I got tired of debugging LangChain agents blind, so I built a local-first observability tool (MIT, no cloud)

I build a fair amount with LangChain and LangGraph, and every time a run broke I hit the same wall: I couldn't see what was going on inside it. Which tool actually got called, which LLM said what, how many tokens each step ate, where the thing fell over. Mostly I ended up scattering print statements around and squinting at logs, which is not a great way to live. I know LangSmith and LangFuse exist and they're good. They just didn't fit how I wanted to work locally. One wants a cloud account, another wants Docker and Postgres running, and some of them send your trace data off to a server you don't own. For day-to-day dev, and definitely for anything with sensitive data in the traces, that was more than I wanted to deal with. So I built TraceSage. You add a few lines of code and it runs entirely on your machine. Nothing goes out. What it actually does: Draws a live topology graph of your agents, tools, LLMs, and MCP servers while the run streams in Lets you replay any run step by step and read the full request and response at each point Tracks tokens per LLM node, in and out, for every call Groups tools by which MCP server loaded them, so it's obvious which ones are local Exports to OpenTelemetry, so when you do want the cloud stuff you can pipe it into Grafana, Datadog, Honeycomb, or anything that speaks OTLP Has a hard off switch for prod: set TRACESAGE_ENABLED=false and it does nothing at all It saves everything to a local SQLite file and works offline. MIT licensed, no account, no key. Install is pip install tracesage[langchain]. Fair warning, it's still young and only does LangChain and LangGraph right now. I'd really like feedback, especially on what's missing or anywhere the API feels wrong. Adding Github and pypi links in comment section