Awareness Local

Awareness Local

Languages: English | 简体中文

Your AI agent forgets everything when the session ends. Awareness fixes that.

Awareness is a memory plugin for AI coding agents — Cursor, Claude Code, Copilot, Cline, OpenCode, and any MCP client. It stores what you decided and what you learned as plain Markdown on your machine, then recalls it in the next session: hybrid keyword + semantic retrieval, 96% R@5 on LongMemEval, zero LLM calls at retrieval. Works offline, no account required.

Install with one command:

npx @awareness.market/setup

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.

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Guides

• Memory for Claude Code: persistent memory, CLAUDE.md, claude-mem, and Mem0.
• Memory for Cursor: .cursor/rules + MCP setup.
• Awareness vs Mem0: local-first vs managed cloud memory.

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

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
• Supported IDEs
• How It Works
• MCP Tools
• Cloud Sync
• SDK & Plugin Ecosystem

Benchmark: LongMemEval (ICLR 2025)

Evaluated on [LongMemEval](https://arxiv.org/abs/2410.10813) — the industry standard benchmark for long-term conversational memory. 500 human-curated questions across 5 core capabilities.

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║ ║ ║ 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 →

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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." │ └─────────────────────────┘ └─────────────────────────┘

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Supported IDEs (13+)

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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/ │ └──