Feynman vs Suprbox — Secure Storage for Autonomous AI Agents: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Feynman and Suprbox — Secure Storage for Autonomous AI Agents — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Feynman
Companion
Open-source AI research agent that reads papers, ranks literature, drafts research and plans experiments from the terminal or a local workbench.
Key features
- 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.
Best for
- 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.
Suprbox — Secure Storage for Autonomous AI Agents
Suprbox
Secure, purpose-built memory fabric that mediates document access and stores context and recall vectors for autonomous AI agents.
Key features
- Purpose-Built Memory Fabric: Provides a dedicated storage layer for agent context and state, optimized for storing and recalling contextual information used by autonomous agents.
- Vector Recall & Retrieval: Stores recall vectors and supports fast retrieval of context vectors so agents can access relevant context quickly during execution.
- Document Gateway: Sits between documents and agents to mediate reads and writes, preventing direct, uncontrolled agent access to source documents.
- Runtime Policy Enforcement: Enforces tight, fine-grained policies at execution time to control what agents can read, write, or execute against stored data.
- Execution Isolation: Isolates agent workspaces and memory to reduce risk of cross-agent data leakage and maintain separation between concurrent agent sessions.
- Controlled Context Delivery: Supplies agents only the allowed slices of context and vectors needed for a task, limiting exposure of sensitive information.
- Interposes between documents and autonomous agents to control access
- Purpose-built memory fabric for storing agent context and recall vectors
- Policy-driven execution and enforcement for agent operations
- Workspace isolation to prevent cross-agent data leakage
- Access controls and document-level gating for agents
- Designed for auditability and runtime visibility of agent access patterns
- Integrates with agent workflows to mediate reads/writes to storage
Best for
- Protecting enterprise document stores from autonomous agents by mediating agent access and preventing unauthorized reads of sensitive files.
- Providing persistent agent memory for multi-step workflows where agents need to store and recall contextual vectors across sessions.
- Enforcing runtime access policies for agents operating in regulated industries (finance, healthcare, legal) to maintain compliance and governance.
- Powering secure Retrieval-Augmented Generation (RAG) pipelines where agents retrieve vetted context vectors rather than raw documents.
- Isolating agent workspaces for research or development teams to experiment with agent behaviors without risking cross-project data leaks.
- Coordinating multi-agent systems by centralizing shared context and controlling which agents can access which pieces of memory.
- Prevent autonomous agents from reading or exfiltrating sensitive documents
- Store and manage agent context and memory vectors securely
- Enforce runtime policies for agentic workflows (who/when/how data is accessed)
- Isolate agent workspaces to reduce risk of data bleed between agents
- Provide a secure intermediary for LLMs and bots accessing corporate documents