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

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.

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:

1. Create D1 database:

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

2. Deploy Pages:

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

3. Configure bindings in Cloudflare Dashboard:

   • Pages → memora-graph → Settings → Bindings
   • Add D1: DB_MEMORA → your database
   • Add R2: R2_MEMORA → your bucket (for images)

4. Configure MCP with D1 URI:

   {
     "env": {
       "MEMORA_STORAGE_URI": "d1://<account-id>/<database-id>",
       "CLOUDFLARE_API_TOKEN": "<your-token>"
     }
   }
   

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

Secure with Zero Trust:

1. Cloudflare Dashboard → Zero Trust → Access → Applications
2. Add application for memora-graph.pages.dev
3. Create policy with allowed emails
4. Pages → Settings → Enable Access Policy

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

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:

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).

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.

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

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")

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)

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)

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.

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.