Omni-Context

Plug long-term memory into any AI client via MCP. Not just storage — a second brain that spots your blind spots and challenges your thinking. Fully local, forever yours.

Build License: MIT

Why Omni-Context

Your AI forgets you every conversation. ChatGPT memory is shallow. Cursor's context lasts one session. Claude doesn't remember across projects.

Cloud memory means someone else's server. Mem0, Letta, Zep — all impressive, all cloud-first. Your second brain lives on their infrastructure.

Omni-Context gives every MCP-compatible AI a shared, persistent knowledge graph that lives on your machine. Plug it into Claude Desktop, Cursor, Cline, Windsurf — they all draw from the same brain. The graph grows as you work. Forever yours.

More than memory. Most "AI memory" tools are fancy databases. Omni-Context actively analyzes your knowledge graph to find gaps — topics you consume but never act on, perspectives you're missing, connections you haven't noticed. It's not just storage; it's a second brain that questions you.

How it works

You capture anything       We build a knowledge graph     Any AI client can query it
───────────────────       ──────────────────────────     ──────────────────────────
Screen · Files · Web       Entities + Relationships      via MCP (standard protocol)
Browser extension          + vectors + full-text         Claude · Cursor · Cline · ...
                           Stored in local SQLite        12+ clients, one brain
  1. Capture — screenshot, drag files, clip web pages, or hit a physical button. Anything.
  2. Extract — OCR + LLM pipeline pulls out entities and their relationships into a local knowledge graph.
  3. Query — any MCP-compatible AI client accesses your graph as long-term memory. Same brain, every conversation.

What makes it different

  • Not a note app — it's an AI memory layer. Your tools don't need their own memory systems; they all share Omni.
  • Not cloud — SQLite on your disk. No accounts, no servers, no data ever leaves your machine.
  • Not locked to one AI — MCP-native. Use Claude Desktop today, Cursor tomorrow, same memory.
  • Active, not passive — the agent scans your graph for connections you've forgotten and surfaces them.
  • Questions your thinking — blind spot detection finds what you're missing. Anti-consensus insights challenge your assumptions. Your graph pushes back.

Tools

25 MCP tools, grouped by what they do.

Decision & retrieval — the "brain"

  • get_decision_context — given a situation, return relevant principles, precedents, conflicts, and graph neighborhood in one call
  • unified_memory_search — three-layer fusion search (full-text + vector + graph traversal) in one natural-language query
  • vector_search — pure semantic vector search; finds conceptually similar entities even with different wording
  • ask_memory — ask a question, get a synthesized answer grounded in your graph
  • graph_answer — graph-grounded Q&A with citation to source entities
  • search_entities — find entities by name / description keywords
  • get_core_context — fetch core principles relevant to a topic (or a compact overview if no topic given)
  • get_entity — get one entity's full info and all its relationships by ID
  • get_graph_neighborhood — get the N-hop subgraph around an entity
  • list_entities — list entities, optionally filtered by type

Capture & write — grow the memory

  • record_capture — store a capture snapshot (screenshot / clipboard / text)
  • extract_from_capture — auto-extract entities + relationships + principles from a text passage
  • add_entity — create a new entity
  • add_relationship — link two existing entities
  • update_entity — modify an entity's name / description / tags / metadata
  • set_core_principle — record or update a core principle

Decision pipeline — sediment thinking

  • save_conclusion — persist a conclusion worth remembering
  • save_decision — record a decision with its context and reasoning
  • analyze_decision — analyze a decision for coherence and potential conflicts
  • discuss_decision — explore a decision from multiple angles
  • get_decision_lineage — trace how a decision evolved over time

Graph maintenance

  • merge_entities — merge duplicate entities into one
  • delete_entity — remove an entity
  • get_stats — entity / relationship counts, type distribution
  • get_decay_report — which memories have crossed the decay threshold (cleanup candidates)

Full parameter schemas: see docs/MCP-INTEGRATION.md.

Install

Windows

Download Omni-Context-Setup-x64.msi from Releases. Double-click, done. Fully offline — Node.js runtime and embedding models are bundled.

macOS / Linux

Build scripts are ready. Community contributors with hardware: pull requests welcome.

Build from source

git clone https://github.com/guo6x/Omni-context.git
cd Omni-context
npm run install:all
npm run package

Omni vs alternatives

Omni ChatGPT Memory Mem0 Letta Obsidian
Runs locally ✓ ✗ ✗ ✓ ✓
MCP-native ✓ ✗ ✗ ✗ ✗
Knowledge graph ✓ ✗ partial ✓ manual
Cross-AI shared ✓ ✗ ✓ ✗ ✗
Own your data ✓ ✗ ✗ ✓ ✓
Offline-first ✓ ✗ ✗ ✗ ✓

Community

MIT License. Built for people who want their AI to actually know them.