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Feynman

Feynman AI Agent
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Overview

Open-source CLI research agent that reads papers, searches the web, drafts research outputs, plans experiments, and cites claims.

Feynman is an open-source AI research agent from Companion for scientific and technical research workflows. It runs from a REPL or shell as commands such as deep research, literature review, critique, reproducibility audit, replication planning, comparison, drafting, and bounded autoresearch. The agent gathers evidence from literature databases, web pages, PDFs, Hugging Face Hub, alphaXiv, and local documents, then links research claims back to sources.

Product overview

Capabilities, pricing and fit

Feynman is an open-source research agent focused on scientific-literature workflows, combining paper, web, code, PDF, and local-document evidence gathering with cited research drafts and review-oriented workflows.

Information as of October 10, 2026

What it can do

  • Scientific research automation

    The official page says Feynman reads papers, searches the web, writes research drafts, plans experiments, and cites research claims.[1]

  • Research workflows

    It exposes workflows including deep research, literature review, internal review, reproducibility audit, replication planning, ML recipe generation, source comparison, drafting, and bounded autoresearch.[1]

  • Plan-first deep research

    The /deepresearch workflow is described as a plan-first investigation across papers, web, and code, with parallel researchers when the topic needs them.[1]

  • Evidence source coverage

    The official page lists literature sources including Semantic Scholar, OpenAlex, arXiv, PubMed, Europe PMC, bioRxiv/medRxiv, and Crossref, plus alphaXiv, Hugging Face Hub, web research, PDFs, and local documents.[1]

  • Cited outputs

    Feynman states that research claims link to their sources and that its draft workflow can produce paper-style drafts with inline citations from findings.[1]

  • Command-line and REPL operation

    Workflows can run as slash commands in the REPL or as feynman workflow commands from the shell.[1]

  • Agent-oriented architecture

    The project describes four research roles: researcher, verifier, reviewer and writer.[2]

Where it fits best

  • Researchers who need literature reviews from primary sources with consensus mapping.[1]
  • Technical teams investigating topics across papers, web sources, and code using a plan-first deep-research workflow.[1]
  • ML and reproducibility teams that want paper-to-code mismatch audits, replication plans, or ranked ML training recipes grounded in papers, datasets, docs, and code.[1]
  • Writers of research drafts who want polished paper-style drafts with inline citations from findings.[1]

Pricing and availability

Open-source installation; budget separately for any model or infrastructure usage and confirm licence and support requirements.[1], [2]

Platforms: CLI, REPL, local shell[1]

Access: Open-source local installation, Standalone bundle via curl or PowerShell with bundled Node.js, npm installation using local Node.js[1]

Important considerations
  • Requires local command-line setup. Standalone bundles include Node.js; npm installation uses your local Node.js.[1]
  • Research drafts and replication plans require expert review before use.[1]
Sources (3)
  1. Feynman — The open source AI research agentOfficial site · checked 2026-10-10
  2. Companion-Inc/feynman: The open source AI research agentGitHub · checked 2026-10-10
  3. Release Notes — Feynman DocsChangelog · checked 2026-10-10

Autonomy level

76/100

Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.

Reasoning: Feynman carries out multi-step literature research, critique, drafting and replication-planning workflows from a supplied research goal. Researchers install and configure it, choose the task and validate citations, analysis and proposed experiments.

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Some of the use cases of Feynman:

  • Literature reviews from primary sources
  • Plan-first deep research across papers, web, and code
  • Research draft generation with inline citations
  • Paper-to-code reproducibility audits
  • Replication planning and experiment planning
  • Comparing sources for agreements and conflicts

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Popularity level: 64%

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Exploring Feynman? For scientific literature research teams, your own AI agent can search relevant literature, reconcile study evidence, and maintain research updates. Start with a setup tailored to this work.

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