# 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**](https://tracely-ai.com) · [**Docs**](https://doc.tracely-ai.com) · [**Product guide**](https://doc.tracely-ai.com/product) · [**Agent skill**](#teach-your-coding-agent-tracely) · [**Guided tour**](guides/OVERVIEW.md) · [**2-min demo**](guides/DEMO.md) · [**Design dossier**](design/README.md) [![CI](https://github.com/jwuthri-cloud/Tracely-ai/actions/workflows/ci.yml/badge.svg)](https://github.com/jwuthri-cloud/Tracely-ai/actions/workflows/ci.yml) [![PyPI](https://img.shields.io/pypi/v/tracelyai-sdk?logo=pypi&logoColor=white)](https://pypi.org/project/tracelyai-sdk/) [![Python](https://img.shields.io/badge/python-3.10%2B-blue?logo=python&logoColor=white)](https://pypi.org/project/tracelyai-sdk/) [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE) [![Stars](https://img.shields.io/github/stars/jwuthri-cloud/Tracely-ai?style=flat&logo=github)](https://github.com/jwuthri-cloud/Tracely-ai/stargazers) [![MCPVault: claimed](https://mcpvault.io/badge/tracely.svg?theme=dark)](https://mcpvault.io/servers/tracely/health?utm_source=external_badge&utm_medium=referral&utm_campaign=mcp_health_report) **Self-host the whole stack in one click** — API, worker, UI, Postgres, ClickHouse, Redis and MinIO: [![Deploy on Railway](https://railway.com/button.svg)](https://railway.com/deploy/n5n_LE?referralCode=WCq5Cn&utm_medium=integration&utm_source=template&utm_campaign=generic) [![Deploy to Railyard](https://app.railyard.run/deploy/badge)](https://app.railyard.run/deploy?repo=https://github.com/jwuthri-cloud/Tracely-ai)
Tracely dashboard — trace and failure counts, the biggest failure clusters, recent traces and regression cases

Tracely dashboard — trace and failure counts, the biggest failure clusters, recent traces and regression cases

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

--- ## Why another agent-observability tool? 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. | | Dataset-first tools | Tracely | |---|---|---| | Where tests come from | You write them | Promoted from real failing traces | | Fidelity to production | A guess | The exact failing run, byte for byte | | Cost to replay in CI | Live model calls | **$0** — recorded tool/LLM fixtures | | What happens on regression | A dashboard number moves | **The PR is blocked** | | How you find out | You go and look | **It comes to you** — Slack, email, your own webhook | --- ## The loop Five steps, five pages in the app. ### 1 · Observe — every run, hierarchically 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. Trace timeline — agent, thinking, skill, generation and delegate spans, with a nested sub-agent and the span's input

Trace timeline — agent, thinking, skill, generation and delegate spans, with a nested sub-agent and the span's input

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. Trace explorer — conversation threads with agent, duration, rolling summary, metadata and usage columns

Trace explorer — conversation threads with agent, duration, rolling summary, metadata and usage columns

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. Fleet view — a conversation rendered as a pixel-art office with one character per agent

Fleet view — a conversation rendered as a pixel-art office with one character per agent

### 2 · Detect & triage — failures group into issues 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. Failure clusters — auto-detected failures grouped into issues with occurrence counts and taxonomy

Failure clusters — auto-detected failures grouped into issues with occurrence counts and taxonomy

### 3 · Test — freeze the failure 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. Regression case — promoted, fail-to-pass validated, with assertions and the reference trajectory

Regression case — promoted, fail-to-pass validated, with assertions and the reference trajectory

### 4 · Ship — block the PR 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 failing CI gate run — FAIL, per-case verdicts and the judge's reason

A failing CI gate run — FAIL, per-case verdicts and the judge's reason

### 5 · Tell me — alerts as a flow 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. The alert flow builder — When → condition → LLM prompt → Slack, with the inspector's input chips, step config and declared outputs

The alert flow builder — When → condition → LLM prompt → Slack, with the inspector's input chips, step config and declared outputs

### Plus — trends, cross-metric analysis, judge calibration 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. Trends — failure rate, gate pass-rate, latency percentiles, token and cost metrics over time

Trends — failure rate, gate pass-rate, latency percentiles, token and cost metrics over time

--- ## Everything in the box | | What it does | Where | |---|---|---| | **Ingest** | OTLP/HTTP from any language; blob-first durability (S3 before the queue); agent semantics as indexed columns; three message conventions normalized | [`backend/`](backend/README.md) | | **Evaluators** | Columns on the trace table. Structural checks (no model) + LLM judges at conversation / run / span level, multi-output (score, boolean, number, text, JSON schema), basic or `@VARIABLE` advanced prompts with live preview, batch or sequential, per-agent/env targeting, deterministic sampling, advisory verdicts | [Docs](https://doc.tracely-ai.com/product/traces) | | **Failure clusters** | Structural signature + semantic embedding clustering, taxonomy, suggested evaluators, promote-to-case | [Docs](https://doc.tracely-ai.com/product/clusters) | | **Regression cases** | Hermetic fixture bundles, fail-to-pass contracts, assertions, reference trajectory, replay history | [Docs](https://doc.tracely-ai.com/product/cases) | | **Scenarios** | Multi-turn conversations Tracely drives at your agent's own endpoint, or an adversarial goal a red-team model pursues (goal achieved = FAIL) | [Docs](https://doc.tracely-ai.com/product/scenarios) | | **CI gates** | `tracely simulate` / `replay` / `gate` + a GitHub Action: commit status, PR comment, non-zero exit | [Docs](https://doc.tracely-ai.com/product/gates) | | **Alerts** | Trigger + visual flow: conditions, Slack, email, webhooks with headers, an LLM step, Python expressions. Event triggers fire inline; thresholds poll a window. Test runs show what each step actually sent | [Docs](https://doc.tracely-ai.com/product/alerts) | | **Trends & analysis** | Daily traces/failures/gate pass-rate, latency + cost, per-agent cross-metric meta-analysis | [Docs](https://doc.tracely-ai.com/product/trends) | | **Judge calibration** | Label judge verdicts against human review; per-evaluator agreement, missed failures vs over-flagging | [Docs](https://doc.tracely-ai.com/product/calibration) | | **Conversation intelligence** | Rolling per-turn summary (backs the judge's `@HISTORY`), declared agent catalog, state deltas, Replay + Fleet views, shareable read-only links | [Docs](https://doc.tracely-ai.com/product/traces) | | **In-app assistant** | ⌘J — an agent over *your* workspace: reads traces, creates evaluators, scenarios, cases and alerts, as you. ⌘K jumps anywhere | [Docs](https://doc.tracely-ai.com/product/assistant) | | **MCP server** | The API doubles as an MCP endpoint (`/mcp`) so a coding agent drives the workspace with an ordinary ingest key | [Docs](https://doc.tracely-ai.com/mcp) | | **Auth & teams** | `dev` (open) · `local` (email/password + invites) · `clerk`. Organizations, workspaces, seats, API keys, per-workspace OpenRouter key | [Docs](https://doc.tracely-ai.com/product/settings) | | **Ops** | Beat self-check (queue depth, worker liveness, ingest freshness) + `/health/queue`, optional Sentry, optional trace quota + Stripe billing | [`guides/DEPLOY.md`](guides/DEPLOY.md) | Near-term plan: [design/part2-tracely/11-prd-next-steps.md](design/part2-tracely/11-prd-next-steps.md) · Long-term roadmap: [10-mvp-and-roadmap.md](design/part2-tracely/10-mvp-and-roadmap.md) --- ## Quickstart **Prerequisites:** Docker + Docker Compose. (For local dev also [uv](https://docs.astral.sh/uv/) and Node 20+ / pnpm.) ### Everything in Docker — the whole product, populated ```bash git clone https://github.com/jwuthri-cloud/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. ```bash 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 ` — **except** the Celery worker, which doesn't hot-reload. ### Local dev (hot reload) ```bash 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) ``` ### Deploy your own [![Deploy on Railway](https://railway.com/button.svg)](https://railway.com/deploy/n5n_LE?referralCode=WCq5Cn&utm_medium=integration&utm_source=template&utm_campaign=generic) One click provisions the whole stack on [Railway](https://railway.com) — 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. **Self-hosting on [Coolify](https://coolify.io)?** One command sets up the same layout, one card per component with its own logs, metrics and CPU/RAM limits: `python3 deploy/coolify/setup.py --coolify-url … --app-domain … --api-domain …`. See [`deploy/coolify/README.md`](deploy/coolify/README.md), which also covers the single-bundle [`docker-compose.coolify.yml`](docker-compose.coolify.yml). Prefer to wire it yourself, or deploying somewhere else? The manual walkthrough is [`deploy/railway/README.md`](deploy/railway/README.md) (every variable pre-written in [`.env.railway.example`](deploy/railway/.env.railway.example)). On AWS EKS or any other Kubernetes cluster, [`deploy/eks/README.md`](deploy/eks/README.md) runs the whole stack in one namespace with no cloud IAM. The production-hardening runbook — auth guards, backups, worker pool, post-deploy verification — is [`guides/DEPLOY.md`](guides/DEPLOY.md). --- ## Send your first trace ```bash pip install "tracelyai-sdk[openai]" # or [anthropic], [langchain], [all] ``` Initialize once at startup, then wrap a run — your normal provider calls are captured automatically, with no span code: ```python 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).

Declare your agents and record your state — two optional lines that make a conversation self-describing

**The agent catalog** tells Tracely which agents, tools, prompts and models the conversation *has* (not just which ones fired) — it fills the **Conversation Agents** panel and the Fleet view, and is readable from judge prompts as `@AGENTS`. **State deltas** record what each step wrote to your shared state, folded into the **Conversation State** drawer and the per-message **State Δ** column: ```python 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 ``` LangGraph users get state for free (node outputs are captured as deltas automatically), and non-Python services can push the catalog with `POST /api/sessions/{conversation_id}/config`.

Full instrumentation guide → **[doc.tracely-ai.com](https://doc.tracely-ai.com)** · [`sdk/README.md`](sdk/README.md) --- ## Gate your PRs 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.

Option A — let Tracely call your agent (no agent code in CI, any language)

Register your agent's HTTP endpoint once, author [scenarios](https://doc.tracely-ai.com/product/scenarios) — multi-turn conversations, or an adversarial goal a red-team model improvises against — and Tracely drives them itself. **Nothing to install, import or shim**, so a TypeScript or Go service gates exactly like a Python one. ```yaml - uses: jwuthri-cloud/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 ``` Scenarios belong to an agent, so leaving `agent` blank gates each one in its own run and fails the job if any of them fails — a new agent is covered the day someone writes its first scenario.

Option B — gate the traces your CI already emits

If your pipeline already runs your agent instrumented with `tracely.env=ci`, the gate matches those traces to your promoted cases (by input) and returns PASS/FAIL. ```yaml # .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-cloud/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 ```

Option C — replay recorded cases against your code (hermetic, $0)

Re-runs your agent on each promoted case's *recorded input*, serving the recorded tool/LLM outputs as fixtures — deterministic, offline, **no API keys, no cost** — then gates. Guarantees the exact failing inputs are tested. ```yaml - run: pip install tracelyai-sdk - 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 }} ``` Hermetic replay requires your agent to route tool/model calls through the SDK's `call_tool` / `call_llm` seam (see [the SDK guide](sdk/README.md)); add `--live` to make real calls instead.

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`](.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. --- ## Get told, don't go looking Settings → **Alerts**. Pick what fires the rule, then draw what happens. | Trigger | Fires when | |---|---| | `CI gate failed` | a gate run finished `FAIL` — or `NO_COVERAGE`, the suite that *could not run*, which is the quietly-green failure nobody notices | | `Conversation failed` | a live turn failed a non-advisory evaluator. Filter by evaluator, or by the text of the judge's own reason | | `New failure mode` | a failure signature no trace has produced before — the alert you cannot get from logs | | `Evaluator FAIL rate` / `Average score` / `Overall failure rate` | a rate or average crosses a line over a sliding window | 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 alerts list — a gallery of use cases, and rules showing their trigger and their flow as a strip of steps

The alerts list — a gallery of use cases, and rules showing their trigger and their flow as a strip of steps

The same rules are reachable from the [in-app assistant](https://doc.tracely-ai.com/product/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 →](https://doc.tracely-ai.com/product/alerts) --- ## Drive it from your editor Every backend serves an [MCP](https://modelcontextprotocol.io) endpoint at `/mcp`, so a coding agent reads your traces and writes your evaluators without any glue code: ```bash 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](https://doc.tracely-ai.com/mcp) --- ## Teach your coding agent Tracely 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. ```bash npx skills add https://github.com/jwuthri-cloud/Tracely-ai --skill tracely ``` Works with Claude Code, Cursor, Copilot, Antigravity and anything else the [`skills`](https://github.com/vercel-labs/skills) CLI supports — add `-g` for a global install, `--agent '*'` for every agent on the machine.

**Knows the whole surface** - **Automatic tracing** — `init(instrument="auto")`, provider + framework extras, `@observe`, drop-ins, LangGraph, LiteLLM, agent SDKs, redaction - **Manual spans** — every observation type, handoffs, RAG, state deltas, multimodal I/O - **Non-Python** — the OTLP conventions to emit from TypeScript, Go or Ruby - **Evaluators** — structural vs judge, levels, `@VARIABLE` templates, advisory verdicts, sampling - **CI gate** — scenarios, red-team runs, hermetic replay, the GitHub Action - **Troubleshooting** — symptom → cause → fix for the failures that look like success

**And the traps that silently produce a useless workspace** - a missing `conversation` id turns one thread into twelve orphan rows - a swallowed tool error is invisible to detection, clustering *and* the gate - no `flush()` and a script loses its last spans - a dropped `traceparent` makes the gate blind to what your agent *did* - an adversarial scenario is **inverted** — goal achieved means the attack won

Prefer to read it yourself? It's plain Markdown: [`skills/tracely/`](skills/tracely/SKILL.md). --- ## Architecture 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 ``` | Layer | Tech | Where | |---|---|---| | Backend (API + domain) | **FastAPI** + Pydantic v2 | [`backend/`](backend/README.md) | | Workers | **Celery + Redis** | [`workers/`](workers/README.md) | | Traces + scores (OLAP) | **ClickHouse** (`ReplacingMergeTree`) | `backend/tracely/infrastructure/clickhouse/ddl` | | Registry (OLTP) | **Postgres + pgvector** + SQLAlchemy 2.0 + Alembic | `backend/migrations` | | Queue / Blobs | **Redis** / **MinIO·S3** (blob-first, source of truth) | — | | Frontend | **Next.js 15** (App Router) + Tailwind + React Flow | [`frontend/`](frontend/README.md) | | SDK + CI gate CLI | **`tracelyai-sdk`** (OTel wrapper + `tracely` CLI) | [`sdk/`](sdk/README.md) | | Tooling | **uv** workspace (Python) · **pnpm** (web) | — | One deliberate adaptation: ClickHouse server-side `async_insert` instead of an in-process write buffer (Celery tasks don't share memory). [Why](design/part2-tracely/01-steal-and-do-not-copy.md) ### Repo map — each folder has its own detailed README | Folder | What's inside | |---|---| | [`backend/`](backend/README.md) | The `tracely` package: FastAPI API + shared domain (OTLP mapping, ClickHouse/Postgres/S3, registry, evaluators, failure intelligence, regression, gate, alert flows, auth, Celery tasks). | | [`workers/`](workers/README.md) | The deployable Celery worker runtime. | | [`frontend/`](frontend/README.md) | The Next.js web app — trace explorer, clusters, cases, gates, trends, alerts builder, settings, auth. | | [`sdk/`](sdk/README.md) | The Python SDK (instrument agents over OTLP, hermetic record-replay) + the `tracely` CI gate CLI. | | [`docs/`](docs/README.md) | The published docs site (Nextra) — SDK reference **and** the per-screen product guide. `make docs` → :3002. | | [`skills/`](skills/tracely/SKILL.md) | The Tracely agent skill — `npx skills add https://github.com/jwuthri-cloud/Tracely-ai --skill tracely`. | | [`scripts/`](scripts/README.md) | Dev/demo helpers (raw-OTLP sender, one-command `seed_demo.py`, gate shim). | | [`design/`](design/README.md) | The full design dossier — reverse-engineered Langfuse + every Tracely design decision. | --- ## Key environment variables | Variable | Default | Purpose | |---|---|---| | `AUTH_MODE` | `dev` | `dev` (open, no login) · `local` (email/password, self-host) · `clerk` (hosted SaaS). | | `SESSION_SECRET` | — | Required when `AUTH_MODE=local`: HS256 signing key for JWTs (≥32 chars). | | `SECRETS_ENCRYPTION_KEY` | — | Encrypts a workspace's own OpenRouter key at rest (≥32 chars). | | `CLERK_ISSUER` | — | Required when `AUTH_MODE=clerk`. | | `OPENROUTER_API_KEY` | — | Server-wide LLM access for judges, failure intelligence, meta-analysis. Workspaces can bring their own key instead. | | `RESEND_API_KEY` | — | Transactional email: invites, password resets, and the alert flow's email step. | | `ALLOW_PRIVATE_URLS` | unset | Whether outbound calls (agent endpoints, alert webhooks) may target private addresses. Unset = allowed everywhere except prod. | | `TRACELY_BACKEND_PORT` | `8000` | Backend host port (Docker compose override). | | `TRACELY_WEB_PORT` | `3001` | Frontend host port (Docker compose override). | 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`](backend/tracely/config.py). --- ## Contributing Issues and PRs welcome. Before pushing: ```bash 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 ``` ## License [MIT](LICENSE) © Julien Wuthrich

**[tracely-ai.com](https://tracely-ai.com)** · [Docs](https://doc.tracely-ai.com) · [Product guide](https://doc.tracely-ai.com/product) · [Star on GitHub](https://github.com/jwuthri-cloud/Tracely-ai) If Tracely is useful to you, a ⭐ helps other people find it.