trace-multi-agent-system SKILL.md skillA
trace-multi-agent-system from contextjet-ai/awesome-llm-observability is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add contextjet-ai/awesome-llm-observability/trace-multi-agent-system@git:20260705.23078bePins git:20260705.23078be in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- contextjet-ai/awesome-llm-observability
- Path
skills/trace-multi-agent-system/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260705.23078be, committed Jul 5, 2026
- Size
- 3,077 bytes
- Repository stars
- 45
- License
- a license file GitHub does not identify
Use this to add observability to a multi-agent or agentic system (multiple agents, sub-agents, tool loops, handoffs). Trigger on "trace my agents", "my agent crew is a black box", "which agent failed", "debug my LangGraph/CrewAI/AutoGen/agent workflow", "the agents loop forever". Get a clear span tree across agents and tool calls so you can see who did what.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Trace a multi-agent system Multi-agent systems fail in ways single calls do not: an agent hands off bad state, a tool loop never terminates, a sub-agent silently fails and the orchestrator carries on. You cannot debug that from the final output. You need the full tree of who called whom. ## Get the structure right Model the run as a nested span tree: - **One root span** per user request or task. - **One span per agent turn** (which agent, its role, its input state). - **Child spans** for each tool call, retrieval, and LLM call inside that turn. - **Handoff spans / events** when one agent passes control or state to another (record what was passed). Most agent frameworks emit this if you turn on their tracing (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, LlamaIndex) or add OpenTelemetry instrumentation (OpenLLMetry, OpenInference auto-instrument several agent frameworks). Prefer the framework's built-in tracing, then fill gaps with manual spans (see `instrument-llm-observability`). ## What to capture per agent - **Identity + role** (`agent.name`, `agent.role`) so spans are attributable. …
Read the whole file at its exact version.
How to install
mdr add contextjet-ai/awesome-llm-observability/trace-multi-agent-system@sha256:3c8d09aa045a7647The line at the top of this page pins the label git:20260705.23078be, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_omc4r7n5gwxpprv7)
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Versions
The registry has recorded 1 version of this file, committed on Jul 5, 2026.
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (3077 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
contextjet-ai/awesome-llm-observability · 45 stars · license a license file GitHub does not identify · pushed 2026-11-02 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_omc4r7n5gwxpprv7 GET https://markdownregistry.com/api/v1/resolve?ref=contextjet-ai/awesome-llm-observability/trace-multi-agent-system GET https://markdownregistry.com/api/v1/blob/3c8d09aa045a7647f1bb5c3082e2cb66a169f47660980e30ec3094f242416946
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.