Awareness Local

io.github.everest-anv0.12.6更新於 Oct 3, 2026

Local-first persistent memory for AI coding agents - 96.0% R@5 on LongMemEval, zero LLM calls.

已驗證STDIO僅桌面Developer ToolsKnowledge & Memory

概覽

AI 產生的概覽

為 AI 程式開發助理提供本機持久記憶，將決策、任務與工作階段脈絡以 Markdown 保存，並支援關鍵字與語意混合檢索。

功能
在使用者電腦上執行輕量常駐程式，透過 MCP 工具提供工作階段脈絡初始化、記憶檢索、決策與程式變更記錄、任務與知識卡片查詢，以及取得針對不同助理的提示詞。記憶以 Markdown 檔案存放在 .awareness 目錄，並以本機 SQLite 索引檢索；檢索採漸進式揭露，先回傳簡短摘要，再依需求取得所選項目的完整內容。本機網頁儀表板可瀏覽記憶、知識卡片與任務。
適用情境
適合橫跨多個工作階段的長期專案，例如長時間的移轉作業、團隊交接、個人開發慣例，以及需要共享決策歷史的多助理協作情境。鎖定希望記憶留在本機並可離線使用的開發者。
執行需求
需要 Node.js 18 以上版本，以及支援 MCP 的 IDE。透過 npm 套件執行安裝，常駐程式會監聽本機連接埠。本機模式不需要雲端帳號、Python 或 Docker。
安裝前請注意
伺服器會在專案的 .awareness 目錄寫入記憶檔案與搜尋索引，記錄內容可能被提交到版本控制。可選的雲端同步可在之後透過安裝指令或儀表板啟用，會把記憶傳送到本機之外並涉及裝置驗證流程；啟用前請確認同步的內容。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Awareness Local，將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only，透過 STDIO。 STDIO 服務會啟動本機處理程序，因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

Awareness Local

Languages: English | 简体中文

[Awareness Local — Persistent Memory for AI Coding Agents]

[LongMemEval R@5 96.0%] [Website] [Docs] [Discord] [License MIT]

[Awareness Local]

Give your AI agent persistent memory. One command. No account. Works offline.

Awareness Local is a local-first MCP memory server for AI coding agents. It gives Cursor, Claude Code, Copilot, Cline, and other MCP IDEs persistent memory, hybrid semantic + keyword retrieval, and reusable knowledge cards for long-running software projects.

It runs a lightweight daemon on your machine, stores memory as Markdown, indexes recall with SQLite FTS5 + embeddings, and keeps your AI workflow fast, explainable, and offline-ready.

bash
npx @awareness.market/setup

That's it. Your AI agent now remembers everything across sessions.

Why Awareness Local

AI coding agents lose context between sessions. Awareness Local provides cross-session memory recall so agents can continue work without re-explaining architecture, past decisions, pending tasks, and implementation constraints.

  • Persistent memory for AI coding agents
  • Local-first MCP server with offline support
  • Hybrid retrieval (keyword + semantic)
  • Knowledge card extraction for decisions, solutions, and risks

Quick Start

bash
npx @awareness.market/setup

Then open your IDE and start coding. Awareness tools become available for recall, record, and session initialization.

Popular Use Cases

  • Long-running codebase migrations across many sessions
  • Team handoffs where AI should remember prior implementation context
  • Personal coding workflows that need durable preferences and conventions
  • Multi-agent setups that share decision history and task memory

FAQ

Does Awareness Local work offline?

Yes. Local mode works fully offline with memory stored on your machine.

Where is data stored?

Memory is stored as Markdown in .awareness/, with a local SQLite index for retrieval.

Do I need a cloud account?

No. Cloud sync is optional and can be enabled later.

Which IDEs are supported?

Any MCP-compatible IDE, including Cursor, Claude Code, Copilot, Cline, Windsurf, and others. ChatGPT Desktop: Settings → MCP servers → Add server → Streamable HTTP → http://127.0.0.1:37800/mcp (local daemon only, no auth needed).

Navigation

Benchmark: LongMemEval (ICLR 2025)

Evaluated on LongMemEval — the industry standard benchmark for long-term conversational memory. 500 human-curated questions across 5 core capabilities.

╔══════════════════════════════════════════════════════════════╗║                                                              ║║   Awareness Memory — LongMemEval Benchmark Results           ║║   ─────────────────────────────────────────────────           ║║                                                              ║║   Benchmark:  LongMemEval (ICLR 2025)                       ║║   Dataset:    500 human-curated questions                    ║║   Variant:    LongMemEval_S (~115k tokens per question)      ║║                                                              ║║   ┌─────────────────────────────────────────────────┐        ║║   │                                                 │        ║║   │   Recall@1    80.2%    (401 / 500)              │        ║║   │   Recall@3    92.8%    (464 / 500)              │        ║║   │   Recall@5    96.0%    (480 / 500)  ◀ PRIMARY   │        ║║   │   Recall@10   98.6%    (493 / 500)              │        ║║   │                                                 │        ║║   └─────────────────────────────────────────────────┘        ║║                                                              ║║   Method:     Hybrid RRF (BM25 + vector, daemon pipeline)    ║║   Embedding:  multilingual-e5-small (production model)       ║║   LLM Calls:  0  (pure retrieval, no generation cost)        ║║   Hardware:   Apple M1, 8GB RAM — 35 min total               ║║                                                              ║╚══════════════════════════════════════════════════════════════╝
┌─────────────────────────────────────────────────────────────┐│          Long-Term Memory Retrieval — R@5 Leaderboard       ││          LongMemEval (ICLR 2025, 500 questions)             │├─────────────────────────────────┬───────────┬───────────────┤│  System                         │  R@5      │  Note         │├─────────────────────────────────┼───────────┼───────────────┤│  MemPalace (ChromaDB raw)       │  96.6%    │  R@5 only *   ││  ★ Awareness Memory (Hybrid)    │  96.0%    │  Hybrid RRF   ││  OMEGA                          │  95.4%    │  QA Accuracy  ││  Mastra (GPT-5-mini)            │  94.9%    │  QA Accuracy  ││  Mastra (GPT-4o)                │  84.2%    │  QA Accuracy  ││  Supermemory                    │  81.6%    │  QA Accuracy  ││  Zep / Graphiti                 │  71.2%    │  QA Accuracy  ││  GPT-4o (full context)          │  60.6%    │  QA Accuracy  │├─────────────────────────────────┴───────────┴───────────────┤│  * MemPalace 96.6% is Recall@5 only, not QA Accuracy.      ││    Palace hierarchy was NOT used in the evaluation.         │└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐│     Awareness Memory — R@5 by Question Type                 ││                                                             ││  knowledge-update        ███████████████████████████ 98.7%  ││  multi-session           ███████████████████████████▊  99.2%││  single-session-asst     ███████████████████████████▌  98.2%││  temporal-reasoning      ██████████████████████████▏   93.2%││  single-session-user     ██████████████████████████    92.9%││  single-session-pref     █████████████████████████▎    90.0%││                                                             ││  Overall                 ██████████████████████████▉   96.0%││                                                             ││  ┌───────────────────────────────────────────────┐          ││  │  Ablation Study                               │          ││  │  ─────────────────────────────────────────    │          ││  │  Vector-only:   92.6%  ▓▓▓▓▓▓▓▓▓▓▓▓▓░░░     │          ││  │  BM25-only:     91.4%  ▓▓▓▓▓▓▓▓▓▓▓▓▓░░░     │          ││  │  Hybrid RRF:    95.6%  ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░  ★  │          ││  │  (2026-04 harness run)                        │          ││  │  Hybrid = +3% over any single method          │          ││  └───────────────────────────────────────────────┘          ││                                                             ││  arxiv.org/abs/2410.10813          awareness.market         │└─────────────────────────────────────────────────────────────┘

Zero LLM calls on retrieval (daemon path). Reproducible benchmark scripts →

What It Does

Before: Every session starts from scratch. You re-explain the codebase, re-justify decisions, watch the agent redo work.

After: Your agent says "I remember you were migrating from MySQL to PostgreSQL. Last session you completed the schema changes and had 2 TODOs remaining..."

Session 1                          Session 2┌─────────────────────────┐       ┌─────────────────────────┐│ Agent: "What database?" │       │ Agent: "I remember we   ││ You: "PostgreSQL..."    │       │ chose PostgreSQL for     ││ Agent: "What framework?"│  →    │ JSON support. You had    ││ You: "FastAPI..."       │       │ 2 TODOs left. Let me     ││ (repeat every session)  │       │ continue from there."    │└─────────────────────────┘       └─────────────────────────┘

Supported IDEs (13+)

IDEAuto-detectedPlugin
Claude Code✅awareness-memory
Cursor✅via MCP
Windsurf✅via MCP
OpenClaw✅@awareness.market/openclaw-memory
Cline✅via MCP
GitHub Copilot✅via MCP
Codex CLI✅via MCP
Kiro✅via MCP
Trae✅via MCP
Zed✅via MCP
JetBrains (Junie)✅via MCP
Augment✅via MCP
AntiGravity (Jules)✅via MCP
ChatGPT Desktop✅via MCP (Streamable HTTP, see FAQ)

How It Works

Your IDE / AI Agent    │    │  MCP Protocol (localhost:37800)    ▼┌────────────────────────────────────┐│  Awareness Local Daemon            ││                                    ││  Markdown files    → Human-readable, git-friendly│  SQLite FTS5       → Fast keyword search│  Local embedding   → Semantic search (optional: npm i @huggingface/transformers)│  Knowledge cards   → Auto-extracted decisions, solutions, risks│  Web Dashboard     → http://localhost:37800/│                                    ││  Cloud sync (optional)             ││  → One-click device-auth           ││  → Bidirectional sync              ││  → Semantic vector search          ││  → Team collaboration              │└────────────────────────────────────┘

Your Data

All memories stored as Markdown files in .awareness/ — human-readable, editable, git-friendly:

.awareness/├── memories/│   ├── 2026-03-22_decided-to-use-postgresql.md│   ├── 2026-03-22_fixed-auth-bug.md│   └── ...├── knowledge/│   ├── decisions/postgresql-over-mysql.md│   └── solutions/auth-token-refresh.md├── tasks/│   └── open/implement-rate-limiting.md└── index.db  (search index, auto-rebuilt)

Features

MCP Tools (available in your IDE)

ToolWhat it does
awareness_initLoad session context — recent knowledge, tasks, rules
awareness_recallSearch memories — progressive disclosure (summary → full)
awareness_recordSave decisions, code changes, insights — with knowledge extraction
awareness_lookupFast lookup — tasks, knowledge cards, session history, risks
awareness_get_agent_promptGet agent-specific prompts for multi-agent setups

Progressive Disclosure (Smart Token Usage)

Instead of dumping everything into context, Awareness uses a two-phase recall:

Phase 1: awareness_recall(query, detail="summary")  → Lightweight index (~80 tokens each): title + summary + score  → Agent reviews and picks what's relevant
Phase 2: awareness_recall(detail="full", ids=[...])  → Complete content for selected items only  → No truncation, no wasted tokens

Web Dashboard

Visit http://localhost:37800/ to browse memories, knowledge cards, tasks, and manage cloud sync.

Cloud Sync (Optional)

Connect to Awareness Cloud for:

  • Semantic vector search (100+ languages)
  • Cross-device real-time sync
  • Team collaboration
  • Memory marketplace
bash
npx @awareness.market/setup --cloud# Or click "Connect to Cloud" in the dashboard

SDK & Plugin Ecosystem

Awareness Local is part of the Awareness ecosystem:

PackageForInstall
Awareness LocalLocal daemon + MCP servernpx @awareness.market/setup
Python SDKwrap_openai() / wrap_anthropic() interceptorspip install awareness-memory-cloud
TypeScript SDKwrapOpenAI() / wrapAnthropic() interceptorsnpm i @awareness-sdk/memory-cloud
OpenClaw PluginAuto-recall + auto-captureopenclaw plugins install @awareness.market/openclaw-memory
Claude Code PluginSkills + hooks/plugin marketplace add everest-an/Awareness-SDK → /plugin install awareness-memory@awareness
Setup CLIOne-command setup for 13+ IDEsnpx @awareness.market/setup

Full SDK docs: awareness.market/docs

Requirements

  • Node.js 18+
  • Any MCP-compatible IDE

No Python, no Docker, no cloud account needed.

⭐ Support the project

If Awareness Local saves you from re-explaining your codebase to your AI agent, give it a ⭐ — it helps more developers discover the project and pushes it toward GitHub Trending.

[Star History Chart]

License

MIT

Tags & Integration

IDE Support: Cursor, Windsurf, Trae, Zed, VS Code, JetBrains. Compatible with: OpenClaw, AutoGPT, LangChain, MetaGPT. Key Technology: OMP (Open Memory Protocol), LatentMAS, Shared Thought Space, One-click Deployment. Focus: Solving AI "Lobster Memory" (Long-term memory loss), Automating complex workflows, Simplifying Agent setup.

來源：README.md，提交 44b40ae

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版本歷史

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  1. v0.12.6最新Oct 3, 2026