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Home/Categories/LLMOps & AI Tooling/ClawTrace
icon of ClawTrace

ClawTrace

Open source alternative to LangSmith, Datadog, New Relic and Dynatrace

  • 47 stars
  • Apache-2.0
  • Active recently
  • Last commit 69 days ago

Last verified Oct 22, 2026

Analyze OpenClaw agent runs with execution trees, per-step token costs, TraceCards, and Apache-2.0 source you can self-host.

Visit websiteGitHub47 stars
image of ClawTrace
TL;DR

ClawTrace is an Apache 2.0 tracing tool for OpenClaw LLM agents. It records model calls, tool use, and sub-agent work into compact TraceCards so AI engineers can see token cost, redundant steps, and skill-distillation opportunities.

Read the full breakdownHide the full breakdownWho it is for, how it works, trade-offs and the FAQ
who it's for

Who ClawTrace is for#

AI engineers reducing agent token spend

ClawTrace fits engineers who need to identify expensive calls, repeated tool use, and steps that can be pruned from a multi-step agent run.

Skip if:

Your agents are simple single-call prompts with no tool execution.

Researchers studying agent skill reuse

TraceCards and CostCraft patches help researchers inspect how trajectories can become reusable skill improvements.

Skip if:

You only need production uptime alerts.

the problem

The problem it solves#

Agent runs become expensive and hard to debug when each model call, tool call, and retry disappears into logs. Teams can see that an agent failed or spent too much, but they often cannot identify which step caused the cost spike or where the workflow repeated work.

Skill improvement has the same visibility problem. Without a compact trace artifact, developers cannot reliably decide which behavior to preserve, prune, or repair across repeated agent runs.

how ClawTrace solves it

How it solves it#

TraceCard summaries

ClawTrace compiles agent sessions into compact YAML TraceCards that include per-step USD cost, token counts, and redundancy flags.

CostCraft skill patches

The README describes three CostCraft patch types: preserve, prune with counterfactual evidence, and repair. That gives teams a structured way to improve agent skills from traces.

OpenClaw-compatible stack

ClawTrace targets OpenClaw agents and ships with a web UI built on Next.js 15 plus a FastAPI backend for trace analysis.

strengths · trade-offs

Strengths and trade-offs#

Strengths

  • Cost detail at the step levelPer-step token and dollar-cost data helps teams optimize agent workflows where raw success or failure logs do not show waste.
  • Research-backed workflowThe project links to an arXiv paper and frames tracing around skill distillation, which gives the tool a clearer method than generic agent dashboards.

Trade-offs

  • -OpenClaw-specific fitClawTrace is most relevant for teams already building OpenClaw agents. Teams on LangChain, custom agent runners, or hosted observability products should verify integration work before adopting it.
tech stack · detected from GitHub

What it's built on#

Languages
PythonTypeScript
Frameworks
FastAPINext.jsReact
frequently asked

FAQ#

What is ClawTrace used for?

ClawTrace traces OpenClaw LLM agent runs and records cost, token usage, tool calls, and redundancy signals for each step.

What license does ClawTrace use?

ClawTrace is Apache 2.0 licensed according to the repository metadata and README badge.

Does ClawTrace help reduce agent cost?

Yes. The project focuses on cost-aware tracing, with TraceCards and CostCraft patches designed to expose expensive or redundant agent behavior.

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