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Evals & observabilityharbor-framework

terminal-bench-1

A benchmark of hard terminal tasks: a set of assignments and a runner where the agent acts as an admin or developer. Has a leaderboard.

Who it is for

For those testing terminal agents on realistic tasks.

How to start

  1. Install: uv tool install terminal-bench (or pip install terminal-bench).
  2. Look at the run options: tb run --help.
  3. Evaluate your agent with tb run and submit the result to the leaderboard.

Steps are taken from the README. Check the current version in the repository before running them.

Stars over the last 30 days

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Author's description

A benchmark for LLMs on complicated tasks in the terminal

langfuse

langfuse

An open platform that shows every model call: traces, cost, quality scores and prompt versions. Use it in the cloud or self-host it.

35Kstars+1.2K in 30 dTypeScript

mlflow

mlflow

A long-standing ML platform that now covers LLM apps too: tracing, evaluation, a prompt registry and a model gateway. Plugs into most frameworks.

28Kstars+455 in 30 dPython

promptfoo

promptfoo

A command-line tool for testing prompts and agents: describe cases in a config and compare models. Also does red teaming and vulnerability scanning. Plugs into CI.

26Kstars+919 in 30 dTypeScript

comet-ml

opik

A platform for debugging and evaluating LLM apps: traces, automated quality checks and dashboards. Supports RAG and agent chains, and can be self-hosted.

22Kstars+618 in 30 dPython

openai

evals

OpenAI's framework for evaluating models plus a registry of ready-made benchmarks. A historically important project that shows how eval templates are structured.

20Kstars+181 in 30 dPython

confident-ai

deepeval

A pytest-style framework for testing LLM apps: ready-made metrics for RAG, agents and chatbots, plus trace-based checks. Results can be pushed to a cloud.

19Kstars+533 in 30 dPython