Memora

Memora

"You never truly know the value of a moment until it becomes a memory."

Give your AI agents persistent memory** An MCP memory layer for agents: structured storage, semantic retrieval, graph relations, and source-backed cross-session context.

Absorb agent work into durable graph memory, then use memory_digest(topic) to retrieve relevant memories, TODOs/issues, related edges, and source IDs.**

** Features ** · ** Preview ** · ** Install ** · ** Usage ** · ** Config ** · ** Live Graph ** · ** Cloud Graph ** · ** Chat ** · ** Semantic Search ** · ** Documents ** · ** LLM Dedup ** · ** Linking ** · ** Neovim **

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

Usage 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

Configuration

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:// / ",
 "CLOUDFLARE_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:// .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:// .r2.cloudflarestorage.com",
 MEMORA_STORAGE_URI = "s3://memories/memories.db",
 MEMORA_CLOUD_ENCRYPT = "true",
 MEMORA_ALLOW_ANY_TAG = "1",
 }

Environment Variables

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

Semantic Search & Embeddings 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

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

Details Panel Timeline Panel

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.

Cloud Graph (Recommended for D1) When using Cloudflare D1 as your database, the graph visualization is hosted on Cloudflare Pages - no local server needed.

Benefits:

• Access from anywhere (no SSH tunneling)

• Real-time updates via WebSocket

• Multi-database support via ?db= parameter

• Secure access with Cloudflare Zero Trust

Setup:

Create D1 database:

npx wrangler d1 create memora-graph
npx wrangler d1 execute memora-graph --file=memora-graph/schema.sql

Deploy Pages:

cd memora-graph
npx wrangler pages deploy ./public --project-name=memora-graph

Configure bindings in Cloudflare Dashboard:

• Pages → memora-graph → Settings → Bindings

• Add D1: DB_MEMORA → your database

• Add R2: R2_MEMORA → your bucket (for images)

Configure MCP with D1 URI:

{
 "env": {
 "MEMORA_STORAGE_URI": "d1:// / ",
 "CLOUDFLARE_API_TOKEN": " "
 }
}

Access: https://memora-graph.pages.dev

Secure with Zero Trust:

• Cloudflare Dashboard → Zero Trust → Access → Applications

• Add application for memora-graph.pages.dev

• Create policy with allowed emails

• Pages → Settings → Enable Access Policy

See memora-graph/ for detailed setup and multi-database configuration.

Chat with Memories Ask questions about your knowledge base directly from the graph UI. The chat panel uses RAG (Retrieval-Augmented Generation) to search relevant memories and stream LLM responses with tool calling support.

• Toggle via the floating chat icon at bottom-right

• Semantic search finds the most relevant memories as context

• Streaming responses with clickable [Memory #ID] references that focus the graph node

• Tool calling — the LLM can create, update, and delete memories directly from chat (e.g., "save this as a memory", "delete memory #42", "update memory #10 with...")

• Works on both the local server and Cloudflare Pages deployment

Configure the chat model:

Backend Variable Default Local server CHAT_MODEL env var Falls back to MEMORA_LLM_MODEL Cloudflare Pages CHAT_MODEL in wrangler.toml deepseek/deepseek-chat

Requires an OpenAI-compatible API (OPENAI_API_KEY + OPENAI_BASE_URL for local, OPENROUTER_API_KEY secret for Cloudflare). The chat model must support tool use (function calling).

LLM Deduplication Find and merge duplicate memories using AI-powered semantic comparison:

# Find potential duplicates (uses cross-refs + optional LLM analysis)
memory_find_duplicates(min_similarity=0.7, max_similarity=0.95, limit=10, use_llm=True)

# Merge duplicates (append, prepend, or replace strategies)
memory_merge(source_id=123, target_id=456, merge_strategy="append")

LLM Comparison analyzes memory pairs and returns:

• verdict: "duplicate", "similar", or "different"

• confidence: 0.0-1.0 score

• reasoning: Brief explanation

• suggested_action: "merge", "keep_both", or "review"

Works with any OpenAI-compatible API (OpenAI, OpenRouter, Azure, etc.) via OPENAI_BASE_URL.

Document Storage Store structured documents (research reports, architecture decisions, post-mortems) as searchable fragment trees:

# Store a markdown document — auto-parsed into typed fragments
memory_store_document(
 content="# Research Report\n\n## Evidence Table\n| Claim | Confidence |\n...",
 document_key="research/memora-enhancements-2026-04-08",
 tags=["memora/research"]
)
# Returns: {root_id: 230, fragment_count: 100, node_map: {claim: [...], plan_item: [...], ...}}

# Retrieve the full document or specific fragment types
memory_get_document(document_key="research/memora-enhancements-2026-04-08")
memory_get_document(document_key="...", node_kinds=["claim"], content_mode="full")

# Delete a document and all its fragments
memory_delete_document(document_key="research/memora-enhancements-2026-04-08")

How it works: The parser splits markdown by structure — tables become individual claims, numbered lists become plan items, URL lists become references, and risk sections become risk fragments. Each fragment is independently searchable via memory_semantic_search while the full document is retrievable as a unit.

Fragment types: claim, plan_item, reference, section_chunk, risk

Integrity guards: Document fragments are protected from accidental modification:

• memory_delete requires force=True for fragments

• memory_merge refuses to merge fragments

• memory_absorb excludes fragments from similarity matching

• memory_find_duplicates and memory_detect_supersessions skip fragments

• Graph UI hides fragments, shows only the document root node

Memory Automation Tools Structured tools for common memory types:

# Create a TODO with status and priority
memory_create_todo(content="Implement feature X", status="open", priority="high", category="backend")

# Create an issue with severity
memory_create_issue(content="Bug in login flow", status="open", severity="major", component="auth")

# Create a section placeholder (hidden from graph)
memory_create_section(content="Architecture", section="docs", subsection="api")

Memory Insights Analyze stored memories and surface actionable insights:

# Full analysis with LLM-powered pattern detection
memory_insights(period="7d", include_llm_analysis=True)

# Quick summary without LLM (faster, no API key needed)
memory_insights(period="1m", include_llm_analysis=False)

Returns:

• Activity summary — memories created in the period, grouped by type and tag

• Open items — open TODOs and issues with stale detection (configurable via MEMORA_STALE_DAYS, default 14)

• Consolidation candidates — similar memory pairs that could be merged

• LLM analysis — themes, focus areas, knowledge gaps, and a summary (requires OPENAI_API_KEY)

Memory Linking Manage relationships between memories:

# Create typed edges between memories
memory_link(from_id=1, to_id=2, edge_type="implements", bidirectional=True)

# Edge types: references, implements, supersedes, extends, contradicts, related_to

# Remove links
memory_unlink(from_id=1, to_id=2)

# Boost memory importance for ranking
memory_boost(memory_id=42, boost_amount=0.5)

# Detect clusters of related memories
memory_clusters(min_cluster_size=2, min_score=0.3)

Knowledge Graph Export (Optional) For offline viewing, export memories as a static HTML file:

memory_export_graph(output_path="~/memories_graph.html", min_score=0.25)

This is optional - the Live Graph Server provides the same visualization with real-time updates.

Neovim Integration Browse memories directly in Neovim with Telescope. Copy the plugin to your config:

# For kickstart.nvim / lazy.nvim
cp nvim/memora.lua ~/.config/nvim/lua/kickstart/plugins/

Usage: Press <leader>sm to open the memory browser with fuzzy search and preview.

Requires: telescope.nvim, plenary.nvim, and memora installed in your Python environment.