mcpField
O
Omni Context

Omni Context

Local
github.com/guo6xMemory & Knowledge6Updated 3d ago

About Omni Context

Omni Context is a local-first memory and decision-control system for long-lived AI agents. It centers on qualifying evidence, binding actions to decisions, and reading outcomes back, with a knowledge graph, hybrid retrieval, and desktop review.

Key features

  1. 01Persistent local memory and knowledge graph
  2. 02Full-text, vector, and graph hybrid retrieval
  3. 03Decision records, lineage, and outcome tracking
  4. 04Evidence qualification and decision context
  5. 05Desktop capture, review, and audit

Use cases

  1. 01Give long-lived agents searchable local memory
  2. 02Check evidence and related decisions before action
  3. 03Record outcomes and compare them with expectations

MCP support

MCP is one of its current interfaces, exposing 26 tools for MCP clients to access memory, retrieval, and decision capabilities.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Omni-Context

English · 简体中文

Evidence-grounded decision control for long-lived AI agents. Local-first, read-back verified, and owned by you.

Omni-Context

Build License: MIT

Download for Windows · Current state · Landing page

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.


What is Omni-Context

Omni-Context is evidence-grounded decision control for long-lived AI agents.

Qualify the evidence before an agent acts, bind execution to the decision that justified it, then read the world back and reopen the decision when reality disagrees.

The judgment loop:

Qualify → Bind → Read-back → Reopen

Agents can already act — write code, open issues, run commands. But memory is not evidence (what an agent remembers is a claim, not a verified fact), and tool success is not outcome truth (exit code 0 says nothing about whether the world actually changed the way you intended). Omni-Context closes that gap: it qualifies evidence before action, binds execution to the exact decision that justified it, reads the world back afterwards, and reopens the decision when reality disagrees.

  • Memory and the knowledge graph are the long-term evidence substrate — they answer "what the agent knows". They are a core part of the product, re-placed at the base of the judgment loop, not deleted.
  • MCP is one interface surface, not the product itself.
  • Desktop is the human control surface: inspect, approve, audit, and reopen decisions.

Read the full thesis: docs/goal24/narrative/thesis-note.en.md · product vision: docs/PRODUCT-VISION.md


Current state

Capability status uses exactly three labels: CURRENTLY_VERIFIED (user-facing today), TARGET (target architecture), FUTURE (planned). "Runtime verified on the development branch" is not the same as "available today". Governance language is frozen in docs/PRODUCT-VISION.md (§ 14).

A. Current user-facing — usable today
  • Persistent local memory — SQLite on your disk by default; no account is required
  • 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, decision lineage, and outcome recording
  • MCP integration — 26 tools, counted from mcp_tool_manifest.json
  • Desktop capture / local desktop application (Windows installer via GitHub Releases)
B. Development-branch runtime verified (CP3–CP8 internal gates)

Under active development on dev/goal24-cli-skills. Each item below has engineering gate evidence, but no public invocation surface yet — it is runtime verified on the development branch, not "available today":

ComponentGate evidence
Restricted execution broker (spawn/kill/timeout, containment, output caps)checkpoint3-security-gate.json — PASS
GitHub read-only CLI adapter (5 semantic capabilities, pinned executable, zero write bindings)checkpoint4-security-gate.json — PASS
Skills registry + importer (quarantine-by-default, integrity-verified)checkpoint5-security-gate.json — PASS
Evidence qualification + surface guard (server-owned eligibility, forged-coverage closure)checkpoint6-security-gate.json — PASS
Approval binding + risk policy (single-use grants, replay defense)checkpoint7-security-gate.json — PASS
Outcome read-back + deterministic evaluator (trusted resolvers, cross-language state/observation vectors)checkpoint8-security-gate.json — PASS (DRG1 prerequisite satisfied)
Real non-synthetic E2E: one approval-gated GitHub issue-close closed loop against real GitHub with independent read-back (exit 0 => PENDING => read-back CLOSED => VERIFIED)drg2-authoritative-gate.json — PASS (DRG2 satisfied)

CP8 full-suite evidence: Brain 1279 passed / 0 failed; Rust 206 passed / 0 failed / 7 ignored; cross-language vectors 26 (state) + 35 (observation), 0 mismatches.

Post-CP8 real E2E (development branch verified): one real, non-synthetic, approval-gated GitHub issue-close closed-loop E2E has been demonstrated against real GitHub with independent read-back. Exit 0 was NOT treated as success — the outcome stayed PENDING until the trusted github.issue.read read-back observed CLOSED and the deterministic evaluator returned VERIFIED. This is internal runtime evidence: there is no public CLI feature for GitHub automation today. See docs/goal24/real-e2e/authoritative-real-e2e-proof.json.

C. Internal control / Future — not published
  • omctx CLI (ask / inspect / approve / verify / history) — CURRENTLY_VERIFIED_INTERNAL. approve and verify require separate short-lived Desktop control sessions; they never start execution, retry writes or rollback. The package remains private and is not an npm user install. See docs/goal24/narrative/cli-product-surface.md.
  • omctx reopen user UX — FUTURE (runtime not implemented).
  • External memory adapters (e.g. MindMemOS, basic-memory) — FUTURE, via EvidenceProvider Adapter → qualification → Evidence Guard. External memory never becomes an evidence authority on its own.
  • Multi-runtime adapters (e.g. OpenClaw, NemoClaw, Claude Code) — FUTURE, as capability transport only; runtimes never receive decision, approval, or outcome authority.

DRG v2: a real, non-synthetic, user-understandable E2E flow now exists and is verified (see the Post-CP8 table row above and docs/goal24/real-e2e/authoritative-real-e2e-proof.json). Public capability claims remain frozen to what repo + gate evidence supports; anything else is explicitly labeled TARGET / FUTURE / DESIGNED TO. Omni is designed to sit between heterogeneous memory/evidence sources and heterogeneous agent runtimes — it does not claim today that it works with any memory OS or any runtime.


How it works

Evidence acquisition (capture / browser extension / desktop capture / imports)
       ↓
Evidence Substrate (local knowledge graph + memory + retrieval)
       ↓
Judgment / Authority Core (qualify → decide → approve)
       ↓
Controlled Execution (restricted broker → capability adapters)
       ↓
Read-back → Outcome → Reopen / Revision
  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. Qualify & decide — evidence qualification gates whether remembered information is trustworthy enough to act on right now.
  4. Execute & verify — approved semantic capabilities run through a restricted broker, then the world is read back and compared to the expectation that justified the decision.

What makes it different

  • Not a note app — it is a decision-control layer. Tools don't need their own memory systems; they share one evidence substrate and one authority core.
  • Local-first, with an explicit network boundary — the memory database stays on your device by default. If you configure a remote/cloud model, only the requests needed for that operation (which can include relevant excerpts) are sent to that provider and may incur its charges. Omni does not automatically upload your whole database.
  • Not locked to one AI — MCP-based today; MCP clients share the same memory. MCP is an interface surface, not the product.
  • 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.

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

The Windows Alpha format is NSIS (Omni-Context_<version>_x64-setup.exe). No installer is currently approved for external trial; do not treat Releases/latest as an Alpha asset. Once a separate trial release is authorized, verify its exact SHA-256 and source commit before installation. Unsigned candidates may show a SmartScreen Unknown publisher warning; do not disable Defender or SmartScreen. See the Windows Alpha installation and privacy guide for data, backup and uninstall guidance.

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

There is no omctx npm package to install today — it is a TARGET. Naming and registry status: docs/goal24/narrative/naming-audit.json.


Why not memory alone? Why not observability alone? Why not a generic runtime?

  • Memory alone remembers, but cannot tell which memories are still trustworthy enough to act on. Omni qualifies evidence before action.
  • Observability alone tells you what happened after the fact, but does not bind execution to a decision or refuse a bad action. Omni binds and gates before and during, then verifies.
  • A generic agent runtime executes whatever it is told, fast. Omni's execution surface only carries approved semantic capabilities and never converts free-form intent into arbitrary shell commands.

Community


MIT License. Own the judgment history — especially the decisions reality proved wrong.

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.