Skip to Content
TaresQuickstart

Quickstart

If you are a coding assistant, start with AI resources.

In about five minutes: install Tares, get events from a real system flowing into one correlated timeline, and connect an agent that reads it.

Using a coding agent? Paste this into Claude Code, Codex or Cursor and it does the whole page for you, then connects itself:

Run npx skills add glassflow/tares --skill tares and use the tares skill to install Tares and connect it to this agent.

Start Tares

uv tool install tares      # or: pipx install tares
tares up                   # daemon + console on http://localhost:8787

Open the console at http://localhost:8787 and add a source.

Add a source

The demo is a self-contained stack (a small api-server, Prometheus, and a traffic generator) plus a catalog that wires up three correlated sources, a view, a trigger, and a built-in agent. Two files, no checkout. Start the stack Tares will ingest from:

curl -O https://raw.githubusercontent.com/glassflow/tares/main/demo/docker-compose.yml
docker compose up -d

Stop the daemon from the first step (Ctrl-C) and restart it seeded with the demo catalog:

curl -O https://raw.githubusercontent.com/glassflow/tares/main/demo/catalog.demo.yaml
TARES_CATALOG=catalog.demo.yaml tares up

The startup log confirms the import: imported catalog.demo.yaml into catalog (3 sources, 1 views, 1 triggers).

The catalog imports only while your catalog is still empty. That is fine here, since you haven’t created anything yet. If you have (say, you tried the other tab first), the import is skipped silently; restart on a fresh data directory instead: TARES_CATALOG=catalog.demo.yaml tares up --data-dir ~/tares-demo

Open Explore, pick the api-server entity, and watch metrics, logs, and alerts merge into one timeline. To break it on purpose and watch an agent diagnose it, continue with Build an AI SRE.

(Prefer no catalog at all? Sources → Discover proposes sources from your running containers; the catalog just pre-wires the correlated view and the triggers.)

Connect an agent

Agents read Tares over MCP. Start the MCP endpoint, a second process that proxies to the daemon (skip this if you’re on the Docker compose; it’s already running on :8788):

tares mcp --transport streamable-http --port 8788 --taresd http://localhost:8787

Point Claude Code at it:

claude mcp add --transport http tares http://localhost:8788/mcp
claude mcp list      # tares: connected

Locally there’s no auth, so the connect command needs no token; on a deployed instance that sets TARES_AUTH_TOKEN, add --header "Authorization: Bearer <token>".

In a Claude Code session:

Use tares: list my sources and summarize what each one contains.

The agent calls the MCP tools (list_sources, catalog_describe, query, …) against the daemon. For other clients (Codex, Claude Desktop), transports, and the full tool list, see Connecting agents.

Verify

tares status prints a checklist and the one next step:

Tares 1.11.0 at http://127.0.0.1:8787   data dir ~/.tares (1.2 MB)
Daemon:            running (uptime 0h 5m)
Auth:              off (open local instance)
Sources:           1 configured, 1 receiving (last event 4s ago)
Views:             0
Triggers:          0 enabled
Tares agents:      none
MCP endpoint:      running at http://127.0.0.1:8788/mcp
Agent clients:     Claude Code: connected
Slack:             not configured

Next: add a second source keyed by the same label so reads correlate. Docs: docs.glassflow.ai/tares/connectors

In the console, Reads lists each read as it happens, with client = mcp.

Next

  • Build an AI SRE: cause an incident and watch an agent diagnose it and write the finding back.
  • Concepts: the data model.
  • Connectors: Vercel, Postgres, Prometheus, OTLP, Claude Code sessions, and more.
  • Deployment: self-host the daemon, MCP server, and TLS with Docker.
Last updated on
IntroductionConcepts