LLM Reasoning Tracer

A modular tracing and explainability toolkit for LangChain agents. Capture step-by-step tool usage, visualize the reasoning process, and analyze how your agent chains behave.

Features

• Traces agent steps (inputs, tool used, outputs)
• Visualizes tool usage and outcomes
• Includes dashboards for Streamlit and Matplotlib
• Supports RAG, search chains, math pipelines, and multi-tool agents

Installation

pip install .

Setup

Create a .env file:

OPENAI_API_KEY=your-key-here

Trace Examples

Run one of the provided pipelines:

python examples/use_with_langchain_agent.py
python examples/math_chain_example.py
python examples/multi_agent_chain_example.py
python examples/rag_pipeline_example.py

Create RAG DB

python examples/create_db.py

It loads data/solar_energy_notes.txt and builds db/ for retrieval.

View Your Trace

1. Streamlit Dashboard

streamlit run examples/streamlit_viewer.py

2. Timeline Plot (if added)

python examples/visualize_trace.py

Output Example (trace_math.json)

{
  "step_1": {
    "input": "What's 15% tip for $80 dinner, and add it to the bill?",
    "tool_used": "Tip Calculator",
    "output": "Tip is $12.0"
  },
  "step_2": {
    "input": "Add 80 + 12.0",
    "tool_used": "Total Calculator",
    "output": "The total bill including tip is $92.0"
  }
}

Structure

• cognitive_tracer/ — the Python module
• examples/ — usage scripts
• traces/ — generated trace files
• data/ — text input for vector DB

Dependencies

langchain
langchain-openai
langchain-community
openai
streamlit
matplotlib
python-dotenv

License

MIT © Nisaharan Genhatharan