# feynman **Repository Path**: doiob/feynman ## Basic Information - **Project Name**: feynman - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-06-23 - **Last Updated**: 2026-06-23 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

Feynman CLI

The open source AI research agent.

Docs License

--- ### Installation **macOS / Linux:** ```bash curl -fsSL https://feynman.is/install | bash ``` **Windows (PowerShell):** ```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:** ```bash curl -fsSL https://feynman.is/install-skills | bash ``` **Windows (PowerShell):** ```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:** ```bash curl -fsSL https://feynman.is/install-skills | bash -s -- --codex ``` **Windows (PowerShell):** ```powershell & ([scriptblock]::Create((irm https://feynman.is/install-skills.ps1))) -Scope Codex ``` For a repo-local Claude/agent install instead: **macOS / Linux:** ```bash curl -fsSL https://feynman.is/install-skills | bash -s -- --repo ``` **Windows (PowerShell):** ```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:** ```bash curl -fsSL https://feynman.is/install-skills | bash -s -- --opencode ``` **Windows (PowerShell):** ```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 ` | Source-heavy multi-agent investigation | | `/lit ` | Literature review from paper search and primary sources | | `/review ` | Simulated peer review with severity and revision plan | | `/audit ` | Paper vs. codebase mismatch audit | | `/replicate ` | Replicate experiments on local or cloud GPUs | | `/recipe ` | Ranked ML training recipes with dataset, method, code, and verification status | | `/compare ` | Source comparison matrix | | `/draft ` | Paper-style draft from research findings | | `/autoresearch ` | Autonomous experiment loop | | `/watch ` | 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](https://www.alphaxiv.org/)** — paper search, Q&A, code reading, annotations (via `alpha` CLI) - **[Hugging Face Hub](https://huggingface.co/docs/hub/api)** — 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](https://github.com/badlogic/pi-mono) for the agent runtime, [alphaXiv](https://www.alphaxiv.org/) for paper search and analysis, and CLI tools for compute and execution. Runtime resources follow Pi's documented package model for [packages](https://github.com/badlogic/pi-mono/blob/main/packages/coding-agent/docs/packages.md), [extensions](https://github.com/badlogic/pi-mono/blob/main/packages/coding-agent/docs/extensions.md), and [skills](https://github.com/badlogic/pi-mono/blob/main/packages/coding-agent/docs/skills.md). Hugging Face inspection uses the public [Hub API endpoints](https://huggingface.co/docs/hub/api) and `HF_TOKEN` / `HUGGINGFACE_HUB_TOKEN` environment variables documented by [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables). The ML recipe workflow was informed by the open-source [Hugging Face `ml-intern`](https://github.com/huggingface/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 Star History Chart --- ### Contributing See [CONTRIBUTING.md](CONTRIBUTING.md) for the full contributor guide. ```bash 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](https://feynman.is/docs) · [Release Notes](RELEASES.md) · [MIT License](LICENSE)