Central Brain guides, comparisons and use cases

Compare Central Brain

• Central Brain vs Mem0: Mem0 is a memory engine and API for developers; Central Brain is a local desktop app for project files and readable AI memory. Compare deployment, data path and fit.
• Central Brain vs Basic Memory: Central Brain brings local document preparation, source-traceable search and readable project memory into one desktop app. Compare its workflow with Basic Memory.
• Central Brain vs Supermemory: Supermemory is a hosted context platform with a separate self-hosted API; Central Brain is a local desktop app with local MCP. Compare deployment, data path and fit.
• Central Brain vs Pieces: Pieces captures work activity through PiecesOS; Central Brain indexes project files you select and keeps a readable brief. Compare the input model, data path and fit.
• ChatGPT memory vs a local AI memory app: Compare ChatGPT native memory with Central Brain local project memory: scope, file search, portability, control and what context reaches a cloud assistant.
• GitHub Copilot memory vs project memory: Compare GitHub Copilot Memory, checked-in instructions and Central Brain local project memory for decisions, documents and cross-client continuity.
• Local AI memory vs hosted APIs: Compare local project storage, retrieved context and optional hosted services before selecting an AI memory workflow for project documents.
• MCP memory server vs CLAUDE.md: CLAUDE.md gives Claude Code always-loaded instructions; an MCP memory server adds searchable, shared memory. When each is enough, and when to use both.
• How much does an AI memory app cost?: AI memory costs range from free (CLAUDE.md, open-source servers) to per-seat, usage-based and flat plans. Pricing models compared, with free options first.

Choose an MCP memory server

• Best MCP memory servers in 2026: There is no single best MCP memory server. Compare Basic Memory, Mem0 OpenMemory, Zep, Letta, Cognee, Supermemory, MemoryLake, AnythingLLM and Central Brain by deployment and the job each one fits.
• Best memory tools for AI coding assistants in 2026: Mem0, Zep, Letta, Cognee, Basic Memory, Supermemory, Pieces, Central Brain and built-in memory compared by what each is best at for coding work.

Connect your AI tools

• Long-term memory for Claude Code: Connect Claude Code to Central Brain for local project memory, source-traceable file search and a repeatable recall and checkpoint workflow.
• One memory for ChatGPT, Claude and Copilot: Use Central Brain as one local project memory for supported GPT Desktop, Claude and GitHub Copilot clients. Set up and verify cross-client recall.
• Search local PDFs and spreadsheets from Claude: Use Central Brain to index PDFs and XLSX files locally and retrieve source context through Claude Desktop MCP without uploading the whole source collection.
• Keep AI project notes in plain files: Build a readable project brief and decision log, then use Central Brain checkpoints and source-traceable retrieval to reuse approved context.
• Share Brain and project permissions: Pair trusted computers, choose shared projects and control remote memory permissions. Learn how Share Brain differs from Fleet Mode and portable copies.
• Fleet Mode Preview: Designed for up to twelve computers: one coordinator and up to eleven workers. Fleet Mode Preview delegates supported document preparation on a local network.
• Make AI remember decisions across coding sessions: Keep a decision log the assistant loads every session, use built-in memory, and add an MCP memory server only when context spans tools or teammates.
• Stop AI chats from forgetting project context: Why AI chats lose project context and how to fix it with a project brief, project features, built-in memory or a memory server shared across tools.
• Back up or export ChatGPT memory: Export your ChatGPT data from Data controls, copy saved memories from Personalization settings, and keep project memory as portable local files.

Plan your installation

• Central Brain system requirements and benchmarks: Check Central Brain platform support, storage and connection needs, then measure preparation, retrieval and project memory on your own machine.

Put project memory to work

• AI memory for legal files: Evaluate Central Brain for authorized legal-file search: local preparation, source-traceable retrieval and matter-specific memory across AI sessions.
• AI memory for regulated files: A practical Central Brain evaluation for controlled document workflows: approved scope, local retrieval, inspectable memory and explicit AI-provider boundaries.
• AI memory for small teams: Give a small team repeatable project search and local AI memory. Understand per-computer licenses, project ownership and optional shared infrastructure.

What does “local-first” mean in these answers?

Local-first means that, in the default workflow, source files, prepared output, vectors and memory stay on storage you choose.

It does not mean that every activity is offline or that no information can ever leave the device. Preparing files, indexing, search and memory run air-gapped, but the 14-day license check, optional cloud connections and cloud-hosted AI clients need an internet connection. Keygen, which handles licensing, receives licensing and device data, not documents. A connected AI tool receives the context it retrieves, and optional cloud connections send selected data only when you enable them. Synced folders, backups and other services you configure change the boundary too.

The useful question is therefore not “is it private?” as an absolute. The useful questions are: where is each copy stored, what process can read it, what gets transmitted for the chosen action, and which person or provider controls retention. The Central Brain security and data-boundary guide is the canonical source for that evaluation.

What can Central Brain prepare and retrieve?

Central Brain prepares PDF, DOCX, TXT, Markdown, CSV and XLSX files and keeps retrieval metadata that leads back to the source where available.

Optional OCR can recover text from scans and photos when it is included in the installed build. Images, audio and video get metadata-only indexing. Support for a file type does not guarantee that every unusual layout, damaged file, handwritten scan, formula, chart or embedded object will be interpreted correctly.

The default semantic search uses a bundled 384-dimension MiniLM embedding model and a local index; cloud embeddings are optional. Exact text search remains useful when you already know the phrase. Results include the source path, prepared-file path, page and chunk where available.

How should an organization evaluate the fit?

Evaluate the workflow with a representative project, known-answer questions and an explicit data-flow review before relying on it for important work.

Choose a project that contains ordinary documents, one difficult file, a changed file and a question whose source is already known. Confirm that preparation succeeds, that both semantic and exact search find the expected material, and that the returned source trail leads a reviewer back to the original.

Then test memory separately: save a decision, an open question and a summary; start a new session in a supported AI tool; and verify that the information can be recalled. Finally, map licensing, connected AI tools, backups, synced storage, optional cloud connections and human access. A successful search demo is not a substitute for that review.