TraceFact MCP Server
Analyzes OpenAI Codex agent traces (rollout JSONL) to generate evidence-linked reliability reports, failure localization, and replay capsules.
Ingests OpenTelemetry spans and log events from observability stacks to produce reliability reports and evidence graphs for agent runs.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@TraceFact MCP Serverlist the latest trace runs and get reliability report for run 42"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.


The 60-second quick start
git clone https://github.com/Alex0AI/tracefact.git
cd tracefact
npm ci
npm run build
node dist/cli.js analyze examples/offline-demo.codex.jsonl --out reportOpen report/report.html. It is standalone and works offline. The command also writes report.json, report.md, report.sarif, and a hash-verified run.tracefact.gz replay capsule.
Prefer the browser? Open the live demo, drag in a trace, and export the report. The file never leaves the page.
Related MCP server: jaeger-mcp
What goes in; what comes out; why it is credible
flowchart LR
A[Codex / Gemini CLI / Browser Use / JSONL / OTel] --> B[Open Agent Trace Schema]
B --> C[Redaction]
C --> D[Evidence Graph]
C --> E[Deterministic failure rules]
D --> F[HTML · JSON · Markdown · SARIF]
E --> F
C --> G[Replay Capsule + SHA-256]TraceFact does not hide correctness behind one LLM-as-judge score. It extracts explicit completion claims and links each one to observable tool results, tests, diffs, browser actions, sources, and artifact hashes. Every failure finding carries exact event IDs. Missing telemetry is shown as uncertainty.
The original core
Open Agent Trace Schema (OATS) 1.0 — a versioned, extensible trace envelope with JSON Schema, strict TypeScript types, and migrations.
Evidence Graph —
supported,weak,unsupported, orconflictingclaims with causal evidence edges and explanations.Replay Capsule — a portable, redacted, gzip-compressed run containing the normalized trace, environment summary, source metadata, report, and hashes. Review does not require the original model or API key.
Failure Taxonomy — deterministic localization of loops, repeated calls, retrieval drift, unverified changes, failed tests, evidence-free completion, cost spikes, premature termination, and failed recovery.
Reliability Report — standalone HTML plus JSON, Markdown, and SARIF. Benchmark scores remain scoped to their original benchmark.
Adapters
Adapter | Status | Public format used |
OpenAI Codex | Stable | rollout |
Gemini CLI | Stable | OpenTelemetry/log event spans and GenAI attributes |
Browser Use | Stable | serialized |
Generic JSONL / OTel | Stable | trace events or recursively discovered spans |
OpenCode | Experimental | session/message/part objects |
agent-browser | Experimental | JSON command responses |
OpenHands | Experimental | action/observation event streams |
Adapters are plugins. Implement the small TraceAdapter interface exported from tracefact/adapter-sdk; unknown fields are preserved in attributes or extensions.
CLI
tracefact analyze <trace> [--adapter auto] [--out tracefact-report]
tracefact replay <run.tracefact.gz> [--out replay.html]
tracefact verify <run.tracefact.gz>
tracefact migrate <old-trace.json> --out migrated.json
tracefact adapters
tracefact serve --dir web-dist --port 4173GitHub Action
- uses: Alex0AI/tracefact/action@v0.1.2
with:
trace: artifacts/codex-rollout.jsonl
adapter: autoThe action appends a job summary, updates a marker-based PR comment when pull-requests: write is granted, and uploads the full HTML/JSON/Markdown/SARIF/capsule bundle. No trace is sent to a third party.
Read-only MCP server
{
"mcpServers": {
"tracefact": {
"command": "node",
"args": ["/absolute/path/to/tracefact/dist/mcp.js"],
"env": { "TRACEFACT_STORE": "/absolute/path/to/traces" }
}
}
}Tools: list_runs, get_run, and get_reliability_report. There are no write tools.
Small, reproducible evidence set
dataset/generated/ contains 60 deterministic, CC0-1.0 synthetic traces spanning success, failed tests, tool loops, unsupported completion, cost anomalies, and successful recovery. Regenerate with npm run dataset:generate; evaluate with npm run experiment.
The v0.1 experiment reports micro precision 0.833, recall 1.000, and F1 0.909 on rule-authored synthetic labels. All 10 apparent false positives are hallucinated_completion findings on fixtures whose primary label is test_failure: the completion claim conflicts with the failed test, but that secondary label was intentionally omitted. This exposes multi-label ambiguity rather than hiding it. The result is regression evidence, not proof of real-world generalization. See the data card, raw results, and technical report.
Where TraceFact fits
Project | Primary job | Hosted tracing | Evidence-linked completion | Portable offline replay | Deterministic failure evidence |
TraceFact | Post-run reliability and reproduction | No | Yes | Yes | Yes |
Tracing, evaluation, datasets | Yes | Evaluation-dependent | No | Not its focus | |
Open-source LLM observability | Optional | Evaluation-dependent | Export, not a capsule | Not its focus | |
Open-source AI observability and evals | Optional | Evaluation-dependent | No | Not its focus | |
Agent monitoring and session replay | Yes | Evaluation-dependent | No | Some analytics |
This is a scope comparison based on public documentation, reviewed 2026-08-20—not a quality ranking. TraceFact can ingest OpenTelemetry exported by existing observability stacks instead of replacing them.
Privacy and safety
Redaction covers common API keys, bearer tokens, cookies, emails, Windows/macOS/Linux home paths, and fields named like secrets. Treat redaction as defense in depth: review a capsule before publishing it. TraceFact defaults to public-repository data, has no telemetry, and never uploads a trace.
Development
npm ci
npm run check
npm run test:coverageCI runs on Windows, Linux, and macOS with Node 20 and 22. See CONTRIBUTING.md, SECURITY.md, and DECISIONS.md.
License
Apache-2.0. It is permissive for commercial and research use, includes an explicit patent grant, and requires preservation of notices. Dataset fixtures are CC0-1.0. External projects and data remain under their own licenses; see THIRD_PARTY.md and DATA_SOURCES.md.
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