The open source AI research agent.

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Installation

macOS / Linux:

curl -fsSL https://feynman.is/install | bash

Windows (PowerShell):

irm https://feynman.is/install.ps1 | iex

The one-line installer fetches the latest tagged release. To pin a version, pass it explicitly, for example curl -fsSL https://feynman.is/install | bash -s -- 0.2.35.

The installer downloads a standalone native bundle with its own Node.js runtime.

To upgrade the standalone app later, rerun the installer. feynman update only refreshes installed Pi packages inside Feynman's environment; it does not replace the standalone runtime bundle itself.

To uninstall the standalone app, remove the launcher and runtime bundle, then optionally remove ~/.feynman if you also want to delete settings, sessions, and installed package state. If you also want to delete alphaXiv login state, remove ~/.ahub. See the installation guide for platform-specific paths.

Local models are supported through the setup flow. For LM Studio, run feynman setup, choose LM Studio, and keep the default http://localhost:1234/v1 unless you changed the server port. For LiteLLM, choose LiteLLM Proxy and keep the default http://localhost:4000/v1. For Ollama or vLLM, choose Custom provider (baseUrl + API key), use openai-completions, and point it at the local /v1 endpoint.

Skills Only

If you want just the research skills without the full terminal app:

macOS / Linux:

curl -fsSL https://feynman.is/install-skills | bash

Windows (PowerShell):

irm https://feynman.is/install-skills.ps1 | iex

That installs the skill library into ~/.codex/skills/feynman for Codex. You can also name the Codex target explicitly:

macOS / Linux:

curl -fsSL https://feynman.is/install-skills | bash -s -- --codex

Windows (PowerShell):

& ([scriptblock]::Create((irm https://feynman.is/install-skills.ps1))) -Scope Codex

For a repo-local Claude/agent install instead:

macOS / Linux:

curl -fsSL https://feynman.is/install-skills | bash -s -- --repo

Windows (PowerShell):

& ([scriptblock]::Create((irm https://feynman.is/install-skills.ps1))) -Scope Repo

That installs into .agents/skills/feynman under the current repository.

For an OpenCode project-local install instead:

macOS / Linux:

curl -fsSL https://feynman.is/install-skills | bash -s -- --opencode

Windows (PowerShell):

& ([scriptblock]::Create((irm https://feynman.is/install-skills.ps1))) -Scope OpenCode

That installs into .opencode/skills/feynman under the current repository.

These installers download the bundled skills/ and prompts/ trees plus the repo guidance files referenced by those skills. They do not install the Feynman terminal, bundled Node runtime, auth storage, or Pi packages.

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What you type → what happens

$ feynman "what do we know about scaling laws"
→ Searches papers and web, produces a cited research brief

$ feynman deepresearch "mechanistic interpretability"
→ Multi-agent investigation with parallel researchers, synthesis, verification

$ feynman lit "RLHF alternatives"
→ Literature review with consensus, disagreements, open questions, and lab/PI corpus mode when the input names a research group

$ feynman rank "mechanistic interpretability sparse autoencoders"
→ Decides what to read first with citation, method, reproducibility, and provenance evidence

$ feynman rank "mechanistic interpretability sparse autoencoders" --expand-citations 2
→ Adds cited and citing papers to the local citation graph before scoring graph prestige

$ feynman rank "mechanistic interpretability sparse autoencoders" --full-text-top 3
→ Adds section-aware full-text evidence and checklist rubric answers before rescoring

$ feynman rank "mechanistic interpretability sparse autoencoders" --critique-top 5
→ Adds research-critique strengths, concerns, and follow-up questions grounded in score evidence

$ feynman rank "mechanistic interpretability sparse autoencoders" --synthesize
→ Writes an auditable model synthesis and names the selected model plus whether it was recommended or explicitly requested

$ feynman paper 10.7717/peerj.4375 --fetch-full-text
→ Resolves legal full-text access candidates for one paper and fetches source-specific text when available

$ feynman audit 2401.12345
→ Compares paper claims against the public codebase

$ feynman replicate "chain-of-thought improves math"
→ Plans replication checks and runs them only after an explicit environment choice

$ feynman recipe "fine-tune a small model for math reasoning"
→ Finds ranked, implementable ML training recipes from papers, datasets, docs, and code

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Workflows

Ask naturally or use slash commands as shortcuts.

Command	What it does
feynman rank <topic>	PaperRank scoring for deciding what to read first, with transparent evidence for citations, methods, reproducibility, and provenance
feynman paper <id-or-title>	Paper access resolver for one DOI, arXiv ID, OpenAlex ID, PMID, PMCID, or title, with OpenAlex, arXiv/alphaXiv, DOI, and Europe PMC candidates plus optional source-specific text fetching
/deepresearch <topic>	Source-heavy multi-agent investigation
/lit <topic-or-lab>	Literature review from paper search and primary sources; lab/PI inputs map publication trajectories and originality-ranked papers
/review <artifact>	Research review with severity and revision plan
/audit <item>	Paper vs. codebase mismatch audit
/replicate <paper>	Plan replication checks; execute only after choosing an environment
/recipe <task-or-paper>	Ranked ML training recipes with dataset, method, code, and verification status
/compare <topic>	Source comparison matrix
/draft <topic>	Paper-style draft from research findings
/autoresearch <idea>	Bounded experiment loop with benchmark evidence
/watch <topic>	Research watch baseline with optional scheduled follow-up
/outputs	Browse all research artifacts

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Agents

Four bundled research agents, invoked by workflow prompts when decomposition helps.

• Researcher — gather evidence across papers, web, repos, docs
• Reviewer — internal research critique with severity-graded feedback
• Writer — structured drafts from research notes
• Verifier — inline citations, source URL verification, dead link cleanup

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Skills & Tools

• AlphaXiv — paper search, Q&A, code reading, annotations (via Feynman's alpha tools and feynman alpha command)
• Hugging Face Hub — dataset metadata, split/schema inspection, and small file reads from model, dataset, and Space repos
• Web search — Exa, Perplexity, or Gemini API; no Chromium cookie access by default
• Session search — indexed recall across prior research sessions
• Preview dependencies — optional browser/PDF rendering support for generated artifacts when preview commands or shell renderers are available
• Observability — PostHog analytics, logs, distributed traces, and Pi AI runtime traces through OpenTelemetry metadata
• Research execution options — Docker, Modal, and RunPod instructions for explicitly chosen replication, benchmark, or dataset-heavy experiment runs; not service deployment or generic cloud administration

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How it works

Built on Pi for the agent runtime, alphaXiv for paper search and analysis, and CLI tools for compute and execution. Runtime resources follow Pi's documented package model for packages, extensions, and skills. Hugging Face inspection uses the public Hub API endpoints and HF_TOKEN / HUGGINGFACE_HUB_TOKEN environment variables documented by huggingface_hub. The ML recipe workflow was informed by the open-source Hugging Face ml-intern research-agent repo, but is implemented as native Feynman prompts, skills, and read-only tools. Research outputs are source-grounded — research claims link to papers, docs, or repos with direct URLs.

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Star History

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Contributing

See CONTRIBUTING.md for the full contributor guide.

git clone https://github.com/companion-inc/feynman.git
cd feynman
nvm use || nvm install
npm install
npm test
npm run typecheck
npm run build

Docs · Release Notes · MIT License