An AI agent that fails silently is one of the most expensive debugging problems you’ll face. It picks tools, branches, retries, rewrites its own plan — and without visibility into those decisions, you’re flying blind. That’s the premise behind Motus Tracing, and it’s a compelling one.

Lithos AI released Motus Tracing in May 2026 as a fully open-source observability layer for AI agents. It’s framework-agnostic, requires zero code changes to your agent, and the entire tracing stack is free to use. Here’s how it works and how to set it up.

What Motus Tracing Captures

Motus records every step of an agent run in detail:

  • Model invocations — which model was called, with what prompt, and what it returned
  • Tool calls — every tool invocation, including arguments and results
  • Sandbox interactions — when your agent executes code or shell commands
  • Sub-agent actions — if your agent spawns child agents, those are traced too
  • Retries and errors — failed calls, retry attempts, and error messages
  • Task transitions — when the agent moves between phases or goals

All of this is organized into a span tree — a hierarchical view of exactly what happened, in what order, and how long each step took.

Framework Support

One of Motus Tracing’s strongest selling points is that it works across frameworks without requiring changes to your agent code:

  • Motus-native agents (built on Lithos AI’s own platform)
  • OpenAI Agents SDK
  • Anthropic SDK
  • Google ADK
  • Plain Python — any agent, any architecture

This means you can use the same tracing tooling whether you’re building on LangGraph, a custom agent loop, or using a hosted platform.

Local Tracing: Enable With One Environment Variable

For local development, enabling Motus Tracing is literally one command:

MOTUS_TRACING=1 uv run python agent.py

That’s it. The runtime writes a self-contained trace_viewer.html file to disk after the run. Open it in a browser and you get the complete span tree: model calls, tool calls, durations, token counts, and payload contents — with no server, no network connection, and no account required.

This is intentional design. The LithosAI team’s stated goal is that “anyone building agents should be able to inspect a run the moment a problem appears, without any agent modifications.”

Confirmed: The MOTUS_TRACING=1 environment variable and trace_viewer.html output are documented on the official LithosAI blog at lithosai.com/blog/motus-agent-tracing and in the GitHub repo.

Installation

Motus Tracing ships as part of the motus Python package:

uv add motus
# or
pip install motus

No additional configuration is required for local tracing. The env var activates it automatically.

Cloud Tracing: Same Schema, Live Streaming

For deployed agents, Motus supports live trace streaming. The cloud and local tracing implementations share the same span schema, which means:

  • Your analysis tooling works identically on local and production traces
  • You can use the same debugging workflow regardless of environment
  • Downstream evaluation pipelines operate on one consistent representation

This is a significant advantage over observability setups that have a local dev format and a separate production format — schema drift between environments is a real problem in practice.

Why Traces Feed Evaluation (and Not Just Debugging)

Motus was designed with a third use case beyond local debugging and production monitoring: evaluation and improvement. The same spans that surface a failure can be fed to a coding agent iterating on the agent, or used to power more advanced techniques like “Learning Agents” (a separate Lithos AI project).

In other words: the trace data you collect from debugging becomes training signal for improving your agent. That’s a compelling flywheel — every run where something goes wrong leaves behind structured evidence you can act on.

Getting Started

  1. Install the motus package via pip or uv
  2. Set MOTUS_TRACING=1 before your next agent run
  3. Open the generated trace_viewer.html to inspect the run
  4. For cloud/production tracing, refer to the Motus documentation

The GitHub repo (lithos-ai/motus) is the primary reference for configuration options and cloud deployment setup.

Sources

  1. Motus Tracing release blog — lithosai.com/blog/motus-agent-tracing
  2. lithos-ai/motus on GitHub
  3. Motus quickstart docs — docs.motus.lithosai.com

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260517-0800

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