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What you can do with Tracely AI

Tracely is a self-hostable tool that grades AI agent traces, clusters failures, freezes bad runs into replayable regression tests, and blocks pull requests that would reintroduce them.

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jwuthri-cloud/Tracely-ai on GitHub

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What do you want to do?

6 use cases

  • Turn a failing agent trace into a PR-blocking test

    Promote a production failure to a replayable case that blocks the PR that would ship it again.

    View prompt
    In the Tracely-ai repository, start the demo stack with `docker compose --profile demo up -d --build --wait`, then promote one failing trace in the UI at http://localhost:3001 to a regression case. Report the case name and confirm the case shows a fail-to-pass contract. Verify that the case appears in the cases list before finishing.

  • Cluster broken agent runs into one issue per failure

    Collapse dozens of failed runs into a single counted issue with a suggested evaluator.

    View prompt
    In the Tracely-ai repository, run `docker compose --profile demo up -d --build --wait`, then open the clusters view at http://localhost:3001. List the top three clusters with their counts and suggested evaluators. Verify that the counts add up to the total number of failed traces shown.

  • Get a Slack or email alert when a live agent run fails

    Build an alert flow that notifies you when a judge fails a live conversation.

    View prompt
    In the Tracely-ai repository, start the stack with `docker compose --profile demo up -d --build --wait` and create an alert rule at http://localhost:3001 that fires when an evaluator fails a live conversation and sends a Slack message. Report the rule name and the output of a test run. Verify that the test run shows the payload each step sent.

  • Check a judge's accuracy before it blocks a release

    Label judge verdicts against human review to catch over-flagging before it gates a PR.

    View prompt
    In the Tracely-ai repository, run `docker compose --profile demo up -d --build --wait` and open the calibration screen at http://localhost:3001. Label at least ten judge verdicts and report each evaluator's agreement, missed failures and over-flagging. Verify that the three numbers are shown for every evaluator you labeled.

  • Deploy the full Tracely stack to Railway in one click

    Run the API, worker, UI, Postgres, ClickHouse, Redis and MinIO on your own Railway project.

    View prompt
    For the Tracely-ai repository, use the Railway deploy template linked in the README, generate SESSION_SECRET and SECRETS_ENCRYPTION_KEY with `openssl rand -hex 32`, and report the frontend domain once the deploy succeeds. Verify that the frontend loads and that you can create a workspace before finishing.

  • Let a coding agent query and edit your Tracely workspace

    Connect a coding agent to the Tracely MCP endpoint with an ingest key.

    View prompt
    Using the Tracely-ai repository running locally at http://localhost:8000, connect to the MCP endpoint at /mcp with the ingest key <ingest-key>. List the five most recent failing traces and report their agent names and failure reasons. Verify that the list matches what the traces view shows at http://localhost:3001.

How to use Tracely AI

  1. Check you have what it needs: Docker and Docker Compose; For local development: uv, Node 20+ and pnpm; A GitHub repository with pull requests, if you want the CI gate.
  2. Open jwuthri-cloud/Tracely-ai on GitHub and read the README and license.
  3. Run docker compose --profile demo up -d --build --wait in the cloned Tracely repository.
  4. Open http://localhost:3001 and find a failing run in the traces view.
  5. Promote the failing trace to a regression case in one click.
  6. Run pip install "tracelyai-sdk[openai]" in your agent project and send traces to the Tracely ingest endpoint.

These steps come from our top use case, Turn a failing agent trace into a PR-blocking test, which also has a copy-paste agent prompt.

Tracely AI alternatives · Best AI repos for Software and DevOps

About this repo

Requirements

  • Docker and Docker Compose
  • For local development: uv, Node 20+ and pnpm
  • A GitHub repository with pull requests, if you want the CI gate
  • Python, to install the tracelyai-sdk package
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Questions about Tracely AI

What is Tracely AI?

Tracely is a self-hostable tool that grades AI agent traces, clusters failures, freezes bad runs into replayable regression tests, and blocks pull requests that would reintroduce them. It is an open-source project on GitHub: https://github.com/jwuthri-cloud/Tracely-ai.

Who is Tracely AI for?

Engineers and teams who run AI agents in production and want failed real runs to become CI tests instead of hand-written datasets.

What can I do with Tracely AI?

6 use cases so far, for example: Turn a failing agent trace into a PR-blocking test; Cluster broken agent runs into one issue per failure; Get a Slack or email alert when a live agent run fails. Each has steps and a copy-paste agent prompt.

What do I need to run Tracely AI?

Docker and Docker Compose; For local development: uv, Node 20+ and pnpm; A GitHub repository with pull requests, if you want the CI gate; Python, to install the tracelyai-sdk package.

What are alternatives to Tracely AI?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is Tracely AI safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.