AI Agent Framework

Intel® Unnati Industrial Training Program 2025

A Pure Python AI Agent Framework with Intel® OpenVINO™ Optimization

📋 TL;DR (Too Long, Didn't Read) short Overview

Built from scratch — A complete AI Agent Framework for orchestrating agentic workflows without using CrewAI, AutoGen, LangGraph, or n8n.

What	Description
What is it?	A Python framework SDK for building AI agents with DAG workflows, tools, memory, and observability
Why?	Intel Unnati Problem Statement #2: Build-Your-Own AI Agent Framework
Key Features	Flow DAG execution, YAML orchestration, Tool registry with schema validation, Memory store, Structured logging, Intel® OpenVINO™ ML optimization
Demo Time	< 30 seconds to run all demos
Lines of Code	~4,500 lines of pure Python (no agent framework dependencies)

🎯 Problem Statement → Implementation Mapping

Problem Statement Requirement	Implementation	Location
Define and execute task flows (DAG)	✅ `Flow` class with topological sort, parallel execution	`framework/flow.py`
Support input handlers, tools/actions	✅ `ToolRegistry`, `BaseTool` with schema validation	`framework/tools.py`
Output actions	✅ `FileWriteTool`, `HTTPTool`, task outputs	`framework/tools.py`
Include memory	✅ `MemoryStore` with namespaces, TTL, persistence	`framework/memory.py`
Guardrails	✅ Schema validation, retries, timeouts, error handling	`framework/tools.py`, `task.py`
Observability (logs, metrics)	✅ `FlowLogger`, `MetricsCollector`, `AuditLog`	`framework/logging.py`
Orchestrator	✅ YAML-based `Orchestrator` with state persistence	`framework/orchestrator.PY`
Apache components	✅ Ready for Kafka/Airflow integration (REST API included)	`api/server.py`
Intel® OpenVINO™ optimization	✅ `OpenVINOTextClassifier`, `OpenVINOEmbedding`	`framework/openvino_tools.py`
Framework SDK with APIs	✅ `Agent` class, decorators, builders	`framework/sdk.py`
Two reference agents	✅ Research Agent, Data Processing Agent	`examples/agents_demo.py`
Performance benchmarks	✅ Before/after OpenVINO comparison	`examples/openvino_benchmark.py`
Retries and timeouts	✅ Exponential backoff, configurable timeouts	`framework/task.py`

🏗️ Architecture

🚀 Quick Start (< 2 minutes)

1. Install Dependencies

git clone https://github.com/Precise-Goals/Intel-Ai-Unnati-Program---Internship.git
cd Intel-Ai-Unnati-Program---Internship
pip install -r requirements.txt

2. Run All Demos

# Step 1: Set PYTHONPATH (required before running demos)

# Linux/macOS:
export PYTHONPATH=$(pwd)

# Windows PowerShell:
$env:PYTHONPATH = (Get-Location).Path

# Windows CMD:
set PYTHONPATH=%cd%

# Step 2: Run the demos
python examples/agents_demo.py       # Reference Agents (Research + Data Processing)
python examples/tools_demo.py        # Tool System with Schema Validation
python examples/orchestrator_demo.py # YAML Orchestrator with State Persistence
python examples/logging_demo.py      # Structured Logging Demo
python examples/openvino_benchmark.py # OpenVINO Benchmark

💡 Tip: On Windows, you can also run run_demos.bat which sets PYTHONPATH automatically.

3. Quick Code Example

from framework import Agent, FunctionTask, tool

# Define a tool with schema validation
@tool(name="analyze", description="Analyze text sentiment")
def analyze(text: str) -> dict:
    return {"sentiment": "positive", "confidence": 0.95}

# Create an agent and workflow
agent = Agent("demo_agent")
flow = agent.create_flow("analysis_flow")

flow.add_task(FunctionTask("fetch", lambda ctx: {"text": "Great product!"}))
flow.add_task(FunctionTask("analyze", lambda ctx: analyze(ctx["fetch_result"]["text"])))
flow.add_dependency("analyze", "fetch")

# Execute
result = agent.run_flow("analysis_flow", {})
print(f"Success: {result.success}")  # True

📊 Benchmark Results: OpenVINO Optimization

Text Classification (Sentiment Analysis)

Metric	PyTorch (Baseline)	Intel® OpenVINO™	Improvement
Avg Latency	45.23 ms	28.41 ms	37.2% faster
Min Latency	42.18 ms	26.54 ms	-
Max Latency	51.87 ms	32.19 ms	-
P95 Latency	49.31 ms	30.87 ms	-
Throughput	22.11 req/s	35.21 req/s	59.2% higher
Speedup	1.0x	1.59x	-

Model: distilbert-base-uncased-finetuned-sst-2-english

Text Embeddings (RAG/Search)

Metric	PyTorch (Baseline)	Intel® OpenVINO™	Improvement
Avg Latency	12.87 ms	7.94 ms	38.3% faster
Min Latency	11.92 ms	7.21 ms	-
Max Latency	15.43 ms	9.18 ms	-
P95 Latency	14.21 ms	8.76 ms	-
Throughput	77.73 req/s	125.94 req/s	62.0% higher
Speedup	1.0x	1.62x	-

Model: sentence-transformers/all-MiniLM-L6-v2

💡 Note: Results measured on Intel CPU. OpenVINO provides best optimization on Intel® processors (CPU, iGPU, VPU).

📝 Sample Agent Outputs

Research Agent Demo

🚀 [20:09:42] FLOW_START: research_workflow (b8e59490...) - 5 tasks
  ▶ [20:09:42] TASK_START: search (function)
  ✓ [20:09:42] TASK_END: search - completed in 0.101s
  ▶ [20:09:42] TASK_START: extract_entities (function)
  ▶ [20:09:42] TASK_START: summarize (function)        ← Parallel execution!
  ✓ [20:09:42] TASK_END: extract_entities - completed in 0.051s
  ✓ [20:09:42] TASK_END: summarize - completed in 0.101s
  ▶ [20:09:42] TASK_START: analyze_sentiment (function)
  ✓ [20:09:42] TASK_END: analyze_sentiment - completed in 0.051s
  ▶ [20:09:42] TASK_START: generate_report (function)
  ✓ [20:09:42] TASK_END: generate_report - completed in 0.000s
✅ [20:09:42] FLOW_END: research_workflow - completed in 0.26s (5 completed, 0 failed)

--- Research Report ---
  query: artificial intelligence applications in healthcare
  entities: {'persons': ['John Doe'], 'organizations': ['Tech Inc'], ...}
  sentiment: {'score': -0.10, 'label': 'neutral', 'confidence': 0.72}

Orchestrator Demo (YAML Workflow)

============================================================
PARALLEL WORKFLOW DEMO
============================================================

Workflow: parallel_processing
Status: completed
Duration: 0.01s

Task States:
  start: completed
  branch_a: completed   ← Parallel branches
  branch_b: completed   ← 
  merge: completed

Merge Result: {'merged': True, 'total': 300}

State persisted to: workflow_states.json

Structured Logging Output (JSONL)

{"timestamp": "2026-01-01T20:09:42", "event_type": "FLOW_START", "flow_id": "b8e59490", "task_count": 5}
{"timestamp": "2026-01-01T20:09:42", "event_type": "TASK_END", "task_name": "search", "duration_seconds": 0.101}
{"timestamp": "2026-01-01T20:09:42", "event_type": "TASK_RETRY", "task_name": "flaky_task", "attempt": 1, "max_attempts": 3}
{"timestamp": "2026-01-01T20:09:42", "event_type": "FLOW_END", "status": "completed", "duration_seconds": 0.26}

📁 Project Structure

intel/
├── framework/                    # Core Framework SDK
│   ├── __init__.py              # Package exports
│   ├── sdk.py                   # Agent class, high-level API
│   ├── task.py                  # Task abstraction (Function, LLM, Tool, Conditional)
│   ├── flow.py                  # DAG execution engine with parallel support
│   ├── tools.py                 # Tool registry, BaseTool, schema validation
│   ├── memory.py                # Memory store with namespaces & TTL
│   ├── logging.py               # FlowLogger, MetricsCollector, AuditLog
│   ├── orchestrator.PY          # YAML-based workflow orchestrator
│   └── openvino_tools.py        # OpenVINO ML optimized tools
│
├── examples/                     # Demo & Reference Implementations
│   ├── agents_demo.py           # Research Agent + Data Processing Agent
│   ├── tools_demo.py            # Tool system demonstration
│   ├── orchestrator_demo.py     # YAML orchestration demo
│   ├── logging_demo.py          # Structured logging demo
│   ├── openvino_benchmark.py    # OpenVINO performance benchmark
│   └── workflows/               # YAML workflow definitions
│       ├── research.yaml
│       └── data_pipeline.yaml
│
├── api/
│   └── server.py                # REST API server (FastAPI)
│
├── dashboard/
│   └── ui.py                    # Streamlit monitoring dashboard
│
├── logs/flows/                  # Persisted flow logs (JSONL)
├── requirements.txt             # Python dependencies
└── README.md                    # This file

✅ Compliance Checklist

❌ Forbidden Frameworks (NOT USED)

Framework	Status	Verification
CrewAI	❌ Not Used	`grep -r "crewai" .` returns nothing
AutoGen	❌ Not Used	`grep -r "autogen" .` returns nothing
LangGraph	❌ Not Used	`grep -r "langgraph" .` returns nothing
n8n	❌ Not Used	`grep -r "n8n" .` returns nothing

✅ Allowed Technologies (USED)

Technology	Usage	Location
Intel® OpenVINO™	ML model optimization (1.5x-1.6x speedup)	`framework/openvino_tools.py`
Apache-compatible	REST API ready for Kafka/Airflow integration	`api/server.py`
Pure Python	All core logic (~4,500 lines)	`framework/*.py`
pydantic	Data validation	`requirements.txt`
PyYAML	YAML workflow parsing	`framework/orchestrator.PY`

✅ Deliverables Completed

Deliverable	Status	Evidence
Framework SDK with APIs	✅	`framework/sdk.py`, `framework/__init__.py`
Flow/DAG execution	✅	`framework/flow.py` (topological sort, parallel execution)
Tool registry	✅	`framework/tools.py` (BaseTool, schema validation)
Memory store	✅	`framework/memory.py` (namespaces, TTL, persistence)
Observability	✅	`framework/logging.py` (JSONL logs, metrics, audit)
YAML orchestrator	✅	`framework/orchestrator.PY` (state persistence)
Reference Agent #1	✅	Research Agent in `examples/agents_demo.py`
Reference Agent #2	✅	Data Processing Agent in `examples/agents_demo.py`
OpenVINO optimization	✅	`framework/openvino_tools.py` with benchmarks
Performance benchmarks	✅	`examples/openvino_benchmark.py`
Retries & timeouts	✅	`framework/task.py` (exponential backoff)

✅ This is a FRAMEWORK, Not Just an App

Framework Characteristic	Evidence
Extensible SDK	`Agent`, `Task`, `Flow`, `Tool` base classes for users to extend
Pluggable components	Tool registry, memory backends, custom task types
Decorators for DX	`@tool`, `@log_execution` decorators
Configuration-driven	YAML workflow definitions
APIs for integration	REST API, programmatic flow builder
Reusable abstractions	`BaseTool`, `Task`, `MemoryStore` abstract patterns

🔧 Advanced Usage

Define a YAML Workflow

# workflows/my_workflow.yaml
name: my_workflow
version: "1.0"

tasks:
  - id: fetch_data
    type: function
    config:
      function: mymodule.fetch

  - id: process
    type: tool
    depends_on: [fetch_data]
    config:
      tool_name: text_processor
      tool_args:
        operation: uppercase

  - id: save
    type: function
    depends_on: [process]
    config:
      function: mymodule.save

Run with Orchestrator

from framework.orchestrator import Orchestrator

orch = Orchestrator(state_dir="./states")
result = orch.load_and_run("workflows/my_workflow.yaml")
print(f"Status: {result.status}")  # completed

Use OpenVINO-Optimized Tools

from framework.openvino_tools import OpenVINOTextClassifier

classifier = OpenVINOTextClassifier(
    model_name="distilbert-base-uncased-finetuned-sst-2-english",
    use_openvino=True  # Enable Intel optimization
)

result = classifier.classify("This product is amazing!")
# {'label': 'POSITIVE', 'confidence': 0.98}

👥 Team Falcons

Name	Role	Email	Contact	CGPA
Sarthak Tulsidas Patil	Developer (CSE)	sarthak.patil@nmiet.edu.in	7387303695	9.57
Prathamesh Santosh Kolhe	Developer (CSE)	prathameshkolhe6099@gmail.com	9975668077	9.18
Dhiraj Takale	Developer (CSE)	dhirajtakale17@gmail.com	8668945438	9.27

Mentor

Name	Role	Email	Contact
Prof. Jordan Choudhari	Assistant Professor	jordan.choudhari@nmiet.edu.in	7709754570

📜 License

MIT License - See LICENSE for details.

Built with ❤️ by Team Falcons for Intel® Unnati Industrial Training Program 2025

No forbidden frameworks. Pure Python. Intel® Optimized.

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AI Agent Framework

Intel® Unnati Industrial Training Program 2025

📋 TL;DR (Too Long, Didn't Read) short Overview

🎯 Problem Statement → Implementation Mapping

🏗️ Architecture

🚀 Quick Start (< 2 minutes)

1. Install Dependencies

2. Run All Demos

3. Quick Code Example

📊 Benchmark Results: OpenVINO Optimization

Text Classification (Sentiment Analysis)

Text Embeddings (RAG/Search)

📝 Sample Agent Outputs

Research Agent Demo

Orchestrator Demo (YAML Workflow)

Structured Logging Output (JSONL)

📁 Project Structure

✅ Compliance Checklist

❌ Forbidden Frameworks (NOT USED)

✅ Allowed Technologies (USED)

✅ Deliverables Completed

✅ This is a FRAMEWORK, Not Just an App

🔧 Advanced Usage

Define a YAML Workflow

Run with Orchestrator

Use OpenVINO-Optimized Tools

👥 Team Falcons

Mentor

📜 License

FilesExpand file tree

readme.md

Latest commit

History

readme.md

File metadata and controls

AI Agent Framework

Intel® Unnati Industrial Training Program 2025

📋 TL;DR (Too Long, Didn't Read) short Overview

🎯 Problem Statement → Implementation Mapping

🏗️ Architecture

🚀 Quick Start (< 2 minutes)

1. Install Dependencies

2. Run All Demos

3. Quick Code Example

📊 Benchmark Results: OpenVINO Optimization

Text Classification (Sentiment Analysis)

Text Embeddings (RAG/Search)

📝 Sample Agent Outputs

Research Agent Demo

Orchestrator Demo (YAML Workflow)

Structured Logging Output (JSONL)

📁 Project Structure

✅ Compliance Checklist

❌ Forbidden Frameworks (NOT USED)

✅ Allowed Technologies (USED)

✅ Deliverables Completed

✅ This is a FRAMEWORK, Not Just an App

🔧 Advanced Usage

Define a YAML Workflow

Run with Orchestrator

Use OpenVINO-Optimized Tools

👥 Team Falcons

Mentor

📜 License