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memory for Claude Code - semantic search, cross-machine sync, learned preferences, error tracking","maintainers":[{"name":"drxelanull","email":"drxelanull@orbitalgrooves.com"}],"readme":"# MIRA3 - Memory Information Retriever and Archiver\n\n[![GitHub](https://img.shields.io/badge/GitHub-Interstitch%2FMIRA3-blue?logo=github)](https://github.com/Interstitch/MIRA3)\n\nAn MCP server that gives Claude Code persistent memory across sessions.\n\n## The Problem\n\nEvery Claude Code session starts fresh. Claude doesn't remember:\n- That you fixed this exact error last Tuesday\n- That `config.py` breaks every time someone touches it\n- That you prefer tests before implementation\n- What you were even working on yesterday\n\nYou end up re-explaining context, re-debugging solved problems, and watching Claude confidently break files that have burned you before.\n\n## The Solution\n\n**MIRA gives Claude a memory.** Ask things like:\n\n> *\"What were we working on last week?\"*\n> *\"How did we fix that authentication bug?\"*\n> *\"Show me the code we used for the caching layer\"*\n\nAnd Claude just *knows* - no re-explanation needed.\n\n## What This Actually Looks Like\n\n**Scenario 1: You forgot where you left off**\n```\nYou: What was I working on Friday?\nClaude: [Calls mira_recent(days=3)] You had 2 sessions Friday:\n        1. Implementing sync worker retry logic - got it working\n        2. Debugging the connection pool - noted you wanted exponential backoff\n```\n\n**Scenario 2: You want to reuse a solution from another project**\n```\nYou: How did we implement rate limiting in the API project?\nClaude: [Searches across projects] Found it in api-gateway (Nov 15). You used\n        a token bucket algorithm with Redis. Want me to show the implementation?\n```\n\n**Scenario 3: You're about to touch a cursed file**\n```\nYou: Let's refactor ingestion.py\nClaude: ⚠️ Heads up - ingestion.py has caused 51 recorded issues in past sessions.\n        Want me to search what went wrong before to avoid repeating mistakes?\n```\n\n## Key Features\n\n- **Zero-configuration context** - Claude knows who you are the moment a session starts\n- **Semantic search** - Find \"that auth conversation\" even if you never used that word\n- **Error pattern database** - Indexed errors linked to solutions\n- **Danger zone warnings** - Claude warns before touching files that caused past issues\n- **Workflow enforcement** - MIRA detects your dev pattern (test first? plan first?) and Claude enforces it\n- **Decision journal** - Track architectural decisions with reasoning\n\n## Installation\n\n```bash\nclaude mcp add claude-mira3 -- npx claude-mira3\n```\n\nThat's it. The SessionStart hook auto-configures on install, injecting MIRA context at the start of every Claude Code session.\n\n**First search:** When you first search, MIRA downloads a ~100MB embedding model in the background for local semantic search. This only happens once. (If you [set up remote storage](#remote-storage-optional) first, the server handles embeddings and this download is skipped.)\n\n### Configuration\n\nMIRA configuration is stored in `.mira/config.json`:\n\n```json\n{\n  \"project_path\": \"/workspaces/MIRA3\"\n}\n```\n\n| Option | Description |\n|--------|-------------|\n| `project_path` | Restrict MIRA to only index this project's conversations |\n\n## How It Works\n\n**The magic is in the SessionStart hook.** Before you type anything, MIRA injects your profile:\n\n```\n=== MIRA Session Context ===\n\n## User Profile\nName: Alex\nSummary: Alex is the lead developer (123 sessions). Prefers planning before implementation.\nDevelopment Lifecycle: Plan → Test → Implement → Commit (85% confidence)\nInteraction Tips:\n  - Be careful with: ingestion.py (51 recorded issues)\n\n## When to Consult MIRA\n- [CRITICAL] Encountering an error → call mira_error_lookup first\n- [CRITICAL] About to say \"I don't know\" → search MIRA before admitting ignorance\n- [CRITICAL] User mentions past discussions → search MIRA to recall context\n```\n\n**What this means:**\n- Claude addresses you by name without asking\n- Claude follows your preferred workflow (test first? plan first?)\n- Claude warns before touching files that caused past issues\n- Claude searches your history before saying \"I don't know\"\n\nNo manual prompting. No \"remember that I prefer...\" every session. MIRA learns from your conversations and Claude just *knows*.\n\n## MCP Tools\n\n| Tool | Purpose |\n|------|---------|\n| `mira_init` | Session initialization (called automatically via hook) |\n| `mira_search` | Search conversations by meaning |\n| `mira_recent` | Recent sessions. Use `days: 7` for last week |\n| `mira_error_lookup` | Search past errors and their solutions |\n| `mira_decisions` | Search architectural decisions |\n| `mira_code_history` | Track file/function changes across sessions |\n| `mira_status` | System health and ingestion stats |\n\n### Search Parameters\n\n| Parameter | Type | Default | Description |\n|-----------|------|---------|-------------|\n| `query` | string | required | Search query |\n| `limit` | number | 10 | Maximum results |\n| `days` | number | - | Filter to last N days |\n| `recency_bias` | boolean | true | Boost recent results. Set `false` for historical searches |\n| `compact` | boolean | true | Compact format (~79% smaller) |\n\n## Search Features\n\n### Semantic Search\n\nMIRA finds conversations by meaning, not just keywords. Search for \"authentication bug\" and find discussions about \"login issues\" or \"JWT token problems.\"\n\n**Local mode:** On first search, MIRA downloads `BAAI/bge-small-en-v1.5` (~100MB ONNX model via fastembed). Vectors are stored in SQLite with sqlite-vec. All processing happens locally.\n\n**Remote mode:** If you configure [remote storage](#remote-storage-optional), the embedding-service on your server handles vector generation using `all-MiniLM-L6-v2` (sentence-transformers) with Qdrant for storage and search. No model download needed on client machines.\n\n### Time Decay\n\nResults are ranked with exponential time decay - recent conversations surface first:\n\n| Age | Score Multiplier |\n|-----|------------------|\n| Today | 1.00 |\n| 30 days | 0.79 |\n| 90 days | 0.50 |\n| 1 year | 0.10 (floor) |\n\nDisable with `recency_bias: false` for historical searches like \"original architecture decision\".\n\n### Fuzzy Matching\n\nTypos are auto-corrected:\n```\nQuery: \"authentcation implementaton\"\n       ↓ (auto-corrected)\nSearch: \"authentication implementation\"\n```\n\n## Data & Indexing\n\nMIRA stores data in `<workspace>/.mira/`. When a new conversation is detected, it extracts and indexes:\n\n| What's Extracted | Stored In | Purpose |\n|------------------|-----------|---------|\n| Summary, keywords, metadata | `local_store.db` | FTS5 full-text search |\n| Semantic vectors | `local_vectors.db` | Meaning-based search |\n| Error patterns & solutions | `insights.db` | `mira_error_lookup` |\n| Architectural decisions | `insights.db` | `mira_decisions` |\n| Code blocks, commands, configs | `artifacts.db` | Structured content retrieval |\n| User profile, preferences | `custodian.db` | Session context injection |\n| Conversation copies | `archives/` | Offline search, excerpts |\n| Python dependencies | `.venv/` | ~50MB runtime |\n\n**Filtering:** Agent sub-conversations and empty sessions are skipped automatically.\n\n## Learning Features\n\n### Custodian Profile\n\nMIRA builds your profile from conversations:\n\n- **Identity** - Your name (from \"I'm Sarah\" or similar)\n- **Preferences** - Tool preferences, coding style\n- **Rules** - \"never commit to main\", \"always run tests first\"\n- **Danger zones** - Files that have caused repeated issues\n- **Workflow** - Your development pattern (test first? plan first?)\n\n### Environment Prerequisites\n\nState prerequisites naturally and MIRA remembers:\n\n```\n\"In Codespaces, I need to start tailscaled first\"\n\"On my workstation, run docker-compose up before tests\"\n```\n\nMIRA detects your environment and reminds you in future sessions.\n\n### Decision Journal\n\nRecord decisions explicitly for high confidence:\n\n```\n\"Decision: use PostgreSQL for the database\"\n\"ADR: all API responses include meta field\"\n\"Going forward, use pnpm instead of npm\"\n```\n\nSearch with `mira_decisions` to understand past choices.\n\n## Architecture\n\n```\nClaude Code → Node.js MCP Server → Python Backend → SQLite (FTS5 + vectors)\n```\n\n- **Node.js layer**: MCP protocol handling\n- **Python layer**: File watching, ingestion, search, extraction\n- **SQLite**: FTS5 for text search, sqlite-vec for semantic search\n\n## FAQ\n\n**How long until MIRA learns my name?**\nUsually 1-2 sessions. Just mention it naturally: \"I'm Sarah\" or sign off with your name.\n\n**Does MIRA read my code?**\nNo. MIRA only indexes Claude Code conversation history. It never reads your source code files directly.\n\n**Does MIRA store code from my conversations?**\nYes. Code snippets, commands, and configs discussed in conversations are stored in MIRA's local SQLite databases (in `.mira/`). This data stays on your machine and is only exchanged with Claude Code during your sessions. If you configure remote storage, conversation data syncs to your server - but never to any third-party service.\n\n**How do I teach MIRA my workflow?**\nJust work normally. MIRA detects patterns: if you consistently write tests before implementing, it learns that as your workflow.\n\n## Requirements\n\n- Node.js >= 20.0.0\n- Python >= 3.8\n- Claude Code\n\n**Note:** MIRA has only been tested on Linux (Ubuntu, Debian, Codespaces). macOS and Windows support is untested.\n\n## Known Limitations\n\n**Fresh Install Testing:** Most development has been done on systems with existing MIRA data. Fresh installs have received limited testing. If you encounter issues during initial setup, please [open an issue](https://github.com/Interstitch/MIRA3/issues).\n\n---\n\n## Remote Storage (Optional)\n\n> **Most users don't need this.** Local semantic search works out of the box. Remote storage is only for users who want history to sync across multiple machines or share between team members.\n\nRemote storage enables:\n- **Cross-machine sync** - History follows you across laptop, desktop, Codespaces\n- **Persistent memory** - Rebuild a Codespace and your history is already there\n- **Team sharing** - Multiple developers share the same memory pool\n\n### Quick Setup\n\n**On your server** (any Linux machine with Docker):\n\n```bash\ncurl -sL https://raw.githubusercontent.com/Interstitch/MIRA3/master/server/install.sh | bash\n```\n\nThe script prompts for server IP, PostgreSQL password, and Qdrant API key.\n\n### Manual Docker Compose\n\n```yaml\nversion: '3.8'\nservices:\n  postgres:\n    image: postgres:15\n    environment:\n      POSTGRES_DB: mira\n      POSTGRES_USER: mira\n      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}\n    volumes:\n      - postgres_data:/var/lib/postgresql/data\n    ports:\n      - \"5432:5432\"\n\n  qdrant:\n    image: qdrant/qdrant:latest\n    environment:\n      QDRANT__SERVICE__API_KEY: ${QDRANT_API_KEY}\n    volumes:\n      - qdrant_data:/qdrant/storage\n    ports:\n      - \"6333:6333\"\n\n  embedding:\n    image: ghcr.io/interstitch/mira-embedding:latest\n    environment:\n      POSTGRES_HOST: postgres\n      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}\n      QDRANT_HOST: qdrant\n      QDRANT_API_KEY: ${QDRANT_API_KEY}\n    ports:\n      - \"8100:8100\"\n    depends_on:\n      - postgres\n      - qdrant\n\nvolumes:\n  postgres_data:\n  qdrant_data:\n```\n\n```bash\nexport POSTGRES_PASSWORD=your-secure-password\nexport QDRANT_API_KEY=your-api-key\ndocker-compose up -d\n```\n\n### Connecting Your Machines\n\nCreate `~/.mira/server.json` on each machine:\n\n```json\n{\n  \"version\": 1,\n  \"central\": {\n    \"enabled\": true,\n    \"qdrant\": { \"host\": \"YOUR_SERVER_IP\", \"port\": 6333, \"api_key\": \"YOUR_QDRANT_KEY\" },\n    \"postgres\": { \"host\": \"YOUR_SERVER_IP\", \"password\": \"YOUR_PG_PASSWORD\" }\n  }\n}\n```\n\n```bash\nchmod 600 ~/.mira/server.json  # Protect credentials\n```\n\nSee [SERVER_SETUP.md](SERVER_SETUP.md) for firewall ports and troubleshooting.\n\n---\n\n## License\n\nMIT\n","readmeFilename":"README.md"}