Latitude provides observability for AI agents and helps teams trace failures and verify fixes.
Open source
24 alternativesKitaru
Kitaru provides agent traces that teams can run and inspect during application workflows.
About Kitaru
Kitaru provides replay-based evaluation for AI agents. It records or imports production runs as sessions and replays them against model, prompt, or code changes to show differences before shipping. During replay it re-executes agent code while answering tool calls from the recording.
It is open source and self-hosted, with Python and TypeScript support. It can import traces from Langfuse, LangSmith, Braintrust, Logfire, Arize Phoenix, and MLflow, and uses coding-assistant skills with an MCP server for review and evaluation workflows.
Key features
- Record production agent runs as replayable sessions
- Import traces from Langfuse, LangSmith, Braintrust, Logfire, Phoenix, MLflow
- Replay sessions against changed model, prompt, or code
- Answer tool calls from recordings during replay
- Pin human judgments to specific trace locations
- Run local FastAPI and Postgres server via Docker or Podman
Best for: Teams building AI agents who need to check changes against recorded production behavior.
Good for
- Open source and self-hosted
- Imports traces from tools teams already use
- Replays use recorded tool responses to avoid duplicate side effects
Watch out for
- Local server requires Docker or Podman
- Replay worker must be installed alongside agent dependencies
Kitaru at a glance
- Website
- github.com/zenml-io/kitaru
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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