Omni-Context

A local, persistent context and decision-intelligence layer for long-lived AI agents. Your memory and decisions stay on your machine.

Build License: MIT

Product baseline status — product/omni-v3-unified-r1 is the historical engineering baseline that was promoted into main during Goal 23.5 repository consolidation; main is now the stable development surface. The engineering starting point 17dc1d0 is NOT an official frozen experiment product: the original Targeted-7 gate FAILED, and the current selector has no formal performance proof. See docs/PRODUCT-BASELINE.md and docs/tag-remediation-proposal.md.

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 context and decisions live on their infrastructure.

Omni-Context is a local, persistent context and decision-intelligence layer for long-lived AI agents. It keeps a knowledge graph, retrieval index, and decision history on your machine, so agents can ground their work in your actual context instead of a fresh chat window.

More than memory. Most "AI memory" tools are fancy databases. Omni-Context also tracks decisions — context, reasoning, lineage, and outcomes — and surfaces evidence for the choices agents make. MCP is one integration surface for this today, not the product itself.

How it works

Capture / Sources
       ↓
Persistent Local Context
       ↓
Knowledge Graph + Retrieval
       ↓
Evidence / Decision Intelligence
       ↓
Integration Surfaces
       ↓
AI Agents
  1. Capture — screenshot, drag files, clip web pages, or hit a physical button. Anything.
  2. Extract — OCR + LLM pipeline pulls entities, relationships, and principles into a local knowledge graph.
  3. Reason — decision context, lineage, and outcomes give agents evidence-qualified context instead of raw memory dumps.
  4. Integrate — today, AI clients access this through MCP; CLI/API adapters are in development.

Today / Current capabilities

  • Persistent local memory (SQLite on your disk — no accounts, no servers)
  • Knowledge graph of entities, relationships, and core principles
  • Hybrid retrieval (full-text + vector + graph traversal)
  • Temporal / provenance-aware context
  • Decision context with principles, precedents, and conflicts
  • Saved decisions and decision lineage
  • Outcome recording (calibration, lessons, follow-ups)
  • MCP integration (current integration surface)
  • Desktop capture / local desktop application

Active development / Roadmap

Under active development on dev/goal24-cli-skills:

  • Transport-agnostic capabilities
  • Skills
  • CLI adapters
  • Evidence-gated execution
  • Approval boundaries
  • Verified outcomes

None of the roadmap items are available yet.

What makes it different

  • Not a note app — it's a context and decision layer. Your tools don't need their own memory systems; they all share the same brain.
  • Not cloud — SQLite on your disk. No accounts, no servers, no data ever leaves your machine.
  • Not locked to one AI — MCP-based today; Claude Desktop, Cursor, Cline, and other MCP clients share the 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

Current MCP interface exposes 26 tools, grouped by what they do. Canonical count is generated in mcp_tool_manifest.json.

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
  • record_decision_outcome — record an observed result for a saved decision (calibration, lessons, follow-ups)

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.