Provides persistent memory for GitHub Copilot through MCP, allowing the agent to remember prior sessions and continue work without re-explaining context.
Provides persistent memory for JetBrains IDEs (Junie) via MCP, enabling cross-session recall of architecture, decisions, and pending tasks.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Awareness Local What did we decide about the database schema last week?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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/setupThat'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
Related MCP server: Tages
npx @awareness.market/setupThen 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. ChatGPT Desktop: Settings → MCP servers → Add server → Streamable HTTP → http://127.0.0.1:37800/mcp (local daemon only, no auth needed).
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 →
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 | ✅ | |
Cursor | ✅ | via MCP |
Windsurf | ✅ | via MCP |
OpenClaw | ✅ | |
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) |
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 |
| Load session context — recent knowledge, tasks, rules |
| Search memories — progressive disclosure (summary → full) |
| Save decisions, code changes, insights — with knowledge extraction |
| Fast lookup — tasks, knowledge cards, session history, risks |
| 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 tokensVisit http://localhost:37800/ to browse memories, knowledge cards, tasks, and manage cloud sync.
Connect to Awareness Cloud 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 dashboardAwareness Local is part of the Awareness ecosystem:
Package | For | Install |
Local daemon + MCP server |
| |
|
| |
|
| |
Auto-recall + auto-capture |
| |
Skills + hooks |
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One-command setup for 13+ IDEs |
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Full SDK docs: 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, 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.
This server cannot be deployed
Long-term memory for AI coding agents: durable project facts, recalled by every MCP client.
Shared memory for coding agents. Stop re-explaining your codebase every session.
Persistent AI memory shared across Claude, ChatGPT, coding agents, and compatible MCP clients.
Give your AI agent persistent, governed memory for every project. At task start it recalls the approved decisions, conventions, risks and architecture (semantic search, ranked by importance); at close it proposes what was learned as typed memories that you review and approve — governance, not a notes dump. Agents propose, humans govern: edits go back to pending and deletion is human-only by design. Connect Claude Code, Cursor, Claude Desktop or any MCP client in two minutes with just your API key — hosted (nothing to install) or locally via `uvx solucortex-mcp`. Built by SoluAI and dogfooded daily: SoluCortex is developed using its own living memory.
