Introduction
TraceAgent gives you complete visibility into what your autonomous agents are actually doing — every tool call, file operation, terminal command, and decision path, recorded and visualized in real time.
Why TraceAgent?
Traditional observability tools were built for microservices — they don't understand what it means for an agent to "call a tool", "write a file", or "retry a failed command".
TraceAgent fills that gap with an agent-first tracing specification and a purpose-built audit UI, giving you full data ownership through a self-hosted setup.
With first-class support for orchestration ecosystems like LangChain, TraceAgent captures and reconstructs complete execution across chains, agents, tools, prompts, and callbacks — enabling reproducibility, debugging, compliance auditing, and performance analysis for production-grade AI systems.
Key Features
| Feature | Description |
|---|---|
| Real-time tracing | Capture tool calls, file operations, shell commands, and artifacts as they happen |
| Execution timeline | Visualize agent behavior through interactive timelines and execution graphs |
| LangChain integration | Native callback handler for LangChain agents and chains, with zero boilerplate |
| Self-hosted | Full data ownership with a FastAPI backend and SQLite/PostgreSQL support |
Packages
TraceAgent is organized into four packages that work together:
| Package | Purpose |
|---|---|
| TraceAgent SDK | Python client for recording agent actions |
| TraceAgent LangChain | Callback handler for automatic LangChain tracing |
| TraceAgent Server | FastAPI backend with SQLite/PostgreSQL storage |
| TraceAgent UI | Interactive dashboard for visualizing traces |
What's Next?
Ready to get started? Follow these steps:
- Install TraceAgent — Get the packages set up
- Quick Start — Your first trace in under 5 minutes
- Architecture — Understand how the pieces fit together
If you're already familiar with agent tracing concepts, jump straight to the Quick Start guide.