# Awareness Local: local-first memory for AI coding agents (96% R 5) > Source: > Published: 2026-08-28 01:02:35+00:00 **Languages:** English | [简体中文](/everest-an/Awareness-Market/blob/main/README.zh-CN.md) **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. ``` npx @awareness.market/setup ``` That's it. Your AI agent now remembers everything across sessions. 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 ``` npx @awareness.market/setup ``` Then open your IDE and start coding. Awareness tools become available for recall, record, and session initialization. - 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 Yes. Local mode works fully offline with memory stored on your machine. Memory is stored as Markdown in `.awareness/` , with a local SQLite index for retrieval. No. Cloud sync is optional and can be enabled later. Any MCP-compatible IDE, including Cursor, Claude Code, Copilot, Cline, Windsurf, and others. 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 →](https://github.com/everest-an/Awareness-Market/tree/main/benchmarks/longmemeval) **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." │ └─────────────────────────┘ └─────────────────────────┘ ``` | IDE | Auto-detected | Plugin | |---|---|---| Claude Code | ✅ | `awareness-memory` | **Cursor****Windsurf****OpenClaw**`@awareness.market/openclaw-memory` **Cline****GitHub Copilot****Codex CLI****Kiro****Trae****Zed****JetBrains (Junie)****Augment****AntiGravity (Jules)** ``` 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 │ └────────────────────────────────────┘ ``` 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) ``` | Tool | What it does | |---|---| `awareness_init` | Load session context — recent knowledge, tasks, rules | `awareness_recall` | Search memories — progressive disclosure (summary → full) | `awareness_record` | Save decisions, code changes, insights — with knowledge extraction | `awareness_lookup` | Fast lookup — tasks, knowledge cards, session history, risks | `awareness_get_agent_prompt` | Get agent-specific prompts for multi-agent setups | 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 ``` Visit `http://localhost:37800/` to browse memories, knowledge cards, tasks, and manage cloud sync. Connect to [Awareness Cloud](https://awareness.market) for: - Semantic vector search (100+ languages) - Cross-device real-time sync - Team collaboration - Memory marketplace ``` npx @awareness.market/setup --cloud # Or click "Connect to Cloud" in the dashboard ``` Awareness Local is part of the Awareness ecosystem: | Package | For | Install | |---|---|---| | Local daemon + MCP server | `npx @awareness.market/setup` | | `wrap_openai()` / `wrap_anthropic()` interceptors | `pip install awareness-memory-cloud` | | `wrapOpenAI()` / `wrapAnthropic()` interceptors | `npm i @awareness-sdk/memory-cloud` | | Auto-recall + auto-capture | `openclaw plugins install @awareness.market/openclaw-memory` | | Skills + hooks | `/plugin marketplace add everest-an/Awareness-SDK` → `/plugin install awareness-memory@awareness` | | One-command setup for 13+ IDEs | `npx @awareness.market/setup` | Full SDK docs: [awareness.market/docs](https://awareness.market/docs) - Node.js 18+ - Any MCP-compatible IDE No Python, no Docker, no cloud account needed. 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. MIT **IDE Support:** [Cursor](https://cursor.com), [Windsurf](https://codeium.com/windsurf), [Trae](https://www.trae.sh), [Zed](https://zed.dev), 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.