ScriptsMay 12, 2026·2 min read

Laminar — Open-Source Observability for AI Agents

Open-source observability for AI agents: self-host with Docker Compose, then use the SDK to trace runs, metrics, and outputs end-to-end.

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Agent ready

This asset can be read and installed directly by agents

TokRepo exposes a universal CLI command, install contract, metadata JSON, adapter-aware plan, and raw content links so agents can judge fit, risk, and next actions.

Stage only · 29/100Stage only
Agent surface
Any MCP/CLI agent
Kind
Script
Install
Manual
Trust
Trust: Established
Entrypoint
docker compose
Universal CLI install command
npx tokrepo install 7a276f53-1d98-4535-9cc3-47cbd6443bae
install contractmetadata JSONadapter planraw content
Intro

Open-source observability for AI agents: self-host with Docker Compose, then use the SDK to trace runs, metrics, and outputs end-to-end.

  • Best for: agent teams who need repeatable tracing and debugging, not just ad-hoc logs, across prompts, tools, and multi-step runs
  • Works with: Docker Compose for self-hosting; SDK instrumentation in your agent app
  • Setup time: 30–60 minutes

Practical Notes

  • Quant: define 3 core metrics per agent workflow (latency, tool-call count, success rate) and baseline them before you optimize prompts.
  • Quant: keep a replay set of 20 representative runs; compare traces after every change to detect regressions.

Observability-first iteration

If you can’t answer these questions with data, you’re guessing:

  • Which step dominates latency?
  • Which tool calls fail most often?
  • Which prompt change improved success rate vs just “felt better”?

Minimal instrumentation strategy

  1. Trace every run with a stable run id.
  2. Attach tool-call spans with inputs/outputs (redact secrets).
  3. Capture final outcomes (pass/fail + reason).

Don’t drown in dashboards

Start with one workflow and one team. Once the metrics are trusted, scale to more services.

FAQ

Q: Do I need to self-host? A: No. The repo documents self-hosting; teams can choose managed options or local-only usage.

Q: What should I instrument first? A: One end-to-end workflow that currently fails or is slow—make it measurable.

Q: How do I compare prompt changes? A: Use a fixed replay set and compare traces/metrics, not anecdotes.

🙏

Source & Thanks

Source: https://github.com/lmnr-ai/lmnr > License: Apache-2.0 > GitHub stars: 2,875 · forks: 195

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