Feynman vs openrig: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Feynman and openrig — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Feynman

Companion

Free

Open-source AI research agent that reads papers, ranks literature, drafts research and plans experiments from the terminal or a local workbench.

Cited Research Briefs: Asking a research question returns a synthesized brief where each claim is tied to the paper or web source it came from, rather than an unsourced summary.
PaperRank Scoring: Ranks papers on a topic with transparent evidence for citations, methodology, reproducibility and provenance so reading order is a decision you can inspect.
Paper Access Resolver: Resolves a single DOI, arXiv ID, OpenAlex ID, PMID, PMCID or title against OpenAlex, arXiv/alphaXiv, DOI and Europe PMC, with optional full-text fetching.
Local Science Workbench: `feynman serve` opens a standalone app with projects, sessions, chat, notebooks, compute, artifact previews and provenance in one place.
Claim Auditing and Replication: Compares a paper's stated claims against what its code actually does, and generates replication plans with compute targets and gated experiment steps.
Local and Hosted Models: Works with hosted providers via OAuth or API key and with local runtimes including LM Studio, Ollama, vLLM and a LiteLLM proxy.
Skills-Only Install: The research skill library can be installed on its own into Claude, Codex or OpenCode projects without the terminal app or bundled runtime.
Science Artifacts: Reports, data files, spreadsheets, notebooks, LaTeX, chemistry sketches and genomes are browsable together with versions, lineage and execution logs.

Deciding What to Read: Ranking a fresh literature pile on a topic by reproducibility and methodology instead of citation count alone.
Writing a Literature Review: Producing a review that separates where the field agrees from where questions remain open, with citations attached.
Verifying a Paper's Claims: Auditing whether the results a paper reports are supported by the code and data it released.
Planning a Replication: Turning a published finding into a concrete replication plan with a compute target and staged experiment steps.
Running Deep Research Passes: Launching a multi-agent deep dive on a topic that synthesizes findings and verifies them before reporting.
Keeping Research Local: Running the whole pipeline against a local model so unpublished work and private data never leave the machine.
Adding Research Skills to a Coding Agent: Installing the skills bundle into an existing Claude or Codex project to get research workflows without a second app.

mvschwarz

Free

OpenRig is open-source software to define a team of Claude Code and Codex agents in YAML and run them as one persistent, managed system.

YAML Rig Definitions: Describe an agent team with roles in YAML and boot it with a single command.
Mixed Harnesses: Run Claude Code and Codex agents together in the same rig, managed as one system.
Lead Agent Coordination: Talk to a lead agent that coordinates specialists across teams and reports back decisions needing attention.
Persistent Teams: Keep team work and context at stable addresses instead of a pile of terminal sessions.
Terminal UI: View the rig as a graph or a table of seats showing runtime, model, context and state, with per-seat detail.
Guided First Use: A getting-started path installs, launches a two-agent team in your repository and produces one reviewed change.
Permission Allowances: Optionally lets your agent add scoped native rules so OpenRig commands run without repeated prompts, with an undo.
CLI Install: Distributed as @openrig/cli on npm, requiring Node.js 22 or 24 and tmux on macOS or Linux.

Multi-Agent Coding Teams: Stand up a persistent team of coding agents with defined roles for a repository.
Cross-Harness Workflows: Combine Claude Code and Codex strengths within one coordinated team.
Outcome-Driven Delegation: Describe a desired outcome to a lead agent and let it split work among specialists.
Agent Monitoring: Watch seats, models, context and state of many agents from one terminal UI.
Reviewed Changes: Have agents produce a change in a repo and another review it before you merge.
Agent Research Experiments: Prototype and study networks of cooperating agents on your own machine.