Feynman Research
Research, review, and compare scholarly work from a CLI, with source provenance for key workflows.
- Source repo
- Companion-Inc/feynman
- Stars
- ★ 10k
- Last updated
- today
- License
- MIT
- Primary language
- TypeScript
- FA score
- 50/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platformCodex (Partial support)
- Cost
- Free software; you pay for model usage
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- Researchers mapping consensus, disagreements, and open questions on a topic can run
/lit. - Not a fit if
- Teams that cannot accept anonymous command, model usage, and error telemetry
- Source review
- 50/100 · Major gaps
What does this agent do, and when should you use it?
Feynman is an open source AI research CLI built on stock Pi, with researcher, verifier, reviewer, and writer roles. Users can ask a question directly or run workflows for literature reviews, deep research, paper reviews, code audits, and experiment recipes. It searches academic indexes, the web, and Hugging Face, and reads local PDFs and documents. Results go to outputs/, drafts to papers/, and literature reviews, deep research, and recipes also produce provenance files listing sources and what was verified. It installs through a shell script or npm and requires signing in to a model provider before use.
After installation, run feynman with a question or use /lit, /deepresearch, /review, /audit, /replicate, /recipe, /compare, /draft, /autoresearch, or /summarize. Feynman searches alphaXiv, Semantic Scholar, OpenAlex, arXiv, PubMed, Europe PMC, bioRxiv, medRxiv, Crossref, the web, and Hugging Face, and reads local PDFs and documents. Deep research plans an investigation with parallel researchers and citation checks; literature review reports consensus, disagreements, and open questions; audit compares paper claims with public code. Outputs are written to outputs/, drafts to papers/, and selected workflows include provenance records.
- Researchers mapping consensus, disagreements, and open questions on a topic can run
/lit. - Teams conducting a planned investigation with parallel researchers and citation checks can run
/deepresearch. - Authors preparing a paper or draft for critique and a severity-ranked revision plan can run
/review. - Researchers checking paper claims against publicly available code can run
/audit. - ML practitioners seeking ranked training recipes or a source comparison matrix can run
/recipeor/compare. - Authors turning research findings into a paper-style draft can run
/draft.
How do you install or deploy this agent?
On macOS or Linux, install with the shell script. On Windows, run the PowerShell installer. Alternatively, use npm with Node.js 22.22 or newer. After installation, run feynman setup and sign in to a model provider. The README also mentions a skills-only bundle for Codex and OpenCode but gives no installation command for it.
How do you use this agent?
After installation and provider sign-in with feynman setup, run a natural-language question or workflow command in the terminal. Results are saved to outputs/ and drafts to papers/.
What are this agent's strengths and limitations?
- Includes concrete research workflows spanning literature review, deep research, paper review, code audit, and replication planning.
- Searches multiple scholarly sources and Hugging Face, and can read local PDFs and documents.
- Literature reviews, deep research, and recipes include provenance files listing sources and what was verified.
- The replication workflow prepares a plan and runs only after the user chooses an environment.
- First use requires choosing and signing in to a model provider; the README does not specify provider or model compatibility in detail.
- Research search requires network access; the README notes shared API rate limits and suggests free OpenAlex and Semantic Scholar keys to avoid them.
- Telemetry is on by default and includes commands, workflows, tools, models, token counts, and errors with messages and stack traces. The README says prompts, model output, paper content, and tool arguments are not sent.
- The README does not document offline operation or model-call pricing.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Feynman Research This agent | 50 · Major gaps | CLIFree + model costs | ★ 10k | today | TypeScript | — |
| Autonomous Agents Research Papers | 28 · Major gaps | Library / SDKFree | ★ 1.4k | 3mo ago | — | — |
| Luxas Research Colleague | 59 · Major gaps | CLIFree + model costs | ★ 1.2k | 9d ago | TypeScript | Claude API |
| AERS: Auto-Empirical Research Skills | 51 · Major gaps | Agent plugin / skillFree + model costs | ★ 4.7k | 1d ago | Stata | Claude Code |
How does FollowAgents rate this agent?
Why each dimension lost points
README discloses telemetry contents (commands, workflows, tools, models, token counts, errors with stack traces, home folder shown as ~) and states prompts/model output/paper content are never sent, with FEYNMAN_TELEMETRY=off, so data_flow_transparency scores 2. However install is curl|bash and irm|iex executing remote scripts with no checksum/signature evidence, so least_privilege and external_effects score 1; /replicate claims to run only after the user chooses an environment but no confirmation mechanism is shown, user_confirmation 1; sensitive data handling (API keys, auth.json) appears only in CI, no product-side key storage policy, 1; dependencies are mostly pinned and CI runs npm-audit, dependency_security 2; rollback/uninstall is only referenced in external docs, rollback 1; provenance files and source listing are explicit, source_attribution 2.
README, package.json and CI workflows agree on Node version, package name and commands, self_consistency 2; heavy reliance on @earendil-works/pi-* and @companion-ai/alpha-hub packages whose availability cannot be confirmed from the repo, dependency_availability 1; tests show auth failures and 503s surface with status text, failure_messages 2.
README lists multiple research workflows and many data sources with a clear researcher audience, audience_and_scenarios 2; capability boundaries (scope of /autoresearch, environment constraints of /replicate) are only one-liners, capability_boundaries 1; trigger precision relies on natural language and slash commands with no ambiguity handling, trigger_precision 1; supports macOS/Linux/Windows, Node 22+ and local models, environment_fit 2.
README is well structured with complete install steps, information_architecture and install_notes 2 each; version 0.5.x is early with no naming stability commitment, naming_stability 1; examples are only a command list with no FAQ, examples_and_faq 1; known limitations are barely mentioned (only telemetry opt-out), known_limitations 1; LICENSE is full MIT text, license 3; RELEASES.md is referenced but not provided, versioning_changelog 1; publisher is unverified and maintenance responsibility only points to CONTRIBUTING.md, maintenance_responsibility 1.
Outputs are saved to outputs/ and papers/ with provenance files, output_usability 2; multiple workflows cover literature review, replication and auditing, giving marginal value over a generic LLM, marginal_value 2; but reliance on multiple external APIs and paid keys with no token cost discussion, cost_benefit 1.
README states /lit, /deepresearch and /recipe write a provenance file listing sources used and what was verified, claim_traceability 2; multi-source cross-checking (alphaXiv, OpenAlex, arXiv) with fallback is exercised in tests, cross_source_corroboration 2; but claims like 'citation checks' and 'verifier agent' lack mechanism detail, blurring fact and inference, fact_inference_separation 1.
- Install scripts execute remote code via curl|bash and irm|iex with no checksum or signature verification in the repo; users should review the script first.
- Telemetry is on by default and sends error stack traces (home folder shown as ~); it must be explicitly disabled with FEYNMAN_TELEMETRY=off.
- Depends on several niche/private npm packages (@earendil-works/pi-*, @companion-ai/alpha-hub) whose supply chain and availability cannot be confirmed from the repo.
- Publisher identity is unverified; maintenance responsibility and update path point only to external docs.
- Version is early 0.5.x, so interfaces and command naming may change.
FAQ
Do I need a paid model account to use Feynman?
What telemetry does it send?
~. It says prompts, model output, paper content, and tool arguments are not sent. Telemetry can be disabled with FEYNMAN_TELEMETRY=off.Can I use API keys to avoid shared search rate limits?
OPENALEX_API_KEY and SEMANTIC_SCHOLAR_API_KEY keys avoid the shared rate limits.Does replication automatically run experiments?
/replicate creates a replication plan and runs only after you choose an environment.