memora

<h1 align="center"><img src="media/memora_new.gif" width="60" alt="Memora Logo" align="absmiddle"> Memora</h1> <p align="center">_{_{<i>"You never truly know the value of a moment until it becomes a memory."</i>}}</p> <p align="center"> <b>Give your AI agents persistent memory</b><br> An MCP memory layer for agents: structured storage, semantic retrieval, graph relations, and source-backed cross-session context. </p> <p align="center"> <a href="https://github.com/agentic-box/memora/releases"><img src="https://img.shields.io/github/v/tag/agentic-box/memora?label=version&color=blue" alt="Version"></a> <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-green" alt="License"></a> <a href="https://github.com/thedotmack/awesome-claude-code"><img src="https://awesome.re/mentioned-badge.svg" alt="Mentioned in Awesome Claude Code"></a> </p> <p align="center"> <img src="media/memora-absorb-digest-flow.gif" alt="Memora absorb and digest flow" width="820"> </p> <p align="center"> <b>Absorb agent work into durable graph memory, then use <code>memory_digest(topic)</code> to retrieve relevant memories, TODOs/issues, related edges, and source IDs.</b> </p> <p align="center"> <b><a href="#features">Features</a></b> · <b><a href="#preview">Preview</a></b> · <b><a href="#install">Install</a></b> · <b><a href="#usage">Usage</a></b> · <b><a href="#configuration">Config</a></b> · <b><a href="#live-graph-server">Live Graph</a></b> · <b><a href="#cloud-graph">Cloud Graph</a></b> · <b><a href="#chat-with-memories">Chat</a></b> · <b><a href="#semantic-search--embeddings">Semantic Search</a></b> · <b><a href="#document-storage">Documents</a></b> · <b><a href="#llm-deduplication">LLM Dedup</a></b> · <b><a href="#memory-linking">Linking</a></b> · <b><a href="#neovim-integration">Neovim</a></b> </p>

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

<p align="center"> <img src="media/demo.gif" alt="Memora memory graph demo" width="320"> <img src="media/demo2.gif" alt="Memora memory interaction demo" width="320"> </p>

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

<details id="usage"> <summary><big><big><strong>Usage</strong></big></big></summary>

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

</details> <details id="configuration"> <summary><big><big><strong>Configuration</strong></big></big></summary>

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",
  }

</details> <details id="environment-variables"> <summary><big><big><strong>Environment Variables</strong></big></big></summary>

| Variable | Description | |------------------------|-----------------------------------------------------------------------------| | MEMORA_DB_PATH | Local SQLite database path (default: ~/.local/share/memora/memories.db) | | MEMORA_STORAGE_URI | Storage URI: d1://<account>/<db-id> (D1) or s3://bucket/memories.db (S3/R2) | | CLOUDFLARE_API_TOKEN | API token for D1 database access (required for d1:// URI) | | MEMORA_CLOUD_ENCRYPT | Encrypt database before uploading to cloud (true/false) | | MEMORA_CLOUD_COMPRESS| Compress database before uploading to cloud (true/false) | | MEMORA_CACHE_DIR | Local cache directory for cloud-synced database | | MEMORA_ALLOW_ANY_TAG | Allow any tag without validation against allowlist (1 to enable) | | MEMORA_TAG_FILE | Path to file containing allowed tags (one per line) | | MEMORA_TAGS | Comma-separated list of allowed tags | | MEMORA_GRAPH_PORT | Port for the knowledge graph visualization server (default: 8765) | | MEMORA_EMBEDDING_MODEL | Embedding backend: openai (default), sentence-transformers, or tfidf | | SENTENCE_TRANSFORMERS_MODEL | Model for sentence-transformers (default: all-MiniLM-L6-v2) | | OPENAI_API_KEY | API key for OpenAI embeddings and LLM deduplication | | OPENAI_BASE_URL | Base URL for OpenAI-compatible APIs (OpenRouter, Azure, etc.) | | OPENAI_EMBEDDING_MODEL | OpenAI embedding model (default: text-embedding-3-small) | | MEMORA_LLM_ENABLED | Enable LLM-powered deduplication comparison (true/false, default: true) | | MEMORA_LLM_MODEL | Model for deduplication comparison (default: gpt-4o-mini) | | CHAT_MODEL | Model for the chat panel (default: deepseek/deepseek-chat, falls back to MEMORA_LLM_MODEL) | | AWS_PROFILE | AWS credentials profile from ~/.aws/credentials (useful for R2) | | AWS_ENDPOINT_URL | S3-compatible endpoint for R2/MinIO | | R2_PUBLIC_DOMAIN | Public domain for R2 image URLs |

</details> <details id="semantic-search--embeddings"> <summary><big><big><strong>Semantic Search & Embeddings</strong></big></big></summary>

Memora supports three embedding backends:

| Backend | Install | Quality | Speed | |---------|---------|---------|-------| | openai (default) | Included | High quality | API latency | | sentence-transformers | pip install memora[local] | Good, runs offline | Medium | | tfidf | Included | Basic keyword matching | Fast |

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

</details> <details id="live-graph-server"> <summary><big><big><strong>Live Graph Server</strong></big></big></summary>

A built-in HTTP server starts automatically with the MCP server, serving an interactive knowledge graph visualization.

<table> <tr> <td align="center"><img src="media/ui_details.png" alt="Details Panel" width="400"><br><em>Details Panel</em></td> <td align="center"><img src="media/ui_timeline.png" alt="Timeline Panel" width="400"><br><em>Timeline Panel</em></td> </tr> </table>

Access locally:

http://localhost:8765/graph

Remote access via SSH:

ssh -L 8765:localhost:8765 user@remote
# Then open http://localhost:8765/graph in your browser

Configuration:

{
  "env": {
    "MEMORA_GRAPH_PORT": "8765"
  }
}

To disable: add "--no-graph" to args in your MCP config.

Graph UI Features

• Details Panel - View memory content, metadata, tags, and related memories
• Timeline Panel - Browse memories chronologically, click to highlight in graph
• History Panel - Action log of all operations with grouped consecutive entries and clickable memory references (deleted memories shown as strikethrough)
• Chat Panel - Ask questions about your memories using RAG-powered LLM chat with streaming responses and clickable [Memory #ID] references
• Time Slider - Filter memories by date range, drag to explore history
• Real-time Updates - Graph, timeline, and history update via SSE when memories change
• Filters - Tag/section dropdowns, zoom controls
• Mermaid Rendering - Code blocks render as diagrams

Node Colors

• 🟣 Tags - Purple shades by tag
• 🔴 Issues - Red (open), Orange (in progress), Green (resolved), Gray (won't fix)
• 🔵 TODOs - Blue (open), Orange (in progress), Green (completed), Red (blocked)

Node size reflects connection count.

</details> <details id="cloud-graph"> <summary><big><big><strong>Cloud Graph (Recommended for D1)</strong></big></big></summary>