OpenClaw Langfuse Observability Plugin Model Card

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OpenClaw Langfuse Observability Plugin Model Card

1. Role in Self Evolve

MCKRUZ/openclaw-langfuse adds Langfuse observability to OpenClaw through a lightweight plugin bridge. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.

2. Working Principle

hook OpenClaw tool and model events into Langfuse traces -> attach session metadata for replayable diagnostics -> surface latency and failure patterns across agent runs -> support regression spotting before releasing updated agent workflows

3. Evidence Path

web-observed GitHub page showed 1 star, 0 forks, 2 commits, MIT license, and JavaScript implementation for OpenClaw-Langfuse integration. Shell GitHub API access remained blocked by DNS and local gh auth was invalid, so this card treats the snapshot as web-observed rather than API-verified.

4. Teaching Use

Use this card to explain OpenClaw Tracing Plugin for Langfuse Observability: it shows how harness/runtime/benchmark layers convert agent behavior into reproducible and auditable engineering workflows.

5. Limits

The repository was not cloned in this iteration; no benchmark run, plugin install, workflow execution, or agent loop experiment was executed. Counts and claims are visible public-page/search signals unless independently revalidated later.