langchain_langgraph

LangChain + LangGraph + Opik

Trace LangChain runnables inside a LangGraph workflow with Opik.

What this does

This example builds a small support-router workflow with LangGraph and LangChain runnables. The graph classifies a support question, routes it to a branch, and generates a deterministic response. When Opik credentials are set, the workflow is wrapped with track_langgraph() and traced with OpikTracer.

Prerequisites

This is a uv project - dependencies live in pyproject.toml.

uv sync

Or, with pip:

pip install opik langchain-core langgraph

Environment variable	Required	Description
OPIK_API_KEY	for a live run	Opik API key from comet.com/opik. Unset -> DRY_RUN.
OPIK_WORKSPACE	for a live run	Your Opik workspace. Unset -> DRY_RUN.
OPIK_PROJECT_NAME	no	Project traces are logged to (default langchain-langgraph).
OPIK_URL_OVERRIDE	no	Base URL for self-hosted Opik (default: Opik Cloud).

Running it

# Dry-run first - no credentials needed.
uv run langchain-langgraph-opik --dry-run

# Full run - set credentials, then the same command logs the graph to Opik.
export OPIK_API_KEY="<your-key>"
export OPIK_WORKSPACE="<your-workspace>"

uv run langchain-langgraph-opik

# or run it the way CI does:
bash run.sh

How it works

1. LangChain runnables - RunnableLambda wraps the classification and response functions so each unit is visible as a LangChain step.
2. LangGraph routing - StateGraph routes the question to greeting, billing, technical, or general response nodes based on the classification.
3. Opik tracing - OpikTracer records the graph execution and track_langgraph() attaches graph structure and node spans to the trace.
4. Dry-run fallback - missing Opik credentials switch the script into DRY_RUN, which prints the same classification and response locally without sending data.