⚡ AgentTrace: Production-Grade Agent Observability & Self-Healing Engine

Live Production Demo:

• 🖥️ Interactive Web Dashboard: https://agent-trace-zeta.vercel.app/
• ⚙️ FastAPI Swagger Docs: https://agenttrace-api-cdav.onrender.com/docs

An end-to-end observability SDK and dashboard for autonomous AI agent pipelines. It monitors multi-step tool calls, visualizes latency bottlenecks, and automatically repairs malformed LLM tool arguments at runtime without crashing workflows.

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🎯 The Problem

LLMs frequently hallucinate tool arguments during multi-step runs:

• Passing strings instead of floats (e.g. "1200 INR" instead of 1200.0)
• Inventing key names (e.g. "user_identifier" instead of "user_id")
• Omitting required schema fields

Normally, these cause immediate runtime crashes. AgentTrace catches these failures and auto-repairs them at runtime.

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💡 Architecture & Tech Stack

• Decorator SDK: Python, Pydantic (Validates schema before tool run)
• Self-Healing Layer: Fast inference via Groq to repair payloads on failure
• Collector Backend: FastAPI with SQLite persistence (traces.db)
• Live Dashboard: Next.js, Tailwind CSS with Payload Diff Inspector

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🚀 How to Run Locally

1. Start Backend

# In project root
python -m uvicorn main:app --reload --port 8000