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

A side-by-side comparison of Feynman and Ruflo — 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Keeping Research Local: Running the whole pipeline against a local model so unpublished work and private data never leave the machine.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.

rUv (ruvnet)

Free

Open-source agent meta-harness for Claude Code and Codex adding swarms, self-learning memory, MCP tools and security guardrails.

Agent Swarms: Over 100 specialized agents self-organize into coordinated swarms to split up and run complex tasks.
Self-Learning Memory: Remembers across sessions and learns from successful trajectories using local vector retrieval and contrastive updates.
MCP Server: Run npx ruflo mcp start to expose Ruflo's tools to Codex or any MCP-enabled client.
Claude Code Plugins: Core tools, a visual console and runtime mods add routing and policy enforcement inside Claude Code.
Terminal UI Console: Shows Overview, Cost, Swarm, Events and Missions pages, and also runs standalone.
Federation: Lets agents on different machines communicate securely without leaking data.
Hook-Based Routing: Hooks automatically route tasks and coordinate agents in the background.

Large Refactors: Split a big codebase change across a swarm of specialist agents.
Persistent Project Context: Keep knowledge across Claude Code sessions with vector memory.
Multi-Client Setups: Connect Codex or other MCP clients to the same tool server.
Cost Monitoring: Track spend and agent activity from the console during long runs.
Multi-Machine Collaboration: Let agents on separate machines coordinate through federation.