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.

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

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

$ feynman replicate "chain-of-thought improves math"
→ Replicates experiments on local or cloud GPUs

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

Workflows

Ask naturally or use slash commands as shortcuts.

| Command | What it does | | --- | --- | | /deepresearch <topic> | Source-heavy multi-agent investigation | | /lit <topic> | Literature review from paper search and primary sources | | /review <artifact> | Simulated peer review with severity and revision plan | | /audit <item> | Paper vs. codebase mismatch audit | | /replicate <paper> | Replicate experiments on local or cloud GPUs | | /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> | Autonomous experiment loop | | /watch <topic> | Recurring research watch | | /outputs | Browse all research artifacts |

Agents

Four bundled research agents, dispatched automatically.

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

Skills & Tools

• AlphaXiv — paper search, Q&A, code reading, annotations (via alpha CLI)
• Hugging Face Hub — dataset metadata, split/schema inspection, and small file reads from model, dataset, and Space repos
• Docker — isolated container execution for safe experiments on your machine
• Web search — Exa, Perplexity, or Gemini API; no Chromium cookie access by default
• Session search — indexed recall across prior research sessions
• Preview — browser and PDF export of generated artifacts
• Modal — serverless GPU compute for burst training and inference
• RunPod — persistent GPU pods with SSH access for long-running experiments

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. Every output is source-grounded — claims link to papers, docs, or repos with direct URLs.

Star History

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