SkillsMay 12, 2026·2 min read

MiroThinker — Open Deep Research Agent with 256K Context

MiroThinker is an open deep-research agent stack with long context, tool-heavy configs, and benchmarked BrowseComp results for serious evaluation work.

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Intro

MiroThinker is an open deep-research agent stack with long context, tool-heavy configs, and benchmarked BrowseComp results for serious evaluation work.

  • Best for: research teams benchmarking long-horizon, tool-rich open agents
  • Works with: Python 3.10+, uv, E2B sandbox, Serper, Jina, summary LLMs, YAML agent configs
  • Setup time: 30-60 minutes

Practical Notes

  • Quant: MiroThinker-1.7 is documented with 256K context and up to 300 tool calls per task; older v1.0 notes mention up to 600 calls.
  • Quant: the README highlights BrowseComp 74.0 and BrowseComp-ZH 75.3 for the 1.7 line, plus multiple keyed environment variables for the minimal tool set.

Rollout pattern

  • Treat the minimal tool set as the baseline and resist adding optional tools until you can reproduce one benchmark-like task.
  • Log environment variables and configs per run so your benchmark notes are reproducible.
  • Use it to learn what long-horizon research agents require operationally, not as a trivial one-command chatbot replacement.

Watchouts

This stack has more moving parts than a typical app-facing agent, so missing keys or mismatched tool configs will produce noisy failures unless you document the environment carefully.

FAQ

Q: Is it a simple local chat app? A: No. The README positions it as a tool-enabled research agent with multiple required services and configs.

Q: Why is it worth studying? A: Because it publishes measurable context, tool-call, and benchmark facts that are useful for replication.

Q: What should I verify first? A: One recommended YAML config plus the three-tool minimal setup for search, scraping, and execution.

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Source & Thanks

Source: https://github.com/MiroMindAI/MiroThinker > License: Apache-2.0 > GitHub stars: 8,223 · forks: 626

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