memora

Features

Core Storage

• 💾 Persistent Storage - SQLite with optional cloud sync (S3, R2, D1)
• 📂 Hierarchical Organization - Section/subsection structure with auto-hierarchy assignment
• 📦 Export/Import - Backup and restore with merge strategies

Search & Intelligence

• 🔍 Semantic Search - Vector embeddings (TF-IDF, sentence-transformers, OpenAI)
• 🎯 Advanced Queries - Full-text, date ranges, tag filters (AND/OR/NOT), hybrid search
• 🔀 Cross-references - Auto-linked related memories based on similarity
• 🤖 LLM Deduplication - Find and merge duplicates with AI-powered comparison
• 🔗 Memory Linking - Typed edges, importance boosting, and cluster detection

Document Storage

• 📄 Structured Documents - Store markdown documents as searchable fragment trees (claims, plan items, references, risks)
• 🔒 Fragment Integrity - Guards against accidental delete/merge/absorb of document fragments
• 🔍 Granular Search - Individual claims and findings are semantically searchable while the full document remains retrievable as a unit

Tools & Visualization

• ⚡ Memory Automation - Structured tools for TODOs, issues, and sections
• 🕸️ Knowledge Graph - Interactive visualization with Mermaid rendering and cluster overlays
• 🌐 Live Graph Server - Built-in HTTP server with cloud-hosted option (D1/Pages)
• 💬 Chat with Memories - RAG-powered chat panel with LLM tool calling to search, create, update, and delete memories via streaming chat
• 📡 Event Notifications - Poll-based system for inter-agent communication
• 📊 Statistics & Analytics - Tag usage, trends, and connection insights
• 🧠 Memory Insights - Activity summary, stale detection, consolidation suggestions, and LLM-powered pattern analysis
• 📜 Action History - Track all memory operations (create, update, delete, merge, boost, link) with grouped timeline view

Preview

Install

pip install git+https://github.com/agentic-box/memora.git

Includes cloud storage (S3/R2) and OpenAI embeddings out of the box.

# Optional: local embeddings (offline, ~2GB for PyTorch)
pip install "memora[local]" @ git+https://github.com/agentic-box/memora.git

The server runs automatically when configured in Claude Code. Manual invocation:

# Default (stdio mode for MCP)
memora-server

# With graph visualization server
memora-server --graph-port 8765

# HTTP transport (alternative to stdio)
memora-server --transport streamable-http --host 127.0.0.1 --port 8080

Claude Code

Add to .mcp.json in your project root:

Local DB:

{
  "mcpServers": {
    "memora": {
      "command": "memora-server",
      "args": [],
      "env": {
        "MEMORA_DB_PATH": "~/.local/share/memora/memories.db",
        "MEMORA_ALLOW_ANY_TAG": "1",
        "MEMORA_GRAPH_PORT": "8765"
      }
    }
  }
}

Cloud DB (Cloudflare D1) - Recommended:

{
  "mcpServers": {
    "memora": {
      "command": "memora-server",
      "args": ["--no-graph"],
      "env": {
        "MEMORA_STORAGE_URI": "d1://<account-id>/<database-id>",
        "CLOUDFLARE_API_TOKEN": "<your-api-token>",
        "MEMORA_ALLOW_ANY_TAG": "1"
      }
    }
  }
}

With D1, use --no-graph to disable the local visualization server. Instead, use the hosted graph at your Cloudflare Pages URL (see Cloud Graph).

Cloud DB (S3/R2) - Sync mode:

{
  "mcpServers": {
    "memora": {
      "command": "memora-server",
      "args": [],
      "env": {
        "AWS_PROFILE": "memora",
        "AWS_ENDPOINT_URL": "https://<account-id>.r2.cloudflarestorage.com",
        "MEMORA_STORAGE_URI": "s3://memories/memories.db",
        "MEMORA_CLOUD_ENCRYPT": "true",
        "MEMORA_ALLOW_ANY_TAG": "1",
        "MEMORA_GRAPH_PORT": "8765"
      }
    }
  }
}

Codex CLI

Add to ~/.codex/config.toml:

[mcp_servers.memora]
  command = "memora-server"  # or full path: /path/to/bin/memora-server
  args = ["--no-graph"]
  env = {
    AWS_PROFILE = "memora",
    AWS_ENDPOINT_URL = "https://<account-id>.r2.cloudflarestorage.com",
    MEMORA_STORAGE_URI = "s3://memories/memories.db",
    MEMORA_CLOUD_ENCRYPT = "true",
    MEMORA_ALLOW_ANY_TAG = "1",
  }

Memora supports three embedding backends:

Automatic: Embeddings and cross-references are computed automatically when you memory_create, memory_update, or memory_create_batch.

Manual rebuild required when:

• Changing MEMORA_EMBEDDING_MODEL after memories exist
• Switching to a different sentence-transformers model

# After changing embedding model, rebuild all embeddings
memory_rebuild_embeddings

# Then rebuild cross-references to update the knowledge graph
memory_rebuild_crossrefs

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