Trace every LLM call, tool call, cost spike, error, and decision path, then turn finished sessions into searchable team memory.
Most observability tools are cloud SaaS — your sessions, costs, and errors transit their infrastructure. Tracea runs entirely on yours.
Zero vendor lock-in. No per-event pricing. No black-box rules you can't inspect or version-control.
Route issues to Slack, email, or any HTTP webhook. Tracea catches behavioral failures your logs never will.
Docker handles everything. One command deploys the backend, dashboard, and database.
docker-compose up --build — server on :8080, dashboard on :5173.localhost:5173 and watch sessions appear in real time.detection_rules.yaml — changes hot-reload without restarting the server.# Clone the repo git clone https://github.com/darshannere/tracea.git cd tracea # Start everything docker-compose up --build # Server: http://localhost:8080 # Dashboard: http://localhost:5173 # API key: ./data/api_key.txt
from tracea.sdk import TracingClient # Patch transport — captures all LLM calls client = TracingClient( url="http://localhost:8080", api_key="your-api-key", agent_id="my-agent", ) client.install() # Use OpenAI / Anthropic as normal import openai response = openai.chat.completions.create(...) # → Auto-traced in tracea
API_KEY=$(cat data/api_key.txt) curl -X POST http://localhost:8080/api/v1/events \ -H "Authorization: Bearer $API_KEY" \ -H "Content-Type: application/json" \ -d '{ "events": [{ "event_id": "evt-001", "session_id": "sess-001", "agent_id": "my-agent", "type": "chat.completion", "provider": "openai", "model": "gpt-4o", "tokens_used": {"total": 512}, "cost_usd": 0.003 }] }'
Run it locally, connect your agents, and start turning agent sessions into timelines, alerts, RCA, and team memory.