Tracely

Production failures become regression tests.

Trace-native CI/CD for AI agents. Tracely grades every agent trace as it lands, clusters the failures into issues, freezes the bad runs into hermetic replayable cases, blocks the pull request that would ship them again — and tells you the moment any of it happens.

production trace → failure detection → regression test → CI gate → alert

Website · Docs · Product guide · Agent skill · Guided tour · 2-min demo · Design dossier

Self-host the whole stack in one click — API, worker, UI, Postgres, ClickHouse, Redis and MinIO:

_{One workspace: what ran, what broke, what is already pinned as a test.}

Because observability stops at the dashboard. You can see that your agent broke — then what?

Every eval tool asks you to hand-author a dataset: sit down, invent questions, write ideal answers, keep them current as the product changes. That dataset is a guess about what might break.

Production already handed you the real thing: a trace of the exact run that failed, with the exact input, the exact tool calls, the exact model responses.

The recorded run is the test. Tracely freezes that trace into a hermetic regression case and replays it on every PR. Everything else — quality scores, failure clusters, suggested fixes, CI verdicts, trends, alerts — is derived from the trace. There are no hand-authored datasets.

Five steps, five pages in the app.

Traces arrive over plain OTLP. Agent semantics (agent.id, conversation.id, turn, step) are promoted to first-class indexed columns, so runs group into conversation threads instead of a flat span soup. The waterfall shows agent → thinking → skill → generation → hand-off, with the failing span in red and its I/O beside it.

Evaluators are columns on the trace table, not a separate tab — each one grades at conversation, run or span level and writes its verdict into the grid. Scores stream in live over SSE as judges finish, so you watch a run get graded in place. Tokens, cost, latency, metadata and the rolling per-turn summary are columns too.

Two extra ways to read one conversation — a step-by-step replay, and a pixel-art office

Replay walks the conversation event by event on a timeline. Fleet renders the same conversation as a room: each agent is a character, hand-offs are walks across the floor, tools are objects on the wall — which turns out to be the fastest way to explain a multi-agent system to someone who doesn't read waterfalls.

Online evaluators grade every run as it lands (LLM-as-judge at conversation / run / span level, plus structural checks that need no model at all). Failures then cluster — structurally and semantically — so 31 broken runs become one issue with a count, not 31 rows to read. Each cluster can suggest the evaluator that would have caught it.

One click promotes a failing trace into a hermetic case: recorded input, tool and LLM outputs bundled as fixtures, and a fail-to-pass contract attached — the case must fail on the old code and pass on the fix, or the promotion isn't trusted. Multi-turn behaviour gets scenarios instead: a scripted conversation, or an adversarial goal a red-team model improvises against.

The suite replays in CI against recorded fixtures: deterministic, offline, no API keys and no model spend. tracely gate exits non-zero, posts a commit status, and upserts a PR comment.

A rule has two halves: when (a gate fails, a live conversation breaks on a judge, a failure mode appears that nobody has seen before, or a rate crosses a line) and what happens — drawn on a canvas. Conditions that gate the rest of the flow, Slack, email, a webhook with your own method and headers, an LLM step whose answer the next step can use, and a Python expression for a number a template can't compute. Every field is a template over the failure's own variables, dragged in as chips. Or describe it to the in-app assistant and it draws the whole thing onto the canvas.

Daily failure and gate pass-rates, latency percentiles, token spend, per-agent meta-analysis (Spearman correlations + z-score outliers, LLM-synthesized) — and a calibration screen where you label judge verdicts against human review and see each judge's agreement, so you catch an over-flagging judge before you let it gate a release.

Near-term plan: design/part2-tracely/11-prd-next-steps.md · Long-term roadmap: 10-mvp-and-roadmap.md

Prerequisites: Docker + Docker Compose. (For local dev also uv and Node 20+ / pnpm.)

git clone https://github.com/Jwuthri/Tracely-ai && cd Tracely
docker compose --profile demo up -d --build --wait
open http://localhost:3001

That brings up ClickHouse, Postgres, Redis and MinIO, runs every migration, seeds the default project and ingest key (tracely_dev_key), then populates traces, clusters, cases and gates — so the app opens with the screenshots above rather than an empty shell.

docker compose down            # stop  (add -v to wipe data)

Host ports default to web :3001 and backend :8000; remap with TRACELY_WEB_PORT / TRACELY_BACKEND_PORT. backend/worker/frontend run off source volume-mounts, so most edits need only docker compose restart <svc> — except the Celery worker, which doesn't hot-reload.

cp .env.example .env
make infra-up      # clickhouse, postgres, redis, minio
make install       # uv sync + pnpm install
make migrate       # ClickHouse DDL + Alembic (Postgres)
make seed          # default project + ingest key → tracely_dev_key

make backend       # FastAPI  :8000  (OpenAPI at /docs)   ┐
make workers       # Celery ingestion/eval worker          ├ three terminals
make frontend      # Next.js  :3001                        ┘

make demo          # populate the WHOLE product: traces + clusters + cases + gates
make test          # backend unit tests (no infra, ~6s)

One click provisions the whole stack on Railway — API, worker, UI, Postgres (pgvector), ClickHouse, Redis and MinIO, wired together with volumes and private networking. Migrations and seeding run on the first deploy; set SESSION_SECRET and SECRETS_ENCRYPTION_KEY (openssl rand -hex 32 each) when prompted, then open the frontend's domain and create your workspace.

Prefer to wire it yourself, or deploying somewhere else? The manual walkthrough is deploy/railway/README.md (every variable pre-written in .env.railway.example), and the production-hardening runbook — auth guards, backups, worker pool, post-deploy verification — is guides/DEPLOY.md.

pip install "tracely-ai[openai]"     # or [anthropic], [langchain], [all]

Initialize once at startup, then wrap a run — your normal provider calls are captured automatically, with no span code:

import tracely_sdk as tracely

tracely.init(
    endpoint="http://localhost:8000",   # your Tracely API
    api_key="tracely_dev_key",          # an ingest key
    service_name="support-agent",
    env="prod",                         # prod | staging | ci | dev — the gating axis
    instrument="auto",                  # auto-detect openai / anthropic / google / mistral / langchain
)

with tracely.trace(agent="support-agent", conversation="conv-1", user="u_42"):
    client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Where is order ORD-4471?"}],
    )

That produces a GENERATION span with model, messages, tokens, latency, tool calls and cost. Need spans for your own logic? @observe and the manual agent / tool / llm / retriever / guardrail context managers are all there.

Any OTLP/HTTP exporter works too — point it at POST {endpoint}/v1/traces with Authorization: Bearer tracely_dev_key. Tracely reads standard gen_ai.* / OpenInference attributes plus first-class hints: tracely.agent.id (auto-registered), tracely.agent.version, tracely.conversation.id / turn.* / step.*, tracely.observation.type, and tracely.env (prod|staging|ci|dev — the gating axis).

AGENTS = [{
    "name": "support",
    "description": "front-line agent; routes billing questions",
    "system_prompt": "You are the support agent for Acme…",   # free-form keys kept verbatim
    "model": "gpt-5.2",
    "tools": {"lookup_order": {"name": "lookup_order", "description": "order by id",
                               "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}}}}},
}]

with tracely.trace(agent="support", conversation="conv-1", agents=AGENTS):
    ...
    tracely.set_state({"cart": cart, "last_action": "add_to_cart"})   # inside any span/@observe

Full instrumentation guide → doc.tracely-ai.com · sdk/README.md

A promoted production failure becomes a regression test that blocks the PR that reintroduces it. All you need is your ingest key (it identifies your workspace) and your Tracely API URL. Three ways to wire it, depending on how your CI can reach your agent.

      - uses: Jwuthri/Tracely-ai/.github/actions/tracely-gate@master
        with:
          api:   https://tracely.your-co.dev
          key:   ${{ secrets.TRACELY_KEY }}
          # agent: planner,support-agent   ← a subset; omit it to gate EVERY agent with scenarios

# .github/workflows/tracely.yml
name: Tracely gate
on: pull_request
permissions:
  contents: read
  statuses: write          # post the blocking commit status
  pull-requests: write     # upsert the results comment
jobs:
  gate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      # → your existing step(s) that run the agent and emit env=ci traces go here ←
      - uses: Jwuthri/Tracely-ai/.github/actions/tracely-gate@master
        with:
          mode:  gate                          # grade the ci traces this workflow emitted
          agent: planner                       # which agent's promoted suite to run
          api:   https://tracely.your-co.dev   # your Tracely backend (TRACELY_API)
          key:   ${{ secrets.TRACELY_KEY }}    # your ingest key = your workspace

      - run: pip install tracely-ai
      - run: tracely replay planner --entrypoint my_pkg.agent:run    # a Python agent
        # …or any language:  tracely replay planner --cmd "node run.js"  (reads $TRACELY_INPUT)
        env:
          TRACELY_API: https://tracely.your-co.dev
          TRACELY_KEY: ${{ secrets.TRACELY_KEY }}

Both commands auto-detect the PR/commit from the Actions context; web-url / TRACELY_WEB_URL is optional and only builds the "view gate run" link in the PR comment.

ℹ️ The repo's own .github/workflows/tracely-gate.yml is Tracely dogfooding itself — it replays the bundled weather_agent example, which is why it uses an in-repo pip install ./sdk. Your integration is one of the three options above, not that file.

Settings → Alerts. Pick what fires the rule, then draw what happens.

Steps: condition (a Jinja gate that stops the rest of the flow), Slack, email, webhook (any verb, your own headers — Authorization: Bearer … — and a templated JSON body), LLM prompt (with declared output fields the next step reads as {{ steps[0].result.field }}), Python expression. Run test executes the flow against a real recent failure and shows what each step actually sent, after templating.

The same rules are reachable from the in-app assistant ("Slack me when the refund judge fails a conversation") and from the MCP server, so an agent can arm one for you. Alerts guide →

Every backend serves an MCP endpoint at /mcp, so a coding agent reads your traces and writes your evaluators without any glue code:

claude mcp add --transport http tracely http://localhost:8000/mcp \
  --header "Authorization: Bearer tracely_dev_key"

Then: "look at the last 20 traces, find what's failing, and add an evaluation column that catches it." Tools over traces, failure clusters, evaluators and trends — scoped to the key's workspace, same as every other call. On hosted Tracely the endpoint is https://api.tracely-ai.com/mcp. Docs

MCP gives your agent your data. The Tracely skill gives it the know-how — how to instrument, what to evaluate, and how to wire the gate — so "add Tracely to this agent" is one sentence instead of a docs tab.

npx skills add https://github.com/Jwuthri/Tracely-ai --skill tracely

Works with Claude Code, Cursor, Copilot, Antigravity and anything else the skills CLI supports — add -g for a global install, --agent '*' for every agent on the machine.

Prefer to read it yourself? It's plain Markdown: skills/tracely/.

The write path deliberately mirrors Langfuse's proven design — reimplemented in Python, with agent semantics promoted to first-class indexed columns (Langfuse keeps them as read-time strings):

SDK/OTLP → POST /v1/traces → S3 blob (durable FIRST) → Redis/Celery
  → worker: otel mapping → registry upsert → ClickHouse events
  → evaluate_run_task → scores + structural clustering → alert flows

One deliberate adaptation: ClickHouse server-side async_insert instead of an in-process write buffer (Celery tasks don't share memory). Why

With no LLM key at all the pipeline still runs — judges, failure intelligence and meta-analysis degrade rather than crash. Full list: backend/tracely/config.py.

Issues and PRs welcome. Before pushing:

uv run pytest -q backend/tests sdk/tests    # what CI runs
uv run ruff check . && uv run ruff format .
cd frontend && pnpm test && pnpm build      # vitest + tsc typecheck + lint

MIT © Julien Wuthrich