From 22d0c545cd572a981c1b573140663e264cb28e69 Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Wed, 19 Nov 2025 13:26:44 -0500 Subject: [PATCH 01/10] Initial port from vivek --- libs/harbor/README.md | 158 + libs/harbor/configs/job.yaml | 47 + .../configs/terminal-bench-daytona.yaml | 42 + .../harbor/configs/terminal-bench-docker.yaml | 40 + libs/harbor/configs/terminal-bench-modal.yaml | 38 + libs/harbor/configs/web-scraper-test.yaml | 51 + libs/harbor/pyproject.toml | 73 + libs/harbor/scripts/export_traces.py | 88 + libs/harbor/src/harbor_deepagents/__init__.py | 46 + .../src/harbor_deepagents/agents/__init__.py | 9 + .../harbor_deepagents/agents/custom_agent.py | 37 + .../agents/deepagent_harbor.py | 435 +++ .../harbor_deepagents/agents/harbor_tools.py | 85 + .../agents/langsmith_integration.py | 86 + .../src/harbor_deepagents/agents/prompts.py | 71 + libs/harbor/src/harbor_deepagents/cli.py | 328 ++ .../src/harbor_deepagents/utils/__init__.py | 5 + .../utils/langsmith_exporter.py | 177 + libs/harbor/src/harness_entry/__init__.py | 43 + libs/harbor/uv.lock | 3181 +++++++++++++++++ 20 files changed, 5040 insertions(+) create mode 100644 libs/harbor/README.md create mode 100644 libs/harbor/configs/job.yaml create mode 100644 libs/harbor/configs/terminal-bench-daytona.yaml create mode 100644 libs/harbor/configs/terminal-bench-docker.yaml create mode 100644 libs/harbor/configs/terminal-bench-modal.yaml create mode 100644 libs/harbor/configs/web-scraper-test.yaml create mode 100644 libs/harbor/pyproject.toml create mode 100755 libs/harbor/scripts/export_traces.py create mode 100644 libs/harbor/src/harbor_deepagents/__init__.py create mode 100644 libs/harbor/src/harbor_deepagents/agents/__init__.py create mode 100644 libs/harbor/src/harbor_deepagents/agents/custom_agent.py create mode 100644 libs/harbor/src/harbor_deepagents/agents/deepagent_harbor.py create mode 100644 libs/harbor/src/harbor_deepagents/agents/harbor_tools.py create mode 100644 libs/harbor/src/harbor_deepagents/agents/langsmith_integration.py create mode 100644 libs/harbor/src/harbor_deepagents/agents/prompts.py create mode 100644 libs/harbor/src/harbor_deepagents/cli.py create mode 100644 libs/harbor/src/harbor_deepagents/utils/__init__.py create mode 100644 libs/harbor/src/harbor_deepagents/utils/langsmith_exporter.py create mode 100644 libs/harbor/src/harness_entry/__init__.py create mode 100644 libs/harbor/uv.lock diff --git a/libs/harbor/README.md b/libs/harbor/README.md new file mode 100644 index 00000000..bc41293c --- /dev/null +++ b/libs/harbor/README.md @@ -0,0 +1,158 @@ +# Building DeepAgent Harnesses for Terminal Bench 2.0 with Harbor + +Build, evaluate, and improve custom agents on Terminal Bench 2.0 with **DeepAgents** (LangChain's agent framework), **Harbor** (a framework for running agents in container environments), and **LangSmith** (built in logging and observability). + +## What is This? + +This repo provides a **DeepAgent harness** - a complete agent implementation built on [LangChain DeepAgents](https://github.com/langchain-ai/deepagents) that can: + +- **Run Harbor tasks** - Execute any Harbor benchmark in sandboxed environments +- **Customize your agent harnesses** - Modify prompts, tools, and behavior for your use case +- **Log to LangSmith** - Automatically trace all runs with full observability +- **Measure & improve** - Analyze traces to optimize your agent harness design + + +## Supported Sandbox Providers + +Run your agent in multiple execution environments: +- 🐳 **Docker** (local) +- ☁️ **Modal, Daytona, E2B** - Cloud sandboxes + +## Quick Start + +### 1. Installation + +TBD + +### 2. Configuration + +Create a `.env` file with your API keys: + +```bash +# Required: Model provider API key +OPENAI_API_KEY=sk-proj-... +# OR +ANTHROPIC_API_KEY=sk-ant-... + +# Optional: LangSmith tracing (recommended) +LANGCHAIN_TRACING_V2=true +LANGCHAIN_API_KEY=lsv2_pt_... +LANGCHAIN_PROJECT=... + +# Optional: Cloud sandbox providers +# NOTE: Sandbox environments may have issues with downloading uv, python, container definition +DAYTONA_API_KEY=dtn_... +modal setup +``` + +### 3. Run Your First Sample Task + +```bash +# Run the web-scraper demo task (configs/job.yaml) +uv run harness --model openai/gpt-5-mini + +# Or rely on MODEL_NAME from .env +uv run harness + +# View Harbor results +cat jobs//result.json | jq '.reward_stats.mean' +``` + +### 4. Run Terminal Bench 2.0 + +```bash +# Single task (Docker local) +uv run tb-docker --task prove-plus-comm --model openai/gpt-5-mini + +# Full benchmark suite +uv run tb-docker --model openai/gpt-5-mini + +# Daytona cloud sandbox (requires DAYTONA_API_KEY) +uv run tb-daytona --task prove-plus-comm --model anthropic/claude-sonnet-4-5-20250620 +``` + +Each run creates a unique job folder: `---`. Override with `--job-name my-run`. + + +### 5. View Traces in LangSmith + +Enable tracing in `.env`: +```bash +LANGCHAIN_TRACING_V2=true +LANGCHAIN_API_KEY=lsv2_pt_... +LANGCHAIN_PROJECT=your-project-name +``` + +View traces at https://smith.langchain.com - Harbor reward scores are automatically logged as feedback. + +## How It Works + +``` +┌─────────────────────────────────────────────────────────┐ +│ Harbor Orchestrator │ +│ (Manages task execution across sandbox providers) │ +└──────────────────────────┬──────────────────────────────┘ + │ + ▼ +┌───────────────────────────────────────────────────────────┐ +│ DeepAgent Harness │ +│ ┌─────────────────────────────────────────────────────┐ │ +│ │ LangChain 1.0 + DeepAgents Framework │ │─────▶ LangSmith +│ │ • Planning (write_todos) │ │ (Traces + +│ │ • Real Filesystem (FilesystemBackend) │ │ Feedback) +│ │ • Subagents (task tool) │ │ +│ │ • Custom Tools (bash from Harbor) │ │ +│ └─────────────────────────────────────────────────────┘ │ +└──────────────────────────┬────────────────────────────────┘ + │ + ▼ +┌───────────────────────────────────────────────────────────┐ +│ Sandbox Environment (choose one) │ +│ • Docker (local) • Modal (cloud) • Daytona • E2B │ +│ │ +│ Provides: /app working directory + bash execution │ +└──────────────────────────┬────────────────────────────────┘ + │ + ▼ + ┌───────────────┐ + │ Task Tests │ + │ (Verifier) │ + └───────┬───────┘ + │ + ▼ + Reward: 0.0 - 1.0 + (logged to LangSmith) +``` + + +## Customizing Your Agent + +**System Prompt:** Edit [src/harbor_deepagents/agents/prompts.py](src/harbor_deepagents/agents/prompts.py). + +**Model:** Update `model_name` inside [configs/job.yaml](configs/job.yaml) (or any Harbor config). +- OpenAI: `openai/gpt-5-mini`, `openai/gpt-4o` +- Anthropic: `anthropic/claude-sonnet-4-5-20250929` + +**Drop-in custom agent:** +1. Start from [src/harbor_deepagents/agents/custom_agent.py](src/harbor_deepagents/agents/custom_agent.py). It subclasses `DeepAgentHarbor` but lets you swap prompts or override methods without touching the base harness. +2. Point your Harbor config at `harbor_deepagents.agents.custom_agent:CustomDeepAgent`. The stock [configs/job.yaml](configs/job.yaml) includes comments showing where to flip the `import_path`. +3. Iterate entirely in-source: run `uv run harness --config configs/job.yaml --dry-run` to verify Harbor resolves the agent, then drop `--dry-run` to execute tasks. LangSmith tracing keeps working so long as your `.env` provides the usual keys. + +## Creating Custom Tasks + +```bash +# Copy template +cp -r tasks/web-scraper-task tasks/my-custom-task + +# Edit instruction.md and tests/test_solution.py +# Update configs/job.yaml with task path + +# Run +uv run harness +``` + +## Learn More +- **Harbor**: https://github.com/HarborBench/harbor - Agent benchmarking framework +- **DeepAgents**: https://github.com/langchain-ai/deepagents - LangChain agent harness +- **LangSmith**: https://docs.smith.langchain.com - LLM observability platform +- **Terminal Bench 2.0**: Standard benchmark suite for coding agents diff --git a/libs/harbor/configs/job.yaml b/libs/harbor/configs/job.yaml new file mode 100644 index 00000000..ae2c9e56 --- /dev/null +++ b/libs/harbor/configs/job.yaml @@ -0,0 +1,47 @@ +# Harbor Job Configuration for DeepAgents +# Run with: harbor run --config configs/job.yaml + +# Job metadata +job_name: deepagents-web-scraper + +# Tasks to evaluate +tasks: + - path: tasks/web-scraper-task + +# Agent configuration using import_path. +# Swap the import_path to harbor_deepagents.agents.custom_agent:CustomDeepAgent +# after tailoring your agent in src/harbor_deepagents/agents/custom_agent.py. +agents: + - import_path: harbor_deepagents.agents.deepagent_harbor:DeepAgentHarbor + model_name: openai/gpt-5-mini # Try: gpt-4o, claude-sonnet-4-5, etc. + kwargs: + max_iterations: 100 + temperature: 0.0 + verbose: true + +# Number of attempts per task-agent combination +n_attempts: 1 + +# Orchestrator configuration (local execution) +orchestrator: + type: local + n_concurrent_trials: 1 + quiet: false + retry: + max_retries: 0 + min_wait_sec: 1 + max_wait_sec: 60 + wait_multiplier: 2 + +# Environment configuration +# Options: docker (local), modal (cloud), e2b, daytona, runloop +environment: + type: docker + +# Timeout multiplier (applies to all timeouts) +timeout_multiplier: 1.0 + +# Metrics to compute +metrics: + - type: mean + kwargs: {} diff --git a/libs/harbor/configs/terminal-bench-daytona.yaml b/libs/harbor/configs/terminal-bench-daytona.yaml new file mode 100644 index 00000000..564c0dbb --- /dev/null +++ b/libs/harbor/configs/terminal-bench-daytona.yaml @@ -0,0 +1,42 @@ +# Harbor Configuration for Terminal Bench 2.0 +# +# Run all tasks: +# harbor run --config configs/terminal-bench.yaml -d terminal-bench@2.0 --env daytona +# +# Run single task: +# harbor run --config configs/terminal-bench.yaml -d terminal-bench@2.0 --task-name prove-plus-comm --env daytona +# +# Or use pure CLI (no config file): +# harbor run \ +# -d terminal-bench@2.0 \ +# --agent-import-path harbor_deepagents.agents.deepagent_harbor:DeepAgentHarbor \ +# -m anthropic/claude-sonnet-4-5-20250929 \ +# --task-name prove-plus-comm \ +# --env daytona + +agents: + - import_path: harbor_deepagents.agents.deepagent_harbor:DeepAgentHarbor + model_name: openai/gpt-5-mini + kwargs: + max_iterations: 500 + temperature: 0.0 + verbose: true + install: + - apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y python3 python3-pip curl + - pip3 install git+https://github.com/vtrivedy/harbor-deepagents.git + - curl -LsSf https://astral.sh/uv/0.9.5/install.sh | sh + +n_attempts: 1 + +orchestrator: + type: local + n_concurrent_trials: 1 # Start with 1 for testing, increase for batch runs + quiet: false + retry: + max_retries: 0 + min_wait_sec: 1 + max_wait_sec: 60 + wait_multiplier: 2 + +environment: + type: daytona # 'docker' for local 'daytona' for cloud environments (requires DAYTONA_API_KEY) diff --git a/libs/harbor/configs/terminal-bench-docker.yaml b/libs/harbor/configs/terminal-bench-docker.yaml new file mode 100644 index 00000000..c070f3bd --- /dev/null +++ b/libs/harbor/configs/terminal-bench-docker.yaml @@ -0,0 +1,40 @@ +# Harbor Configuration for Terminal Bench 2.0 +# +# Run all tasks: +# harbor run --config configs/terminal-bench.yaml -d terminal-bench@2.0 --env daytona +# +# Run single task: +# harbor run --config configs/terminal-bench.yaml -d terminal-bench@2.0 --task-name prove-plus-comm --env daytona +# +# Or use pure CLI (no config file): +# harbor run \ +# -d terminal-bench@2.0 \ +# --agent-import-path harbor_deepagents.agents.deepagent_harbor:DeepAgentHarbor \ +# -m anthropic/claude-sonnet-4-5-20250929 \ +# --task-name prove-plus-comm \ +# --env daytona + +job_name: deepagent-terminal-bench-docker + +agents: + - import_path: harbor_deepagents.agents.deepagent_harbor:DeepAgentHarbor + model_name: openai/gpt-5-mini + kwargs: + max_iterations: 500 + temperature: 0.0 + verbose: true + +n_attempts: 2 + +orchestrator: + type: local + n_concurrent_trials: 2 # run trials simultaenously, beware of local resource consumption + quiet: false + retry: + max_retries: 0 + min_wait_sec: 1 + max_wait_sec: 60 + wait_multiplier: 2 + +environment: + type: docker # 'docker' for local 'daytona' for cloud environments (requires DAYTONA_API_KEY) diff --git a/libs/harbor/configs/terminal-bench-modal.yaml b/libs/harbor/configs/terminal-bench-modal.yaml new file mode 100644 index 00000000..52e66bbf --- /dev/null +++ b/libs/harbor/configs/terminal-bench-modal.yaml @@ -0,0 +1,38 @@ +# Harbor Configuration for Terminal Bench 2.0 on Modal +# +# Run all tasks: +# harbor run --config configs/terminal-bench-modal.yaml -d terminal-bench@2.0 +# +# Run single task: +# harbor run --config configs/terminal-bench-modal.yaml -d terminal-bench@2.0 --task-name prove-plus-comm +# +# Or use pure CLI (no config file): +# harbor run \ +# -d terminal-bench@2.0 \ +# --agent-import-path harbor_deepagents.agents.deepagent_harbor:DeepAgentHarbor \ +# -m anthropic/claude-sonnet-4-5-20250929 \ +# --task-name prove-plus-comm \ +# --env modal + +agents: + - import_path: harbor_deepagents.agents.deepagent_harbor:DeepAgentHarbor + model_name: openai/gpt-5-mini + kwargs: + max_iterations: 500 + temperature: 0.0 + verbose: true + +n_attempts: 1 + +orchestrator: + type: local + n_concurrent_trials: 1 # Start with 1 for testing, increase for batch runs + quiet: false + retry: + max_retries: 0 + min_wait_sec: 1 + max_wait_sec: 60 + wait_multiplier: 2 + +environment: + type: modal # Modal cloud sandboxes (requires MODAL_TOKEN_ID and MODAL_TOKEN_SECRET) diff --git a/libs/harbor/configs/web-scraper-test.yaml b/libs/harbor/configs/web-scraper-test.yaml new file mode 100644 index 00000000..de3024e8 --- /dev/null +++ b/libs/harbor/configs/web-scraper-test.yaml @@ -0,0 +1,51 @@ +# Harbor Job Configuration for DeepAgents +# Run with: harbor run --config configs/job.yaml + +# Job metadata +job_name: deepagents-web-scraper + +# Tasks to evaluate +tasks: + - path: tasks/web-scraper-task + +# Agent configuration using import_path +# This allows Harbor to load our custom agent without modifying core code +agents: + - import_path: harbor_deepagents.agents.deepagent_harbor:DeepAgentHarbor + model_name: openai/gpt-5-mini # Can also use: sonnet-4.5, openai/gpt-4o, etc. + kwargs: + max_iterations: 100 + temperature: 0.0 + verbose: true + +# Number of attempts per task-agent combination +n_attempts: 1 + +# Orchestrator configuration (local execution) +orchestrator: + type: local + n_concurrent_trials: 1 + quiet: false + retry: + max_retries: 0 + min_wait_sec: 1 + max_wait_sec: 60 + wait_multiplier: 2 + +# Environment configuration +# Options: docker (local), modal (cloud), e2b, daytona, runloop +# environment: +# type: daytona # Changed from docker to daytona +# force_build: false +# delete: false + +environment: + type: docker + +# Timeout multiplier (applies to all timeouts) +timeout_multiplier: 1.0 + +# Metrics to compute +metrics: + - type: mean + kwargs: {} diff --git a/libs/harbor/pyproject.toml b/libs/harbor/pyproject.toml new file mode 100644 index 00000000..4621cebc --- /dev/null +++ b/libs/harbor/pyproject.toml @@ -0,0 +1,73 @@ +[project] +name = "harbor-deepagents" +version = "0.1.0" +description = "Harbor integration with LangChain DeepAgents and LangSmith tracing" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + # Harbor runtime + "harbor>=0.1.12", + + # LangChain + DeepAgents (upgraded to 0.2.5 for LocalFilesystemBackend support) + "langchain>=1.0.2", + "langchain-anthropic>=1.0.0", + "langchain-openai>=1.0.0", + "deepagents>=0.2.5", + "openai>=1.109.1,<2.0.0", + + # LangSmith for observability + "langsmith>=0.4.0", + + # Additional tools + "tavily-python>=0.5.0", + + # Modal for cloud execution + "modal>=0.64.0", + + # Utilities + "python-dotenv>=1.0.0", + "rich>=13.0.0", + "pydantic>=2.0.0", + "e2b==2.4.3", +] + +[project.optional-dependencies] +dev = [ + "pytest>=8.4.2", + "pytest-asyncio>=1.2.0", + "ipython>=8.30.0", + "ruff>=0.8.0", +] + +[project.scripts] +harness = "harbor_deepagents.cli:main" +tb-docker = "harbor_deepagents.cli:run_tb_docker" +tb-daytona = "harbor_deepagents.cli:run_tb_daytona" + +[build-system] +requires = ["setuptools>=75.0"] +build-backend = "setuptools.build_meta" + +[tool.setuptools] +package-dir = {"" = "src"} + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.uv] +override-dependencies = [ + "openai>=1.109.1,<2.0.0", + "e2b==2.4.3", +] + +[tool.ruff] +line-length = 100 +target-version = "py312" + +[tool.ruff.lint] +select = ["E", "F", "I"] +ignore = ["E501"] + +[tool.pytest.ini_options] +testpaths = ["tests"] +asyncio_mode = "auto" diff --git a/libs/harbor/scripts/export_traces.py b/libs/harbor/scripts/export_traces.py new file mode 100755 index 00000000..d5fda50e --- /dev/null +++ b/libs/harbor/scripts/export_traces.py @@ -0,0 +1,88 @@ +#!/usr/bin/env python3 +"""Export Harbor ATIF trajectories to LangSmith for visualization.""" + +import sys +from pathlib import Path + +from dotenv import load_dotenv + +# Load environment variables +load_dotenv() + +# Add src to path +sys.path.insert(0, str(Path(__file__).parent.parent / "src")) + +from harbor_deepagents.utils.langsmith_exporter import LangSmithTrajectoryExporter + + +def main(): + """Export all trajectories from latest job to LangSmith.""" + jobs_dir = Path("jobs") + + if not jobs_dir.exists(): + print("✗ No jobs directory found. Run a job first!") + print(" → uv run harness --model openai/gpt-5-mini") + return 1 + + # Find most recent job + job_dirs = sorted( + [d for d in jobs_dir.iterdir() if d.is_dir()], + key=lambda p: p.stat().st_mtime, + reverse=True, + ) + + if not job_dirs: + print("✗ No jobs found in jobs directory") + return 1 + + latest_job = job_dirs[0] + print(f"📦 Exporting trajectories from: {latest_job.name}") + print("") + + # Find all trajectory files + trajectory_files = list(latest_job.rglob("trajectory.json")) + + if not trajectory_files: + print("✗ No trajectory files found") + print(" Trajectories are saved to: jobs///agent/trajectory.json") + return 1 + + print(f"Found {len(trajectory_files)} trajectory file(s)") + print("") + + # Export to LangSmith + exporter = LangSmithTrajectoryExporter() + + exported_count = 0 + failed_count = 0 + + for trajectory_path in trajectory_files: + trial_name = trajectory_path.parent.parent.name + try: + run_id = exporter.export_trajectory( + trajectory_path, + project_name="harbor-deepagents", + ) + print(f"✓ Exported {trial_name}: {run_id[:12]}...") + exported_count += 1 + except Exception as e: + print(f"✗ Failed to export {trial_name}: {e}") + failed_count += 1 + + print("") + print("=" * 60) + print(f"Export complete! {exported_count} succeeded, {failed_count} failed") + print("=" * 60) + print("") + + if exported_count > 0: + print("View in LangSmith:") + print(" → https://smith.langchain.com") + print(" → Project: harbor-deepagents") + print("") + + return 0 if failed_count == 0 else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/libs/harbor/src/harbor_deepagents/__init__.py b/libs/harbor/src/harbor_deepagents/__init__.py new file mode 100644 index 00000000..f0ad31b2 --- /dev/null +++ b/libs/harbor/src/harbor_deepagents/__init__.py @@ -0,0 +1,46 @@ +"""Harbor integration with LangChain DeepAgents. + +This package provides a complete integration between Harbor (agent benchmarking) +and LangChain DeepAgents (agent framework) with automatic LangSmith tracing. + +Quick Start: + >>> from harbor_deepagents.agents import DeepAgentHarbor + >>> agent = DeepAgentHarbor( + ... logs_dir=Path("logs"), + ... model_name="anthropic/claude-sonnet-4-5-20250929" + ... ) + +Example Usage with Harbor: + In configs/job.yaml: + ```yaml + agents: + - import_path: harbor_deepagents.agents:DeepAgentHarbor + model_name: anthropic/claude-sonnet-4-5-20250929 + ``` + + Then run: + ```bash + harbor run --config configs/job.yaml + ``` + +Features: + - DeepAgents framework with planning, filesystem, and subagents + - Automatic LangSmith tracing (zero config) + - ATIF trajectory format for RL/fine-tuning + - Works with any Harbor environment (Docker, Modal, E2B, etc.) + +See Also: + - Harbor: https://harborframework.com + - DeepAgents: https://github.com/langchain-ai/deepagents + - LangSmith: https://smith.langchain.com +""" + +__version__ = "1.0.0" + +from harbor_deepagents.agents import DeepAgentHarbor +from harbor_deepagents.utils import LangSmithTrajectoryExporter + +__all__ = [ + "DeepAgentHarbor", + "LangSmithTrajectoryExporter", +] diff --git a/libs/harbor/src/harbor_deepagents/agents/__init__.py b/libs/harbor/src/harbor_deepagents/agents/__init__.py new file mode 100644 index 00000000..42e55f97 --- /dev/null +++ b/libs/harbor/src/harbor_deepagents/agents/__init__.py @@ -0,0 +1,9 @@ +"""Harbor DeepAgents - Agent implementations.""" + +from harbor_deepagents.agents.custom_agent import CustomDeepAgent +from harbor_deepagents.agents.deepagent_harbor import DeepAgentHarbor + +__all__ = [ + "CustomDeepAgent", + "DeepAgentHarbor", +] diff --git a/libs/harbor/src/harbor_deepagents/agents/custom_agent.py b/libs/harbor/src/harbor_deepagents/agents/custom_agent.py new file mode 100644 index 00000000..53fea86b --- /dev/null +++ b/libs/harbor/src/harbor_deepagents/agents/custom_agent.py @@ -0,0 +1,37 @@ +"""Simple extension template for quickly experimenting with custom agents.""" + +from __future__ import annotations + +from pathlib import Path + +from .deepagent_harbor import DeepAgentHarbor + +#Change this to what you want +CUSTOM_SYSTEM_PROMPT = """You are a Harbor DeepAgent specialized for custom experiments. + +Adjust this prompt, override methods, or replace the class entirely to suit +your use case. The default behavior inherits DeepAgentHarbor's tooling stack. +""" + + +class CustomDeepAgent(DeepAgentHarbor): + """Starter agent that forwards to DeepAgentHarbor with a tweakable prompt. + + Replace the prompt, override `run`, or layer in additional initialization + without having to touch the stock DeepAgentHarbor implementation. Point + Harbor configs at `harbor_deepagents.agents.custom_agent:CustomDeepAgent` + once you're ready to iterate on your own behavior. + """ + + def __init__( + self, + logs_dir: Path, + *, + system_prompt: str | None = None, + **kwargs, + ) -> None: + super().__init__( + logs_dir=logs_dir, + system_prompt=system_prompt or CUSTOM_SYSTEM_PROMPT, + **kwargs, + ) diff --git a/libs/harbor/src/harbor_deepagents/agents/deepagent_harbor.py b/libs/harbor/src/harbor_deepagents/agents/deepagent_harbor.py new file mode 100644 index 00000000..5b56affb --- /dev/null +++ b/libs/harbor/src/harbor_deepagents/agents/deepagent_harbor.py @@ -0,0 +1,435 @@ +"""Harbor agent implementation using LangChain DeepAgents.""" + +import json +import os +import uuid +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Optional + +from deepagents import create_deep_agent +from deepagents.backends import FilesystemBackend +from langchain_anthropic import ChatAnthropic +from langchain_openai import ChatOpenAI +from langchain_core.messages import BaseMessage +from langsmith import Client +from pydantic import BaseModel + +from harbor.agents.base import BaseAgent +from harbor.environments.base import BaseEnvironment +from harbor.models.agent.context import AgentContext +from harbor.models.trajectories import ( + Agent, + FinalMetrics, + Metrics, + Observation, + ObservationResult, + Step, + ToolCall, + Trajectory, +) + +from .harbor_tools import create_harbor_environment_tools +from .langsmith_integration import send_harbor_feedback +from .prompts import HARBOR_SYSTEM_PROMPT + + +class DeepAgentHarbor(BaseAgent): + """Harbor agent that uses LangChain DeepAgents for intelligent task completion. + + DeepAgents provides: + - Planning via write_todos tool + - Real filesystem access via FilesystemBackend (ls, read_file, write_file, edit_file, glob_search, grep_search) + - Subagent spawning for complex tasks + - Built on LangChain 1.0 + LangGraph + + Harbor provides: + - bash tool for command execution in sandboxed environment + """ + + def __init__( + self, + logs_dir: Path, + model_name: str | None = None, + max_iterations: int = 50, + temperature: float = 0.0, + verbose: bool = True, + system_prompt: str | None = None, + *args, + **kwargs, + ): + super().__init__(logs_dir, model_name, *args, **kwargs) + + if model_name is None: + # Use DeepAgents default + model_name = "claude-sonnet-4-5-20250929" + + self._model_name = model_name + self._max_iterations = max_iterations + self._temperature = temperature + self._verbose = verbose + self._session_id = str(uuid.uuid4()) + self._environment: BaseEnvironment | None = None + self._system_prompt = system_prompt or HARBOR_SYSTEM_PROMPT + + # Initialize LLM based on provider prefix (LangSmith will trace if LANGCHAIN_TRACING_V2=true) + if model_name.startswith("openai/") or model_name.startswith("gpt-"): + # OpenAI models: openai/gpt-4o or gpt-4o-mini + actual_model = model_name.split("/")[-1] if "/" in model_name else model_name + self._llm = ChatOpenAI( + model=actual_model, + temperature=temperature, + ) + elif model_name.startswith("anthropic/") or model_name.startswith("claude-"): + # Anthropic models: anthropic/claude-sonnet-4-5-20250929 or claude-sonnet-4-5-20250929 + actual_model = model_name.split("/")[-1] if "/" in model_name else model_name + self._llm = ChatAnthropic( + model=actual_model, + temperature=temperature, + ) + else: + # Default to Anthropic for backward compatibility + self._llm = ChatAnthropic( + model=model_name, + temperature=temperature, + ) + + # Trajectory tracking (ATIF format) + self._trajectory_steps: list[Step] = [] + self._step_counter = 0 + self._total_prompt_tokens = 0 + self._total_completion_tokens = 0 + self._total_cost = 0.0 + + # LangSmith run tracking for feedback + self._langsmith_run_id: str | None = None + self._task_name: str | None = None + + @staticmethod + def name() -> str: + return "deepagent-harbor" + + def version(self) -> str | None: + return "1.0.0" + + async def setup(self, environment: BaseEnvironment) -> None: + """Setup phase - store environment reference.""" + self._environment = environment + + # Add system step to trajectory + self._add_step( + source="system", + message=self._system_prompt, + ) + + async def run( + self, + instruction: str, + environment: BaseEnvironment, + context: AgentContext, + ) -> None: + """Execute the DeepAgent on the given instruction. + + Args: + instruction: The task to complete + environment: Harbor environment (Docker, Modal, etc.) + context: Context to populate with metrics + """ + self._environment = environment + + # Add user instruction as a step + self._add_step( + source="user", + message=instruction, + ) + + # Create Harbor-specific tools that interact with the environment + harbor_tools = create_harbor_environment_tools(environment) + + # Extract only bash tool - DeepAgents will handle filesystem via backend + bash_tool = next((tool for tool in harbor_tools if tool.name == "bash"), None) + if not bash_tool: + raise RuntimeError("bash tool not found in harbor_tools") + + # Configure FilesystemBackend for real filesystem access in Harbor's /app directory + filesystem_backend = FilesystemBackend( + root_dir="/app", # Terminal Bench working directory + virtual_mode=False, # Use real filesystem, not virtual/mock + ) + + # Create DeepAgent with: + # - Harbor's bash tool (for command execution) + # - FilesystemBackend for real file I/O (no mock filesystem) + # - Built-in planning (write_todos) and subagent (task) tools + deep_agent = create_deep_agent( + model=self._llm, + tools=[bash_tool], # Only bash from Harbor + system_prompt=self._system_prompt, + backend=filesystem_backend, # Real filesystem backend + ) + + try: + # Invoke deep agent with LangSmith tracing + result = await deep_agent.ainvoke( + {"messages": [{"role": "user", "content": instruction}]}, + config={ + "run_name": f"harbor-deepagent-{self._session_id[:8]}", + "tags": ["harbor", "deepagent", self._model_name, self._session_id], + "metadata": { + "task_instruction": instruction, + "model": self._model_name, + "session_id": self._session_id, + }, + "recursion_limit": self._max_iterations, + }, + ) + + # Store task name for feedback + self._task_name = instruction[:100] # Truncate for readability + + # Query LangSmith to get the run_id by searching for our unique run_name + self._langsmith_run_id = self._session_id # Default fallback + if os.getenv("LANGCHAIN_TRACING_V2"): + try: + client = Client() + project_name = os.getenv("LANGCHAIN_PROJECT", "default") + run_name = f"harbor-deepagent-{self._session_id[:8]}" + # Search by exact run_name which is unique + runs = list(client.list_runs( + project_name=project_name, + filter=f'eq(name, "{run_name}")', + limit=1, + )) + if runs: + self._langsmith_run_id = str(runs[0].id) + if self._verbose: + print(f"✓ Found LangSmith run_id: {self._langsmith_run_id}") + except Exception as e: + if self._verbose: + print(f"Warning: Could not query LangSmith run_id: {e}") + + # Extract messages from result + messages = result.get("messages", []) + + # Process messages into ATIF steps + self._process_messages_to_steps(messages) + + # Extract final output + final_message = self._extract_final_message(messages) or "Task completed" + last_step = self._trajectory_steps[-1] if self._trajectory_steps else None + if not ( + last_step + and last_step.source == "agent" + and (last_step.message or "").strip() == final_message.strip() + ): + self._add_step( + source="agent", + message=final_message, + ) + + # Build and save trajectory + trajectory = self._build_trajectory() + trajectory_path = self.logs_dir / "trajectory.json" + trajectory_path.write_text(json.dumps(trajectory.to_json_dict(), indent=2)) + + if self._verbose: + print(f"✓ Trajectory saved to {trajectory_path}") + + # Populate context with metrics + if trajectory.final_metrics: + context.n_input_tokens = trajectory.final_metrics.total_prompt_tokens + context.n_output_tokens = trajectory.final_metrics.total_completion_tokens + context.cost_usd = trajectory.final_metrics.total_cost_usd + + except Exception as e: + error_msg = f"DeepAgent execution failed: {str(e)}" + self._add_step( + source="system", + message=error_msg, + ) + if self._verbose: + print(f"✗ Error: {error_msg}") + raise + + def _process_messages_to_steps(self, messages: list[BaseMessage]) -> None: + """Process LangChain messages into ATIF trajectory steps. + + Args: + messages: List of messages from agent execution + """ + for msg in messages: + if msg.type == "ai": + # AI message with potential tool calls + message_content = getattr(msg, "content", "") + + # Check for tool calls + tool_calls = getattr(msg, "tool_calls", None) + + if tool_calls: + # Has tool calls + for tc in tool_calls: + tool_call_id = tc.get("id", f"call_{self._step_counter + 1}") + tool_name = tc.get("name", "unknown") + tool_args = tc.get("args", {}) + + self._add_step( + source="agent", + message=f"Using tool: {tool_name}", + tool_calls=[ + ToolCall( + tool_call_id=tool_call_id, + function_name=tool_name, + arguments=tool_args, + ) + ], + ) + elif message_content: + # Regular AI message + self._add_step( + source="agent", + message=str(message_content), + ) + + # Extract usage metadata if available + usage_metadata = getattr(msg, "usage_metadata", None) + if usage_metadata: + self._update_token_usage(usage_metadata) + + elif msg.type == "tool": + # Tool result + content = getattr(msg, "content", "") + tool_call_id = getattr(msg, "tool_call_id", None) + + # Find the corresponding step and add observation + if self._trajectory_steps: + last_step = self._trajectory_steps[-1] + if last_step.tool_calls: + # Add observation to the last tool call step + last_step.observation = Observation( + results=[ + ObservationResult( + source_call_id=tool_call_id, + content=str(content), + ) + ] + ) + + def _extract_final_message(self, messages: list[BaseMessage]) -> str: + """Extract the final agent message.""" + for msg in reversed(messages): + if msg.type == "ai": + content = getattr(msg, "content", "") + if content: + return str(content) + return "" + + def _update_token_usage(self, usage_metadata: dict[str, Any]) -> None: + """Update token usage from message metadata.""" + input_tokens = usage_metadata.get("input_tokens", 0) + output_tokens = usage_metadata.get("output_tokens", 0) + + self._total_prompt_tokens += input_tokens + self._total_completion_tokens += output_tokens + + # Estimate cost based on model provider + if self._model_name.startswith("openai/") or self._model_name.startswith("gpt-"): + # OpenAI pricing (approximate, as of Nov 2025) + if "gpt-5-mini" in self._model_name or "gpt-4o-mini" in self._model_name: + # Mini models: $0.15 per 1M input, $0.60 per 1M output + input_cost = input_tokens * 0.00000015 + output_cost = output_tokens * 0.0000006 + elif "gpt-5" in self._model_name or "gpt-4o" in self._model_name: + # Standard models: $2.50 per 1M input, $10 per 1M output + input_cost = input_tokens * 0.0000025 + output_cost = output_tokens * 0.00001 + else: + # Default OpenAI pricing + input_cost = input_tokens * 0.0000015 + output_cost = output_tokens * 0.000006 + else: + # Anthropic pricing (Claude Sonnet) + # $3 per 1M input, $15 per 1M output + input_cost = input_tokens * 0.000003 + output_cost = output_tokens * 0.000015 + + self._total_cost += input_cost + output_cost + + def _add_step( + self, + source: str, + message: str, + tool_calls: list[ToolCall] | None = None, + observation: Observation | None = None, + metrics: Metrics | None = None, + ) -> None: + """Add a step to the ATIF trajectory.""" + self._step_counter += 1 + + step = Step( + step_id=self._step_counter, + timestamp=datetime.now(timezone.utc).isoformat(), + source=source, + message=message, + tool_calls=tool_calls, + observation=observation, + metrics=metrics, + ) + + self._trajectory_steps.append(step) + + def _build_trajectory(self) -> Trajectory: + """Build final ATIF trajectory.""" + final_metrics = FinalMetrics( + total_prompt_tokens=self._total_prompt_tokens or None, + total_completion_tokens=self._total_completion_tokens or None, + total_cost_usd=self._total_cost or None, + total_steps=len(self._trajectory_steps), + ) + + return Trajectory( + schema_version="ATIF-v1.2", + session_id=self._session_id, + agent=Agent( + name=self.name(), + version=self.version() or "unknown", + model_name=self._model_name, + extra={ + "framework": "deepagents", + "langchain_version": "1.0+", + "langsmith_run_id": self._langsmith_run_id, # Store for feedback + }, + ), + steps=self._trajectory_steps, + final_metrics=final_metrics, + ) + + def send_verification_feedback(self, reward: float) -> None: + """Send Harbor verification results to LangSmith as feedback. + + This should be called after Harbor's verifier runs to push the + reward score to LangSmith, making it visible in the trace UI. + + Args: + reward: Reward score from Harbor verifier (0.0 to 1.0) + + Example: + >>> agent = DeepAgentHarbor(...) + >>> await agent.run(instruction, environment, context) + >>> # After Harbor verifies the task: + >>> agent.send_verification_feedback(reward=1.0) + """ + if not self._langsmith_run_id or not self._task_name: + if self._verbose: + print("Warning: No run_id or task_name available for feedback") + return + + send_harbor_feedback( + run_id=self._langsmith_run_id, + task_name=self._task_name, + reward=reward, + agent_cost_usd=self._total_cost, + total_steps=len(self._trajectory_steps), + ) + + if self._verbose: + print(f"✓ Sent feedback to LangSmith: reward={reward * 100:.0f}%") diff --git a/libs/harbor/src/harbor_deepagents/agents/harbor_tools.py b/libs/harbor/src/harbor_deepagents/agents/harbor_tools.py new file mode 100644 index 00000000..7b2d11e3 --- /dev/null +++ b/libs/harbor/src/harbor_deepagents/agents/harbor_tools.py @@ -0,0 +1,85 @@ +"""Harbor-specific tools for DeepAgents to interact with the environment.""" + +from typing import Annotated + +from langchain_core.tools import tool + +from harbor.environments.base import BaseEnvironment + + +def create_harbor_environment_tools(environment: BaseEnvironment) -> list: + """Create tools that allow DeepAgent to interact with Harbor environment. + + These tools wrap Harbor's environment API to provide: + - Bash command execution + - File reading + - File writing + + Args: + environment: Harbor environment instance (Docker, Modal, etc.) + + Returns: + List of LangChain tool functions + """ + + @tool + async def bash(command: Annotated[str, "The bash command to execute"]) -> str: + """Execute a bash command in the task environment. + + Use this for running Python scripts, installing packages, listing files, + checking output, etc. Returns stdout, stderr, and exit code. + + Examples: + - bash("python script.py") + - bash("ls -la /app") + - bash("cat file.txt") + """ + result = await environment.exec(command) + + output_parts = [] + if result.stdout: + output_parts.append(f"[stdout]\n{result.stdout.strip()}") + if result.stderr: + output_parts.append(f"[stderr]\n{result.stderr.strip()}") + output_parts.append(f"[exit_code] {result.return_code}") + + return "\n\n".join(output_parts) + + @tool + async def read_file(path: Annotated[str, "Path to the file to read"]) -> str: + """Read the contents of a file from the task environment. + + Provide the full path to the file (e.g., /app/script.py). + + Returns the file contents as a string. + """ + result = await environment.exec(f"cat {path}") + + if result.return_code != 0: + return f"Error reading file: {result.stderr}" + + return result.stdout or "" + + @tool + async def write_file( + path: Annotated[str, "Path to the file to write"], + content: Annotated[str, "Content to write to the file"], + ) -> str: + """Write content to a file in the task environment. + + This will create or overwrite the file. Provide the full path + and the content to write. + + Returns success message or error. + """ + # Escape content for heredoc + escaped_content = content.replace("'", "'\"'\"'") + + result = await environment.exec(f"cat > {path} << 'EOF'\n{content}\nEOF") + + if result.return_code != 0: + return f"Error writing file: {result.stderr}" + + return f"Successfully wrote {len(content)} bytes to {path}" + + return [bash, read_file, write_file] diff --git a/libs/harbor/src/harbor_deepagents/agents/langsmith_integration.py b/libs/harbor/src/harbor_deepagents/agents/langsmith_integration.py new file mode 100644 index 00000000..5f0dfa78 --- /dev/null +++ b/libs/harbor/src/harbor_deepagents/agents/langsmith_integration.py @@ -0,0 +1,86 @@ +"""LangSmith integration for Harbor results feedback.""" + +import os +from typing import Optional + +from langsmith import Client + + +def send_harbor_feedback( + run_id: str, + task_name: str, + reward: float, + agent_cost_usd: Optional[float] = None, + total_steps: Optional[int] = None, +) -> None: + """Send Harbor verification results to LangSmith as feedback. + + This function pushes Harbor's reward score and metadata to LangSmith + as feedback on the agent's run, making it visible in the LangSmith UI + alongside the execution trace. + + Args: + run_id: LangSmith run ID from the agent execution + task_name: Name of the Harbor task + reward: Reward score from Harbor verifier (0.0 to 1.0, where 1.0 = 100% pass) + agent_cost_usd: Optional cost in USD + total_steps: Optional total number of steps taken + + Example: + >>> send_harbor_feedback( + ... run_id="abc123", + ... task_name="web-scraper-task", + ... reward=1.0, + ... agent_cost_usd=0.012, + ... total_steps=11, + ... ) + """ + # Check if LangSmith tracing is enabled + if not os.getenv("LANGCHAIN_TRACING_V2"): + return + + try: + client = Client() + + # Main reward score feedback + client.create_feedback( + run_id=run_id, + key="harbor_reward", + score=reward, + comment=f"Harbor Task: {task_name} | Score: {reward * 100:.0f}%", + ) + + # Optional cost feedback + if agent_cost_usd is not None: + client.create_feedback( + run_id=run_id, + key="harbor_cost_usd", + score=agent_cost_usd, + comment=f"Agent execution cost: ${agent_cost_usd:.4f}", + ) + + # Optional steps feedback + if total_steps is not None: + client.create_feedback( + run_id=run_id, + key="harbor_steps", + score=total_steps, + comment=f"Total steps: {total_steps}", + ) + + except Exception as e: + # Don't fail the task if feedback fails + print(f"Warning: Failed to send LangSmith feedback: {e}") + + +def get_langsmith_url(run_id: str) -> str: + """Generate LangSmith URL for a given run ID. + + Args: + run_id: LangSmith run ID + + Returns: + Full URL to the run in LangSmith UI + """ + project_name = os.getenv("LANGCHAIN_PROJECT", "default") + return f"https://smith.langchain.com/o/default/projects/p/{project_name}/r/{run_id}" diff --git a/libs/harbor/src/harbor_deepagents/agents/prompts.py b/libs/harbor/src/harbor_deepagents/agents/prompts.py new file mode 100644 index 00000000..e28bf97d --- /dev/null +++ b/libs/harbor/src/harbor_deepagents/agents/prompts.py @@ -0,0 +1,71 @@ +"""System prompts for DeepAgent Harbor integration.""" + +HARBOR_SYSTEM_PROMPT = """You are an expert software engineer working on programming tasks in a sandboxed Linux environment. + +Your goal is to complete the given task by writing code, running commands, and verifying your solution. + +## Environment Details + +- **Working directory**: `/app` +- **Environment**: Sandboxed Linux container +- **Task verification**: Tests will run automatically after completion + +## Available Tools + +You have access to the following tools: + +### Command Execution +1. **bash** - Execute bash commands in the sandboxed environment (run scripts, install packages, check outputs) + +### File Operations (Real Filesystem) +2. **ls** - List files and directories (provide path like /app or /app/subdir) +3. **read_file** - Read file contents (supports offset and limit for large files) +4. **write_file** - Create new files with content +5. **edit_file** - Edit existing files with string replacement (supports replace_all flag) +6. **glob_search** - Search for files matching patterns (e.g., "*.py", "test_*.json") +7. **grep_search** - Search file contents with regex patterns + +### Planning & Orchestration +8. **write_todos** - Break down complex tasks into steps and track progress +9. **task** - Spawn subagents for complex, independent subtasks + +**Important**: All file operations work on the REAL filesystem in /app. No mock/virtual filesystem is used. + +## Best Practices + +### Planning +- Use `write_todos` to break down the task into clear steps +- Update todos as you make progress +- Think step-by-step + +### Development +- Write clean, well-commented code +- Test incrementally (write → test → refine) +- Handle errors gracefully +- Use descriptive variable names + +### Verification +- Run your solution to verify it works +- Check that all required files are created +- Test edge cases +- Review the task requirements + +### File Management +- Always work in `/app` directory +- Use paths like `/app/script.py` or just `script.py` (will resolve to /app) +- Use `ls` to explore directory structure before reading files +- Use `glob_search` to find files matching patterns +- Use `grep_search` to search content across files +- Use `edit_file` for modifying existing files (more reliable than rewriting) + +## Task Completion + +When you believe the task is complete: + +1. Run your solution one final time +2. Verify all requirements are met +3. Check that output files are created correctly +4. Provide a brief summary of your solution + +Remember: The tests will verify your solution automatically. Make sure to follow +the requirements exactly as specified in the task instruction.""" diff --git a/libs/harbor/src/harbor_deepagents/cli.py b/libs/harbor/src/harbor_deepagents/cli.py new file mode 100644 index 00000000..396e302f --- /dev/null +++ b/libs/harbor/src/harbor_deepagents/cli.py @@ -0,0 +1,328 @@ +""" +Command-line entry point for running Harbor + DeepAgents harness workflows via uv. +""" + +from __future__ import annotations + +import argparse +import json +import os +import subprocess +import sys +from datetime import datetime +from pathlib import Path +from typing import Dict, Iterable, List, Optional, Sequence + +from dotenv import load_dotenv +from langsmith import Client +from rich.console import Console + +from harbor_deepagents.agents.langsmith_integration import send_harbor_feedback + +console = Console() + +DEFAULT_JOB_CONFIG = Path("configs/job.yaml") +TERMINAL_BENCH_CONFIGS: Dict[str, Path] = { + "docker": Path("configs/terminal-bench-docker.yaml"), + "daytona": Path("configs/terminal-bench-daytona.yaml"), +} +TERMINAL_BENCH_DATASET = "terminal-bench@2.0" + + +def parse_args(argv: Optional[Sequence[str]] = None) -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Run Harbor DeepAgents harness with simple uv-friendly commands.", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--suite", + choices=["job", "terminal-bench"], + default="job", + help="Which pre-defined job config to use when --config is not provided.", + ) + parser.add_argument( + "--env-type", + choices=["docker", "daytona"], + default="docker", + help="Execution environment preset (used for Terminal Bench configs).", + ) + parser.add_argument( + "--config", + dest="config_path", + help="Explicit Harbor YAML config to run (overrides --suite/--env-type).", + ) + parser.add_argument( + "--dataset", + help="Optional Harbor dataset (e.g., terminal-bench@2.0).", + ) + parser.add_argument( + "--task", + dest="task_name", + help="Optional single task name to run (maps to --task-name).", + ) + parser.add_argument( + "--model", + help="Model identifier (fallback to MODEL_NAME env var).", + ) + parser.add_argument( + "--env-file", + default=".env", + help="Dotenv file to load provider keys + MODEL_NAME from.", + ) + parser.add_argument( + "--no-env-file", + action="store_true", + help="Disable automatic dotenv loading.", + ) + parser.add_argument( + "--dry-run", + action="store_true", + help="Print the Harbor command without executing it.", + ) + parser.add_argument( + "--job-name", + help="Optional job name override. Defaults to --.", + ) + parser.add_argument( + "harbor_args", + nargs=argparse.REMAINDER, + help="Additional arguments after '--' are passed directly to Harbor.", + ) + return parser.parse_args(argv) + + +def load_environment(env_file: Optional[str], skip: bool) -> None: + if skip or not env_file: + return + env_path = Path(env_file) + if not env_path.exists(): + console.print(f"[yellow]No dotenv file found at {env_path}. Continuing without it.[/]") + return + + load_dotenv(env_path, override=False) + console.print(f"[green]Loaded environment variables from {env_path}[/]") + + +def resolve_config(suite: str, env_type: str, explicit: Optional[str]) -> Path: + if explicit: + return Path(explicit) + if suite == "terminal-bench": + config_path = TERMINAL_BENCH_CONFIGS.get(env_type) + if not config_path: + raise ValueError(f"Unsupported env_type '{env_type}' for Terminal Bench runs.") + return config_path + return DEFAULT_JOB_CONFIG + + +def determine_model(cli_model: Optional[str]) -> str: + model = cli_model or os.environ.get("MODEL_NAME") + if not model: + console.print( + "[red]MODEL_NAME is not set. Pass --model or export MODEL_NAME in your environment.[/]" + ) + sys.exit(1) + return model + + +def infer_provider(model: str) -> Optional[str]: + lowered = model.lower() + if lowered.startswith(("openai/", "gpt-")): + return "openai" + if lowered.startswith(("anthropic/", "claude-")): + return "anthropic" + return None + + +def ensure_provider_keys(model: str) -> None: + provider = infer_provider(model) + if provider == "openai" and not os.environ.get("OPENAI_API_KEY"): + console.print("[red]OPENAI_API_KEY is required for OpenAI models.[/]") + sys.exit(1) + if provider == "anthropic" and not os.environ.get("ANTHROPIC_API_KEY"): + console.print("[red]ANTHROPIC_API_KEY is required for Anthropic models.[/]") + sys.exit(1) + + +def ensure_env_requirements(env_type: str) -> None: + if env_type == "daytona" and not os.environ.get("DAYTONA_API_KEY"): + console.print("[red]DAYTONA_API_KEY is required when env-type is 'daytona'.[/]") + sys.exit(1) + + +def build_command(config_path: Path, dataset: Optional[str], task_name: Optional[str]) -> List[str]: + command: List[str] = ["harbor", "run", "--config", str(config_path)] + if dataset: + command.extend(["-d", dataset]) + if task_name: + command.extend(["--task-name", task_name]) + return command + + +def normalize_remainder(args: Iterable[str]) -> List[str]: + remainder = list(args) + if remainder and remainder[0] == "--": + remainder = remainder[1:] + return remainder + + +def main(argv: Optional[Sequence[str]] = None) -> None: + args = parse_args(argv) + load_environment(args.env_file, args.no_env_file) + + config_path = resolve_config(args.suite, args.env_type, args.config_path) + if not config_path.exists(): + console.print(f"[red]Config file not found: {config_path}[/]") + sys.exit(1) + + dataset = args.dataset or ( + TERMINAL_BENCH_DATASET if args.suite == "terminal-bench" else None + ) + + model = determine_model(args.model) + os.environ["MODEL_NAME"] = model + + ensure_provider_keys(model) + ensure_env_requirements(args.env_type) + + extra_args = normalize_remainder(args.harbor_args) + command = build_command(config_path, dataset, args.task_name) + if args.job_name: + command.extend(["--job-name", args.job_name]) + elif "--job-name" not in extra_args: + auto_name = generate_job_name(config_path, args.task_name, model) + command.extend(["--job-name", auto_name]) + if extra_args: + command.extend(extra_args) + + job_name = extract_job_name(command) + if job_name: + console.print(f"[cyan]Running Harbor job {job_name}[/]") + else: + console.print("[cyan]Running Harbor job[/]") + + if args.dry_run: + console.print("[yellow]Dry run enabled; skipping execution.[/]") + return + + try: + subprocess.run(command, check=True) + send_feedback_for_job(job_name) + except subprocess.CalledProcessError as exc: + console.print(f"[red]Harbor exited with code {exc.returncode}[/]") + sys.exit(exc.returncode) + + +def _run_with_preset(preset_args: List[str]) -> None: + extra_args = sys.argv[1:] + main(preset_args + list(extra_args)) + + +def run_tb_docker() -> None: + preset = ["--suite", "terminal-bench", "--env-type", "docker"] + _run_with_preset(preset) + + +def run_tb_daytona() -> None: + preset = ["--suite", "terminal-bench", "--env-type", "daytona"] + _run_with_preset(preset) + + +def generate_job_name(config_path: Path, task_name: Optional[str], model: str) -> str: + """Create a readable job name like tb-daytona-prove-plus-comm-20251112-0730.""" + + timestamp = datetime.utcnow().strftime("%Y%m%d-%H%M%S") + base = config_path.stem + task_segment = task_name or "all-tasks" + model_segment = model.split("/")[-1] + slug = "-".join( + _slugify(segment) + for segment in (base, task_segment, model_segment, timestamp) + if segment + ) + return slug or f"harbor-job-{timestamp}" + + +def _slugify(value: str) -> str: + return "".join(ch.lower() if ch.isalnum() else "-" for ch in value).strip("-") + + +def send_feedback_for_job(job_name: Optional[str]) -> None: + """Push LangSmith feedback for every trial under jobs/.""" + + if not job_name: + return + + job_dir = Path("jobs") / job_name + if not job_dir.exists(): + return + + trajectory_files = sorted(job_dir.glob("**/agent/trajectory.json")) + if not trajectory_files: + return + + sent_count = 0 + total_trials = len(trajectory_files) + + for trajectory_path in trajectory_files: + trial_dir = trajectory_path.parent.parent + result_path = trial_dir / "result.json" + + if not result_path.exists(): + continue + + try: + trajectory = json.loads(trajectory_path.read_text()) + trial_result = json.loads(result_path.read_text()) + except Exception: + continue + + run_id = trajectory.get("agent", {}).get("extra", {}).get("langsmith_run_id") or ( + trajectory.get("session_id") + ) + if not run_id: + continue + + reward = ( + trial_result.get("verifier_result", {}) + .get("rewards", {}) + .get("reward") + ) + if reward is None: + continue + + task_name = trial_result.get("task_name") or trial_dir.name + cost_usd = trial_result.get("agent_result", {}).get("cost_usd") + total_steps = len(trajectory.get("steps", [])) + + try: + send_harbor_feedback( + run_id=run_id, + task_name=task_name, + reward=reward, + agent_cost_usd=cost_usd, + total_steps=total_steps, + ) + sent_count += 1 + except Exception as exc: + console.print( + f"[yellow]Warning: failed to send LangSmith feedback for {trial_dir.name}: {exc}[/]" + ) + + if sent_count: + console.print( + f"[green]✓ Sent LangSmith feedback for {sent_count}/{total_trials} trial(s) in {job_dir.name}[/]" + ) + + +def extract_job_name(command: List[str]) -> Optional[str]: + """Extract --job-name value from the constructed Harbor command.""" + + if "--job-name" in command: + idx = command.index("--job-name") + if idx + 1 < len(command): + return command[idx + 1] + return None + + +if __name__ == "__main__": + main() diff --git a/libs/harbor/src/harbor_deepagents/utils/__init__.py b/libs/harbor/src/harbor_deepagents/utils/__init__.py new file mode 100644 index 00000000..d9816806 --- /dev/null +++ b/libs/harbor/src/harbor_deepagents/utils/__init__.py @@ -0,0 +1,5 @@ +"""Harbor DeepAgents - Utility functions.""" + +from harbor_deepagents.utils.langsmith_exporter import LangSmithTrajectoryExporter + +__all__ = ["LangSmithTrajectoryExporter"] diff --git a/libs/harbor/src/harbor_deepagents/utils/langsmith_exporter.py b/libs/harbor/src/harbor_deepagents/utils/langsmith_exporter.py new file mode 100644 index 00000000..7e4e3c24 --- /dev/null +++ b/libs/harbor/src/harbor_deepagents/utils/langsmith_exporter.py @@ -0,0 +1,177 @@ +"""Export Harbor ATIF trajectories to LangSmith for visualization.""" + +import json +from pathlib import Path +from typing import Any + +from langsmith import Client + + +class LangSmithTrajectoryExporter: + """Export ATIF trajectories to LangSmith. + + This allows you to: + - Visualize agent execution in LangSmith UI + - Compare different runs + - Analyze costs and latency + - Debug agent behavior + """ + + def __init__(self, api_key: str | None = None): + """Initialize exporter with optional API key. + + Args: + api_key: LangSmith API key (or use LANGCHAIN_API_KEY env var) + """ + self.client = Client(api_key=api_key) + + def export_trajectory( + self, + trajectory_path: Path | str, + project_name: str = "harbor-deepagents", + ) -> str: + """Export an ATIF trajectory file to LangSmith. + + Args: + trajectory_path: Path to ATIF trajectory JSON file + project_name: LangSmith project name + + Returns: + The run ID in LangSmith + """ + trajectory_path = Path(trajectory_path) + + with open(trajectory_path) as f: + atif = json.load(f) + + # Create root run + run_id = self.client.create_run( + name=f"harbor-{atif['agent']['name']}-{atif['session_id'][:8]}", + run_type="chain", + inputs={"instruction": self._extract_user_message(atif)}, + project_name=project_name, + tags=["harbor", atif["agent"]["name"], "deepagents"], + extra={ + "metadata": { + "agent_name": atif["agent"]["name"], + "agent_version": atif["agent"]["version"], + "model": atif["agent"].get("model_name"), + "session_id": atif["session_id"], + "schema_version": atif["schema_version"], + **atif["agent"].get("extra", {}), + } + }, + ).id + + # Add steps as child runs + for step in atif["steps"]: + self._add_step_run( + step=step, + parent_run_id=run_id, + project_name=project_name, + ) + + # Update root run with final output and metrics + final_message = self._extract_final_message(atif) + final_metrics = atif.get("final_metrics", {}) + + self.client.update_run( + run_id=run_id, + outputs={"result": final_message}, + extra={"metadata": final_metrics}, + ) + + return run_id + + def export_job_trajectories( + self, + job_dir: Path | str, + project_name: str = "harbor-deepagents", + ) -> list[str]: + """Export all trajectories from a Harbor job directory. + + Args: + job_dir: Path to Harbor job directory + project_name: LangSmith project name + + Returns: + List of LangSmith run IDs + """ + job_dir = Path(job_dir) + trajectory_files = list(job_dir.rglob("trajectory.json")) + + run_ids = [] + for trajectory_path in trajectory_files: + try: + run_id = self.export_trajectory(trajectory_path, project_name) + run_ids.append(run_id) + print(f"✓ Exported {trajectory_path.parent.name}: {run_id}") + except Exception as e: + print(f"✗ Failed to export {trajectory_path}: {e}") + + return run_ids + + def _extract_user_message(self, atif: dict[str, Any]) -> str: + """Extract the initial user message from ATIF.""" + for step in atif["steps"]: + if step["source"] == "user": + return step["message"] + return "" + + def _extract_final_message(self, atif: dict[str, Any]) -> str: + """Extract the final agent message from ATIF.""" + for step in reversed(atif["steps"]): + if step["source"] == "agent": + return step["message"] + return "" + + def _add_step_run( + self, + step: dict[str, Any], + parent_run_id: str, + project_name: str, + ) -> None: + """Add a step as a child run in LangSmith.""" + run_type = self._get_run_type(step) + + inputs = {"message": step.get("message", "")} + outputs = {} + + # Add tool call information + if step.get("tool_calls"): + tool_call = step["tool_calls"][0] # Simplified: take first tool call + inputs["tool"] = tool_call.get("function_name") + inputs["arguments"] = tool_call.get("arguments") + + # Add observation information + if step.get("observation"): + results = step["observation"].get("results", []) + if results: + outputs["observation"] = results[0].get("content") + + # Create child run + self.client.create_run( + name=f"step-{step['step_id']}-{step['source']}", + run_type=run_type, + inputs=inputs, + outputs=outputs if outputs else None, + parent_run_id=parent_run_id, + project_name=project_name, + extra={ + "metadata": { + "step_id": step["step_id"], + "source": step["source"], + "timestamp": step.get("timestamp"), + **(step.get("metrics", {}) or {}), + } + }, + ) + + def _get_run_type(self, step: dict[str, Any]) -> str: + """Determine LangSmith run type from ATIF step.""" + if step.get("tool_calls"): + return "tool" + elif step["source"] == "agent": + return "llm" + else: + return "chain" diff --git a/libs/harbor/src/harness_entry/__init__.py b/libs/harbor/src/harness_entry/__init__.py new file mode 100644 index 00000000..50db82c3 --- /dev/null +++ b/libs/harbor/src/harness_entry/__init__.py @@ -0,0 +1,43 @@ +"""Bootstrap wrapper so console scripts can import from src without editable installs.""" + +from __future__ import annotations + +import os +import sys +from pathlib import Path + + +def _maybe_add_src_path() -> None: + """Ensure the repo's src directory is importable when developing locally.""" + + candidate_roots = [ + Path.cwd(), + Path(os.environ.get("HARBOR_DEEPAGENTS_ROOT", "")), + ] + + for root in candidate_roots: + if not root: + continue + src_dir = root / "src" + if src_dir.is_dir(): + sys.path.insert(0, str(src_dir)) + return + + +_maybe_add_src_path() + +from harbor_deepagents.cli import main as 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14:29:27 -0500 Subject: [PATCH 02/10] x --- libs/harbor/Makefile | 63 +++ libs/harbor/harbor_deepagents/__init__.py | 46 ++ .../harbor_deepagents/agents/__init__.py | 9 + .../harbor_deepagents/agents/custom_agent.py | 37 ++ .../agents/deepagent_harbor.py | 435 ++++++++++++++++++ .../harbor_deepagents/agents/harbor_tools.py | 85 ++++ .../agents/langsmith_integration.py | 86 ++++ .../harbor_deepagents/agents/prompts.py | 71 +++ libs/harbor/harbor_deepagents/cli.py | 328 +++++++++++++ .../harbor_deepagents/utils/__init__.py | 5 + .../utils/langsmith_exporter.py | 177 +++++++ libs/harbor/pyproject.toml | 2 +- libs/harbor/scripts/harbor_entry.py | 43 ++ libs/harbor/tests/unit_tests/test_imports.py | 4 + 14 files changed, 1390 insertions(+), 1 deletion(-) create mode 100644 libs/harbor/Makefile create mode 100644 libs/harbor/harbor_deepagents/__init__.py create mode 100644 libs/harbor/harbor_deepagents/agents/__init__.py create mode 100644 libs/harbor/harbor_deepagents/agents/custom_agent.py create mode 100644 libs/harbor/harbor_deepagents/agents/deepagent_harbor.py create mode 100644 libs/harbor/harbor_deepagents/agents/harbor_tools.py create mode 100644 libs/harbor/harbor_deepagents/agents/langsmith_integration.py create mode 100644 libs/harbor/harbor_deepagents/agents/prompts.py create mode 100644 libs/harbor/harbor_deepagents/cli.py create mode 100644 libs/harbor/harbor_deepagents/utils/__init__.py create mode 100644 libs/harbor/harbor_deepagents/utils/langsmith_exporter.py create mode 100644 libs/harbor/scripts/harbor_entry.py create mode 100644 libs/harbor/tests/unit_tests/test_imports.py diff --git a/libs/harbor/Makefile b/libs/harbor/Makefile new file mode 100644 index 00000000..2fac930e --- /dev/null +++ b/libs/harbor/Makefile @@ -0,0 +1,63 @@ +.PHONY: all lint format test help + +# Default target executed when no arguments are given to make. +all: help + +###################### +# TESTING AND COVERAGE +###################### + +# Define a variable for the test file path. +TEST_FILE ?= tests/unit_tests +INTEGRATION_FILES ?= tests/integration_tests + +test: + uv run pytest --disable-socket --allow-unix-socket $(TEST_FILE) + +test_integration: + uv run pytest $(INTEGRATION_FILES) + +test_watch: + uv run ptw . -- $(TEST_FILE) + + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +lint format: PYTHON_FILES=deepagents_cli/ tests/ +lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=. --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') + +lint lint_diff: + [ "$(PYTHON_FILES)" = "" ] || uv run ruff format $(PYTHON_FILES) --diff + @if [ "$(LINT)" != "minimal" ]; then \ + if [ "$(PYTHON_FILES)" != "" ]; then \ + uv run ruff check $(PYTHON_FILES) --diff; \ + fi; \ + fi + # [ "$(PYTHON_FILES)" = "" ] || uv run mypy $(PYTHON_FILES) + +format format_diff: + [ "$(PYTHON_FILES)" = "" ] || uv run ruff format $(PYTHON_FILES) + [ "$(PYTHON_FILES)" = "" ] || uv run ruff check --fix $(PYTHON_FILES) + +format_unsafe: + [ "$(PYTHON_FILES)" = "" ] || uv run ruff format --unsafe-fixes $(PYTHON_FILES) + + +###################### +# HELP +###################### + +help: + @echo '====================' + @echo '-- LINTING --' + @echo 'format - run code formatters' + @echo 'lint - run linters' + @echo '-- TESTS --' + @echo 'test - run unit tests' + @echo 'test TEST_FILE= - run all tests in file' + @echo '-- DOCUMENTATION tasks are from the top-level Makefile --' + + diff --git a/libs/harbor/harbor_deepagents/__init__.py b/libs/harbor/harbor_deepagents/__init__.py new file mode 100644 index 00000000..f0ad31b2 --- /dev/null +++ b/libs/harbor/harbor_deepagents/__init__.py @@ -0,0 +1,46 @@ +"""Harbor integration with LangChain DeepAgents. + +This package provides a complete integration between Harbor (agent benchmarking) +and LangChain DeepAgents (agent framework) with automatic LangSmith tracing. + +Quick Start: + >>> from harbor_deepagents.agents import DeepAgentHarbor + >>> agent = DeepAgentHarbor( + ... logs_dir=Path("logs"), + ... model_name="anthropic/claude-sonnet-4-5-20250929" + ... ) + +Example Usage with Harbor: + In configs/job.yaml: + ```yaml + agents: + - import_path: harbor_deepagents.agents:DeepAgentHarbor + model_name: anthropic/claude-sonnet-4-5-20250929 + ``` + + Then run: + ```bash + harbor run --config configs/job.yaml + ``` + +Features: + - DeepAgents framework with planning, filesystem, and subagents + - Automatic LangSmith tracing (zero config) + - ATIF trajectory format for RL/fine-tuning + - Works with any Harbor environment (Docker, Modal, E2B, etc.) + +See Also: + - Harbor: https://harborframework.com + - DeepAgents: https://github.com/langchain-ai/deepagents + - LangSmith: https://smith.langchain.com +""" + +__version__ = "1.0.0" + +from harbor_deepagents.agents import DeepAgentHarbor +from harbor_deepagents.utils import LangSmithTrajectoryExporter + +__all__ = [ + "DeepAgentHarbor", + "LangSmithTrajectoryExporter", +] diff --git a/libs/harbor/harbor_deepagents/agents/__init__.py b/libs/harbor/harbor_deepagents/agents/__init__.py new file mode 100644 index 00000000..42e55f97 --- /dev/null +++ b/libs/harbor/harbor_deepagents/agents/__init__.py @@ -0,0 +1,9 @@ +"""Harbor DeepAgents - Agent implementations.""" + +from harbor_deepagents.agents.custom_agent import CustomDeepAgent +from harbor_deepagents.agents.deepagent_harbor import DeepAgentHarbor + +__all__ = [ + "CustomDeepAgent", + "DeepAgentHarbor", +] diff --git a/libs/harbor/harbor_deepagents/agents/custom_agent.py b/libs/harbor/harbor_deepagents/agents/custom_agent.py new file mode 100644 index 00000000..1acdd216 --- /dev/null +++ b/libs/harbor/harbor_deepagents/agents/custom_agent.py @@ -0,0 +1,37 @@ +"""Simple extension template for quickly experimenting with custom agents.""" + +from __future__ import annotations + +from pathlib import Path + +from harbor_deepagents.agents.deepagent_harbor import DeepAgentHarbor + +#Change this to what you want +CUSTOM_SYSTEM_PROMPT = """You are a Harbor DeepAgent specialized for custom experiments. + +Adjust this prompt, override methods, or replace the class entirely to suit +your use case. The default behavior inherits DeepAgentHarbor's tooling stack. +""" + + +class CustomDeepAgent(DeepAgentHarbor): + """Starter agent that forwards to DeepAgentHarbor with a tweakable prompt. + + Replace the prompt, override `run`, or layer in additional initialization + without having to touch the stock DeepAgentHarbor implementation. Point + Harbor configs at `harbor_deepagents.agents.custom_agent:CustomDeepAgent` + once you're ready to iterate on your own behavior. + """ + + def __init__( + self, + logs_dir: Path, + *, + system_prompt: str | None = None, + **kwargs, + ) -> None: + super().__init__( + logs_dir=logs_dir, + system_prompt=system_prompt or CUSTOM_SYSTEM_PROMPT, + **kwargs, + ) diff --git a/libs/harbor/harbor_deepagents/agents/deepagent_harbor.py b/libs/harbor/harbor_deepagents/agents/deepagent_harbor.py new file mode 100644 index 00000000..443525a3 --- /dev/null +++ b/libs/harbor/harbor_deepagents/agents/deepagent_harbor.py @@ -0,0 +1,435 @@ +"""Harbor agent implementation using LangChain DeepAgents.""" + +import json +import os +import uuid +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Optional + +from deepagents import create_deep_agent +from deepagents.backends import FilesystemBackend +from langchain_anthropic import ChatAnthropic +from langchain_openai import ChatOpenAI +from langchain_core.messages import BaseMessage +from langsmith import Client +from pydantic import BaseModel + +from harbor.agents.base import BaseAgent +from harbor.environments.base import BaseEnvironment +from harbor.models.agent.context import AgentContext +from harbor.models.trajectories import ( + Agent, + FinalMetrics, + Metrics, + Observation, + ObservationResult, + Step, + ToolCall, + Trajectory, +) + +from harbor_deepagents.agents.harbor_tools import create_harbor_environment_tools +from harbor_deepagents.agents.langsmith_integration import send_harbor_feedback +from harbor_deepagents.agents.prompts import HARBOR_SYSTEM_PROMPT + + +class DeepAgentHarbor(BaseAgent): + """Harbor agent that uses LangChain DeepAgents for intelligent task completion. + + DeepAgents provides: + - Planning via write_todos tool + - Real filesystem access via FilesystemBackend (ls, read_file, write_file, edit_file, glob_search, grep_search) + - Subagent spawning for complex tasks + - Built on LangChain 1.0 + LangGraph + + Harbor provides: + - bash tool for command execution in sandboxed environment + """ + + def __init__( + self, + logs_dir: Path, + model_name: str | None = None, + max_iterations: int = 50, + temperature: float = 0.0, + verbose: bool = True, + system_prompt: str | None = None, + *args, + **kwargs, + ): + super().__init__(logs_dir, model_name, *args, **kwargs) + + if model_name is None: + # Use DeepAgents default + model_name = "claude-sonnet-4-5-20250929" + + self._model_name = model_name + self._max_iterations = max_iterations + self._temperature = temperature + self._verbose = verbose + self._session_id = str(uuid.uuid4()) + self._environment: BaseEnvironment | None = None + self._system_prompt = system_prompt or HARBOR_SYSTEM_PROMPT + + # Initialize LLM based on provider prefix (LangSmith will trace if LANGCHAIN_TRACING_V2=true) + if model_name.startswith("openai/") or model_name.startswith("gpt-"): + # OpenAI models: openai/gpt-4o or gpt-4o-mini + actual_model = model_name.split("/")[-1] if "/" in model_name else model_name + self._llm = ChatOpenAI( + model=actual_model, + temperature=temperature, + ) + elif model_name.startswith("anthropic/") or model_name.startswith("claude-"): + # Anthropic models: anthropic/claude-sonnet-4-5-20250929 or claude-sonnet-4-5-20250929 + actual_model = model_name.split("/")[-1] if "/" in model_name else model_name + self._llm = ChatAnthropic( + model=actual_model, + temperature=temperature, + ) + else: + # Default to Anthropic for backward compatibility + self._llm = ChatAnthropic( + model=model_name, + temperature=temperature, + ) + + # Trajectory tracking (ATIF format) + self._trajectory_steps: list[Step] = [] + self._step_counter = 0 + self._total_prompt_tokens = 0 + self._total_completion_tokens = 0 + self._total_cost = 0.0 + + # LangSmith run tracking for feedback + self._langsmith_run_id: str | None = None + self._task_name: str | None = None + + @staticmethod + def name() -> str: + return "deepagent-harbor" + + def version(self) -> str | None: + return "1.0.0" + + async def setup(self, environment: BaseEnvironment) -> None: + """Setup phase - store environment reference.""" + self._environment = environment + + # Add system step to trajectory + self._add_step( + source="system", + message=self._system_prompt, + ) + + async def run( + self, + instruction: str, + environment: BaseEnvironment, + context: AgentContext, + ) -> None: + """Execute the DeepAgent on the given instruction. + + Args: + instruction: The task to complete + environment: Harbor environment (Docker, Modal, etc.) + context: Context to populate with metrics + """ + self._environment = environment + + # Add user instruction as a step + self._add_step( + source="user", + message=instruction, + ) + + # Create Harbor-specific tools that interact with the environment + harbor_tools = create_harbor_environment_tools(environment) + + # Extract only bash tool - DeepAgents will handle filesystem via backend + bash_tool = next((tool for tool in harbor_tools if tool.name == "bash"), None) + if not bash_tool: + raise RuntimeError("bash tool not found in harbor_tools") + + # Configure FilesystemBackend for real filesystem access in Harbor's /app directory + filesystem_backend = FilesystemBackend( + root_dir="/app", # Terminal Bench working directory + virtual_mode=False, # Use real filesystem, not virtual/mock + ) + + # Create DeepAgent with: + # - Harbor's bash tool (for command execution) + # - FilesystemBackend for real file I/O (no mock filesystem) + # - Built-in planning (write_todos) and subagent (task) tools + deep_agent = create_deep_agent( + model=self._llm, + tools=[bash_tool], # Only bash from Harbor + system_prompt=self._system_prompt, + backend=filesystem_backend, # Real filesystem backend + ) + + try: + # Invoke deep agent with LangSmith tracing + result = await deep_agent.ainvoke( + {"messages": [{"role": "user", "content": instruction}]}, + config={ + "run_name": f"harbor-deepagent-{self._session_id[:8]}", + "tags": ["harbor", "deepagent", self._model_name, self._session_id], + "metadata": { + "task_instruction": instruction, + "model": self._model_name, + "session_id": self._session_id, + }, + "recursion_limit": self._max_iterations, + }, + ) + + # Store task name for feedback + self._task_name = instruction[:100] # Truncate for readability + + # Query LangSmith to get the run_id by searching for our unique run_name + self._langsmith_run_id = self._session_id # Default fallback + if os.getenv("LANGCHAIN_TRACING_V2"): + try: + client = Client() + project_name = os.getenv("LANGCHAIN_PROJECT", "default") + run_name = f"harbor-deepagent-{self._session_id[:8]}" + # Search by exact run_name which is unique + runs = list(client.list_runs( + project_name=project_name, + filter=f'eq(name, "{run_name}")', + limit=1, + )) + if runs: + self._langsmith_run_id = str(runs[0].id) + if self._verbose: + print(f"✓ Found LangSmith run_id: {self._langsmith_run_id}") + except Exception as e: + if self._verbose: + print(f"Warning: Could not query LangSmith run_id: {e}") + + # Extract messages from result + messages = result.get("messages", []) + + # Process messages into ATIF steps + self._process_messages_to_steps(messages) + + # Extract final output + final_message = self._extract_final_message(messages) or "Task completed" + last_step = self._trajectory_steps[-1] if self._trajectory_steps else None + if not ( + last_step + and last_step.source == "agent" + and (last_step.message or "").strip() == final_message.strip() + ): + self._add_step( + source="agent", + message=final_message, + ) + + # Build and save trajectory + trajectory = self._build_trajectory() + trajectory_path = self.logs_dir / "trajectory.json" + trajectory_path.write_text(json.dumps(trajectory.to_json_dict(), indent=2)) + + if self._verbose: + print(f"✓ Trajectory saved to {trajectory_path}") + + # Populate context with metrics + if trajectory.final_metrics: + context.n_input_tokens = trajectory.final_metrics.total_prompt_tokens + context.n_output_tokens = trajectory.final_metrics.total_completion_tokens + context.cost_usd = trajectory.final_metrics.total_cost_usd + + except Exception as e: + error_msg = f"DeepAgent execution failed: {str(e)}" + self._add_step( + source="system", + message=error_msg, + ) + if self._verbose: + print(f"✗ Error: {error_msg}") + raise + + def _process_messages_to_steps(self, messages: list[BaseMessage]) -> None: + """Process LangChain messages into ATIF trajectory steps. + + Args: + messages: List of messages from agent execution + """ + for msg in messages: + if msg.type == "ai": + # AI message with potential tool calls + message_content = getattr(msg, "content", "") + + # Check for tool calls + tool_calls = getattr(msg, "tool_calls", None) + + if tool_calls: + # Has tool calls + for tc in tool_calls: + tool_call_id = tc.get("id", f"call_{self._step_counter + 1}") + tool_name = tc.get("name", "unknown") + tool_args = tc.get("args", {}) + + self._add_step( + source="agent", + message=f"Using tool: {tool_name}", + tool_calls=[ + ToolCall( + tool_call_id=tool_call_id, + function_name=tool_name, + arguments=tool_args, + ) + ], + ) + elif message_content: + # Regular AI message + self._add_step( + source="agent", + message=str(message_content), + ) + + # Extract usage metadata if available + usage_metadata = getattr(msg, "usage_metadata", None) + if usage_metadata: + self._update_token_usage(usage_metadata) + + elif msg.type == "tool": + # Tool result + content = getattr(msg, "content", "") + tool_call_id = getattr(msg, "tool_call_id", None) + + # Find the corresponding step and add observation + if self._trajectory_steps: + last_step = self._trajectory_steps[-1] + if last_step.tool_calls: + # Add observation to the last tool call step + last_step.observation = Observation( + results=[ + ObservationResult( + source_call_id=tool_call_id, + content=str(content), + ) + ] + ) + + def _extract_final_message(self, messages: list[BaseMessage]) -> str: + """Extract the final agent message.""" + for msg in reversed(messages): + if msg.type == "ai": + content = getattr(msg, "content", "") + if content: + return str(content) + return "" + + def _update_token_usage(self, usage_metadata: dict[str, Any]) -> None: + """Update token usage from message metadata.""" + input_tokens = usage_metadata.get("input_tokens", 0) + output_tokens = usage_metadata.get("output_tokens", 0) + + self._total_prompt_tokens += input_tokens + self._total_completion_tokens += output_tokens + + # Estimate cost based on model provider + if self._model_name.startswith("openai/") or self._model_name.startswith("gpt-"): + # OpenAI pricing (approximate, as of Nov 2025) + if "gpt-5-mini" in self._model_name or "gpt-4o-mini" in self._model_name: + # Mini models: $0.15 per 1M input, $0.60 per 1M output + input_cost = input_tokens * 0.00000015 + output_cost = output_tokens * 0.0000006 + elif "gpt-5" in self._model_name or "gpt-4o" in self._model_name: + # Standard models: $2.50 per 1M input, $10 per 1M output + input_cost = input_tokens * 0.0000025 + output_cost = output_tokens * 0.00001 + else: + # Default OpenAI pricing + input_cost = input_tokens * 0.0000015 + output_cost = output_tokens * 0.000006 + else: + # Anthropic pricing (Claude Sonnet) + # $3 per 1M input, $15 per 1M output + input_cost = input_tokens * 0.000003 + output_cost = output_tokens * 0.000015 + + self._total_cost += input_cost + output_cost + + def _add_step( + self, + source: str, + message: str, + tool_calls: list[ToolCall] | None = None, + observation: Observation | None = None, + metrics: Metrics | None = None, + ) -> None: + """Add a step to the ATIF trajectory.""" + self._step_counter += 1 + + step = Step( + step_id=self._step_counter, + timestamp=datetime.now(timezone.utc).isoformat(), + source=source, + message=message, + tool_calls=tool_calls, + observation=observation, + metrics=metrics, + ) + + self._trajectory_steps.append(step) + + def _build_trajectory(self) -> Trajectory: + """Build final ATIF trajectory.""" + final_metrics = FinalMetrics( + total_prompt_tokens=self._total_prompt_tokens or None, + total_completion_tokens=self._total_completion_tokens or None, + total_cost_usd=self._total_cost or None, + total_steps=len(self._trajectory_steps), + ) + + return Trajectory( + schema_version="ATIF-v1.2", + session_id=self._session_id, + agent=Agent( + name=self.name(), + version=self.version() or "unknown", + model_name=self._model_name, + extra={ + "framework": "deepagents", + "langchain_version": "1.0+", + "langsmith_run_id": self._langsmith_run_id, # Store for feedback + }, + ), + steps=self._trajectory_steps, + final_metrics=final_metrics, + ) + + def send_verification_feedback(self, reward: float) -> None: + """Send Harbor verification results to LangSmith as feedback. + + This should be called after Harbor's verifier runs to push the + reward score to LangSmith, making it visible in the trace UI. + + Args: + reward: Reward score from Harbor verifier (0.0 to 1.0) + + Example: + >>> agent = DeepAgentHarbor(...) + >>> await agent.run(instruction, environment, context) + >>> # After Harbor verifies the task: + >>> agent.send_verification_feedback(reward=1.0) + """ + if not self._langsmith_run_id or not self._task_name: + if self._verbose: + print("Warning: No run_id or task_name available for feedback") + return + + send_harbor_feedback( + run_id=self._langsmith_run_id, + task_name=self._task_name, + reward=reward, + agent_cost_usd=self._total_cost, + total_steps=len(self._trajectory_steps), + ) + + if self._verbose: + print(f"✓ Sent feedback to LangSmith: reward={reward * 100:.0f}%") diff --git a/libs/harbor/harbor_deepagents/agents/harbor_tools.py b/libs/harbor/harbor_deepagents/agents/harbor_tools.py new file mode 100644 index 00000000..7b2d11e3 --- /dev/null +++ b/libs/harbor/harbor_deepagents/agents/harbor_tools.py @@ -0,0 +1,85 @@ +"""Harbor-specific tools for DeepAgents to interact with the environment.""" + +from typing import Annotated + +from langchain_core.tools import tool + +from harbor.environments.base import BaseEnvironment + + +def create_harbor_environment_tools(environment: BaseEnvironment) -> list: + """Create tools that allow DeepAgent to interact with Harbor environment. + + These tools wrap Harbor's environment API to provide: + - Bash command execution + - File reading + - File writing + + Args: + environment: Harbor environment instance (Docker, Modal, etc.) + + Returns: + List of LangChain tool functions + """ + + @tool + async def bash(command: Annotated[str, "The bash command to execute"]) -> str: + """Execute a bash command in the task environment. + + Use this for running Python scripts, installing packages, listing files, + checking output, etc. Returns stdout, stderr, and exit code. + + Examples: + - bash("python script.py") + - bash("ls -la /app") + - bash("cat file.txt") + """ + result = await environment.exec(command) + + output_parts = [] + if result.stdout: + output_parts.append(f"[stdout]\n{result.stdout.strip()}") + if result.stderr: + output_parts.append(f"[stderr]\n{result.stderr.strip()}") + output_parts.append(f"[exit_code] {result.return_code}") + + return "\n\n".join(output_parts) + + @tool + async def read_file(path: Annotated[str, "Path to the file to read"]) -> str: + """Read the contents of a file from the task environment. + + Provide the full path to the file (e.g., /app/script.py). + + Returns the file contents as a string. + """ + result = await environment.exec(f"cat {path}") + + if result.return_code != 0: + return f"Error reading file: {result.stderr}" + + return result.stdout or "" + + @tool + async def write_file( + path: Annotated[str, "Path to the file to write"], + content: Annotated[str, "Content to write to the file"], + ) -> str: + """Write content to a file in the task environment. + + This will create or overwrite the file. Provide the full path + and the content to write. + + Returns success message or error. + """ + # Escape content for heredoc + escaped_content = content.replace("'", "'\"'\"'") + + result = await environment.exec(f"cat > {path} << 'EOF'\n{content}\nEOF") + + if result.return_code != 0: + return f"Error writing file: {result.stderr}" + + return f"Successfully wrote {len(content)} bytes to {path}" + + return [bash, read_file, write_file] diff --git a/libs/harbor/harbor_deepagents/agents/langsmith_integration.py b/libs/harbor/harbor_deepagents/agents/langsmith_integration.py new file mode 100644 index 00000000..5f0dfa78 --- /dev/null +++ b/libs/harbor/harbor_deepagents/agents/langsmith_integration.py @@ -0,0 +1,86 @@ +"""LangSmith integration for Harbor results feedback.""" + +import os +from typing import Optional + +from langsmith import Client + + +def send_harbor_feedback( + run_id: str, + task_name: str, + reward: float, + agent_cost_usd: Optional[float] = None, + total_steps: Optional[int] = None, +) -> None: + """Send Harbor verification results to LangSmith as feedback. + + This function pushes Harbor's reward score and metadata to LangSmith + as feedback on the agent's run, making it visible in the LangSmith UI + alongside the execution trace. + + Args: + run_id: LangSmith run ID from the agent execution + task_name: Name of the Harbor task + reward: Reward score from Harbor verifier (0.0 to 1.0, where 1.0 = 100% pass) + agent_cost_usd: Optional cost in USD + total_steps: Optional total number of steps taken + + Example: + >>> send_harbor_feedback( + ... run_id="abc123", + ... task_name="web-scraper-task", + ... reward=1.0, + ... agent_cost_usd=0.012, + ... total_steps=11, + ... ) + """ + # Check if LangSmith tracing is enabled + if not os.getenv("LANGCHAIN_TRACING_V2"): + return + + try: + client = Client() + + # Main reward score feedback + client.create_feedback( + run_id=run_id, + key="harbor_reward", + score=reward, + comment=f"Harbor Task: {task_name} | Score: {reward * 100:.0f}%", + ) + + # Optional cost feedback + if agent_cost_usd is not None: + client.create_feedback( + run_id=run_id, + key="harbor_cost_usd", + score=agent_cost_usd, + comment=f"Agent execution cost: ${agent_cost_usd:.4f}", + ) + + # Optional steps feedback + if total_steps is not None: + client.create_feedback( + run_id=run_id, + key="harbor_steps", + score=total_steps, + comment=f"Total steps: {total_steps}", + ) + + except Exception as e: + # Don't fail the task if feedback fails + print(f"Warning: Failed to send LangSmith feedback: {e}") + + +def get_langsmith_url(run_id: str) -> str: + """Generate LangSmith URL for a given run ID. + + Args: + run_id: LangSmith run ID + + Returns: + Full URL to the run in LangSmith UI + """ + project_name = os.getenv("LANGCHAIN_PROJECT", "default") + return f"https://smith.langchain.com/o/default/projects/p/{project_name}/r/{run_id}" diff --git a/libs/harbor/harbor_deepagents/agents/prompts.py b/libs/harbor/harbor_deepagents/agents/prompts.py new file mode 100644 index 00000000..e28bf97d --- /dev/null +++ b/libs/harbor/harbor_deepagents/agents/prompts.py @@ -0,0 +1,71 @@ +"""System prompts for DeepAgent Harbor integration.""" + +HARBOR_SYSTEM_PROMPT = """You are an expert software engineer working on programming tasks in a sandboxed Linux environment. + +Your goal is to complete the given task by writing code, running commands, and verifying your solution. + +## Environment Details + +- **Working directory**: `/app` +- **Environment**: Sandboxed Linux container +- **Task verification**: Tests will run automatically after completion + +## Available Tools + +You have access to the following tools: + +### Command Execution +1. **bash** - Execute bash commands in the sandboxed environment (run scripts, install packages, check outputs) + +### File Operations (Real Filesystem) +2. **ls** - List files and directories (provide path like /app or /app/subdir) +3. **read_file** - Read file contents (supports offset and limit for large files) +4. **write_file** - Create new files with content +5. **edit_file** - Edit existing files with string replacement (supports replace_all flag) +6. **glob_search** - Search for files matching patterns (e.g., "*.py", "test_*.json") +7. **grep_search** - Search file contents with regex patterns + +### Planning & Orchestration +8. **write_todos** - Break down complex tasks into steps and track progress +9. **task** - Spawn subagents for complex, independent subtasks + +**Important**: All file operations work on the REAL filesystem in /app. No mock/virtual filesystem is used. + +## Best Practices + +### Planning +- Use `write_todos` to break down the task into clear steps +- Update todos as you make progress +- Think step-by-step + +### Development +- Write clean, well-commented code +- Test incrementally (write → test → refine) +- Handle errors gracefully +- Use descriptive variable names + +### Verification +- Run your solution to verify it works +- Check that all required files are created +- Test edge cases +- Review the task requirements + +### File Management +- Always work in `/app` directory +- Use paths like `/app/script.py` or just `script.py` (will resolve to /app) +- Use `ls` to explore directory structure before reading files +- Use `glob_search` to find files matching patterns +- Use `grep_search` to search content across files +- Use `edit_file` for modifying existing files (more reliable than rewriting) + +## Task Completion + +When you believe the task is complete: + +1. Run your solution one final time +2. Verify all requirements are met +3. Check that output files are created correctly +4. Provide a brief summary of your solution + +Remember: The tests will verify your solution automatically. Make sure to follow +the requirements exactly as specified in the task instruction.""" diff --git a/libs/harbor/harbor_deepagents/cli.py b/libs/harbor/harbor_deepagents/cli.py new file mode 100644 index 00000000..396e302f --- /dev/null +++ b/libs/harbor/harbor_deepagents/cli.py @@ -0,0 +1,328 @@ +""" +Command-line entry point for running Harbor + DeepAgents harness workflows via uv. +""" + +from __future__ import annotations + +import argparse +import json +import os +import subprocess +import sys +from datetime import datetime +from pathlib import Path +from typing import Dict, Iterable, List, Optional, Sequence + +from dotenv import load_dotenv +from langsmith import Client +from rich.console import Console + +from harbor_deepagents.agents.langsmith_integration import send_harbor_feedback + +console = Console() + +DEFAULT_JOB_CONFIG = Path("configs/job.yaml") +TERMINAL_BENCH_CONFIGS: Dict[str, Path] = { + "docker": Path("configs/terminal-bench-docker.yaml"), + "daytona": Path("configs/terminal-bench-daytona.yaml"), +} +TERMINAL_BENCH_DATASET = "terminal-bench@2.0" + + +def parse_args(argv: Optional[Sequence[str]] = None) -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Run Harbor DeepAgents harness with simple uv-friendly commands.", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument( + "--suite", + choices=["job", "terminal-bench"], + default="job", + help="Which pre-defined job config to use when --config is not provided.", + ) + parser.add_argument( + "--env-type", + choices=["docker", "daytona"], + default="docker", + help="Execution environment preset (used for Terminal Bench configs).", + ) + parser.add_argument( + "--config", + dest="config_path", + help="Explicit Harbor YAML config to run (overrides --suite/--env-type).", + ) + parser.add_argument( + "--dataset", + help="Optional Harbor dataset (e.g., terminal-bench@2.0).", + ) + parser.add_argument( + "--task", + dest="task_name", + help="Optional single task name to run (maps to --task-name).", + ) + parser.add_argument( + "--model", + help="Model identifier (fallback to MODEL_NAME env var).", + ) + parser.add_argument( + "--env-file", + default=".env", + help="Dotenv file to load provider keys + MODEL_NAME from.", + ) + parser.add_argument( + "--no-env-file", + action="store_true", + help="Disable automatic dotenv loading.", + ) + parser.add_argument( + "--dry-run", + action="store_true", + help="Print the Harbor command without executing it.", + ) + parser.add_argument( + "--job-name", + help="Optional job name override. Defaults to --.", + ) + parser.add_argument( + "harbor_args", + nargs=argparse.REMAINDER, + help="Additional arguments after '--' are passed directly to Harbor.", + ) + return parser.parse_args(argv) + + +def load_environment(env_file: Optional[str], skip: bool) -> None: + if skip or not env_file: + return + env_path = Path(env_file) + if not env_path.exists(): + console.print(f"[yellow]No dotenv file found at {env_path}. Continuing without it.[/]") + return + + load_dotenv(env_path, override=False) + console.print(f"[green]Loaded environment variables from {env_path}[/]") + + +def resolve_config(suite: str, env_type: str, explicit: Optional[str]) -> Path: + if explicit: + return Path(explicit) + if suite == "terminal-bench": + config_path = TERMINAL_BENCH_CONFIGS.get(env_type) + if not config_path: + raise ValueError(f"Unsupported env_type '{env_type}' for Terminal Bench runs.") + return config_path + return DEFAULT_JOB_CONFIG + + +def determine_model(cli_model: Optional[str]) -> str: + model = cli_model or os.environ.get("MODEL_NAME") + if not model: + console.print( + "[red]MODEL_NAME is not set. Pass --model or export MODEL_NAME in your environment.[/]" + ) + sys.exit(1) + return model + + +def infer_provider(model: str) -> Optional[str]: + lowered = model.lower() + if lowered.startswith(("openai/", "gpt-")): + return "openai" + if lowered.startswith(("anthropic/", "claude-")): + return "anthropic" + return None + + +def ensure_provider_keys(model: str) -> None: + provider = infer_provider(model) + if provider == "openai" and not os.environ.get("OPENAI_API_KEY"): + console.print("[red]OPENAI_API_KEY is required for OpenAI models.[/]") + sys.exit(1) + if provider == "anthropic" and not os.environ.get("ANTHROPIC_API_KEY"): + console.print("[red]ANTHROPIC_API_KEY is required for Anthropic models.[/]") + sys.exit(1) + + +def ensure_env_requirements(env_type: str) -> None: + if env_type == "daytona" and not os.environ.get("DAYTONA_API_KEY"): + console.print("[red]DAYTONA_API_KEY is required when env-type is 'daytona'.[/]") + sys.exit(1) + + +def build_command(config_path: Path, dataset: Optional[str], task_name: Optional[str]) -> List[str]: + command: List[str] = ["harbor", "run", "--config", str(config_path)] + if dataset: + command.extend(["-d", dataset]) + if task_name: + command.extend(["--task-name", task_name]) + return command + + +def normalize_remainder(args: Iterable[str]) -> List[str]: + remainder = list(args) + if remainder and remainder[0] == "--": + remainder = remainder[1:] + return remainder + + +def main(argv: Optional[Sequence[str]] = None) -> None: + args = parse_args(argv) + load_environment(args.env_file, args.no_env_file) + + config_path = resolve_config(args.suite, args.env_type, args.config_path) + if not config_path.exists(): + console.print(f"[red]Config file not found: {config_path}[/]") + sys.exit(1) + + dataset = args.dataset or ( + TERMINAL_BENCH_DATASET if args.suite == "terminal-bench" else None + ) + + model = determine_model(args.model) + os.environ["MODEL_NAME"] = model + + ensure_provider_keys(model) + ensure_env_requirements(args.env_type) + + extra_args = normalize_remainder(args.harbor_args) + command = build_command(config_path, dataset, args.task_name) + if args.job_name: + command.extend(["--job-name", args.job_name]) + elif "--job-name" not in extra_args: + auto_name = generate_job_name(config_path, args.task_name, model) + command.extend(["--job-name", auto_name]) + if extra_args: + command.extend(extra_args) + + job_name = extract_job_name(command) + if job_name: + console.print(f"[cyan]Running Harbor job {job_name}[/]") + else: + console.print("[cyan]Running Harbor job[/]") + + if args.dry_run: + console.print("[yellow]Dry run enabled; skipping execution.[/]") + return + + try: + subprocess.run(command, check=True) + send_feedback_for_job(job_name) + except subprocess.CalledProcessError as exc: + console.print(f"[red]Harbor exited with code {exc.returncode}[/]") + sys.exit(exc.returncode) + + +def _run_with_preset(preset_args: List[str]) -> None: + extra_args = sys.argv[1:] + main(preset_args + list(extra_args)) + + +def run_tb_docker() -> None: + preset = ["--suite", "terminal-bench", "--env-type", "docker"] + _run_with_preset(preset) + + +def run_tb_daytona() -> None: + preset = ["--suite", "terminal-bench", "--env-type", "daytona"] + _run_with_preset(preset) + + +def generate_job_name(config_path: Path, task_name: Optional[str], model: str) -> str: + """Create a readable job name like tb-daytona-prove-plus-comm-20251112-0730.""" + + timestamp = datetime.utcnow().strftime("%Y%m%d-%H%M%S") + base = config_path.stem + task_segment = task_name or "all-tasks" + model_segment = model.split("/")[-1] + slug = "-".join( + _slugify(segment) + for segment in (base, task_segment, model_segment, timestamp) + if segment + ) + return slug or f"harbor-job-{timestamp}" + + +def _slugify(value: str) -> str: + return "".join(ch.lower() if ch.isalnum() else "-" for ch in value).strip("-") + + +def send_feedback_for_job(job_name: Optional[str]) -> None: + """Push LangSmith feedback for every trial under jobs/.""" + + if not job_name: + return + + job_dir = Path("jobs") / job_name + if not job_dir.exists(): + return + + trajectory_files = sorted(job_dir.glob("**/agent/trajectory.json")) + if not trajectory_files: + return + + sent_count = 0 + total_trials = len(trajectory_files) + + for trajectory_path in trajectory_files: + trial_dir = trajectory_path.parent.parent + result_path = trial_dir / "result.json" + + if not result_path.exists(): + continue + + try: + trajectory = json.loads(trajectory_path.read_text()) + trial_result = json.loads(result_path.read_text()) + except Exception: + continue + + run_id = trajectory.get("agent", {}).get("extra", {}).get("langsmith_run_id") or ( + trajectory.get("session_id") + ) + if not run_id: + continue + + reward = ( + trial_result.get("verifier_result", {}) + .get("rewards", {}) + .get("reward") + ) + if reward is None: + continue + + task_name = trial_result.get("task_name") or trial_dir.name + cost_usd = trial_result.get("agent_result", {}).get("cost_usd") + total_steps = len(trajectory.get("steps", [])) + + try: + send_harbor_feedback( + run_id=run_id, + task_name=task_name, + reward=reward, + agent_cost_usd=cost_usd, + total_steps=total_steps, + ) + sent_count += 1 + except Exception as exc: + console.print( + f"[yellow]Warning: failed to send LangSmith feedback for {trial_dir.name}: {exc}[/]" + ) + + if sent_count: + console.print( + f"[green]✓ Sent LangSmith feedback for {sent_count}/{total_trials} trial(s) in {job_dir.name}[/]" + ) + + +def extract_job_name(command: List[str]) -> Optional[str]: + """Extract --job-name value from the constructed Harbor command.""" + + if "--job-name" in command: + idx = command.index("--job-name") + if idx + 1 < len(command): + return command[idx + 1] + return None + + +if __name__ == "__main__": + main() diff --git a/libs/harbor/harbor_deepagents/utils/__init__.py b/libs/harbor/harbor_deepagents/utils/__init__.py new file mode 100644 index 00000000..d9816806 --- /dev/null +++ b/libs/harbor/harbor_deepagents/utils/__init__.py @@ -0,0 +1,5 @@ +"""Harbor DeepAgents - Utility functions.""" + +from harbor_deepagents.utils.langsmith_exporter import LangSmithTrajectoryExporter + +__all__ = ["LangSmithTrajectoryExporter"] diff --git a/libs/harbor/harbor_deepagents/utils/langsmith_exporter.py b/libs/harbor/harbor_deepagents/utils/langsmith_exporter.py new file mode 100644 index 00000000..7e4e3c24 --- /dev/null +++ b/libs/harbor/harbor_deepagents/utils/langsmith_exporter.py @@ -0,0 +1,177 @@ +"""Export Harbor ATIF trajectories to LangSmith for visualization.""" + +import json +from pathlib import Path +from typing import Any + +from langsmith import Client + + +class LangSmithTrajectoryExporter: + """Export ATIF trajectories to LangSmith. + + This allows you to: + - Visualize agent execution in LangSmith UI + - Compare different runs + - Analyze costs and latency + - Debug agent behavior + """ + + def __init__(self, api_key: str | None = None): + """Initialize exporter with optional API key. + + Args: + api_key: LangSmith API key (or use LANGCHAIN_API_KEY env var) + """ + self.client = Client(api_key=api_key) + + def export_trajectory( + self, + trajectory_path: Path | str, + project_name: str = "harbor-deepagents", + ) -> str: + """Export an ATIF trajectory file to LangSmith. + + Args: + trajectory_path: Path to ATIF trajectory JSON file + project_name: LangSmith project name + + Returns: + The run ID in LangSmith + """ + trajectory_path = Path(trajectory_path) + + with open(trajectory_path) as f: + atif = json.load(f) + + # Create root run + run_id = self.client.create_run( + name=f"harbor-{atif['agent']['name']}-{atif['session_id'][:8]}", + run_type="chain", + inputs={"instruction": self._extract_user_message(atif)}, + project_name=project_name, + tags=["harbor", atif["agent"]["name"], "deepagents"], + extra={ + "metadata": { + "agent_name": atif["agent"]["name"], + "agent_version": atif["agent"]["version"], + "model": atif["agent"].get("model_name"), + "session_id": atif["session_id"], + "schema_version": atif["schema_version"], + **atif["agent"].get("extra", {}), + } + }, + ).id + + # Add steps as child runs + for step in atif["steps"]: + self._add_step_run( + step=step, + parent_run_id=run_id, + project_name=project_name, + ) + + # Update root run with final output and metrics + final_message = self._extract_final_message(atif) + final_metrics = atif.get("final_metrics", {}) + + self.client.update_run( + run_id=run_id, + outputs={"result": final_message}, + extra={"metadata": final_metrics}, + ) + + return run_id + + def export_job_trajectories( + self, + job_dir: Path | str, + project_name: str = "harbor-deepagents", + ) -> list[str]: + """Export all trajectories from a Harbor job directory. + + Args: + job_dir: Path to Harbor job directory + project_name: LangSmith project name + + Returns: + List of LangSmith run IDs + """ + job_dir = Path(job_dir) + trajectory_files = list(job_dir.rglob("trajectory.json")) + + run_ids = [] + for trajectory_path in trajectory_files: + try: + run_id = self.export_trajectory(trajectory_path, project_name) + run_ids.append(run_id) + print(f"✓ Exported {trajectory_path.parent.name}: {run_id}") + except Exception as e: + print(f"✗ Failed to export {trajectory_path}: {e}") + + return run_ids + + def _extract_user_message(self, atif: dict[str, Any]) -> str: + """Extract the initial user message from ATIF.""" + for step in atif["steps"]: + if step["source"] == "user": + return step["message"] + return "" + + def _extract_final_message(self, atif: dict[str, Any]) -> str: + """Extract the final agent message from ATIF.""" + for step in reversed(atif["steps"]): + if step["source"] == "agent": + return step["message"] + return "" + + def _add_step_run( + self, + step: dict[str, Any], + parent_run_id: str, + project_name: str, + ) -> None: + """Add a step as a child run in LangSmith.""" + run_type = self._get_run_type(step) + + inputs = {"message": step.get("message", "")} + outputs = {} + + # Add tool call information + if step.get("tool_calls"): + tool_call = step["tool_calls"][0] # Simplified: take first tool call + inputs["tool"] = tool_call.get("function_name") + inputs["arguments"] = tool_call.get("arguments") + + # Add observation information + if step.get("observation"): + results = step["observation"].get("results", []) + if results: + outputs["observation"] = results[0].get("content") + + # Create child run + self.client.create_run( + name=f"step-{step['step_id']}-{step['source']}", + run_type=run_type, + inputs=inputs, + outputs=outputs if outputs else None, + parent_run_id=parent_run_id, + project_name=project_name, + extra={ + "metadata": { + "step_id": step["step_id"], + "source": step["source"], + "timestamp": step.get("timestamp"), + **(step.get("metrics", {}) or {}), + } + }, + ) + + def _get_run_type(self, step: dict[str, Any]) -> str: + """Determine LangSmith run type from ATIF step.""" + if step.get("tool_calls"): + return "tool" + elif step["source"] == "agent": + return "llm" + else: + return "chain" diff --git a/libs/harbor/pyproject.toml b/libs/harbor/pyproject.toml index 4621cebc..99e76f11 100644 --- a/libs/harbor/pyproject.toml +++ b/libs/harbor/pyproject.toml @@ -49,7 +49,7 @@ requires = ["setuptools>=75.0"] build-backend = "setuptools.build_meta" [tool.setuptools] -package-dir = {"" = "src"} +package-dir = {"" = "harbor_deepagents"} [tool.setuptools.packages.find] where = ["src"] diff --git a/libs/harbor/scripts/harbor_entry.py b/libs/harbor/scripts/harbor_entry.py new file mode 100644 index 00000000..50db82c3 --- /dev/null +++ b/libs/harbor/scripts/harbor_entry.py @@ -0,0 +1,43 @@ +"""Bootstrap wrapper so console scripts can import from src without editable installs.""" + +from __future__ import annotations + +import os +import sys +from pathlib import Path + + +def _maybe_add_src_path() -> None: + """Ensure the repo's src directory is importable when developing locally.""" + + candidate_roots = [ + Path.cwd(), + Path(os.environ.get("HARBOR_DEEPAGENTS_ROOT", "")), + ] + + for root in candidate_roots: + if not root: + continue + src_dir = root / "src" + if src_dir.is_dir(): + sys.path.insert(0, str(src_dir)) + return + + +_maybe_add_src_path() + +from harbor_deepagents.cli import main as _main +from harbor_deepagents.cli import run_tb_daytona as _run_tb_daytona +from harbor_deepagents.cli import run_tb_docker as _run_tb_docker + + +def main() -> None: + _main() + + +def tb_docker() -> None: + _run_tb_docker() + + +def tb_daytona() -> None: + _run_tb_daytona() diff --git a/libs/harbor/tests/unit_tests/test_imports.py b/libs/harbor/tests/unit_tests/test_imports.py new file mode 100644 index 00000000..7fd6882b --- /dev/null +++ b/libs/harbor/tests/unit_tests/test_imports.py @@ -0,0 +1,4 @@ +"""Place holder unit tests for imports here.""" + +def test_placeholder() -> None: + """A placeholder test to ensure the test suite runs.""" From 85864ce0e527a109acd2d6de96da109c1e29a6ea Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Wed, 19 Nov 2025 14:31:52 -0500 Subject: [PATCH 03/10] x --- libs/harbor/src/harbor_deepagents/__init__.py | 46 -- .../src/harbor_deepagents/agents/__init__.py | 9 - .../harbor_deepagents/agents/custom_agent.py | 37 -- .../agents/deepagent_harbor.py | 435 ------------------ .../harbor_deepagents/agents/harbor_tools.py | 85 ---- .../agents/langsmith_integration.py | 86 ---- .../src/harbor_deepagents/agents/prompts.py | 71 --- libs/harbor/src/harbor_deepagents/cli.py | 328 ------------- .../src/harbor_deepagents/utils/__init__.py | 5 - .../utils/langsmith_exporter.py | 177 ------- libs/harbor/src/harness_entry/__init__.py | 43 -- 11 files changed, 1322 deletions(-) delete mode 100644 libs/harbor/src/harbor_deepagents/__init__.py delete mode 100644 libs/harbor/src/harbor_deepagents/agents/__init__.py delete mode 100644 libs/harbor/src/harbor_deepagents/agents/custom_agent.py delete mode 100644 libs/harbor/src/harbor_deepagents/agents/deepagent_harbor.py delete mode 100644 libs/harbor/src/harbor_deepagents/agents/harbor_tools.py delete mode 100644 libs/harbor/src/harbor_deepagents/agents/langsmith_integration.py delete mode 100644 libs/harbor/src/harbor_deepagents/agents/prompts.py delete mode 100644 libs/harbor/src/harbor_deepagents/cli.py delete mode 100644 libs/harbor/src/harbor_deepagents/utils/__init__.py delete mode 100644 libs/harbor/src/harbor_deepagents/utils/langsmith_exporter.py delete mode 100644 libs/harbor/src/harness_entry/__init__.py diff --git a/libs/harbor/src/harbor_deepagents/__init__.py b/libs/harbor/src/harbor_deepagents/__init__.py deleted file mode 100644 index f0ad31b2..00000000 --- a/libs/harbor/src/harbor_deepagents/__init__.py +++ /dev/null @@ -1,46 +0,0 @@ -"""Harbor integration with LangChain DeepAgents. - -This package provides a complete integration between Harbor (agent benchmarking) -and LangChain DeepAgents (agent framework) with automatic LangSmith tracing. - -Quick Start: - >>> from harbor_deepagents.agents import DeepAgentHarbor - >>> agent = DeepAgentHarbor( - ... logs_dir=Path("logs"), - ... model_name="anthropic/claude-sonnet-4-5-20250929" - ... ) - -Example Usage with Harbor: - In configs/job.yaml: - ```yaml - agents: - - import_path: harbor_deepagents.agents:DeepAgentHarbor - model_name: anthropic/claude-sonnet-4-5-20250929 - ``` - - Then run: - ```bash - harbor run --config configs/job.yaml - ``` - -Features: - - DeepAgents framework with planning, filesystem, and subagents - - Automatic LangSmith tracing (zero config) - - ATIF trajectory format for RL/fine-tuning - - Works with any Harbor environment (Docker, Modal, E2B, etc.) - -See Also: - - Harbor: https://harborframework.com - - DeepAgents: https://github.com/langchain-ai/deepagents - - LangSmith: https://smith.langchain.com -""" - -__version__ = "1.0.0" - -from harbor_deepagents.agents import DeepAgentHarbor -from harbor_deepagents.utils import LangSmithTrajectoryExporter - -__all__ = [ - "DeepAgentHarbor", - "LangSmithTrajectoryExporter", -] diff --git a/libs/harbor/src/harbor_deepagents/agents/__init__.py b/libs/harbor/src/harbor_deepagents/agents/__init__.py deleted file mode 100644 index 42e55f97..00000000 --- a/libs/harbor/src/harbor_deepagents/agents/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -"""Harbor DeepAgents - Agent implementations.""" - -from harbor_deepagents.agents.custom_agent import CustomDeepAgent -from harbor_deepagents.agents.deepagent_harbor import DeepAgentHarbor - -__all__ = [ - "CustomDeepAgent", - "DeepAgentHarbor", -] diff --git a/libs/harbor/src/harbor_deepagents/agents/custom_agent.py b/libs/harbor/src/harbor_deepagents/agents/custom_agent.py deleted file mode 100644 index 53fea86b..00000000 --- a/libs/harbor/src/harbor_deepagents/agents/custom_agent.py +++ /dev/null @@ -1,37 +0,0 @@ -"""Simple extension template for quickly experimenting with custom agents.""" - -from __future__ import annotations - -from pathlib import Path - -from .deepagent_harbor import DeepAgentHarbor - -#Change this to what you want -CUSTOM_SYSTEM_PROMPT = """You are a Harbor DeepAgent specialized for custom experiments. - -Adjust this prompt, override methods, or replace the class entirely to suit -your use case. The default behavior inherits DeepAgentHarbor's tooling stack. -""" - - -class CustomDeepAgent(DeepAgentHarbor): - """Starter agent that forwards to DeepAgentHarbor with a tweakable prompt. - - Replace the prompt, override `run`, or layer in additional initialization - without having to touch the stock DeepAgentHarbor implementation. Point - Harbor configs at `harbor_deepagents.agents.custom_agent:CustomDeepAgent` - once you're ready to iterate on your own behavior. - """ - - def __init__( - self, - logs_dir: Path, - *, - system_prompt: str | None = None, - **kwargs, - ) -> None: - super().__init__( - logs_dir=logs_dir, - system_prompt=system_prompt or CUSTOM_SYSTEM_PROMPT, - **kwargs, - ) diff --git a/libs/harbor/src/harbor_deepagents/agents/deepagent_harbor.py b/libs/harbor/src/harbor_deepagents/agents/deepagent_harbor.py deleted file mode 100644 index 5b56affb..00000000 --- a/libs/harbor/src/harbor_deepagents/agents/deepagent_harbor.py +++ /dev/null @@ -1,435 +0,0 @@ -"""Harbor agent implementation using LangChain DeepAgents.""" - -import json -import os -import uuid -from datetime import datetime, timezone -from pathlib import Path -from typing import Any, Optional - -from deepagents import create_deep_agent -from deepagents.backends import FilesystemBackend -from langchain_anthropic import ChatAnthropic -from langchain_openai import ChatOpenAI -from langchain_core.messages import BaseMessage -from langsmith import Client -from pydantic import BaseModel - -from harbor.agents.base import BaseAgent -from harbor.environments.base import BaseEnvironment -from harbor.models.agent.context import AgentContext -from harbor.models.trajectories import ( - Agent, - FinalMetrics, - Metrics, - Observation, - ObservationResult, - Step, - ToolCall, - Trajectory, -) - -from .harbor_tools import create_harbor_environment_tools -from .langsmith_integration import send_harbor_feedback -from .prompts import HARBOR_SYSTEM_PROMPT - - -class DeepAgentHarbor(BaseAgent): - """Harbor agent that uses LangChain DeepAgents for intelligent task completion. - - DeepAgents provides: - - Planning via write_todos tool - - Real filesystem access via FilesystemBackend (ls, read_file, write_file, edit_file, glob_search, grep_search) - - Subagent spawning for complex tasks - - Built on LangChain 1.0 + LangGraph - - Harbor provides: - - bash tool for command execution in sandboxed environment - """ - - def __init__( - self, - logs_dir: Path, - model_name: str | None = None, - max_iterations: int = 50, - temperature: float = 0.0, - verbose: bool = True, - system_prompt: str | None = None, - *args, - **kwargs, - ): - super().__init__(logs_dir, model_name, *args, **kwargs) - - if model_name is None: - # Use DeepAgents default - model_name = "claude-sonnet-4-5-20250929" - - self._model_name = model_name - self._max_iterations = max_iterations - self._temperature = temperature - self._verbose = verbose - self._session_id = str(uuid.uuid4()) - self._environment: BaseEnvironment | None = None - self._system_prompt = system_prompt or HARBOR_SYSTEM_PROMPT - - # Initialize LLM based on provider prefix (LangSmith will trace if LANGCHAIN_TRACING_V2=true) - if model_name.startswith("openai/") or model_name.startswith("gpt-"): - # OpenAI models: openai/gpt-4o or gpt-4o-mini - actual_model = model_name.split("/")[-1] if "/" in model_name else model_name - self._llm = ChatOpenAI( - model=actual_model, - temperature=temperature, - ) - elif model_name.startswith("anthropic/") or model_name.startswith("claude-"): - # Anthropic models: anthropic/claude-sonnet-4-5-20250929 or claude-sonnet-4-5-20250929 - actual_model = model_name.split("/")[-1] if "/" in model_name else model_name - self._llm = ChatAnthropic( - model=actual_model, - temperature=temperature, - ) - else: - # Default to Anthropic for backward compatibility - self._llm = ChatAnthropic( - model=model_name, - temperature=temperature, - ) - - # Trajectory tracking (ATIF format) - self._trajectory_steps: list[Step] = [] - self._step_counter = 0 - self._total_prompt_tokens = 0 - self._total_completion_tokens = 0 - self._total_cost = 0.0 - - # LangSmith run tracking for feedback - self._langsmith_run_id: str | None = None - self._task_name: str | None = None - - @staticmethod - def name() -> str: - return "deepagent-harbor" - - def version(self) -> str | None: - return "1.0.0" - - async def setup(self, environment: BaseEnvironment) -> None: - """Setup phase - store environment reference.""" - self._environment = environment - - # Add system step to trajectory - self._add_step( - source="system", - message=self._system_prompt, - ) - - async def run( - self, - instruction: str, - environment: BaseEnvironment, - context: AgentContext, - ) -> None: - """Execute the DeepAgent on the given instruction. - - Args: - instruction: The task to complete - environment: Harbor environment (Docker, Modal, etc.) - context: Context to populate with metrics - """ - self._environment = environment - - # Add user instruction as a step - self._add_step( - source="user", - message=instruction, - ) - - # Create Harbor-specific tools that interact with the environment - harbor_tools = create_harbor_environment_tools(environment) - - # Extract only bash tool - DeepAgents will handle filesystem via backend - bash_tool = next((tool for tool in harbor_tools if tool.name == "bash"), None) - if not bash_tool: - raise RuntimeError("bash tool not found in harbor_tools") - - # Configure FilesystemBackend for real filesystem access in Harbor's /app directory - filesystem_backend = FilesystemBackend( - root_dir="/app", # Terminal Bench working directory - virtual_mode=False, # Use real filesystem, not virtual/mock - ) - - # Create DeepAgent with: - # - Harbor's bash tool (for command execution) - # - FilesystemBackend for real file I/O (no mock filesystem) - # - Built-in planning (write_todos) and subagent (task) tools - deep_agent = create_deep_agent( - model=self._llm, - tools=[bash_tool], # Only bash from Harbor - system_prompt=self._system_prompt, - backend=filesystem_backend, # Real filesystem backend - ) - - try: - # Invoke deep agent with LangSmith tracing - result = await deep_agent.ainvoke( - {"messages": [{"role": "user", "content": instruction}]}, - config={ - "run_name": f"harbor-deepagent-{self._session_id[:8]}", - "tags": ["harbor", "deepagent", self._model_name, self._session_id], - "metadata": { - "task_instruction": instruction, - "model": self._model_name, - "session_id": self._session_id, - }, - "recursion_limit": self._max_iterations, - }, - ) - - # Store task name for feedback - self._task_name = instruction[:100] # Truncate for readability - - # Query LangSmith to get the run_id by searching for our unique run_name - self._langsmith_run_id = self._session_id # Default fallback - if os.getenv("LANGCHAIN_TRACING_V2"): - try: - client = Client() - project_name = os.getenv("LANGCHAIN_PROJECT", "default") - run_name = f"harbor-deepagent-{self._session_id[:8]}" - # Search by exact run_name which is unique - runs = list(client.list_runs( - project_name=project_name, - filter=f'eq(name, "{run_name}")', - limit=1, - )) - if runs: - self._langsmith_run_id = str(runs[0].id) - if self._verbose: - print(f"✓ Found LangSmith run_id: {self._langsmith_run_id}") - except Exception as e: - if self._verbose: - print(f"Warning: Could not query LangSmith run_id: {e}") - - # Extract messages from result - messages = result.get("messages", []) - - # Process messages into ATIF steps - self._process_messages_to_steps(messages) - - # Extract final output - final_message = self._extract_final_message(messages) or "Task completed" - last_step = self._trajectory_steps[-1] if self._trajectory_steps else None - if not ( - last_step - and last_step.source == "agent" - and (last_step.message or "").strip() == final_message.strip() - ): - self._add_step( - source="agent", - message=final_message, - ) - - # Build and save trajectory - trajectory = self._build_trajectory() - trajectory_path = self.logs_dir / "trajectory.json" - trajectory_path.write_text(json.dumps(trajectory.to_json_dict(), indent=2)) - - if self._verbose: - print(f"✓ Trajectory saved to {trajectory_path}") - - # Populate context with metrics - if trajectory.final_metrics: - context.n_input_tokens = trajectory.final_metrics.total_prompt_tokens - context.n_output_tokens = trajectory.final_metrics.total_completion_tokens - context.cost_usd = trajectory.final_metrics.total_cost_usd - - except Exception as e: - error_msg = f"DeepAgent execution failed: {str(e)}" - self._add_step( - source="system", - message=error_msg, - ) - if self._verbose: - print(f"✗ Error: {error_msg}") - raise - - def _process_messages_to_steps(self, messages: list[BaseMessage]) -> None: - """Process LangChain messages into ATIF trajectory steps. - - Args: - messages: List of messages from agent execution - """ - for msg in messages: - if msg.type == "ai": - # AI message with potential tool calls - message_content = getattr(msg, "content", "") - - # Check for tool calls - tool_calls = getattr(msg, "tool_calls", None) - - if tool_calls: - # Has tool calls - for tc in tool_calls: - tool_call_id = tc.get("id", f"call_{self._step_counter + 1}") - tool_name = tc.get("name", "unknown") - tool_args = tc.get("args", {}) - - self._add_step( - source="agent", - message=f"Using tool: {tool_name}", - tool_calls=[ - ToolCall( - tool_call_id=tool_call_id, - function_name=tool_name, - arguments=tool_args, - ) - ], - ) - elif message_content: - # Regular AI message - self._add_step( - source="agent", - message=str(message_content), - ) - - # Extract usage metadata if available - usage_metadata = getattr(msg, "usage_metadata", None) - if usage_metadata: - self._update_token_usage(usage_metadata) - - elif msg.type == "tool": - # Tool result - content = getattr(msg, "content", "") - tool_call_id = getattr(msg, "tool_call_id", None) - - # Find the corresponding step and add observation - if self._trajectory_steps: - last_step = self._trajectory_steps[-1] - if last_step.tool_calls: - # Add observation to the last tool call step - last_step.observation = Observation( - results=[ - ObservationResult( - source_call_id=tool_call_id, - content=str(content), - ) - ] - ) - - def _extract_final_message(self, messages: list[BaseMessage]) -> str: - """Extract the final agent message.""" - for msg in reversed(messages): - if msg.type == "ai": - content = getattr(msg, "content", "") - if content: - return str(content) - return "" - - def _update_token_usage(self, usage_metadata: dict[str, Any]) -> None: - """Update token usage from message metadata.""" - input_tokens = usage_metadata.get("input_tokens", 0) - output_tokens = usage_metadata.get("output_tokens", 0) - - self._total_prompt_tokens += input_tokens - self._total_completion_tokens += output_tokens - - # Estimate cost based on model provider - if self._model_name.startswith("openai/") or self._model_name.startswith("gpt-"): - # OpenAI pricing (approximate, as of Nov 2025) - if "gpt-5-mini" in self._model_name or "gpt-4o-mini" in self._model_name: - # Mini models: $0.15 per 1M input, $0.60 per 1M output - input_cost = input_tokens * 0.00000015 - output_cost = output_tokens * 0.0000006 - elif "gpt-5" in self._model_name or "gpt-4o" in self._model_name: - # Standard models: $2.50 per 1M input, $10 per 1M output - input_cost = input_tokens * 0.0000025 - output_cost = output_tokens * 0.00001 - else: - # Default OpenAI pricing - input_cost = input_tokens * 0.0000015 - output_cost = output_tokens * 0.000006 - else: - # Anthropic pricing (Claude Sonnet) - # $3 per 1M input, $15 per 1M output - input_cost = input_tokens * 0.000003 - output_cost = output_tokens * 0.000015 - - self._total_cost += input_cost + output_cost - - def _add_step( - self, - source: str, - message: str, - tool_calls: list[ToolCall] | None = None, - observation: Observation | None = None, - metrics: Metrics | None = None, - ) -> None: - """Add a step to the ATIF trajectory.""" - self._step_counter += 1 - - step = Step( - step_id=self._step_counter, - timestamp=datetime.now(timezone.utc).isoformat(), - source=source, - message=message, - tool_calls=tool_calls, - observation=observation, - metrics=metrics, - ) - - self._trajectory_steps.append(step) - - def _build_trajectory(self) -> Trajectory: - """Build final ATIF trajectory.""" - final_metrics = FinalMetrics( - total_prompt_tokens=self._total_prompt_tokens or None, - total_completion_tokens=self._total_completion_tokens or None, - total_cost_usd=self._total_cost or None, - total_steps=len(self._trajectory_steps), - ) - - return Trajectory( - schema_version="ATIF-v1.2", - session_id=self._session_id, - agent=Agent( - name=self.name(), - version=self.version() or "unknown", - model_name=self._model_name, - extra={ - "framework": "deepagents", - "langchain_version": "1.0+", - "langsmith_run_id": self._langsmith_run_id, # Store for feedback - }, - ), - steps=self._trajectory_steps, - final_metrics=final_metrics, - ) - - def send_verification_feedback(self, reward: float) -> None: - """Send Harbor verification results to LangSmith as feedback. - - This should be called after Harbor's verifier runs to push the - reward score to LangSmith, making it visible in the trace UI. - - Args: - reward: Reward score from Harbor verifier (0.0 to 1.0) - - Example: - >>> agent = DeepAgentHarbor(...) - >>> await agent.run(instruction, environment, context) - >>> # After Harbor verifies the task: - >>> agent.send_verification_feedback(reward=1.0) - """ - if not self._langsmith_run_id or not self._task_name: - if self._verbose: - print("Warning: No run_id or task_name available for feedback") - return - - send_harbor_feedback( - run_id=self._langsmith_run_id, - task_name=self._task_name, - reward=reward, - agent_cost_usd=self._total_cost, - total_steps=len(self._trajectory_steps), - ) - - if self._verbose: - print(f"✓ Sent feedback to LangSmith: reward={reward * 100:.0f}%") diff --git a/libs/harbor/src/harbor_deepagents/agents/harbor_tools.py b/libs/harbor/src/harbor_deepagents/agents/harbor_tools.py deleted file mode 100644 index 7b2d11e3..00000000 --- a/libs/harbor/src/harbor_deepagents/agents/harbor_tools.py +++ /dev/null @@ -1,85 +0,0 @@ -"""Harbor-specific tools for DeepAgents to interact with the environment.""" - -from typing import Annotated - -from langchain_core.tools import tool - -from harbor.environments.base import BaseEnvironment - - -def create_harbor_environment_tools(environment: BaseEnvironment) -> list: - """Create tools that allow DeepAgent to interact with Harbor environment. - - These tools wrap Harbor's environment API to provide: - - Bash command execution - - File reading - - File writing - - Args: - environment: Harbor environment instance (Docker, Modal, etc.) - - Returns: - List of LangChain tool functions - """ - - @tool - async def bash(command: Annotated[str, "The bash command to execute"]) -> str: - """Execute a bash command in the task environment. - - Use this for running Python scripts, installing packages, listing files, - checking output, etc. Returns stdout, stderr, and exit code. - - Examples: - - bash("python script.py") - - bash("ls -la /app") - - bash("cat file.txt") - """ - result = await environment.exec(command) - - output_parts = [] - if result.stdout: - output_parts.append(f"[stdout]\n{result.stdout.strip()}") - if result.stderr: - output_parts.append(f"[stderr]\n{result.stderr.strip()}") - output_parts.append(f"[exit_code] {result.return_code}") - - return "\n\n".join(output_parts) - - @tool - async def read_file(path: Annotated[str, "Path to the file to read"]) -> str: - """Read the contents of a file from the task environment. - - Provide the full path to the file (e.g., /app/script.py). - - Returns the file contents as a string. - """ - result = await environment.exec(f"cat {path}") - - if result.return_code != 0: - return f"Error reading file: {result.stderr}" - - return result.stdout or "" - - @tool - async def write_file( - path: Annotated[str, "Path to the file to write"], - content: Annotated[str, "Content to write to the file"], - ) -> str: - """Write content to a file in the task environment. - - This will create or overwrite the file. Provide the full path - and the content to write. - - Returns success message or error. - """ - # Escape content for heredoc - escaped_content = content.replace("'", "'\"'\"'") - - result = await environment.exec(f"cat > {path} << 'EOF'\n{content}\nEOF") - - if result.return_code != 0: - return f"Error writing file: {result.stderr}" - - return f"Successfully wrote {len(content)} bytes to {path}" - - return [bash, read_file, write_file] diff --git a/libs/harbor/src/harbor_deepagents/agents/langsmith_integration.py b/libs/harbor/src/harbor_deepagents/agents/langsmith_integration.py deleted file mode 100644 index 5f0dfa78..00000000 --- a/libs/harbor/src/harbor_deepagents/agents/langsmith_integration.py +++ /dev/null @@ -1,86 +0,0 @@ -"""LangSmith integration for Harbor results feedback.""" - -import os -from typing import Optional - -from langsmith import Client - - -def send_harbor_feedback( - run_id: str, - task_name: str, - reward: float, - agent_cost_usd: Optional[float] = None, - total_steps: Optional[int] = None, -) -> None: - """Send Harbor verification results to LangSmith as feedback. - - This function pushes Harbor's reward score and metadata to LangSmith - as feedback on the agent's run, making it visible in the LangSmith UI - alongside the execution trace. - - Args: - run_id: LangSmith run ID from the agent execution - task_name: Name of the Harbor task - reward: Reward score from Harbor verifier (0.0 to 1.0, where 1.0 = 100% pass) - agent_cost_usd: Optional cost in USD - total_steps: Optional total number of steps taken - - Example: - >>> send_harbor_feedback( - ... run_id="abc123", - ... task_name="web-scraper-task", - ... reward=1.0, - ... agent_cost_usd=0.012, - ... total_steps=11, - ... ) - """ - # Check if LangSmith tracing is enabled - if not os.getenv("LANGCHAIN_TRACING_V2"): - return - - try: - client = Client() - - # Main reward score feedback - client.create_feedback( - run_id=run_id, - key="harbor_reward", - score=reward, - comment=f"Harbor Task: {task_name} | Score: {reward * 100:.0f}%", - ) - - # Optional cost feedback - if agent_cost_usd is not None: - client.create_feedback( - run_id=run_id, - key="harbor_cost_usd", - score=agent_cost_usd, - comment=f"Agent execution cost: ${agent_cost_usd:.4f}", - ) - - # Optional steps feedback - if total_steps is not None: - client.create_feedback( - run_id=run_id, - key="harbor_steps", - score=total_steps, - comment=f"Total steps: {total_steps}", - ) - - except Exception as e: - # Don't fail the task if feedback fails - print(f"Warning: Failed to send LangSmith feedback: {e}") - - -def get_langsmith_url(run_id: str) -> str: - """Generate LangSmith URL for a given run ID. - - Args: - run_id: LangSmith run ID - - Returns: - Full URL to the run in LangSmith UI - """ - project_name = os.getenv("LANGCHAIN_PROJECT", "default") - return f"https://smith.langchain.com/o/default/projects/p/{project_name}/r/{run_id}" diff --git a/libs/harbor/src/harbor_deepagents/agents/prompts.py b/libs/harbor/src/harbor_deepagents/agents/prompts.py deleted file mode 100644 index e28bf97d..00000000 --- a/libs/harbor/src/harbor_deepagents/agents/prompts.py +++ /dev/null @@ -1,71 +0,0 @@ -"""System prompts for DeepAgent Harbor integration.""" - -HARBOR_SYSTEM_PROMPT = """You are an expert software engineer working on programming tasks in a sandboxed Linux environment. - -Your goal is to complete the given task by writing code, running commands, and verifying your solution. - -## Environment Details - -- **Working directory**: `/app` -- **Environment**: Sandboxed Linux container -- **Task verification**: Tests will run automatically after completion - -## Available Tools - -You have access to the following tools: - -### Command Execution -1. **bash** - Execute bash commands in the sandboxed environment (run scripts, install packages, check outputs) - -### File Operations (Real Filesystem) -2. **ls** - List files and directories (provide path like /app or /app/subdir) -3. **read_file** - Read file contents (supports offset and limit for large files) -4. **write_file** - Create new files with content -5. **edit_file** - Edit existing files with string replacement (supports replace_all flag) -6. **glob_search** - Search for files matching patterns (e.g., "*.py", "test_*.json") -7. **grep_search** - Search file contents with regex patterns - -### Planning & Orchestration -8. **write_todos** - Break down complex tasks into steps and track progress -9. **task** - Spawn subagents for complex, independent subtasks - -**Important**: All file operations work on the REAL filesystem in /app. No mock/virtual filesystem is used. - -## Best Practices - -### Planning -- Use `write_todos` to break down the task into clear steps -- Update todos as you make progress -- Think step-by-step - -### Development -- Write clean, well-commented code -- Test incrementally (write → test → refine) -- Handle errors gracefully -- Use descriptive variable names - -### Verification -- Run your solution to verify it works -- Check that all required files are created -- Test edge cases -- Review the task requirements - -### File Management -- Always work in `/app` directory -- Use paths like `/app/script.py` or just `script.py` (will resolve to /app) -- Use `ls` to explore directory structure before reading files -- Use `glob_search` to find files matching patterns -- Use `grep_search` to search content across files -- Use `edit_file` for modifying existing files (more reliable than rewriting) - -## Task Completion - -When you believe the task is complete: - -1. Run your solution one final time -2. Verify all requirements are met -3. Check that output files are created correctly -4. Provide a brief summary of your solution - -Remember: The tests will verify your solution automatically. Make sure to follow -the requirements exactly as specified in the task instruction.""" diff --git a/libs/harbor/src/harbor_deepagents/cli.py b/libs/harbor/src/harbor_deepagents/cli.py deleted file mode 100644 index 396e302f..00000000 --- a/libs/harbor/src/harbor_deepagents/cli.py +++ /dev/null @@ -1,328 +0,0 @@ -""" -Command-line entry point for running Harbor + DeepAgents harness workflows via uv. -""" - -from __future__ import annotations - -import argparse -import json -import os -import subprocess -import sys -from datetime import datetime -from pathlib import Path -from typing import Dict, Iterable, List, Optional, Sequence - -from dotenv import load_dotenv -from langsmith import Client -from rich.console import Console - -from harbor_deepagents.agents.langsmith_integration import send_harbor_feedback - -console = Console() - -DEFAULT_JOB_CONFIG = Path("configs/job.yaml") -TERMINAL_BENCH_CONFIGS: Dict[str, Path] = { - "docker": Path("configs/terminal-bench-docker.yaml"), - "daytona": Path("configs/terminal-bench-daytona.yaml"), -} -TERMINAL_BENCH_DATASET = "terminal-bench@2.0" - - -def parse_args(argv: Optional[Sequence[str]] = None) -> argparse.Namespace: - parser = argparse.ArgumentParser( - description="Run Harbor DeepAgents harness with simple uv-friendly commands.", - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - ) - parser.add_argument( - "--suite", - choices=["job", "terminal-bench"], - default="job", - help="Which pre-defined job config to use when --config is not provided.", - ) - parser.add_argument( - "--env-type", - choices=["docker", "daytona"], - default="docker", - help="Execution environment preset (used for Terminal Bench configs).", - ) - parser.add_argument( - "--config", - dest="config_path", - help="Explicit Harbor YAML config to run (overrides --suite/--env-type).", - ) - parser.add_argument( - "--dataset", - help="Optional Harbor dataset (e.g., terminal-bench@2.0).", - ) - parser.add_argument( - "--task", - dest="task_name", - help="Optional single task name to run (maps to --task-name).", - ) - parser.add_argument( - "--model", - help="Model identifier (fallback to MODEL_NAME env var).", - ) - parser.add_argument( - "--env-file", - default=".env", - help="Dotenv file to load provider keys + MODEL_NAME from.", - ) - parser.add_argument( - "--no-env-file", - action="store_true", - help="Disable automatic dotenv loading.", - ) - parser.add_argument( - "--dry-run", - action="store_true", - help="Print the Harbor command without executing it.", - ) - parser.add_argument( - "--job-name", - help="Optional job name override. Defaults to --.", - ) - parser.add_argument( - "harbor_args", - nargs=argparse.REMAINDER, - help="Additional arguments after '--' are passed directly to Harbor.", - ) - return parser.parse_args(argv) - - -def load_environment(env_file: Optional[str], skip: bool) -> None: - if skip or not env_file: - return - env_path = Path(env_file) - if not env_path.exists(): - console.print(f"[yellow]No dotenv file found at {env_path}. Continuing without it.[/]") - return - - load_dotenv(env_path, override=False) - console.print(f"[green]Loaded environment variables from {env_path}[/]") - - -def resolve_config(suite: str, env_type: str, explicit: Optional[str]) -> Path: - if explicit: - return Path(explicit) - if suite == "terminal-bench": - config_path = TERMINAL_BENCH_CONFIGS.get(env_type) - if not config_path: - raise ValueError(f"Unsupported env_type '{env_type}' for Terminal Bench runs.") - return config_path - return DEFAULT_JOB_CONFIG - - -def determine_model(cli_model: Optional[str]) -> str: - model = cli_model or os.environ.get("MODEL_NAME") - if not model: - console.print( - "[red]MODEL_NAME is not set. Pass --model or export MODEL_NAME in your environment.[/]" - ) - sys.exit(1) - return model - - -def infer_provider(model: str) -> Optional[str]: - lowered = model.lower() - if lowered.startswith(("openai/", "gpt-")): - return "openai" - if lowered.startswith(("anthropic/", "claude-")): - return "anthropic" - return None - - -def ensure_provider_keys(model: str) -> None: - provider = infer_provider(model) - if provider == "openai" and not os.environ.get("OPENAI_API_KEY"): - console.print("[red]OPENAI_API_KEY is required for OpenAI models.[/]") - sys.exit(1) - if provider == "anthropic" and not os.environ.get("ANTHROPIC_API_KEY"): - console.print("[red]ANTHROPIC_API_KEY is required for Anthropic models.[/]") - sys.exit(1) - - -def ensure_env_requirements(env_type: str) -> None: - if env_type == "daytona" and not os.environ.get("DAYTONA_API_KEY"): - console.print("[red]DAYTONA_API_KEY is required when env-type is 'daytona'.[/]") - sys.exit(1) - - -def build_command(config_path: Path, dataset: Optional[str], task_name: Optional[str]) -> List[str]: - command: List[str] = ["harbor", "run", "--config", str(config_path)] - if dataset: - command.extend(["-d", dataset]) - if task_name: - command.extend(["--task-name", task_name]) - return command - - -def normalize_remainder(args: Iterable[str]) -> List[str]: - remainder = list(args) - if remainder and remainder[0] == "--": - remainder = remainder[1:] - return remainder - - -def main(argv: Optional[Sequence[str]] = None) -> None: - args = parse_args(argv) - load_environment(args.env_file, args.no_env_file) - - config_path = resolve_config(args.suite, args.env_type, args.config_path) - if not config_path.exists(): - console.print(f"[red]Config file not found: {config_path}[/]") - sys.exit(1) - - dataset = args.dataset or ( - TERMINAL_BENCH_DATASET if args.suite == "terminal-bench" else None - ) - - model = determine_model(args.model) - os.environ["MODEL_NAME"] = model - - ensure_provider_keys(model) - ensure_env_requirements(args.env_type) - - extra_args = normalize_remainder(args.harbor_args) - command = build_command(config_path, dataset, args.task_name) - if args.job_name: - command.extend(["--job-name", args.job_name]) - elif "--job-name" not in extra_args: - auto_name = generate_job_name(config_path, args.task_name, model) - command.extend(["--job-name", auto_name]) - if extra_args: - command.extend(extra_args) - - job_name = extract_job_name(command) - if job_name: - console.print(f"[cyan]Running Harbor job {job_name}[/]") - else: - console.print("[cyan]Running Harbor job[/]") - - if args.dry_run: - console.print("[yellow]Dry run enabled; skipping execution.[/]") - return - - try: - subprocess.run(command, check=True) - send_feedback_for_job(job_name) - except subprocess.CalledProcessError as exc: - console.print(f"[red]Harbor exited with code {exc.returncode}[/]") - sys.exit(exc.returncode) - - -def _run_with_preset(preset_args: List[str]) -> None: - extra_args = sys.argv[1:] - main(preset_args + list(extra_args)) - - -def run_tb_docker() -> None: - preset = ["--suite", "terminal-bench", "--env-type", "docker"] - _run_with_preset(preset) - - -def run_tb_daytona() -> None: - preset = ["--suite", "terminal-bench", "--env-type", "daytona"] - _run_with_preset(preset) - - -def generate_job_name(config_path: Path, task_name: Optional[str], model: str) -> str: - """Create a readable job name like tb-daytona-prove-plus-comm-20251112-0730.""" - - timestamp = datetime.utcnow().strftime("%Y%m%d-%H%M%S") - base = config_path.stem - task_segment = task_name or "all-tasks" - model_segment = model.split("/")[-1] - slug = "-".join( - _slugify(segment) - for segment in (base, task_segment, model_segment, timestamp) - if segment - ) - return slug or f"harbor-job-{timestamp}" - - -def _slugify(value: str) -> str: - return "".join(ch.lower() if ch.isalnum() else "-" for ch in value).strip("-") - - -def send_feedback_for_job(job_name: Optional[str]) -> None: - """Push LangSmith feedback for every trial under jobs/.""" - - if not job_name: - return - - job_dir = Path("jobs") / job_name - if not job_dir.exists(): - return - - trajectory_files = sorted(job_dir.glob("**/agent/trajectory.json")) - if not trajectory_files: - return - - sent_count = 0 - total_trials = len(trajectory_files) - - for trajectory_path in trajectory_files: - trial_dir = trajectory_path.parent.parent - result_path = trial_dir / "result.json" - - if not result_path.exists(): - continue - - try: - trajectory = json.loads(trajectory_path.read_text()) - trial_result = json.loads(result_path.read_text()) - except Exception: - continue - - run_id = trajectory.get("agent", {}).get("extra", {}).get("langsmith_run_id") or ( - trajectory.get("session_id") - ) - if not run_id: - continue - - reward = ( - trial_result.get("verifier_result", {}) - .get("rewards", {}) - .get("reward") - ) - if reward is None: - continue - - task_name = trial_result.get("task_name") or trial_dir.name - cost_usd = trial_result.get("agent_result", {}).get("cost_usd") - total_steps = len(trajectory.get("steps", [])) - - try: - send_harbor_feedback( - run_id=run_id, - task_name=task_name, - reward=reward, - agent_cost_usd=cost_usd, - total_steps=total_steps, - ) - sent_count += 1 - except Exception as exc: - console.print( - f"[yellow]Warning: failed to send LangSmith feedback for {trial_dir.name}: {exc}[/]" - ) - - if sent_count: - console.print( - f"[green]✓ Sent LangSmith feedback for {sent_count}/{total_trials} trial(s) in {job_dir.name}[/]" - ) - - -def extract_job_name(command: List[str]) -> Optional[str]: - """Extract --job-name value from the constructed Harbor command.""" - - if "--job-name" in command: - idx = command.index("--job-name") - if idx + 1 < len(command): - return command[idx + 1] - return None - - -if __name__ == "__main__": - main() diff --git a/libs/harbor/src/harbor_deepagents/utils/__init__.py b/libs/harbor/src/harbor_deepagents/utils/__init__.py deleted file mode 100644 index d9816806..00000000 --- a/libs/harbor/src/harbor_deepagents/utils/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -"""Harbor DeepAgents - Utility functions.""" - -from harbor_deepagents.utils.langsmith_exporter import LangSmithTrajectoryExporter - -__all__ = ["LangSmithTrajectoryExporter"] diff --git a/libs/harbor/src/harbor_deepagents/utils/langsmith_exporter.py b/libs/harbor/src/harbor_deepagents/utils/langsmith_exporter.py deleted file mode 100644 index 7e4e3c24..00000000 --- a/libs/harbor/src/harbor_deepagents/utils/langsmith_exporter.py +++ /dev/null @@ -1,177 +0,0 @@ -"""Export Harbor ATIF trajectories to LangSmith for visualization.""" - -import json -from pathlib import Path -from typing import Any - -from langsmith import Client - - -class LangSmithTrajectoryExporter: - """Export ATIF trajectories to LangSmith. - - This allows you to: - - Visualize agent execution in LangSmith UI - - Compare different runs - - Analyze costs and latency - - Debug agent behavior - """ - - def __init__(self, api_key: str | None = None): - """Initialize exporter with optional API key. - - Args: - api_key: LangSmith API key (or use LANGCHAIN_API_KEY env var) - """ - self.client = Client(api_key=api_key) - - def export_trajectory( - self, - trajectory_path: Path | str, - project_name: str = "harbor-deepagents", - ) -> str: - """Export an ATIF trajectory file to LangSmith. - - Args: - trajectory_path: Path to ATIF trajectory JSON file - project_name: LangSmith project name - - Returns: - The run ID in LangSmith - """ - trajectory_path = Path(trajectory_path) - - with open(trajectory_path) as f: - atif = json.load(f) - - # Create root run - run_id = self.client.create_run( - name=f"harbor-{atif['agent']['name']}-{atif['session_id'][:8]}", - run_type="chain", - inputs={"instruction": self._extract_user_message(atif)}, - project_name=project_name, - tags=["harbor", atif["agent"]["name"], "deepagents"], - extra={ - "metadata": { - "agent_name": atif["agent"]["name"], - "agent_version": atif["agent"]["version"], - "model": atif["agent"].get("model_name"), - "session_id": atif["session_id"], - "schema_version": atif["schema_version"], - **atif["agent"].get("extra", {}), - } - }, - ).id - - # Add steps as child runs - for step in atif["steps"]: - self._add_step_run( - step=step, - parent_run_id=run_id, - project_name=project_name, - ) - - # Update root run with final output and metrics - final_message = self._extract_final_message(atif) - final_metrics = atif.get("final_metrics", {}) - - self.client.update_run( - run_id=run_id, - outputs={"result": final_message}, - extra={"metadata": final_metrics}, - ) - - return run_id - - def export_job_trajectories( - self, - job_dir: Path | str, - project_name: str = "harbor-deepagents", - ) -> list[str]: - """Export all trajectories from a Harbor job directory. - - Args: - job_dir: Path to Harbor job directory - project_name: LangSmith project name - - Returns: - List of LangSmith run IDs - """ - job_dir = Path(job_dir) - trajectory_files = list(job_dir.rglob("trajectory.json")) - - run_ids = [] - for trajectory_path in trajectory_files: - try: - run_id = self.export_trajectory(trajectory_path, project_name) - run_ids.append(run_id) - print(f"✓ Exported {trajectory_path.parent.name}: {run_id}") - except Exception as e: - print(f"✗ Failed to export {trajectory_path}: {e}") - - return run_ids - - def _extract_user_message(self, atif: dict[str, Any]) -> str: - """Extract the initial user message from ATIF.""" - for step in atif["steps"]: - if step["source"] == "user": - return step["message"] - return "" - - def _extract_final_message(self, atif: dict[str, Any]) -> str: - """Extract the final agent message from ATIF.""" - for step in reversed(atif["steps"]): - if step["source"] == "agent": - return step["message"] - return "" - - def _add_step_run( - self, - step: dict[str, Any], - parent_run_id: str, - project_name: str, - ) -> None: - """Add a step as a child run in LangSmith.""" - run_type = self._get_run_type(step) - - inputs = {"message": step.get("message", "")} - outputs = {} - - # Add tool call information - if step.get("tool_calls"): - tool_call = step["tool_calls"][0] # Simplified: take first tool call - inputs["tool"] = tool_call.get("function_name") - inputs["arguments"] = tool_call.get("arguments") - - # Add observation information - if step.get("observation"): - results = step["observation"].get("results", []) - if results: - outputs["observation"] = results[0].get("content") - - # Create child run - self.client.create_run( - name=f"step-{step['step_id']}-{step['source']}", - run_type=run_type, - inputs=inputs, - outputs=outputs if outputs else None, - parent_run_id=parent_run_id, - project_name=project_name, - extra={ - "metadata": { - "step_id": step["step_id"], - "source": step["source"], - "timestamp": step.get("timestamp"), - **(step.get("metrics", {}) or {}), - } - }, - ) - - def _get_run_type(self, step: dict[str, Any]) -> str: - """Determine LangSmith run type from ATIF step.""" - if step.get("tool_calls"): - return "tool" - elif step["source"] == "agent": - return "llm" - else: - return "chain" diff --git a/libs/harbor/src/harness_entry/__init__.py b/libs/harbor/src/harness_entry/__init__.py deleted file mode 100644 index 50db82c3..00000000 --- a/libs/harbor/src/harness_entry/__init__.py +++ /dev/null @@ -1,43 +0,0 @@ -"""Bootstrap wrapper so console scripts can import from src without editable installs.""" - -from __future__ import annotations - -import os -import sys -from pathlib import Path - - -def _maybe_add_src_path() -> None: - """Ensure the repo's src directory is importable when developing locally.""" - - candidate_roots = [ - Path.cwd(), - Path(os.environ.get("HARBOR_DEEPAGENTS_ROOT", "")), - ] - - for root in candidate_roots: - if not root: - continue - src_dir = root / "src" - if src_dir.is_dir(): - sys.path.insert(0, str(src_dir)) - return - - -_maybe_add_src_path() - -from harbor_deepagents.cli import main as _main -from harbor_deepagents.cli import run_tb_daytona as _run_tb_daytona -from harbor_deepagents.cli import run_tb_docker as _run_tb_docker - - -def main() -> None: - _main() - - -def tb_docker() -> None: - _run_tb_docker() - - -def tb_daytona() -> None: - _run_tb_daytona() From c4d08613ec4c005dbead0f10ed24738398f55a17 Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Wed, 19 Nov 2025 15:31:24 -0500 Subject: [PATCH 04/10] x --- .../harbor_deepagents/agents/__init__.py | 2 - .../harbor_deepagents/agents/custom_agent.py | 37 ------------- .../agents/deepagent_harbor.py | 42 ++++----------- .../harbor_deepagents/agents/harbor_tools.py | 5 +- .../{langsmith_integration.py => tracing.py} | 52 ++++++++----------- 5 files changed, 34 insertions(+), 104 deletions(-) delete mode 100644 libs/harbor/harbor_deepagents/agents/custom_agent.py rename libs/harbor/harbor_deepagents/agents/{langsmith_integration.py => tracing.py} (63%) diff --git a/libs/harbor/harbor_deepagents/agents/__init__.py b/libs/harbor/harbor_deepagents/agents/__init__.py index 42e55f97..e709621a 100644 --- a/libs/harbor/harbor_deepagents/agents/__init__.py +++ b/libs/harbor/harbor_deepagents/agents/__init__.py @@ -1,9 +1,7 @@ """Harbor DeepAgents - Agent implementations.""" -from harbor_deepagents.agents.custom_agent import CustomDeepAgent from harbor_deepagents.agents.deepagent_harbor import DeepAgentHarbor __all__ = [ - "CustomDeepAgent", "DeepAgentHarbor", ] diff --git a/libs/harbor/harbor_deepagents/agents/custom_agent.py b/libs/harbor/harbor_deepagents/agents/custom_agent.py deleted file mode 100644 index 1acdd216..00000000 --- a/libs/harbor/harbor_deepagents/agents/custom_agent.py +++ /dev/null @@ -1,37 +0,0 @@ -"""Simple extension template for quickly experimenting with custom agents.""" - -from __future__ import annotations - -from pathlib import Path - -from harbor_deepagents.agents.deepagent_harbor import DeepAgentHarbor - -#Change this to what you want -CUSTOM_SYSTEM_PROMPT = """You are a Harbor DeepAgent specialized for custom experiments. - -Adjust this prompt, override methods, or replace the class entirely to suit -your use case. The default behavior inherits DeepAgentHarbor's tooling stack. -""" - - -class CustomDeepAgent(DeepAgentHarbor): - """Starter agent that forwards to DeepAgentHarbor with a tweakable prompt. - - Replace the prompt, override `run`, or layer in additional initialization - without having to touch the stock DeepAgentHarbor implementation. Point - Harbor configs at `harbor_deepagents.agents.custom_agent:CustomDeepAgent` - once you're ready to iterate on your own behavior. - """ - - def __init__( - self, - logs_dir: Path, - *, - system_prompt: str | None = None, - **kwargs, - ) -> None: - super().__init__( - logs_dir=logs_dir, - system_prompt=system_prompt or CUSTOM_SYSTEM_PROMPT, - **kwargs, - ) diff --git a/libs/harbor/harbor_deepagents/agents/deepagent_harbor.py b/libs/harbor/harbor_deepagents/agents/deepagent_harbor.py index 443525a3..6aec02ee 100644 --- a/libs/harbor/harbor_deepagents/agents/deepagent_harbor.py +++ b/libs/harbor/harbor_deepagents/agents/deepagent_harbor.py @@ -5,15 +5,7 @@ import uuid from datetime import datetime, timezone from pathlib import Path -from typing import Any, Optional - -from deepagents import create_deep_agent -from deepagents.backends import FilesystemBackend -from langchain_anthropic import ChatAnthropic -from langchain_openai import ChatOpenAI -from langchain_core.messages import BaseMessage -from langsmith import Client -from pydantic import BaseModel +from typing import Any from harbor.agents.base import BaseAgent from harbor.environments.base import BaseEnvironment @@ -28,10 +20,15 @@ ToolCall, Trajectory, ) +from langchain.chat_models import init_chat_model +from langchain_core.messages import BaseMessage +from langsmith import Client +from deepagents import create_deep_agent +from deepagents.backends import FilesystemBackend from harbor_deepagents.agents.harbor_tools import create_harbor_environment_tools -from harbor_deepagents.agents.langsmith_integration import send_harbor_feedback from harbor_deepagents.agents.prompts import HARBOR_SYSTEM_PROMPT +from harbor_deepagents.agents.tracing import send_harbor_feedback class DeepAgentHarbor(BaseAgent): @@ -62,7 +59,7 @@ def __init__( if model_name is None: # Use DeepAgents default - model_name = "claude-sonnet-4-5-20250929" + model_name = "anthropic:claude-sonnet-4-5-20250929" self._model_name = model_name self._max_iterations = max_iterations @@ -71,28 +68,7 @@ def __init__( self._session_id = str(uuid.uuid4()) self._environment: BaseEnvironment | None = None self._system_prompt = system_prompt or HARBOR_SYSTEM_PROMPT - - # Initialize LLM based on provider prefix (LangSmith will trace if LANGCHAIN_TRACING_V2=true) - if model_name.startswith("openai/") or model_name.startswith("gpt-"): - # OpenAI models: openai/gpt-4o or gpt-4o-mini - actual_model = model_name.split("/")[-1] if "/" in model_name else model_name - self._llm = ChatOpenAI( - model=actual_model, - temperature=temperature, - ) - elif model_name.startswith("anthropic/") or model_name.startswith("claude-"): - # Anthropic models: anthropic/claude-sonnet-4-5-20250929 or claude-sonnet-4-5-20250929 - actual_model = model_name.split("/")[-1] if "/" in model_name else model_name - self._llm = ChatAnthropic( - model=actual_model, - temperature=temperature, - ) - else: - # Default to Anthropic for backward compatibility - self._llm = ChatAnthropic( - model=model_name, - temperature=temperature, - ) + self._model = init_chat_model(model_name, temperature=temperature) # Trajectory tracking (ATIF format) self._trajectory_steps: list[Step] = [] diff --git a/libs/harbor/harbor_deepagents/agents/harbor_tools.py b/libs/harbor/harbor_deepagents/agents/harbor_tools.py index 7b2d11e3..ce788777 100644 --- a/libs/harbor/harbor_deepagents/agents/harbor_tools.py +++ b/libs/harbor/harbor_deepagents/agents/harbor_tools.py @@ -2,12 +2,11 @@ from typing import Annotated -from langchain_core.tools import tool - from harbor.environments.base import BaseEnvironment +from langchain_core.tools import tool, BaseTool -def create_harbor_environment_tools(environment: BaseEnvironment) -> list: +def create_harbor_environment_tools(environment: BaseEnvironment) -> list[BaseTool]: """Create tools that allow DeepAgent to interact with Harbor environment. These tools wrap Harbor's environment API to provide: diff --git a/libs/harbor/harbor_deepagents/agents/langsmith_integration.py b/libs/harbor/harbor_deepagents/agents/tracing.py similarity index 63% rename from libs/harbor/harbor_deepagents/agents/langsmith_integration.py rename to libs/harbor/harbor_deepagents/agents/tracing.py index 5f0dfa78..4e5d0f83 100644 --- a/libs/harbor/harbor_deepagents/agents/langsmith_integration.py +++ b/libs/harbor/harbor_deepagents/agents/tracing.py @@ -39,39 +39,33 @@ def send_harbor_feedback( if not os.getenv("LANGCHAIN_TRACING_V2"): return - try: - client = Client() - - # Main reward score feedback + client = Client() + + # Main reward score feedback + client.create_feedback( + run_id=run_id, + key="harbor_reward", + score=reward, + comment=f"Harbor Task: {task_name} | Score: {reward * 100:.0f}%", + ) + + # Optional cost feedback + if agent_cost_usd is not None: client.create_feedback( run_id=run_id, - key="harbor_reward", - score=reward, - comment=f"Harbor Task: {task_name} | Score: {reward * 100:.0f}%", + key="harbor_cost_usd", + score=agent_cost_usd, + comment=f"Agent execution cost: ${agent_cost_usd:.4f}", ) - # Optional cost feedback - if agent_cost_usd is not None: - client.create_feedback( - run_id=run_id, - key="harbor_cost_usd", - score=agent_cost_usd, - comment=f"Agent execution cost: ${agent_cost_usd:.4f}", - ) - - # Optional steps feedback - if total_steps is not None: - client.create_feedback( - run_id=run_id, - key="harbor_steps", - score=total_steps, - comment=f"Total steps: {total_steps}", - ) - - except Exception as e: - # Don't fail the task if feedback fails - print(f"Warning: Failed to send LangSmith feedback: {e}") - + # Optional steps feedback + if total_steps is not None: + client.create_feedback( + run_id=run_id, + key="harbor_steps", + score=total_steps, + comment=f"Total steps: {total_steps}", + ) def get_langsmith_url(run_id: str) -> str: """Generate LangSmith URL for a given run ID. From 99e242f1eaefb59624e9e7b2e851de0443b9c042 Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Wed, 19 Nov 2025 17:59:17 -0500 Subject: [PATCH 05/10] x --- libs/harbor/Makefile | 2 +- .../harbor_deepagents/agents/deepagent.py | 344 ++ .../agents/deepagent_harbor.py | 411 -- .../agents/harbor_backend.py | 33 + .../harbor_deepagents/agents/harbor_tools.py | 84 - libs/harbor/harbor_deepagents/cli.py | 12 +- .../harbor_deepagents/langsmith_exporter.py | 177 + libs/harbor/pyproject.toml | 2 +- libs/harbor/scripts/export_traces.py | 176 +- libs/harbor/scripts/harbor_entry.py | 6 +- libs/harbor/uv.lock | 3498 ++++++++--------- 11 files changed, 2402 insertions(+), 2343 deletions(-) create mode 100644 libs/harbor/harbor_deepagents/agents/deepagent.py delete mode 100644 libs/harbor/harbor_deepagents/agents/deepagent_harbor.py create mode 100644 libs/harbor/harbor_deepagents/agents/harbor_backend.py delete mode 100644 libs/harbor/harbor_deepagents/agents/harbor_tools.py create mode 100644 libs/harbor/harbor_deepagents/langsmith_exporter.py diff --git a/libs/harbor/Makefile b/libs/harbor/Makefile index 2fac930e..b408a1ea 100644 --- a/libs/harbor/Makefile +++ b/libs/harbor/Makefile @@ -26,7 +26,7 @@ test_watch: ###################### # Define a variable for Python and notebook files. -lint format: PYTHON_FILES=deepagents_cli/ tests/ +lint format: PYTHON_FILES=harbor_deepagents/ tests/ lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=. --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') lint lint_diff: diff --git a/libs/harbor/harbor_deepagents/agents/deepagent.py b/libs/harbor/harbor_deepagents/agents/deepagent.py new file mode 100644 index 00000000..d77fb521 --- /dev/null +++ b/libs/harbor/harbor_deepagents/agents/deepagent.py @@ -0,0 +1,344 @@ +"""Harbor agent implementation using LangChain DeepAgents.""" + +import json +import os +import uuid +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Literal + +from harbor.agents.base import BaseAgent +from harbor.environments.base import BaseEnvironment +from harbor.models.agent.context import AgentContext +from harbor.models.trajectories import ( + Agent, + FinalMetrics, + Metrics, + Observation, + ObservationResult, + Step, + ToolCall, + Trajectory, +) +from langchain.chat_models import init_chat_model +from langchain_core.messages import AIMessage, ToolMessage +from langchain_core.messages import BaseMessage +from langsmith import Client + +from deepagents import create_deep_agent +from harbor_deepagents.agents.harbor_backend import HarborSandbox +from harbor_deepagents.agents.tracing import send_harbor_feedback + + +class DeepAgentHarbor(BaseAgent): + """Harbor agent that uses LangChain DeepAgents for intelligent task completion.""" + + def __init__( + self, + logs_dir: Path, + model_name: str | None = None, + max_iterations: int = 50, + temperature: float = 0.0, + verbose: bool = True, + *args, + **kwargs, + ) -> None: + super().__init__(logs_dir, model_name, *args, **kwargs) + + if model_name is None: + # Use DeepAgents default + model_name = "anthropic:claude-sonnet-4-5-20250929" + + self._model_name = model_name + self._max_iterations = max_iterations + self._temperature = temperature + self._verbose = verbose + self._session_id = str(uuid.uuid4()) + self._environment: BaseEnvironment | None = None + self._model = init_chat_model(model_name, temperature=temperature) + + # Trajectory tracking (ATIF format) + self._trajectory_steps: list[Step] = [] + self._step_counter = 0 + self._total_prompt_tokens = 0 + self._total_completion_tokens = 0 + self._total_cost = 0.0 + + # LangSmith run tracking for feedback + self._langsmith_run_id: str | None = None + self._task_name: str | None = None + + @staticmethod + def name() -> str: + return "deepagent-harbor" + + def version(self) -> str | None: + return "1.0.0" + + async def run( + self, + instruction: str, + environment: BaseEnvironment, + context: AgentContext, + ) -> None: + """Execute the DeepAgent on the given instruction. + + Args: + instruction: The task to complete + environment: Harbor environment (Docker, Modal, etc.) + context: Context to populate with metrics + """ + raise ValueError(BaseEnvironment) + self._add_step( + source="user", + message=instruction, + ) + backend = HarborSandbox(environment) + + # Create DeepAgent with: + # - Harbor's bash tool (for command execution) + # - FilesystemBackend for real file I/O (no mock filesystem) + # - Built-in planning (write_todos) and subagent (task) tools + deep_agent = create_deep_agent( + model=self._model, + backend=backend + ) + + # Invoke deep agent with LangSmith tracing + result = await deep_agent.ainvoke( + {"messages": [{"role": "user", "content": instruction}]}, + config={ + "run_name": f"harbor-deepagent-{self._session_id[:8]}", + "tags": ["harbor", "deepagent", self._model_name, self._session_id], + "metadata": { + "task_instruction": instruction, + "model": self._model_name, + "session_id": self._session_id, + }, + "recursion_limit": self._max_iterations, + }, + ) + + # Store task name for feedback + self._task_name = instruction[:100] # Truncate for readability + + # Query LangSmith to get the run_id by searching for our unique run_name + self._langsmith_run_id = self._session_id # Default fallback + + if os.getenv("LANGCHAIN_TRACING_V2"): + client = Client() + project_name = os.getenv("LANGCHAIN_PROJECT", "default") + run_name = f"harbor-deepagent-{self._session_id[:8]}" + # Search by exact run_name which is unique + runs = list(client.list_runs( + project_name=project_name, + filter=f'eq(name, "{run_name}")', + limit=1, + )) + if runs: + self._langsmith_run_id = str(runs[0].id) + if self._verbose: + print(f"✓ Found LangSmith run_id: {self._langsmith_run_id}") + + # Extract messages from result + messages = result.get("messages", []) + + # Process messages into ATIF steps + self._process_messages_to_steps(messages) + + # Extract final output + final_message = self._extract_final_message(messages) or "Task completed" + last_step = self._trajectory_steps[-1] if self._trajectory_steps else None + if not ( + last_step + and last_step.source == "agent" + and (last_step.message or "").strip() == final_message.strip() + ): + self._add_step( + source="agent", + message=final_message, + ) + + # Build and save trajectory + trajectory = self._build_trajectory() + trajectory_path = self.logs_dir / "trajectory.json" + trajectory_path.write_text(json.dumps(trajectory.to_json_dict(), indent=2)) + + if self._verbose: + print(f"✓ Trajectory saved to {trajectory_path}") + + # Populate context with metrics + if trajectory.final_metrics: + context.n_input_tokens = trajectory.final_metrics.total_prompt_tokens + context.n_output_tokens = trajectory.final_metrics.total_completion_tokens + context.cost_usd = trajectory.final_metrics.total_cost_usd + + def _process_message(self, msg: BaseMessage) -> None: + """Process LangChain messages into ATIF trajectory steps.""" + if isinstance(msg, AIMessage): + tool_calls = msg.tool_calls + for tc in tool_calls: + tool_call_id = tc.get("id", f"call_{self._step_counter + 1}") + tool_name = tc.get("name", "unknown") + tool_args = tc.get("args", {}) + + self._add_step( + source="agent", + message=f"Using tool: {tool_name}", + tool_calls=[ + ToolCall( + tool_call_id=tool_call_id, + function_name=tool_name, + arguments=tool_args, + ) + ], + ) + else: + self._add_step( + source="agent", + message=str(msg.content_blocks), + ) + + # Extract usage metadata if available + usage_metadata = getattr(msg, "usage_metadata", None) + if usage_metadata: + self._update_token_usage(usage_metadata) + elif isinstance(msg, ToolMessage): + content_blocks = msg.content_blocks + tool_call_id = msg.tool_call_id + # Find the corresponding step and add observation + if self._trajectory_steps: + last_step = self._trajectory_steps[-1] + if last_step.tool_calls: + # Add observation to the last tool call step + last_step.observation = Observation( + results=[ + ObservationResult( + source_call_id=tool_call_id, + content=str(msg.content_blocks), + ) + ] + ) + else: + raise NotImplementedError( + f"Message type {type(msg)} not supported for step conversion" + ) + + def _extract_final_message(self, messages: list[BaseMessage]) -> str: + """Extract the final agent message.""" + for msg in reversed(messages): + if msg.type == "ai": + content = getattr(msg, "content", "") + if content: + return str(content) + return "" + + def _update_token_usage(self, usage_metadata: dict[str, Any]) -> None: + """Update token usage from message metadata.""" + input_tokens = usage_metadata.get("input_tokens", 0) + output_tokens = usage_metadata.get("output_tokens", 0) + + self._total_prompt_tokens += input_tokens + self._total_completion_tokens += output_tokens + + # Estimate cost based on model provider + if self._model_name.startswith("openai/") or self._model_name.startswith("gpt-"): + # OpenAI pricing (approximate, as of Nov 2025) + if "gpt-5-mini" in self._model_name or "gpt-4o-mini" in self._model_name: + # Mini models: $0.15 per 1M input, $0.60 per 1M output + input_cost = input_tokens * 0.00000015 + output_cost = output_tokens * 0.0000006 + elif "gpt-5" in self._model_name or "gpt-4o" in self._model_name: + # Standard models: $2.50 per 1M input, $10 per 1M output + input_cost = input_tokens * 0.0000025 + output_cost = output_tokens * 0.00001 + else: + # Default OpenAI pricing + input_cost = input_tokens * 0.0000015 + output_cost = output_tokens * 0.000006 + else: + # Anthropic pricing (Claude Sonnet) + # $3 per 1M input, $15 per 1M output + input_cost = input_tokens * 0.000003 + output_cost = output_tokens * 0.000015 + + self._total_cost += input_cost + output_cost + + def _add_step( + self, + source: Literal["system", "user", "agent"], + message: str, + tool_calls: list[ToolCall] | None = None, + observation: Observation | None = None, + metrics: Metrics | None = None, + ) -> None: + """Add a step to the ATIF trajectory.""" + self._step_counter += 1 + step = Step( + step_id=self._step_counter, + timestamp=datetime.now(timezone.utc).isoformat(), + source=source, + message=message, + tool_calls=tool_calls, + observation=observation, + metrics=metrics, + ) + + self._trajectory_steps.append(step) + + def _build_trajectory(self) -> Trajectory: + """Build final ATIF trajectory.""" + final_metrics = FinalMetrics( + total_prompt_tokens=self._total_prompt_tokens or None, + total_completion_tokens=self._total_completion_tokens or None, + total_cost_usd=self._total_cost or None, + total_steps=len(self._trajectory_steps), + ) + + return Trajectory( + schema_version="ATIF-v1.2", + session_id=self._session_id, + agent=Agent( + name=self.name(), + version=self.version() or "unknown", + model_name=self._model_name, + extra={ + "framework": "deepagents", + "langchain_version": "1.0+", + "langsmith_run_id": self._langsmith_run_id, # Store for feedback + }, + ), + steps=self._trajectory_steps, + final_metrics=final_metrics, + ) + + def send_verification_feedback(self, reward: float) -> None: + """Send Harbor verification results to LangSmith as feedback. + + This should be called after Harbor's verifier runs to push the + reward score to LangSmith, making it visible in the trace UI. + + Args: + reward: Reward score from Harbor verifier (0.0 to 1.0) + + Example: + >>> agent = DeepAgentHarbor(...) + >>> await agent.run(instruction, environment, context) + >>> # After Harbor verifies the task: + >>> agent.send_verification_feedback(reward=1.0) + """ + if not self._langsmith_run_id or not self._task_name: + if self._verbose: + print("Warning: No run_id or task_name available for feedback") + return + + send_harbor_feedback( + run_id=self._langsmith_run_id, + task_name=self._task_name, + reward=reward, + agent_cost_usd=self._total_cost, + total_steps=len(self._trajectory_steps), + ) + + if self._verbose: + print(f"✓ Sent feedback to LangSmith: reward={reward * 100:.0f}%") diff --git a/libs/harbor/harbor_deepagents/agents/deepagent_harbor.py b/libs/harbor/harbor_deepagents/agents/deepagent_harbor.py deleted file mode 100644 index 6aec02ee..00000000 --- a/libs/harbor/harbor_deepagents/agents/deepagent_harbor.py +++ /dev/null @@ -1,411 +0,0 @@ -"""Harbor agent implementation using LangChain DeepAgents.""" - -import json -import os -import uuid -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - -from harbor.agents.base import BaseAgent -from harbor.environments.base import BaseEnvironment -from harbor.models.agent.context import AgentContext -from harbor.models.trajectories import ( - Agent, - FinalMetrics, - Metrics, - Observation, - ObservationResult, - Step, - ToolCall, - Trajectory, -) -from langchain.chat_models import init_chat_model -from langchain_core.messages import BaseMessage -from langsmith import Client - -from deepagents import create_deep_agent -from deepagents.backends import FilesystemBackend -from harbor_deepagents.agents.harbor_tools import create_harbor_environment_tools -from harbor_deepagents.agents.prompts import HARBOR_SYSTEM_PROMPT -from harbor_deepagents.agents.tracing import send_harbor_feedback - - -class DeepAgentHarbor(BaseAgent): - """Harbor agent that uses LangChain DeepAgents for intelligent task completion. - - DeepAgents provides: - - Planning via write_todos tool - - Real filesystem access via FilesystemBackend (ls, read_file, write_file, edit_file, glob_search, grep_search) - - Subagent spawning for complex tasks - - Built on LangChain 1.0 + LangGraph - - Harbor provides: - - bash tool for command execution in sandboxed environment - """ - - def __init__( - self, - logs_dir: Path, - model_name: str | None = None, - max_iterations: int = 50, - temperature: float = 0.0, - verbose: bool = True, - system_prompt: str | None = None, - *args, - **kwargs, - ): - super().__init__(logs_dir, model_name, *args, **kwargs) - - if model_name is None: - # Use DeepAgents default - model_name = "anthropic:claude-sonnet-4-5-20250929" - - self._model_name = model_name - self._max_iterations = max_iterations - self._temperature = temperature - self._verbose = verbose - self._session_id = str(uuid.uuid4()) - self._environment: BaseEnvironment | None = None - self._system_prompt = system_prompt or HARBOR_SYSTEM_PROMPT - self._model = init_chat_model(model_name, temperature=temperature) - - # Trajectory tracking (ATIF format) - self._trajectory_steps: list[Step] = [] - self._step_counter = 0 - self._total_prompt_tokens = 0 - self._total_completion_tokens = 0 - self._total_cost = 0.0 - - # LangSmith run tracking for feedback - self._langsmith_run_id: str | None = None - self._task_name: str | None = None - - @staticmethod - def name() -> str: - return "deepagent-harbor" - - def version(self) -> str | None: - return "1.0.0" - - async def setup(self, environment: BaseEnvironment) -> None: - """Setup phase - store environment reference.""" - self._environment = environment - - # Add system step to trajectory - self._add_step( - source="system", - message=self._system_prompt, - ) - - async def run( - self, - instruction: str, - environment: BaseEnvironment, - context: AgentContext, - ) -> None: - """Execute the DeepAgent on the given instruction. - - Args: - instruction: The task to complete - environment: Harbor environment (Docker, Modal, etc.) - context: Context to populate with metrics - """ - self._environment = environment - - # Add user instruction as a step - self._add_step( - source="user", - message=instruction, - ) - - # Create Harbor-specific tools that interact with the environment - harbor_tools = create_harbor_environment_tools(environment) - - # Extract only bash tool - DeepAgents will handle filesystem via backend - bash_tool = next((tool for tool in harbor_tools if tool.name == "bash"), None) - if not bash_tool: - raise RuntimeError("bash tool not found in harbor_tools") - - # Configure FilesystemBackend for real filesystem access in Harbor's /app directory - filesystem_backend = FilesystemBackend( - root_dir="/app", # Terminal Bench working directory - virtual_mode=False, # Use real filesystem, not virtual/mock - ) - - # Create DeepAgent with: - # - Harbor's bash tool (for command execution) - # - FilesystemBackend for real file I/O (no mock filesystem) - # - Built-in planning (write_todos) and subagent (task) tools - deep_agent = create_deep_agent( - model=self._llm, - tools=[bash_tool], # Only bash from Harbor - system_prompt=self._system_prompt, - backend=filesystem_backend, # Real filesystem backend - ) - - try: - # Invoke deep agent with LangSmith tracing - result = await deep_agent.ainvoke( - {"messages": [{"role": "user", "content": instruction}]}, - config={ - "run_name": f"harbor-deepagent-{self._session_id[:8]}", - "tags": ["harbor", "deepagent", self._model_name, self._session_id], - "metadata": { - "task_instruction": instruction, - "model": self._model_name, - "session_id": self._session_id, - }, - "recursion_limit": self._max_iterations, - }, - ) - - # Store task name for feedback - self._task_name = instruction[:100] # Truncate for readability - - # Query LangSmith to get the run_id by searching for our unique run_name - self._langsmith_run_id = self._session_id # Default fallback - if os.getenv("LANGCHAIN_TRACING_V2"): - try: - client = Client() - project_name = os.getenv("LANGCHAIN_PROJECT", "default") - run_name = f"harbor-deepagent-{self._session_id[:8]}" - # Search by exact run_name which is unique - runs = list(client.list_runs( - project_name=project_name, - filter=f'eq(name, "{run_name}")', - limit=1, - )) - if runs: - self._langsmith_run_id = str(runs[0].id) - if self._verbose: - print(f"✓ Found LangSmith run_id: {self._langsmith_run_id}") - except Exception as e: - if self._verbose: - print(f"Warning: Could not query LangSmith run_id: {e}") - - # Extract messages from result - messages = result.get("messages", []) - - # Process messages into ATIF steps - self._process_messages_to_steps(messages) - - # Extract final output - final_message = self._extract_final_message(messages) or "Task completed" - last_step = self._trajectory_steps[-1] if self._trajectory_steps else None - if not ( - last_step - and last_step.source == "agent" - and (last_step.message or "").strip() == final_message.strip() - ): - self._add_step( - source="agent", - message=final_message, - ) - - # Build and save trajectory - trajectory = self._build_trajectory() - trajectory_path = self.logs_dir / "trajectory.json" - trajectory_path.write_text(json.dumps(trajectory.to_json_dict(), indent=2)) - - if self._verbose: - print(f"✓ Trajectory saved to {trajectory_path}") - - # Populate context with metrics - if trajectory.final_metrics: - context.n_input_tokens = trajectory.final_metrics.total_prompt_tokens - context.n_output_tokens = trajectory.final_metrics.total_completion_tokens - context.cost_usd = trajectory.final_metrics.total_cost_usd - - except Exception as e: - error_msg = f"DeepAgent execution failed: {str(e)}" - self._add_step( - source="system", - message=error_msg, - ) - if self._verbose: - print(f"✗ Error: {error_msg}") - raise - - def _process_messages_to_steps(self, messages: list[BaseMessage]) -> None: - """Process LangChain messages into ATIF trajectory steps. - - Args: - messages: List of messages from agent execution - """ - for msg in messages: - if msg.type == "ai": - # AI message with potential tool calls - message_content = getattr(msg, "content", "") - - # Check for tool calls - tool_calls = getattr(msg, "tool_calls", None) - - if tool_calls: - # Has tool calls - for tc in tool_calls: - tool_call_id = tc.get("id", f"call_{self._step_counter + 1}") - tool_name = tc.get("name", "unknown") - tool_args = tc.get("args", {}) - - self._add_step( - source="agent", - message=f"Using tool: {tool_name}", - tool_calls=[ - ToolCall( - tool_call_id=tool_call_id, - function_name=tool_name, - arguments=tool_args, - ) - ], - ) - elif message_content: - # Regular AI message - self._add_step( - source="agent", - message=str(message_content), - ) - - # Extract usage metadata if available - usage_metadata = getattr(msg, "usage_metadata", None) - if usage_metadata: - self._update_token_usage(usage_metadata) - - elif msg.type == "tool": - # Tool result - content = getattr(msg, "content", "") - tool_call_id = getattr(msg, "tool_call_id", None) - - # Find the corresponding step and add observation - if self._trajectory_steps: - last_step = self._trajectory_steps[-1] - if last_step.tool_calls: - # Add observation to the last tool call step - last_step.observation = Observation( - results=[ - ObservationResult( - source_call_id=tool_call_id, - content=str(content), - ) - ] - ) - - def _extract_final_message(self, messages: list[BaseMessage]) -> str: - """Extract the final agent message.""" - for msg in reversed(messages): - if msg.type == "ai": - content = getattr(msg, "content", "") - if content: - return str(content) - return "" - - def _update_token_usage(self, usage_metadata: dict[str, Any]) -> None: - """Update token usage from message metadata.""" - input_tokens = usage_metadata.get("input_tokens", 0) - output_tokens = usage_metadata.get("output_tokens", 0) - - self._total_prompt_tokens += input_tokens - self._total_completion_tokens += output_tokens - - # Estimate cost based on model provider - if self._model_name.startswith("openai/") or self._model_name.startswith("gpt-"): - # OpenAI pricing (approximate, as of Nov 2025) - if "gpt-5-mini" in self._model_name or "gpt-4o-mini" in self._model_name: - # Mini models: $0.15 per 1M input, $0.60 per 1M output - input_cost = input_tokens * 0.00000015 - output_cost = output_tokens * 0.0000006 - elif "gpt-5" in self._model_name or "gpt-4o" in self._model_name: - # Standard models: $2.50 per 1M input, $10 per 1M output - input_cost = input_tokens * 0.0000025 - output_cost = output_tokens * 0.00001 - else: - # Default OpenAI pricing - input_cost = input_tokens * 0.0000015 - output_cost = output_tokens * 0.000006 - else: - # Anthropic pricing (Claude Sonnet) - # $3 per 1M input, $15 per 1M output - input_cost = input_tokens * 0.000003 - output_cost = output_tokens * 0.000015 - - self._total_cost += input_cost + output_cost - - def _add_step( - self, - source: str, - message: str, - tool_calls: list[ToolCall] | None = None, - observation: Observation | None = None, - metrics: Metrics | None = None, - ) -> None: - """Add a step to the ATIF trajectory.""" - self._step_counter += 1 - - step = Step( - step_id=self._step_counter, - timestamp=datetime.now(timezone.utc).isoformat(), - source=source, - message=message, - tool_calls=tool_calls, - observation=observation, - metrics=metrics, - ) - - self._trajectory_steps.append(step) - - def _build_trajectory(self) -> Trajectory: - """Build final ATIF trajectory.""" - final_metrics = FinalMetrics( - total_prompt_tokens=self._total_prompt_tokens or None, - total_completion_tokens=self._total_completion_tokens or None, - total_cost_usd=self._total_cost or None, - total_steps=len(self._trajectory_steps), - ) - - return Trajectory( - schema_version="ATIF-v1.2", - session_id=self._session_id, - agent=Agent( - name=self.name(), - version=self.version() or "unknown", - model_name=self._model_name, - extra={ - "framework": "deepagents", - "langchain_version": "1.0+", - "langsmith_run_id": self._langsmith_run_id, # Store for feedback - }, - ), - steps=self._trajectory_steps, - final_metrics=final_metrics, - ) - - def send_verification_feedback(self, reward: float) -> None: - """Send Harbor verification results to LangSmith as feedback. - - This should be called after Harbor's verifier runs to push the - reward score to LangSmith, making it visible in the trace UI. - - Args: - reward: Reward score from Harbor verifier (0.0 to 1.0) - - Example: - >>> agent = DeepAgentHarbor(...) - >>> await agent.run(instruction, environment, context) - >>> # After Harbor verifies the task: - >>> agent.send_verification_feedback(reward=1.0) - """ - if not self._langsmith_run_id or not self._task_name: - if self._verbose: - print("Warning: No run_id or task_name available for feedback") - return - - send_harbor_feedback( - run_id=self._langsmith_run_id, - task_name=self._task_name, - reward=reward, - agent_cost_usd=self._total_cost, - total_steps=len(self._trajectory_steps), - ) - - if self._verbose: - print(f"✓ Sent feedback to LangSmith: reward={reward * 100:.0f}%") diff --git a/libs/harbor/harbor_deepagents/agents/harbor_backend.py b/libs/harbor/harbor_deepagents/agents/harbor_backend.py new file mode 100644 index 00000000..47b40ebb --- /dev/null +++ b/libs/harbor/harbor_deepagents/agents/harbor_backend.py @@ -0,0 +1,33 @@ +"""Harbor-specific tools for DeepAgents to interact with the environment.""" + +import asyncio + +from harbor.environments.base import BaseEnvironment + +from deepagents.backends.protocol import ExecuteResponse +from deepagents.backends.sandbox import BaseSandbox + + +class HarborSandbox(BaseSandbox): + def __init__(self, environment: BaseEnvironment) -> None: + """Initialize HarborSandbox with the given environment.""" + self.environment = environment + + def execute( + self, + command: str, + ) -> ExecuteResponse: + """Execute a bash command in the task environment.""" + # TODO: This is a temporary hack to run async code from a sync function. + # We need to add async support in deepagents. + result = asyncio.run(self.environment.exec(command)) + output = (result.stdout or "") + "\n stderr: " + (result.stderr or "") + return ExecuteResponse( + output=output, + exit_code=result.returncode, + ) + + @property + def id(self) -> str: + """Unique identifier for the sandbox backend.""" + return self.environment.session_id diff --git a/libs/harbor/harbor_deepagents/agents/harbor_tools.py b/libs/harbor/harbor_deepagents/agents/harbor_tools.py deleted file mode 100644 index ce788777..00000000 --- a/libs/harbor/harbor_deepagents/agents/harbor_tools.py +++ /dev/null @@ -1,84 +0,0 @@ -"""Harbor-specific tools for DeepAgents to interact with the environment.""" - -from typing import Annotated - -from harbor.environments.base import BaseEnvironment -from langchain_core.tools import tool, BaseTool - - -def create_harbor_environment_tools(environment: BaseEnvironment) -> list[BaseTool]: - """Create tools that allow DeepAgent to interact with Harbor environment. - - These tools wrap Harbor's environment API to provide: - - Bash command execution - - File reading - - File writing - - Args: - environment: Harbor environment instance (Docker, Modal, etc.) - - Returns: - List of LangChain tool functions - """ - - @tool - async def bash(command: Annotated[str, "The bash command to execute"]) -> str: - """Execute a bash command in the task environment. - - Use this for running Python scripts, installing packages, listing files, - checking output, etc. Returns stdout, stderr, and exit code. - - Examples: - - bash("python script.py") - - bash("ls -la /app") - - bash("cat file.txt") - """ - result = await environment.exec(command) - - output_parts = [] - if result.stdout: - output_parts.append(f"[stdout]\n{result.stdout.strip()}") - if result.stderr: - output_parts.append(f"[stderr]\n{result.stderr.strip()}") - output_parts.append(f"[exit_code] {result.return_code}") - - return "\n\n".join(output_parts) - - @tool - async def read_file(path: Annotated[str, "Path to the file to read"]) -> str: - """Read the contents of a file from the task environment. - - Provide the full path to the file (e.g., /app/script.py). - - Returns the file contents as a string. - """ - result = await environment.exec(f"cat {path}") - - if result.return_code != 0: - return f"Error reading file: {result.stderr}" - - return result.stdout or "" - - @tool - async def write_file( - path: Annotated[str, "Path to the file to write"], - content: Annotated[str, "Content to write to the file"], - ) -> str: - """Write content to a file in the task environment. - - This will create or overwrite the file. Provide the full path - and the content to write. - - Returns success message or error. - """ - # Escape content for heredoc - escaped_content = content.replace("'", "'\"'\"'") - - result = await environment.exec(f"cat > {path} << 'EOF'\n{content}\nEOF") - - if result.return_code != 0: - return f"Error writing file: {result.stderr}" - - return f"Successfully wrote {len(content)} bytes to {path}" - - return [bash, read_file, write_file] diff --git a/libs/harbor/harbor_deepagents/cli.py b/libs/harbor/harbor_deepagents/cli.py index 396e302f..e020ca22 100644 --- a/libs/harbor/harbor_deepagents/cli.py +++ b/libs/harbor/harbor_deepagents/cli.py @@ -1,6 +1,4 @@ -""" -Command-line entry point for running Harbor + DeepAgents harness workflows via uv. -""" +"""Command-line entry point for running Harbor + DeepAgents harness workflows.""" from __future__ import annotations @@ -14,10 +12,9 @@ from typing import Dict, Iterable, List, Optional, Sequence from dotenv import load_dotenv -from langsmith import Client from rich.console import Console -from harbor_deepagents.agents.langsmith_integration import send_harbor_feedback +from harbor_deepagents.agents.tracing import send_harbor_feedback console = Console() @@ -31,7 +28,7 @@ def parse_args(argv: Optional[Sequence[str]] = None) -> argparse.Namespace: parser = argparse.ArgumentParser( - description="Run Harbor DeepAgents harness with simple uv-friendly commands.", + description="Run Harbor DeepAgents harness.", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) parser.add_argument( @@ -104,6 +101,7 @@ def load_environment(env_file: Optional[str], skip: bool) -> None: def resolve_config(suite: str, env_type: str, explicit: Optional[str]) -> Path: + """Determine the Harbor config path based on CLI args.""" if explicit: return Path(explicit) if suite == "terminal-bench": @@ -115,6 +113,7 @@ def resolve_config(suite: str, env_type: str, explicit: Optional[str]) -> Path: def determine_model(cli_model: Optional[str]) -> str: + """Get the model name from CLI or environment.""" model = cli_model or os.environ.get("MODEL_NAME") if not model: console.print( @@ -125,6 +124,7 @@ def determine_model(cli_model: Optional[str]) -> str: def infer_provider(model: str) -> Optional[str]: + """Infer the LLM provider from the model name.""" lowered = model.lower() if lowered.startswith(("openai/", "gpt-")): return "openai" diff --git a/libs/harbor/harbor_deepagents/langsmith_exporter.py b/libs/harbor/harbor_deepagents/langsmith_exporter.py new file mode 100644 index 00000000..7e4e3c24 --- /dev/null +++ b/libs/harbor/harbor_deepagents/langsmith_exporter.py @@ -0,0 +1,177 @@ +"""Export Harbor ATIF trajectories to LangSmith for visualization.""" + +import json +from pathlib import Path +from typing import Any + +from langsmith import Client + + +class LangSmithTrajectoryExporter: + """Export ATIF trajectories to LangSmith. + + This allows you to: + - Visualize agent execution in LangSmith UI + - Compare different runs + - Analyze costs and latency + - Debug agent behavior + """ + + def __init__(self, api_key: str | None = None): + """Initialize exporter with optional API key. + + Args: + api_key: LangSmith API key (or use LANGCHAIN_API_KEY env var) + """ + self.client = Client(api_key=api_key) + + def export_trajectory( + self, + trajectory_path: Path | str, + project_name: str = "harbor-deepagents", + ) -> str: + """Export an ATIF trajectory file to LangSmith. + + Args: + trajectory_path: Path to ATIF trajectory JSON file + project_name: LangSmith project name + + Returns: + The run ID in LangSmith + """ + trajectory_path = Path(trajectory_path) + + with open(trajectory_path) as f: + atif = json.load(f) + + # Create root run + run_id = self.client.create_run( + name=f"harbor-{atif['agent']['name']}-{atif['session_id'][:8]}", + run_type="chain", + inputs={"instruction": self._extract_user_message(atif)}, + project_name=project_name, + tags=["harbor", atif["agent"]["name"], "deepagents"], + extra={ + "metadata": { + "agent_name": atif["agent"]["name"], + "agent_version": atif["agent"]["version"], + "model": atif["agent"].get("model_name"), + "session_id": atif["session_id"], + "schema_version": atif["schema_version"], + **atif["agent"].get("extra", {}), + } + }, + ).id + + # Add steps as child runs + for step in atif["steps"]: + self._add_step_run( + step=step, + parent_run_id=run_id, + project_name=project_name, + ) + + # Update root run with final output and metrics + final_message = self._extract_final_message(atif) + final_metrics = atif.get("final_metrics", {}) + + self.client.update_run( + run_id=run_id, + outputs={"result": final_message}, + extra={"metadata": final_metrics}, + ) + + return run_id + + def export_job_trajectories( + self, + job_dir: Path | str, + project_name: str = "harbor-deepagents", + ) -> list[str]: + """Export all trajectories from a Harbor job directory. + + Args: + job_dir: Path to Harbor job directory + project_name: LangSmith project name + + Returns: + List of LangSmith run IDs + """ + job_dir = Path(job_dir) + trajectory_files = list(job_dir.rglob("trajectory.json")) + + run_ids = [] + for trajectory_path in trajectory_files: + try: + run_id = self.export_trajectory(trajectory_path, project_name) + run_ids.append(run_id) + print(f"✓ Exported {trajectory_path.parent.name}: {run_id}") + except Exception as e: + print(f"✗ Failed to export {trajectory_path}: {e}") + + return run_ids + + def _extract_user_message(self, atif: dict[str, Any]) -> str: + """Extract the initial user message from ATIF.""" + for step in atif["steps"]: + if step["source"] == "user": + return step["message"] + return "" + + def _extract_final_message(self, atif: dict[str, Any]) -> str: + """Extract the final agent message from ATIF.""" + for step in reversed(atif["steps"]): + if step["source"] == "agent": + return step["message"] + return "" + + def _add_step_run( + self, + step: dict[str, Any], + parent_run_id: str, + project_name: str, + ) -> None: + """Add a step as a child run in LangSmith.""" + run_type = self._get_run_type(step) + + inputs = {"message": step.get("message", "")} + outputs = {} + + # Add tool call information + if step.get("tool_calls"): + tool_call = step["tool_calls"][0] # Simplified: take first tool call + inputs["tool"] = tool_call.get("function_name") + inputs["arguments"] = tool_call.get("arguments") + + # Add observation information + if step.get("observation"): + results = step["observation"].get("results", []) + if results: + outputs["observation"] = results[0].get("content") + + # Create child run + self.client.create_run( + name=f"step-{step['step_id']}-{step['source']}", + run_type=run_type, + inputs=inputs, + outputs=outputs if outputs else None, + parent_run_id=parent_run_id, + project_name=project_name, + extra={ + "metadata": { + "step_id": step["step_id"], + "source": step["source"], + "timestamp": step.get("timestamp"), + **(step.get("metrics", {}) or {}), + } + }, + ) + + def _get_run_type(self, step: dict[str, Any]) -> str: + """Determine LangSmith run type from ATIF step.""" + if step.get("tool_calls"): + return "tool" + elif step["source"] == "agent": + return "llm" + else: + return "chain" diff --git a/libs/harbor/pyproject.toml b/libs/harbor/pyproject.toml index 99e76f11..0ee38e1e 100644 --- a/libs/harbor/pyproject.toml +++ b/libs/harbor/pyproject.toml @@ -32,7 +32,7 @@ dependencies = [ ] [project.optional-dependencies] -dev = [ +test = [ "pytest>=8.4.2", "pytest-asyncio>=1.2.0", "ipython>=8.30.0", diff --git a/libs/harbor/scripts/export_traces.py b/libs/harbor/scripts/export_traces.py index d5fda50e..b78d5bc8 100755 --- a/libs/harbor/scripts/export_traces.py +++ b/libs/harbor/scripts/export_traces.py @@ -1,88 +1,88 @@ -#!/usr/bin/env python3 -"""Export Harbor ATIF trajectories to LangSmith for visualization.""" - -import sys -from pathlib import Path - -from dotenv import load_dotenv - -# Load environment variables -load_dotenv() - -# Add src to path -sys.path.insert(0, str(Path(__file__).parent.parent / "src")) - -from harbor_deepagents.utils.langsmith_exporter import LangSmithTrajectoryExporter - - -def main(): - """Export all trajectories from latest job to LangSmith.""" - jobs_dir = Path("jobs") - - if not jobs_dir.exists(): - print("✗ No jobs directory found. Run a job first!") - print(" → uv run harness --model openai/gpt-5-mini") - return 1 - - # Find most recent job - job_dirs = sorted( - [d for d in jobs_dir.iterdir() if d.is_dir()], - key=lambda p: p.stat().st_mtime, - reverse=True, - ) - - if not job_dirs: - print("✗ No jobs found in jobs directory") - return 1 - - latest_job = job_dirs[0] - print(f"📦 Exporting trajectories from: {latest_job.name}") - print("") - - # Find all trajectory files - trajectory_files = list(latest_job.rglob("trajectory.json")) - - if not trajectory_files: - print("✗ No trajectory files found") - print(" Trajectories are saved to: jobs///agent/trajectory.json") - return 1 - - print(f"Found {len(trajectory_files)} trajectory file(s)") - print("") - - # Export to LangSmith - exporter = LangSmithTrajectoryExporter() - - exported_count = 0 - failed_count = 0 - - for trajectory_path in trajectory_files: - trial_name = trajectory_path.parent.parent.name - try: - run_id = exporter.export_trajectory( - trajectory_path, - project_name="harbor-deepagents", - ) - print(f"✓ Exported {trial_name}: {run_id[:12]}...") - exported_count += 1 - except Exception as e: - print(f"✗ Failed to export {trial_name}: {e}") - failed_count += 1 - - print("") - print("=" * 60) - print(f"Export complete! {exported_count} succeeded, {failed_count} failed") - print("=" * 60) - print("") - - if exported_count > 0: - print("View in LangSmith:") - print(" → https://smith.langchain.com") - print(" → Project: harbor-deepagents") - print("") - - return 0 if failed_count == 0 else 1 - - -if __name__ == "__main__": - sys.exit(main()) +# #!/usr/bin/env python3 +# """Export Harbor ATIF trajectories to LangSmith for visualization.""" +# +# import sys +# from pathlib import Path +# +# from dotenv import load_dotenv +# +# # Load environment variables +# load_dotenv() +# +# # Add src to path +# sys.path.insert(0, str(Path(__file__).parent.parent / "src")) +# +# from harbor_deepagents.utils.langsmith_exporter import LangSmithTrajectoryExporter +# +# +# def main(): +# """Export all trajectories from latest job to LangSmith.""" +# jobs_dir = Path("jobs") +# +# if not jobs_dir.exists(): +# print("✗ No jobs directory found. Run a job first!") +# print(" → uv run harness --model openai/gpt-5-mini") +# return 1 +# +# # Find most recent job +# job_dirs = sorted( +# [d for d in jobs_dir.iterdir() if d.is_dir()], +# key=lambda p: p.stat().st_mtime, +# reverse=True, +# ) +# +# if not job_dirs: +# print("✗ No jobs found in jobs directory") +# return 1 +# +# latest_job = job_dirs[0] +# print(f"📦 Exporting trajectories from: {latest_job.name}") +# print("") +# +# # Find all trajectory files +# trajectory_files = list(latest_job.rglob("trajectory.json")) +# +# if not trajectory_files: +# print("✗ No trajectory files found") +# print(" Trajectories are saved to: jobs///agent/trajectory.json") +# return 1 +# +# print(f"Found {len(trajectory_files)} trajectory file(s)") +# print("") +# +# # Export to LangSmith +# exporter = LangSmithTrajectoryExporter() +# +# exported_count = 0 +# failed_count = 0 +# +# for trajectory_path in trajectory_files: +# trial_name = trajectory_path.parent.parent.name +# try: +# run_id = exporter.export_trajectory( +# trajectory_path, +# project_name="harbor-deepagents", +# ) +# print(f"✓ Exported {trial_name}: {run_id[:12]}...") +# exported_count += 1 +# except Exception as e: +# print(f"✗ Failed to export {trial_name}: {e}") +# failed_count += 1 +# +# print("") +# print("=" * 60) +# print(f"Export complete! {exported_count} succeeded, {failed_count} failed") +# print("=" * 60) +# print("") +# +# if exported_count > 0: +# print("View in LangSmith:") +# print(" → https://smith.langchain.com") +# print(" → Project: harbor-deepagents") +# print("") +# +# return 0 if failed_count == 0 else 1 +# +# +# if __name__ == "__main__": +# sys.exit(main()) diff --git a/libs/harbor/scripts/harbor_entry.py b/libs/harbor/scripts/harbor_entry.py index 50db82c3..77a2c7bb 100644 --- a/libs/harbor/scripts/harbor_entry.py +++ b/libs/harbor/scripts/harbor_entry.py @@ -2,6 +2,9 @@ from __future__ import annotations +from harbor_deepagents.cli import main as _main +from harbor_deepagents.cli import run_tb_daytona as _run_tb_daytona +from harbor_deepagents.cli import run_tb_docker as _run_tb_docker import os import sys from pathlib import Path @@ -26,9 +29,6 @@ def _maybe_add_src_path() -> None: _maybe_add_src_path() -from harbor_deepagents.cli import main as _main -from harbor_deepagents.cli import run_tb_daytona as _run_tb_daytona -from harbor_deepagents.cli import run_tb_docker as _run_tb_docker def main() -> None: diff --git a/libs/harbor/uv.lock b/libs/harbor/uv.lock index d1eaf221..ca2359be 100644 --- a/libs/harbor/uv.lock +++ b/libs/harbor/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 1 +revision = 2 requires-python = ">=3.12" [manifest] @@ -12,18 +12,18 @@ overrides = [ name = "aiofiles" version = "24.1.0" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = 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.../agents/{harbor_backend.py => backend.py} | 0 .../harbor_deepagents/agents/deepagent.py | 332 +++++++++--------- libs/harbor/pyproject.toml | 8 +- libs/harbor/scripts/harbor_entry.py | 43 --- libs/harbor/uv.lock | 30 +- 7 files changed, 194 insertions(+), 225 deletions(-) rename libs/harbor/harbor_deepagents/agents/{harbor_backend.py => backend.py} (100%) delete mode 100644 libs/harbor/scripts/harbor_entry.py diff --git a/libs/harbor/harbor_deepagents/__init__.py b/libs/harbor/harbor_deepagents/__init__.py index f0ad31b2..14b4ee66 100644 --- a/libs/harbor/harbor_deepagents/__init__.py +++ b/libs/harbor/harbor_deepagents/__init__.py @@ -38,9 +38,9 @@ __version__ = "1.0.0" from harbor_deepagents.agents import DeepAgentHarbor -from harbor_deepagents.utils import LangSmithTrajectoryExporter +# from harbor_deepagents.utils import LangSmithTrajectoryExporter __all__ = [ "DeepAgentHarbor", - "LangSmithTrajectoryExporter", + # "LangSmithTrajectoryExporter", ] diff --git a/libs/harbor/harbor_deepagents/agents/__init__.py b/libs/harbor/harbor_deepagents/agents/__init__.py index e709621a..bd7a23d2 100644 --- a/libs/harbor/harbor_deepagents/agents/__init__.py +++ b/libs/harbor/harbor_deepagents/agents/__init__.py @@ -1,6 +1,6 @@ """Harbor DeepAgents - Agent implementations.""" -from harbor_deepagents.agents.deepagent_harbor import DeepAgentHarbor +from harbor_deepagents.agents.deepagent import DeepAgentHarbor __all__ = [ "DeepAgentHarbor", diff --git a/libs/harbor/harbor_deepagents/agents/harbor_backend.py b/libs/harbor/harbor_deepagents/agents/backend.py similarity index 100% rename from libs/harbor/harbor_deepagents/agents/harbor_backend.py rename to libs/harbor/harbor_deepagents/agents/backend.py diff --git a/libs/harbor/harbor_deepagents/agents/deepagent.py b/libs/harbor/harbor_deepagents/agents/deepagent.py index d77fb521..0909535d 100644 --- a/libs/harbor/harbor_deepagents/agents/deepagent.py +++ b/libs/harbor/harbor_deepagents/agents/deepagent.py @@ -1,11 +1,10 @@ """Harbor agent implementation using LangChain DeepAgents.""" import json -import os import uuid from datetime import datetime, timezone from pathlib import Path -from typing import Any, Literal +from typing import Literal from harbor.agents.base import BaseAgent from harbor.environments.base import BaseEnvironment @@ -21,17 +20,20 @@ Trajectory, ) from langchain.chat_models import init_chat_model +from langchain.messages import UsageMetadata from langchain_core.messages import AIMessage, ToolMessage from langchain_core.messages import BaseMessage -from langsmith import Client +from langchain_core.runnables import RunnableConfig from deepagents import create_deep_agent -from harbor_deepagents.agents.harbor_backend import HarborSandbox -from harbor_deepagents.agents.tracing import send_harbor_feedback +from harbor_deepagents.agents.backend import HarborSandbox -class DeepAgentHarbor(BaseAgent): - """Harbor agent that uses LangChain DeepAgents for intelligent task completion.""" +class DeepAgentsWrapper(BaseAgent): + """Harbor agent implementation using LangChain DeepAgents. + + Wraps DeepAgents to execute tasks in Harbor environments. + """ def __init__( self, @@ -43,6 +45,7 @@ def __init__( *args, **kwargs, ) -> None: + """Initialize DeepAgentsWrapper.""" super().__init__(logs_dir, model_name, *args, **kwargs) if model_name is None: @@ -72,6 +75,14 @@ def __init__( def name() -> str: return "deepagent-harbor" + async def setup(self, environment: BaseEnvironment) -> None: + """Setup the agent with the given environment. + + Args: + environment: Harbor environment (Docker, Modal, etc.) + """ + self._environment = environment + def version(self) -> str | None: return "1.0.0" @@ -88,79 +99,100 @@ async def run( environment: Harbor environment (Docker, Modal, etc.) context: Context to populate with metrics """ - raise ValueError(BaseEnvironment) self._add_step( source="user", message=instruction, ) backend = HarborSandbox(environment) - - # Create DeepAgent with: - # - Harbor's bash tool (for command execution) - # - FilesystemBackend for real file I/O (no mock filesystem) - # - Built-in planning (write_todos) and subagent (task) tools deep_agent = create_deep_agent( model=self._model, backend=backend ) + config: RunnableConfig = { + "run_name": f"harbor-deepagent-{self._session_id[:8]}", + "tags": ["harbor", "deepagent", self._model_name, self._session_id], + "metadata": { + "task_instruction": instruction, + "model": self._model_name, + "session_id": self._session_id, + }, + "recursion_limit": self._max_iterations, + } + # Invoke deep agent with LangSmith tracing result = await deep_agent.ainvoke( - {"messages": [{"role": "user", "content": instruction}]}, - config={ - "run_name": f"harbor-deepagent-{self._session_id[:8]}", - "tags": ["harbor", "deepagent", self._model_name, self._session_id], - "metadata": { - "task_instruction": instruction, - "model": self._model_name, - "session_id": self._session_id, - }, - "recursion_limit": self._max_iterations, - }, + {"messages": [{"role": "user", "content": instruction}]}, # type: ignore + config=config, ) - - # Store task name for feedback - self._task_name = instruction[:100] # Truncate for readability - - # Query LangSmith to get the run_id by searching for our unique run_name - self._langsmith_run_id = self._session_id # Default fallback - - if os.getenv("LANGCHAIN_TRACING_V2"): - client = Client() - project_name = os.getenv("LANGCHAIN_PROJECT", "default") - run_name = f"harbor-deepagent-{self._session_id[:8]}" - # Search by exact run_name which is unique - runs = list(client.list_runs( - project_name=project_name, - filter=f'eq(name, "{run_name}")', - limit=1, - )) - if runs: - self._langsmith_run_id = str(runs[0].id) - if self._verbose: - print(f"✓ Found LangSmith run_id: {self._langsmith_run_id}") - - # Extract messages from result - messages = result.get("messages", []) - + messages = result['messages'] # Process messages into ATIF steps - self._process_messages_to_steps(messages) - - # Extract final output - final_message = self._extract_final_message(messages) or "Task completed" - last_step = self._trajectory_steps[-1] if self._trajectory_steps else None - if not ( - last_step - and last_step.source == "agent" - and (last_step.message or "").strip() == final_message.strip() - ): - self._add_step( - source="agent", - message=final_message, - ) + self._process_message(messages) + + + # # Store task name for feedback + # self._task_name = instruction[:100] # Truncate for readability + # + # # Query LangSmith to get the run_id by searching for our unique run_name + # self._langsmith_run_id = self._session_id # Default fallback + # + # if os.getenv("LANGCHAIN_TRACING_V2"): + # client = Client() + # project_name = os.getenv("LANGCHAIN_PROJECT", "default") + # run_name = f"harbor-deepagent-{self._session_id[:8]}" + # # Search by exact run_name which is unique + # runs = list(client.list_runs( + # project_name=project_name, + # filter=f'eq(name, "{run_name}")', + # limit=1, + # )) + # if runs: + # self._langsmith_run_id = str(runs[0].id) + # if self._verbose: + # print(f"✓ Found LangSmith run_id: {self._langsmith_run_id}") + # + + ### Extract messages from result + ### + ### # Extract final output + ### final_message = self._extract_final_message(messages) or "Task completed" + ### last_step = self._trajectory_steps[-1] if self._trajectory_steps else None + ### if not ( + ### last_step + ### and last_step.source == "agent" + ### and (last_step.message or "").strip() == final_message.strip() + ### ): + ### self._add_step( + ### source="agent", + ### message=final_message, + ### ) # Build and save trajectory - trajectory = self._build_trajectory() + + final_metrics = FinalMetrics( + total_prompt_tokens=self._total_prompt_tokens or None, + total_completion_tokens=self._total_completion_tokens or None, + total_cost_usd=self._total_cost or None, + total_steps=len(self._trajectory_steps), + ) + + trajectory = Trajectory( + schema_version="ATIF-v1.2", + session_id=self._session_id, + agent=Agent( + name=self.name(), + version=self.version() or "unknown", + model_name=self._model_name, + extra={ + "framework": "deepagents", + "langchain_version": "1.0+", + "langsmith_run_id": self._langsmith_run_id, # Store for feedback + }, + ), + steps=self._trajectory_steps, + final_metrics=final_metrics, + ) + trajectory_path = self.logs_dir / "trajectory.json" trajectory_path.write_text(json.dumps(trajectory.to_json_dict(), indent=2)) @@ -176,64 +208,53 @@ async def run( def _process_message(self, msg: BaseMessage) -> None: """Process LangChain messages into ATIF trajectory steps.""" if isinstance(msg, AIMessage): - tool_calls = msg.tool_calls - for tc in tool_calls: - tool_call_id = tc.get("id", f"call_{self._step_counter + 1}") - tool_name = tc.get("name", "unknown") - tool_args = tc.get("args", {}) - - self._add_step( - source="agent", - message=f"Using tool: {tool_name}", - tool_calls=[ + atf_tool_calls = [] + message = "" + for cb in msg.content_blocks: + if cb['type'] == "text": + message += cb['text'] + elif cb['type'] == "reasoning": + message += cb['reasoning'] + elif cb["type"] == "tool_call": + atf_tool_calls.append( ToolCall( - tool_call_id=tool_call_id, - function_name=tool_name, - arguments=tool_args, + tool_call_id=cb['id'], + function_name=cb['name'], + arguments=cb['args'] ) - ], - ) - else: - self._add_step( - source="agent", - message=str(msg.content_blocks), - ) + ) + else: + # TODO: Add server side tool call results. + continue + self._add_step( + source="agent", + tool_calls=atf_tool_calls, + message=message, + ) + # # Extract usage metadata if available + # if msg.usage_metadata: + # self._update_token_usage(msg.usage_metadata) - # Extract usage metadata if available - usage_metadata = getattr(msg, "usage_metadata", None) - if usage_metadata: - self._update_token_usage(usage_metadata) elif isinstance(msg, ToolMessage): - content_blocks = msg.content_blocks - tool_call_id = msg.tool_call_id - # Find the corresponding step and add observation - if self._trajectory_steps: - last_step = self._trajectory_steps[-1] - if last_step.tool_calls: - # Add observation to the last tool call step - last_step.observation = Observation( - results=[ - ObservationResult( - source_call_id=tool_call_id, - content=str(msg.content_blocks), - ) - ] - ) + # TODO: Merge the observation into the previous step + observation = [ + ObservationResult( + source_call_id=msg.tool_call_id, + content=str(msg.content), + ) + ] + + self._add_step( + source="agent", + observation=Observation(results=observation), + message="", + ) else: raise NotImplementedError( f"Message type {type(msg)} not supported for step conversion" ) - def _extract_final_message(self, messages: list[BaseMessage]) -> str: - """Extract the final agent message.""" - for msg in reversed(messages): - if msg.type == "ai": - content = getattr(msg, "content", "") - if content: - return str(content) - return "" - - def _update_token_usage(self, usage_metadata: dict[str, Any]) -> None: + def _update_token_usage(self, usage_metadata: UsageMetadata) -> None: """Update token usage from message metadata.""" input_tokens = usage_metadata.get("input_tokens", 0) output_tokens = usage_metadata.get("output_tokens", 0) @@ -286,59 +307,34 @@ def _add_step( self._trajectory_steps.append(step) - def _build_trajectory(self) -> Trajectory: - """Build final ATIF trajectory.""" - final_metrics = FinalMetrics( - total_prompt_tokens=self._total_prompt_tokens or None, - total_completion_tokens=self._total_completion_tokens or None, - total_cost_usd=self._total_cost or None, - total_steps=len(self._trajectory_steps), - ) - - return Trajectory( - schema_version="ATIF-v1.2", - session_id=self._session_id, - agent=Agent( - name=self.name(), - version=self.version() or "unknown", - model_name=self._model_name, - extra={ - "framework": "deepagents", - "langchain_version": "1.0+", - "langsmith_run_id": self._langsmith_run_id, # Store for feedback - }, - ), - steps=self._trajectory_steps, - final_metrics=final_metrics, - ) - - def send_verification_feedback(self, reward: float) -> None: - """Send Harbor verification results to LangSmith as feedback. - - This should be called after Harbor's verifier runs to push the - reward score to LangSmith, making it visible in the trace UI. - - Args: - reward: Reward score from Harbor verifier (0.0 to 1.0) - - Example: - >>> agent = DeepAgentHarbor(...) - >>> await agent.run(instruction, environment, context) - >>> # After Harbor verifies the task: - >>> agent.send_verification_feedback(reward=1.0) - """ - if not self._langsmith_run_id or not self._task_name: - if self._verbose: - print("Warning: No run_id or task_name available for feedback") - return - - send_harbor_feedback( - run_id=self._langsmith_run_id, - task_name=self._task_name, - reward=reward, - agent_cost_usd=self._total_cost, - total_steps=len(self._trajectory_steps), - ) - if self._verbose: - print(f"✓ Sent feedback to LangSmith: reward={reward * 100:.0f}%") + # def send_verification_feedback(self, reward: float) -> None: + # """Send Harbor verification results to LangSmith as feedback. + # + # This should be called after Harbor's verifier runs to push the + # reward score to LangSmith, making it visible in the trace UI. + # + # Args: + # reward: Reward score from Harbor verifier (0.0 to 1.0) + # + # Example: + # >>> agent = DeepAgentHarbor(...) + # >>> await agent.run(instruction, environment, context) + # >>> # After Harbor verifies the task: + # >>> agent.send_verification_feedback(reward=1.0) + # """ + # if not self._langsmith_run_id or not self._task_name: + # if self._verbose: + # print("Warning: No run_id or task_name available for feedback") + # return + # + # send_harbor_feedback( + # run_id=self._langsmith_run_id, + # task_name=self._task_name, + # reward=reward, + # agent_cost_usd=self._total_cost, + # total_steps=len(self._trajectory_steps), + # ) + # + # if self._verbose: + # print(f"✓ Sent feedback to LangSmith: reward={reward * 100:.0f}%") diff --git a/libs/harbor/pyproject.toml b/libs/harbor/pyproject.toml index 0ee38e1e..23eef614 100644 --- a/libs/harbor/pyproject.toml +++ b/libs/harbor/pyproject.toml @@ -7,23 +7,18 @@ requires-python = ">=3.12" dependencies = [ # Harbor runtime "harbor>=0.1.12", - # LangChain + DeepAgents (upgraded to 0.2.5 for LocalFilesystemBackend support) "langchain>=1.0.2", "langchain-anthropic>=1.0.0", "langchain-openai>=1.0.0", "deepagents>=0.2.5", "openai>=1.109.1,<2.0.0", - # LangSmith for observability "langsmith>=0.4.0", - # Additional tools "tavily-python>=0.5.0", - # Modal for cloud execution "modal>=0.64.0", - # Utilities "python-dotenv>=1.0.0", "rich>=13.0.0", @@ -60,6 +55,9 @@ override-dependencies = [ "e2b==2.4.3", ] +[tool.uv.sources] +deepagents = { path = "../deepagents" } + [tool.ruff] line-length = 100 target-version = "py312" diff --git a/libs/harbor/scripts/harbor_entry.py b/libs/harbor/scripts/harbor_entry.py deleted file mode 100644 index 77a2c7bb..00000000 --- a/libs/harbor/scripts/harbor_entry.py +++ /dev/null @@ -1,43 +0,0 @@ -"""Bootstrap wrapper so console scripts can import from src without editable installs.""" - -from __future__ import annotations - -from harbor_deepagents.cli import main as _main -from harbor_deepagents.cli import run_tb_daytona as _run_tb_daytona -from harbor_deepagents.cli import run_tb_docker as _run_tb_docker -import os -import sys -from pathlib import Path - - -def _maybe_add_src_path() -> None: - """Ensure the repo's src directory is importable when developing locally.""" - - candidate_roots = [ - Path.cwd(), - Path(os.environ.get("HARBOR_DEEPAGENTS_ROOT", "")), - ] - - for root in candidate_roots: - if not root: - continue - src_dir = root / "src" - if src_dir.is_dir(): - sys.path.insert(0, str(src_dir)) - return - - -_maybe_add_src_path() - - - -def main() -> None: - _main() - - -def tb_docker() -> None: - _run_tb_docker() - - -def tb_daytona() -> None: - _run_tb_daytona() diff --git a/libs/harbor/uv.lock b/libs/harbor/uv.lock index ca2359be..7285168a 100644 --- a/libs/harbor/uv.lock +++ b/libs/harbor/uv.lock @@ -415,17 +415,35 @@ wheels = [ [[package]] name = "deepagents" -version = "0.2.5" -source = { registry = "https://pypi.org/simple" } +version = "0.2.7" +source = { directory = "../deepagents" } dependencies = [ { name = "langchain" }, { name = "langchain-anthropic" }, { name = "langchain-core" }, { name = "wcmatch" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/81/2b/37edbe9c42602d7e09e16a048dca004efe0f93f2319a22267d4b18bf1223/deepagents-0.2.5.tar.gz", hash = "sha256:784f665b4f9d8d0101afdbfa7de36303ee1d968febe51ca241d9541bb9d6335d", size = 76847, upload-time = "2025-11-04T22:39:06.707Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/ab/ff/73c4eb9e18bda614ea55c561db26e307662c7cfd02b69eb81fdc66ea518b/deepagents-0.2.5-py3-none-any.whl", hash = "sha256:0a7881a6284188d66b5a7ecc8143e655a1b81fa8192a61cae82abdb599adf5eb", size = 86574, upload-time = "2025-11-04T22:39:05.492Z" }, + +[package.metadata] +requires-dist = [ + { name = "langchain", specifier = ">=1.0.2,<2.0.0" }, + { name = "langchain-anthropic", specifier = ">=1.0.0,<2.0.0" }, + { name = "langchain-core", specifier = ">=1.0.0,<2.0.0" }, + { name = "wcmatch" }, +] + +[package.metadata.requires-dev] +dev = [ + { name = "build" }, + { name = "langchain-openai" }, + { name = "twine" }, +] +test = [ + { name = "mypy", specifier = ">=1.18.1,<1.19.0" }, + { name = "pytest" }, + { name = "pytest-cov" }, + { name = "pytest-xdist" }, + { name = "ruff", specifier = ">=0.12.2,<0.13.0" }, ] [[package]] @@ -777,7 +795,7 @@ test = [ [package.metadata] requires-dist = [ - { name = "deepagents", specifier = ">=0.2.5" }, + { name = "deepagents", directory = "../deepagents" }, { name = "e2b", specifier = "==2.4.3" }, { name = "harbor", specifier = ">=0.1.12" }, { name = "ipython", marker = "extra == 'test'", specifier = ">=8.30.0" }, From 1754e420365a8de866f2d9afeea29b0bc5c844f4 Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Thu, 20 Nov 2025 15:18:09 -0500 Subject: [PATCH 07/10] x --- .../harbor_deepagents/agents/deepagent.py | 2 +- .../harbor_deepagents/utils/__init__.py | 5 - .../utils/langsmith_exporter.py | 177 ------------------ 3 files changed, 1 insertion(+), 183 deletions(-) delete mode 100644 libs/harbor/harbor_deepagents/utils/__init__.py delete mode 100644 libs/harbor/harbor_deepagents/utils/langsmith_exporter.py diff --git a/libs/harbor/harbor_deepagents/agents/deepagent.py b/libs/harbor/harbor_deepagents/agents/deepagent.py index 0909535d..fe86abef 100644 --- a/libs/harbor/harbor_deepagents/agents/deepagent.py +++ b/libs/harbor/harbor_deepagents/agents/deepagent.py @@ -84,7 +84,7 @@ async def setup(self, environment: BaseEnvironment) -> None: self._environment = environment def version(self) -> str | None: - return "1.0.0" + return "0.0.1" async def run( self, diff --git a/libs/harbor/harbor_deepagents/utils/__init__.py b/libs/harbor/harbor_deepagents/utils/__init__.py deleted file mode 100644 index d9816806..00000000 --- a/libs/harbor/harbor_deepagents/utils/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -"""Harbor DeepAgents - Utility functions.""" - -from harbor_deepagents.utils.langsmith_exporter import LangSmithTrajectoryExporter - -__all__ = ["LangSmithTrajectoryExporter"] diff --git a/libs/harbor/harbor_deepagents/utils/langsmith_exporter.py b/libs/harbor/harbor_deepagents/utils/langsmith_exporter.py deleted file mode 100644 index 7e4e3c24..00000000 --- a/libs/harbor/harbor_deepagents/utils/langsmith_exporter.py +++ /dev/null @@ -1,177 +0,0 @@ -"""Export Harbor ATIF trajectories to LangSmith for visualization.""" - -import json -from pathlib import Path -from typing import Any - -from langsmith import Client - - -class LangSmithTrajectoryExporter: - """Export ATIF trajectories to LangSmith. - - This allows you to: - - Visualize agent execution in LangSmith UI - - Compare different runs - - Analyze costs and latency - - Debug agent behavior - """ - - def __init__(self, api_key: str | None = None): - """Initialize exporter with optional API key. - - Args: - api_key: LangSmith API key (or use LANGCHAIN_API_KEY env var) - """ - self.client = Client(api_key=api_key) - - def export_trajectory( - self, - trajectory_path: Path | str, - project_name: str = "harbor-deepagents", - ) -> str: - """Export an ATIF trajectory file to LangSmith. - - Args: - trajectory_path: Path to ATIF trajectory JSON file - project_name: LangSmith project name - - Returns: - The run ID in LangSmith - """ - trajectory_path = Path(trajectory_path) - - with open(trajectory_path) as f: - atif = json.load(f) - - # Create root run - run_id = self.client.create_run( - name=f"harbor-{atif['agent']['name']}-{atif['session_id'][:8]}", - run_type="chain", - inputs={"instruction": self._extract_user_message(atif)}, - project_name=project_name, - tags=["harbor", atif["agent"]["name"], "deepagents"], - extra={ - "metadata": { - "agent_name": atif["agent"]["name"], - "agent_version": atif["agent"]["version"], - "model": atif["agent"].get("model_name"), - "session_id": atif["session_id"], - "schema_version": atif["schema_version"], - **atif["agent"].get("extra", {}), - } - }, - ).id - - # Add steps as child runs - for step in atif["steps"]: - self._add_step_run( - step=step, - parent_run_id=run_id, - project_name=project_name, - ) - - # Update root run with final output and metrics - final_message = self._extract_final_message(atif) - final_metrics = atif.get("final_metrics", {}) - - self.client.update_run( - run_id=run_id, - outputs={"result": final_message}, - extra={"metadata": final_metrics}, - ) - - return run_id - - def export_job_trajectories( - self, - job_dir: Path | str, - project_name: str = "harbor-deepagents", - ) -> list[str]: - """Export all trajectories from a Harbor job directory. - - Args: - job_dir: Path to Harbor job directory - project_name: LangSmith project name - - Returns: - List of LangSmith run IDs - """ - job_dir = Path(job_dir) - trajectory_files = list(job_dir.rglob("trajectory.json")) - - run_ids = [] - for trajectory_path in trajectory_files: - try: - run_id = self.export_trajectory(trajectory_path, project_name) - run_ids.append(run_id) - print(f"✓ Exported {trajectory_path.parent.name}: {run_id}") - except Exception as e: - print(f"✗ Failed to export {trajectory_path}: {e}") - - return run_ids - - def _extract_user_message(self, atif: dict[str, Any]) -> str: - """Extract the initial user message from ATIF.""" - for step in atif["steps"]: - if step["source"] == "user": - return step["message"] - return "" - - def _extract_final_message(self, atif: dict[str, Any]) -> str: - """Extract the final agent message from ATIF.""" - for step in reversed(atif["steps"]): - if step["source"] == "agent": - return step["message"] - return "" - - def _add_step_run( - self, - step: dict[str, Any], - parent_run_id: str, - project_name: str, - ) -> None: - """Add a step as a child run in LangSmith.""" - run_type = self._get_run_type(step) - - inputs = {"message": step.get("message", "")} - outputs = {} - - # Add tool call information - if step.get("tool_calls"): - tool_call = step["tool_calls"][0] # Simplified: take first tool call - inputs["tool"] = tool_call.get("function_name") - inputs["arguments"] = tool_call.get("arguments") - - # Add observation information - if step.get("observation"): - results = step["observation"].get("results", []) - if results: - outputs["observation"] = results[0].get("content") - - # Create child run - self.client.create_run( - name=f"step-{step['step_id']}-{step['source']}", - run_type=run_type, - inputs=inputs, - outputs=outputs if outputs else None, - parent_run_id=parent_run_id, - project_name=project_name, - extra={ - "metadata": { - "step_id": step["step_id"], - "source": step["source"], - "timestamp": step.get("timestamp"), - **(step.get("metrics", {}) or {}), - } - }, - ) - - def _get_run_type(self, step: dict[str, Any]) -> str: - """Determine LangSmith run type from ATIF step.""" - if step.get("tool_calls"): - return "tool" - elif step["source"] == "agent": - return "llm" - else: - return "chain" From 9e16afa9675bbb0b994b030577cf8f7d326ee47c Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Thu, 20 Nov 2025 15:53:12 -0500 Subject: [PATCH 08/10] x --- .../harbor_deepagents/agents/__init__.py | 4 +- .../harbor_deepagents/agents/backend.py | 2 +- .../harbor_deepagents/agents/deepagent.py | 160 +++++++++--------- 3 files changed, 87 insertions(+), 79 deletions(-) diff --git a/libs/harbor/harbor_deepagents/agents/__init__.py b/libs/harbor/harbor_deepagents/agents/__init__.py index bd7a23d2..3a1b896a 100644 --- a/libs/harbor/harbor_deepagents/agents/__init__.py +++ b/libs/harbor/harbor_deepagents/agents/__init__.py @@ -1,7 +1,7 @@ """Harbor DeepAgents - Agent implementations.""" -from harbor_deepagents.agents.deepagent import DeepAgentHarbor +from harbor_deepagents.agents.deepagent import DeepAgentsWrapper __all__ = [ - "DeepAgentHarbor", + "DeepAgentsWrapper", ] diff --git a/libs/harbor/harbor_deepagents/agents/backend.py b/libs/harbor/harbor_deepagents/agents/backend.py index 47b40ebb..9f36bf27 100644 --- a/libs/harbor/harbor_deepagents/agents/backend.py +++ b/libs/harbor/harbor_deepagents/agents/backend.py @@ -24,7 +24,7 @@ def execute( output = (result.stdout or "") + "\n stderr: " + (result.stderr or "") return ExecuteResponse( output=output, - exit_code=result.returncode, + exit_code=result.return_code, ) @property diff --git a/libs/harbor/harbor_deepagents/agents/deepagent.py b/libs/harbor/harbor_deepagents/agents/deepagent.py index fe86abef..82e8d522 100644 --- a/libs/harbor/harbor_deepagents/agents/deepagent.py +++ b/libs/harbor/harbor_deepagents/agents/deepagent.py @@ -21,7 +21,7 @@ ) from langchain.chat_models import init_chat_model from langchain.messages import UsageMetadata -from langchain_core.messages import AIMessage, ToolMessage +from langchain_core.messages import AIMessage, ToolMessage, HumanMessage from langchain_core.messages import BaseMessage from langchain_core.runnables import RunnableConfig @@ -62,7 +62,6 @@ def __init__( # Trajectory tracking (ATIF format) self._trajectory_steps: list[Step] = [] - self._step_counter = 0 self._total_prompt_tokens = 0 self._total_completion_tokens = 0 self._total_cost = 0.0 @@ -99,10 +98,6 @@ async def run( environment: Harbor environment (Docker, Modal, etc.) context: Context to populate with metrics """ - self._add_step( - source="user", - message=instruction, - ) backend = HarborSandbox(environment) deep_agent = create_deep_agent( model=self._model, @@ -125,9 +120,89 @@ async def run( {"messages": [{"role": "user", "content": instruction}]}, # type: ignore config=config, ) - messages = result['messages'] - # Process messages into ATIF steps - self._process_message(messages) + + # Create trajectory + + steps = [ + Step( + step_id=0, + timestamp=datetime.now(timezone.utc).isoformat(), + source="system", + message="Agent initialized and ready to execute the task.", + ), + Step( + step_id=1, + timestamp=datetime.now(timezone.utc).isoformat(), + source="user", + message=instruction + ) + ] + + observations = [] + + current_step: Step | None = None + + for msg in result['messages']: + if isinstance(msg, AIMessage): + if current_step: + steps.append(current_step) + current_step = Step( + step_id=steps[-1].step_id + 1, + timestamp=datetime.now(timezone.utc).isoformat(), + source="agent", + ) + + atf_tool_calls = [] + message = "" + for cb in msg.content_blocks: + if cb['type'] == "text": + message += cb['text'] + elif cb['type'] == "reasoning": + message += cb['reasoning'] + elif cb["type"] == "tool_call": + atf_tool_calls.append( + ToolCall( + tool_call_id=cb['id'], + function_name=cb['name'], + arguments=cb['args'] + ) + ) + else: + # TODO: Add server side tool call results. + continue + + if observations: + observation = Observation( + results=observations + ) + + step = Step( + step_id=steps[-1].step_id + 1, + timestamp=datetime.now(timezone.utc).isoformat(), + source="agent", + message=message, + tool_calls=atf_tool_calls, + observation=observation, + ) + + # Reset observations for the next step + observations = [] + + elif isinstance(msg, ToolMessage): + # TODO: Merge the observation into the previous step + observations.append( + ObservationResult( + source_call_id=msg.tool_call_id, + content=str(msg.content), + ) + ) + elif isinstance(msg, HumanMessage): + pass + else: + raise NotImplementedError( + f"Message type {type(msg)} not supported for step conversion" + ) + # # Store task name for feedback @@ -207,52 +282,6 @@ async def run( def _process_message(self, msg: BaseMessage) -> None: """Process LangChain messages into ATIF trajectory steps.""" - if isinstance(msg, AIMessage): - atf_tool_calls = [] - message = "" - for cb in msg.content_blocks: - if cb['type'] == "text": - message += cb['text'] - elif cb['type'] == "reasoning": - message += cb['reasoning'] - elif cb["type"] == "tool_call": - atf_tool_calls.append( - ToolCall( - tool_call_id=cb['id'], - function_name=cb['name'], - arguments=cb['args'] - ) - ) - else: - # TODO: Add server side tool call results. - continue - self._add_step( - source="agent", - tool_calls=atf_tool_calls, - message=message, - ) - # # Extract usage metadata if available - # if msg.usage_metadata: - # self._update_token_usage(msg.usage_metadata) - - elif isinstance(msg, ToolMessage): - # TODO: Merge the observation into the previous step - observation = [ - ObservationResult( - source_call_id=msg.tool_call_id, - content=str(msg.content), - ) - ] - - self._add_step( - source="agent", - observation=Observation(results=observation), - message="", - ) - else: - raise NotImplementedError( - f"Message type {type(msg)} not supported for step conversion" - ) def _update_token_usage(self, usage_metadata: UsageMetadata) -> None: """Update token usage from message metadata.""" @@ -285,27 +314,6 @@ def _update_token_usage(self, usage_metadata: UsageMetadata) -> None: self._total_cost += input_cost + output_cost - def _add_step( - self, - source: Literal["system", "user", "agent"], - message: str, - tool_calls: list[ToolCall] | None = None, - observation: Observation | None = None, - metrics: Metrics | None = None, - ) -> None: - """Add a step to the ATIF trajectory.""" - self._step_counter += 1 - step = Step( - step_id=self._step_counter, - timestamp=datetime.now(timezone.utc).isoformat(), - source=source, - message=message, - tool_calls=tool_calls, - observation=observation, - metrics=metrics, - ) - - self._trajectory_steps.append(step) # def send_verification_feedback(self, reward: float) -> None: From 3259f409089e7634fb71eccc2dfbbf812442e72e Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Thu, 20 Nov 2025 16:08:52 -0500 Subject: [PATCH 09/10] x --- libs/harbor/harbor_deepagents/__init__.py | 44 +------------- .../harbor_deepagents/agents/deepagent.py | 58 ++++++++++--------- 2 files changed, 34 insertions(+), 68 deletions(-) diff --git a/libs/harbor/harbor_deepagents/__init__.py b/libs/harbor/harbor_deepagents/__init__.py index 14b4ee66..7849373f 100644 --- a/libs/harbor/harbor_deepagents/__init__.py +++ b/libs/harbor/harbor_deepagents/__init__.py @@ -1,46 +1,6 @@ -"""Harbor integration with LangChain DeepAgents. - -This package provides a complete integration between Harbor (agent benchmarking) -and LangChain DeepAgents (agent framework) with automatic LangSmith tracing. - -Quick Start: - >>> from harbor_deepagents.agents import DeepAgentHarbor - >>> agent = DeepAgentHarbor( - ... logs_dir=Path("logs"), - ... model_name="anthropic/claude-sonnet-4-5-20250929" - ... ) - -Example Usage with Harbor: - In configs/job.yaml: - ```yaml - agents: - - import_path: harbor_deepagents.agents:DeepAgentHarbor - model_name: anthropic/claude-sonnet-4-5-20250929 - ``` - - Then run: - ```bash - harbor run --config configs/job.yaml - ``` - -Features: - - DeepAgents framework with planning, filesystem, and subagents - - Automatic LangSmith tracing (zero config) - - ATIF trajectory format for RL/fine-tuning - - Works with any Harbor environment (Docker, Modal, E2B, etc.) - -See Also: - - Harbor: https://harborframework.com - - DeepAgents: https://github.com/langchain-ai/deepagents - - LangSmith: https://smith.langchain.com -""" - -__version__ = "1.0.0" - -from harbor_deepagents.agents import DeepAgentHarbor -# from harbor_deepagents.utils import LangSmithTrajectoryExporter +from harbor_deepagents.agents import DeepAgentsWrapper __all__ = [ - "DeepAgentHarbor", + "DeepAgentsWrapper", # "LangSmithTrajectoryExporter", ] diff --git a/libs/harbor/harbor_deepagents/agents/deepagent.py b/libs/harbor/harbor_deepagents/agents/deepagent.py index 82e8d522..08216327 100644 --- a/libs/harbor/harbor_deepagents/agents/deepagent.py +++ b/libs/harbor/harbor_deepagents/agents/deepagent.py @@ -139,19 +139,20 @@ async def run( ] observations = [] - - current_step: Step | None = None + pending_step: Step | None = None for msg in result['messages']: if isinstance(msg, AIMessage): - if current_step: - steps.append(current_step) - current_step = Step( - step_id=steps[-1].step_id + 1, - timestamp=datetime.now(timezone.utc).isoformat(), - source="agent", - ) - + # If there's a pending step with tool calls, add it now with observations + if pending_step is not None: + if pending_step.tool_calls and observations: + # Add observations to the pending step + pending_step.observation = Observation(results=observations) + observations = [] + steps.append(pending_step) + pending_step = None + + # Extract content and tool calls from current AIMessage atf_tool_calls = [] message = "" for cb in msg.content_blocks: @@ -171,25 +172,24 @@ async def run( # TODO: Add server side tool call results. continue - if observations: - observation = Observation( - results=observations - ) - - step = Step( - step_id=steps[-1].step_id + 1, - timestamp=datetime.now(timezone.utc).isoformat(), - source="agent", - message=message, - tool_calls=atf_tool_calls, - observation=observation, - ) + # Create new step + new_step = Step( + step_id=steps[-1].step_id + 1 if steps else 0, + timestamp=datetime.now(timezone.utc).isoformat(), + source="agent", + message=message, + tool_calls=atf_tool_calls if atf_tool_calls else None, + ) - # Reset observations for the next step - observations = [] + # If this AIMessage has tool calls, make it pending (wait for observations) + # Otherwise, add it immediately + if atf_tool_calls: + pending_step = new_step + else: + steps.append(new_step) elif isinstance(msg, ToolMessage): - # TODO: Merge the observation into the previous step + # Collect observations for the pending step observations.append( ObservationResult( source_call_id=msg.tool_call_id, @@ -203,6 +203,12 @@ async def run( f"Message type {type(msg)} not supported for step conversion" ) + # Add any remaining pending step + if pending_step is not None: + if pending_step.tool_calls and observations: + pending_step.observation = Observation(results=observations) + steps.append(pending_step) + # # Store task name for feedback From ad124336f30c70c8c676bf38b9634523fc019e44 Mon Sep 17 00:00:00 2001 From: Eugene Yurtsev Date: Thu, 20 Nov 2025 16:16:20 -0500 Subject: [PATCH 10/10] x --- libs/harbor/harbor_deepagents/agents/deepagent.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/libs/harbor/harbor_deepagents/agents/deepagent.py b/libs/harbor/harbor_deepagents/agents/deepagent.py index 08216327..77abba11 100644 --- a/libs/harbor/harbor_deepagents/agents/deepagent.py +++ b/libs/harbor/harbor_deepagents/agents/deepagent.py @@ -60,8 +60,6 @@ def __init__( self._environment: BaseEnvironment | None = None self._model = init_chat_model(model_name, temperature=temperature) - # Trajectory tracking (ATIF format) - self._trajectory_steps: list[Step] = [] self._total_prompt_tokens = 0 self._total_completion_tokens = 0 self._total_cost = 0.0 @@ -125,13 +123,13 @@ async def run( steps = [ Step( - step_id=0, + step_id=1, timestamp=datetime.now(timezone.utc).isoformat(), source="system", message="Agent initialized and ready to execute the task.", ), Step( - step_id=1, + step_id=2, timestamp=datetime.now(timezone.utc).isoformat(), source="user", message=instruction @@ -254,7 +252,7 @@ async def run( total_prompt_tokens=self._total_prompt_tokens or None, total_completion_tokens=self._total_completion_tokens or None, total_cost_usd=self._total_cost or None, - total_steps=len(self._trajectory_steps), + total_steps=len(steps), ) trajectory = Trajectory( @@ -270,7 +268,7 @@ async def run( "langsmith_run_id": self._langsmith_run_id, # Store for feedback }, ), - steps=self._trajectory_steps, + steps=steps, final_metrics=final_metrics, )