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

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Keeping Research Local: Running the whole pipeline against a local model so unpublished work and private data never leave the machine.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

Flint AI

Free

Shared workspace that puts human teammates and AI agents in the same room, preserving context and history across handoffs.

Shared Rooms: People, agents, decisions, and work history live in one persistent room so context survives handoffs between sessions and teammates.
Agent Framework Support: Works with Claude Code, LangChain, Google ADK, OpenAI, Amazon Bedrock, and custom agents without migration or lock-in.
Messaging Connectors: Brings agent collaboration into Slack, Microsoft Teams, Discord, and Mattermost where teams already work.
Cross-Platform Desktop Console: Native downloads for macOS Apple Silicon and Intel, Windows x64, and Linux as AppImage or Debian package.
Extensible Integrations: Designed to connect to whatever additional tools a team already relies on.
Fast Deployment: Set up in minutes on top of existing agents rather than rebuilding workflows around a new platform.

An engineering team wants Claude Code and a research agent to share the same project context instead of re-explaining it to each.
A company running agents from several vendors needs one coordination layer that does not lock it into a single provider.
A team already living in Slack or Discord wants to invite agents into existing channels rather than adopt a new app.
A project handed between two people needs the agent work history to carry over intact.
An operations lead wants a durable record of what agents decided and why, auditable after the fact.
A developer evaluating agent frameworks wants a neutral room to run several side by side on the same task.