CLI ToolsMay 14, 2026·2 min read

DSPy Micro Agent — CLI + FastAPI + Evals

evalops/dspy-micro-agent is a minimal agent runtime (CLI + FastAPI + eval harness); verified 73★ and documents `micro-agent ask` and `run_evals.py --n 50`.

Agent Toolkit
Agent Toolkit · Community
Agent ready

This asset can be read and installed directly by agents

TokRepo exposes a universal CLI command, install contract, metadata JSON, adapter-aware plan, and raw content links so agents can judge fit, risk, and next actions.

Native · 94/100Policy: allow
Agent surface
Any MCP/CLI agent
Kind
Cli
Install
Uv|Pip
Trust
Trust: Established
Entrypoint
micro-agent ask --question "What's 2*(3+5)?" --utc
Universal CLI install command
npx tokrepo install 5da29604-b199-589f-a026-ea1832dcdffc
install contractmetadata JSONadapter planraw content
Intro

evalops/dspy-micro-agent is a minimal agent runtime (CLI + FastAPI + eval harness); verified 73★ and documents micro-agent ask and run_evals.py --n 50.

Best for: Builders who want a small, readable Plan/Act loop with traces, plus quick evals before adding complexity

Works with: Python 3.10+, OpenAI or Ollama providers, CLI + FastAPI deployments

Setup time: 10-25 minutes

Key facts (verified)

  • GitHub: 73 stars · 6 forks · pushed 2026-04-25.
  • License: MIT · owner avatar + repo URL verified via GitHub API.
  • README-backed entrypoint: micro-agent ask --question "What's 2*(3+5)?" --utc.

Main

  • Treat traces as a first-class artifact: README stores JSONL traces under traces/ and includes replay support.

  • Use the eval harness early (--n 50) to guard against regressions when you add tools, prompts, or provider switches.

  • Start with the CLI, then graduate to the HTTP API when you need multi-client access or UI integrations.

Source-backed notes

  • README quickstart shows micro-agent ask ... --utc, a FastAPI server via uvicorn, and evals via python evals/run_evals.py --n 50.
  • Docs describe provider config for OpenAI and Ollama, plus tracing under traces/<id>.jsonl.
  • README lists eval metrics like success_rate/avg_latency_sec/avg_cost_usd and notes usage/cost capture can be best-effort.

FAQ

  • Is this a full framework?: No — README frames it as a minimal runtime + DSPy modules you can read end-to-end.
  • Can I run it without OpenAI?: Yes — README includes an Ollama provider path via env vars.
  • How do I keep changes safe?: Use the built-in eval harness and store/replay traces to compare behavior over time.
🙏

Source & Thanks

Source: https://github.com/evalops/dspy-micro-agent > License: MIT > GitHub stars: 73 · forks: 6

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