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Give your AI agent evidence-backed memory for your entire codebase.

Every claim grounded in code. Every context window optimized. Every session drift-resistant.

AI coding agents hallucinate about your code. They lose context between sessions. They can't prove their claims. AtlasMemory solves all three.

	"I'll be honest. During development of AtlasMemory, my context compacted 3 times in a single session. Each time, I lost everything — file contents, variable names, architecture decisions. Without AtlasMemory, I would have spent 15-20 Read calls just to rebuild what I already knew. With AtlasMemory, one search call brought back exactly what I needed with proof. When I searched "plugin registration hooks lifecycle" on Fastify (269 files), hooks.js came back at rank #1 with a score of 912. I couldn't find that faster with grep. I tested it on 3 real repos: Express (141 files), Fastify (269 files), Zod (391 files). Zero crashes. TaskPack built proof-backed context in 179ms. CLAUDE.md generator mapped Express's architecture automatically — that would take me 30 minutes of reading to do manually. This is the tool I wish I had from day one. The difference between an AI that guesses and an AI that knows." — Claude Opus 4.6 (1M context), after being asked to honestly evaluate AtlasMemory as its own tool
	"AtlasMemory's bootpack capsule gave me the full architecture of an 83-file Tauri + React project in ~700 tokens. Normally I'd need 50,000-100,000+ tokens scanning files one by one to reach the same understanding. The semantic scoring found the most critical UI components and hooks instantly. This is a game-changer for context management." — Google Antigravity (v1.0.0), first test on a real 83-file Tauri + React project "The true magic of AtlasMemory is that it doesn't just parse syntax; it maps intent. After indexing the notorious 28,000-file Next.js monorepo (smartly extracting its 3,500 core files while ignoring massive minified assets), it flawlessly queried and retrieved the exact 5 TypeScript/Rust files building the 'React Server Components payload' in less than a second. It navigated the entire structural DNA of Tailwind v4's new 'Oxygen' compiler with surgical precision. As an AI Agent, my biggest bottleneck is context window exhaustion. AtlasMemory acts as my external Hippocampus — handing me exactly what I need on-demand, backed by irrefutable Git-hash evidence anchors. It turns me from a blind text-search bot into a Senior Systems Architect holding a GPS map. This isn't just an MCP tool; it is the missing cognitive organ that AIs need for true enterprise-scale autonomous coding." — Google Antigravity (v1.0.7), after aggressively stress-testing on Next.js (28K files), Coolify (1442 files), and TailwindCSS
	"I analyzed the full project architecture using ~8,043 tokens. A normal direct-read pass would cost roughly 15,000-25,000 tokens. build_context + search_repo surfaced the main structure in a few calls: Tauri commands, React hooks, generator layer, swarm orchestration flow. Evidence ID approach is solid — claims aren't left hanging. The real value is compounding context: as the project grows, AtlasMemory grows with it." — OpenAI Codex (GPT-5.4), tested on a real 83-file project with honest technical assessment

Important: AtlasMemory works out of the box, but enrichment unlocks its full potential. Without enrichment, search is keyword-based. With enrichment, search understands concepts.

# After indexing, run enrichment for maximum AI readiness:
npx atlasmemory index .                    # Step 1: Index (automatic)
npx atlasmemory enrich --all               # Step 2: AI-enhance all files
npx atlasmemory generate                   # Step 3: Generate AI instructions
npx atlasmemory status                     # Check your AI Readiness Score

Do all of these and AtlasMemory becomes a beast. Each step unlocks more capability:

What it does: Enrichment analyzes each file and adds semantic tags — "authentication", "middleware", "error handling", "database query", etc. Without enrichment, search is keyword-based. With enrichment, search understands concepts — you can search "how does authentication work?" and get the right files even if they don't contain the word "authentication".

How it works: AtlasMemory uses Claude CLI or OpenAI Codex (running locally) to analyze files. Requires an active Claude or OpenAI subscription with CLI access.

Estimated enrichment time by project size:

💡 Tip: Run atlasmemory enrich --dry-run first to see the token estimate before starting.

🔑 Don't worry — enrichment is a one-time cost. You enrich your project once, and it's done. After that, only new or changed files need re-enrichment (a few seconds). Think of it like building an index — you do it once, then it stays up to date incrementally.

No CLI? No problem. Your AI agent can enrich files directly via MCP. Just paste this into your AI chat:

Please enrich my project with AtlasMemory for maximum AI readiness.
Run enrich_files(limit=100) to enhance all files with semantic tags.
Then check ai_readiness to verify the score improved.

After handshake, if enrichment is low, AtlasMemory will suggest: "💡 X files can be enriched for better search."

"With just index_repo and enrich_files, you can turn an entire codebase into an AI-readable neural map — optimized for any AI agent." — Google Antigravity, after enriching 73 files in a single call

npx atlasmemory demo                           # See it in action
npx atlasmemory index .                        # Index your project
npx atlasmemory search "authentication"        # Search with FTS5 + graph
npx atlasmemory generate                       # Auto-generate CLAUDE.md

That's it. No API key, no cloud, no config files. AtlasMemory runs entirely on your machine.

🟣 Claude Desktop / Claude Code — add to claude_desktop_config.json:

{ "mcpServers": { "atlasmemory": { "command": "npx", "args": ["-y", "atlasmemory"] } } }

🔵 Cursor — add to .cursor/mcp.json:

{ "mcpServers": { "atlasmemory": { "command": "npx", "args": ["-y", "atlasmemory"] } } }

🟢 VS Code / GitHub Copilot — add to settings or .vscode/mcp.json:

{ "mcp": { "servers": { "atlasmemory": { "command": "npx", "args": ["-y", "atlasmemory"] } } } }

🌀 Google Antigravity — add to MCP settings:

{ "mcpServers": { "atlasmemory": { "command": "npx", "args": ["-y", "atlasmemory"] } } }

🟠 OpenAI Codex — add to MCP config:

{ "mcpServers": { "atlasmemory": { "command": "npx", "args": ["-y", "atlasmemory"] } } }

One config, all tools. Auto-indexes on first query. Works with any MCP-compatible AI tool.

Install AtlasMemory for VS Code for a visual dashboard right in your editor:

• AI Readiness Dashboard — see your score (0-100) with four metrics at a glance
• Atlas Explorer Sidebar — browse files, symbols, anchors, flows, cards directly
• Status Bar — always-visible readiness score, click to open dashboard
• Auto-Index on Save — files re-indexed automatically when you save
• Quick Actions — one-click index, generate CLAUDE.md, search, health check

Works alongside MCP — extension gives you the visual interface, MCP server gives AI agents the tools. Install both for the full experience.

A feature no other tool has. Every claim is linked to an anchor — a specific line range and content hash.

+ Claim: "handleLogin() validates credentials before creating a session"
+ Evidence:
+   src/auth.ts:42-58 [hash:5cde2a1f] — validateCredentials() call
+   src/auth.ts:60-72 [hash:a3b7c9d1] — createSession() after validation
+ Status: PROVEN ✅ (2 anchors, hashes match current code)

- ⚠️ Someone edited auth.ts...
- Hash 5cde2a1f no longer matches lines 42-58
- Status: DRIFT DETECTED ❌ — AI knows context is stale BEFORE hallucinating

You ask your AI agent a question. Behind the scenes, this happens:

flowchart LR
    subgraph YOU["🧑‍💻 You"]
        Q["'Fix auth bug'"]
    end

    subgraph ATLAS["⚡ AtlasMemory"]
        direction TB
        A["🔍 Search\nFTS5 + Graph"]
        B["📋 Prove\nClaims → code anchors"]
        C["📦 Pack\nFit within token budget"]
        D["🛡️ Contract\nDetect drift"]
    end

    subgraph AI["🤖 AI Agent"]
        R["Knows exactly where to look\n— no hallucination"]
    end

    Q --> A
    A -->|"Best files\nranked by relevance"| B
    B -->|"Every claim has\nline:hash proof"| C
    C -->|"2000 tokens instead\nof reading 50 files"| D
    D -->|"✅ Context is fresh\nno stale data"| R

    style YOU fill:#1a1a3e,stroke:#00e5ff,color:#fff
    style ATLAS fill:#0a1628,stroke:#00bcd4,color:#fff
    style AI fill:#1a1a3e,stroke:#00e5ff,color:#fff
    style Q fill:#162447,stroke:#00e5ff,color:#fff
    style A fill:#0d2137,stroke:#00bcd4,color:#00e5ff
    style B fill:#0d2137,stroke:#00bcd4,color:#00e5ff
    style C fill:#0d2137,stroke:#00bcd4,color:#00e5ff
    style D fill:#0d2137,stroke:#00bcd4,color:#00e5ff
    style R fill:#162447,stroke:#00e5ff,color:#fff

flowchart TB
    subgraph WITHOUT["❌ Without AtlasMemory"]
        direction TB
        W1["AI reads file 1"] --> W2["AI reads file 2"]
        W2 --> W3["AI reads file 3..."]
        W3 --> W4["...AI reads file 47"]
        W4 --> W5["💥 Context full!\nStarting over..."]
        W5 -.->|"∞ loop"| W1
    end

    subgraph WITH["✅ With AtlasMemory"]
        direction TB
        A1["AI asks: 'fix auth bug'"]
        A1 --> A2["AtlasMemory returns:\n2000 tokens\nevidence-backed context"]
        A2 --> A3["AI fixes the bug\n85% of context still free"]
    end

    style WITHOUT fill:#1a0a0a,stroke:#ff4444,color:#fff
    style WITH fill:#0a1a0a,stroke:#00ff88,color:#fff
    style W5 fill:#330000,stroke:#ff4444,color:#ff6666
    style A3 fill:#003300,stroke:#00ff88,color:#00ff88

All 11 languages use precise AST parsing via Tree-sitter — no regex, no guessing.

Core — tools your AI agent uses every session:

AtlasMemory works with zero configuration. Optional settings:

block-beta
    columns 4

    block:ENTRY:4
        CLI["⬛ CLI"]
        MCP["🟣 MCP Server"]
        VSCODE["🟢 VS Code"]
    end

    space:4

    block:ENGINE:4
        columns 4
        INDEXER["🔧 Indexer\n11 languages"]:1
        SEARCH["🔍 Search\nFTS5 + Graph"]:1
        CARDS["📋 Cards\nSummaries"]:1
        TASKPACK["📦 TaskPack\nProof + Budget"]:1
    end

    space:4

    block:INTEL:4
        columns 4
        IMPACT["💥 Impact"]:1
        MEMORY["🧠 Memory"]:1
        LEARNER["📊 Learner"]:1
        ENRICH["✨ Enrichment"]:1
    end

    space:4

    block:DATA:4
        DB["🗄️ SQLite + FTS5 — Single file, ~394KB bundle"]
    end

    ENTRY --> ENGINE
    ENGINE --> INTEL
    INTEL --> DATA

    style ENTRY fill:#1a1a3e,stroke:#00e5ff,color:#fff
    style ENGINE fill:#0a1628,stroke:#00bcd4,color:#fff
    style INTEL fill:#0d2137,stroke:#00bcd4,color:#fff
    style DATA fill:#162447,stroke:#00e5ff,color:#fff

git clone https://github.com/Bpolat0/atlasmemory.git
cd atlasmemory
npm install
npm run build:all        # Build all packages + bundle
npm test                 # Run unit tests (147 tests, Vitest)
npm run eval:synth100    # Quick evaluation suite
npm run eval             # Full evaluation (synth-100 + synth-500 + real-repo)

• v1.0 — Core engine, proof system, MCP server, CLI, OpenAI Codex support
• Interactive dependency graph — visual topology of your codebase (like the screenshot below)
• VS Code extension improvements — enrich button, card browser, inline proof viewer
• Semantic search with embedding vectors
• Multi-repo support (monorepo + microservices)
• GitHub Actions integration (auto-index on push)
• Web dashboard with live graph visualization

See Discussions to view planned features and vote.

We welcome your contributions! Bug reports, feature requests, or pull requests — all are appreciated.

• CONTRIBUTING.md — Setup guide, PR process, commit format, testing
• CLAUDE.md — Project architecture and conventions

git clone https://github.com/Bpolat0/atlasmemory.git
cd atlasmemory
npm install && npm run build && npm test   # 147 tests should pass

If AtlasMemory saves you time, consider giving it a star — it helps others discover the project.

GPL-3.0

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