mnemograph

Mnemograph

A persistent, event-sourced knowledge graph for AI coding agents. Unlike simple key-value memory, Mnemograph captures entities, relations, and observations — enabling semantic search, tiered context retrieval, and git-based version control of your AI's memory.

Works with: Claude Code, opencode, codex CLI, Zed, Continue.dev, and any MCP-compatible agent.

Why Mnemograph?

AI coding sessions are ephemeral. Mnemograph gives your AI partner persistent memory that:

• Survives across sessions — decisions, patterns, learnings persist
• Supports semantic search — find relevant context by meaning, not just keywords
• Provides tiered retrieval — shallow summaries to deep subgraphs based on need
• Versions like code — branch, commit, diff, revert your knowledge graph
• Enables collaboration — share memory repos across users or projects

Memory Scope: Local vs Global

Before using mnemograph, decide where to store memory:

Scope	Path	Use When
Project-local	./.claude/memory	Knowledge specific to this repo (architecture, decisions, patterns)
Global	~/.claude/memory	Cross-project knowledge (personal learnings, universal patterns, preferences)
Custom	Any path via MEMORY_PATH	Shared team memory, org-wide knowledge bases

Important: Agents should ask the user which scope to use when first setting up mnemograph for a project. This affects where knowledge is stored and whether it's shared across projects.

# Project-local (default)
MEMORY_PATH=".claude/memory"

# Global (cross-project)
MEMORY_PATH="$HOME/.claude/memory"

# CLI: use --global flag
mnemograph --global status
mnemograph --global graph

Quick Start

Option 1: Let Claude Code install it

Give Claude Code this repo URL and ask it to set up mnemograph:

https://github.com/tm42/mnemograph

Or point Claude to the setup instructions directly:

Read https://raw.githubusercontent.com/tm42/mnemograph/main/SETUP_CLAUDE_CODE.md and follow them

Option 2: Manual installation

# Install from PyPI
pip install mnemograph

# Add to Claude Code (global, available in all projects)
claude mcp add --scope user mnemograph \
  -e MEMORY_PATH="$HOME/.claude/memory" \
  -- uvx mnemograph

# Initialize memory directory
mkdir -p ~/.claude/memory

Option 3: Other MCP Clients

Each MCP client has a different configuration format. See UNIVERSAL_MCP_COMPATIBILITY.md for copy-paste configs for:

• opencode — ~/.config/opencode/opencode.json
• Codex CLI — ~/.codex/config.yaml
• Zed — ~/.config/zed/settings.json
• Continue.dev — ~/.continue/config.json

The key environment variable is MEMORY_PATH — set it to where you want the knowledge graph stored.

Option 4: Install from source

git clone https://github.com/tm42/mnemograph.git
cd mnemograph
uv sync

# Add to Claude Code (or adapt for your MCP client)
claude mcp add --scope user mnemograph \
  -e MEMORY_PATH="$HOME/.claude/memory" \
  -- uv run --directory /path/to/mnemograph mnemograph

Usage

MCP Tools (used by any agent)

Mnemograph exposes these tools via MCP:

Core Operations:

Tool	Description
remember	Primary storage: Store knowledge atomically (entity + observations + relations in one call)
recall	Primary retrieval: Get relevant context with auto token management. Use focus=['Entity'] for full details. Default output is human-readable prose.
create_entities	Create entities (auto-blocks duplicates >80% match)
create_relations	Link entities with typed edges (implements, uses, decided_for, etc.)
add_observations	Add facts/notes to existing entities
read_graph	Get the full knowledge graph (warning: may be large)
delete_entities	Remove entities (cascades to relations)
delete_relations	Remove specific relations
delete_observations	Remove specific observations

Session Lifecycle:

Tool	Description
session_start	Signal session start, get initial context. Returns quick_start guide.
session_end	Signal session end, optionally save summary
get_primer	Get oriented with the knowledge graph (call at session start)

Branching (Parallel Workstreams):

Tool	Description
create_branch	Create a named branch for isolated work (e.g., "feature/auth-refactor")
switch_branch	Switch to a different branch
list_branches	List all branches
merge_branch	Merge a branch into main
delete_branch	Delete a branch
get_current_branch	Get the current branch name

Graph Maintenance:

Tool	Description
find_similar	Find entities with similar names (duplicate detection)
find_orphans	Find entities with no relations
merge_entities	Merge duplicate entities (consolidates observations, redirects relations)
get_graph_health	Assess graph quality: orphans, duplicates, overloaded entities
suggest_relations	Suggest potential relations based on semantic similarity
create_entities_force	Create entities bypassing duplicate check
clear_graph	Clear all entities/relations (event-sourced, can rewind)

Time Travel:

Tool	Description
get_state_at	View graph state at any point in history
diff_timerange	Show what changed between two points in time
get_entity_history	Full changelog for a specific entity
rewind	Rewind graph to a previous state using git
restore_state_at	Restore graph to state at timestamp (audit-preserving)
reload	Reload graph state from disk (after git operations)

Edge Weights:

Tool	Description
get_relation_weight	Get weight breakdown (recency, co-access, explicit)
set_relation_importance	Set explicit importance weight (0.0-1.0)
get_strongest_connections	Find entity's most important connections
get_weak_relations	Find pruning candidates (low-weight relations)

Recall: Prose vs Graph Format

The recall tool returns context in prose format by default — human-readable text that agents can consume directly without parsing JSON:

# Default: prose format (human-readable)
recall(depth="medium", query="authentication")
# Returns:
# **MyApp** (project)
# A Python web service. Uses OAuth2 for user auth.
# Uses: PostgreSQL, Redis
#
# **Decisions:**
# • Decision: Use JWT — Stateless tokens for API authentication
#
# **Gotchas:**
# • Token expiry is 1 hour by default
# • Refresh tokens stored in Redis

# Optional: graph format (structured JSON)
recall(depth="medium", query="authentication", format="graph")

Depth levels:

• shallow — Quick summary: entity counts, recent activity, gotchas
• medium — Semantic search + 1-hop neighbors (~2000 tokens)
• deep — Multi-hop traversal from focus entities (~5000 tokens)

Gotcha extraction: Observations prefixed with Gotcha:, Warning:, Note:, or Important: are automatically extracted into a dedicated section.

CLI Tools

mnemograph — Unified CLI for all memory operations:

# Basic operations
mnemograph status                # Show entity/relation counts, recent events
mnemograph log                   # View event history
mnemograph log --session X       # Filter by session
mnemograph sessions              # List all sessions
mnemograph export                # Export graph as JSON

# VCS commands (git-based version control)
mnemograph vcs init              # Initialize memory as git repo
mnemograph vcs commit -m "msg"   # Commit current state
mnemograph vcs log               # View commit history
mnemograph vcs revert --event ID # Undo specific events (compensating events)
mnemograph vcs revert --session X # Undo entire session

# Graph visualization
mnemograph graph                 # Open interactive graph viewer
mnemograph graph --watch         # Live reload mode (refresh button)

# Time travel
mnemograph show --at "2 days ago"  # View state at a point in time
mnemograph diff "1 week ago"       # Show changes since then
mnemograph history "EntityName"    # Full changelog for an entity
mnemograph rewind -n 1             # Git-based rewind by N commits
mnemograph restore --to "yesterday" # Event-based restore (audit-preserving)

# Graph health and maintenance
mnemograph health                # Show graph health report (orphans, duplicates, etc.)
mnemograph health --fix          # Interactive cleanup mode
mnemograph similar "React"       # Find entities similar to "React" (duplicate check)
mnemograph orphans               # List entities with no relations
mnemograph suggest "FastAPI"     # Suggest relations for an entity
mnemograph clear                 # Clear all entities and relations (with confirmation)

# Global options (come *before* the subcommand)
mnemograph --global status       # Use global memory (~/.claude/memory)
mnemograph --memory-path /path graph  # Custom memory location

Running from anywhere (without activating the venv):

# Using uv (recommended)
uv run --directory /path/to/mnemograph mnemograph graph

# Using uvx (if installed from PyPI)
uvx --from mnemograph mnemograph status

Graph Visualization — Interactive D3.js viewer:

• Layout algorithms: Force-directed, Radial (hubs at center), Clustered (by component)
• Color modes: By entity type, connected component, or degree centrality
• Edge weight slider: Filter connections by strength
• Live refresh: --watch mode with Refresh button for real-time updates

Architecture

~/.mnemograph/memory/    # or ~/.claude/memory, ~/.opencode/memory, etc.
├── mnemograph.db        # SQLite database (events + vectors)
├── state.json           # Cached materialized state (derived)
└── .git/                # Version history

Event sourcing means all changes are recorded as immutable events in SQLite. The current state is computed by replaying events. This enables:

• Full history of all changes
• Revert any operation
• Branch/merge knowledge graphs
• Audit trail of what Claude learned and when

Two-layer versioning:

• mnemograph vcs revert — fine-grained, undo specific events via compensating events
• mnemograph rewind / mnemograph restore — coarse-grained, git-level or timestamp-based restore

Branching

Branches let you work on isolated knowledge without affecting the main graph. Perfect for:

• Exploratory work — try approaches without polluting shared knowledge
• Feature-specific context — "feature/auth-refactor" keeps auth decisions separate
• Multiple projects — switch context between different codebases

Creating and Using Branches

# Create a branch for your feature
create_branch(name="feature/auth-refactor")

# Work normally — all operations happen on this branch
remember(name="OAuth2", entity_type="concept",
         observations=["Implementing OAuth2 flow"])

# Switch back to main to see clean state
switch_branch(name="main")

# Merge when ready
merge_branch(source="feature/auth-refactor", target="main")

How Branching Works

• Main branch always exists, contains shared knowledge
• Feature branches inherit from main but additions stay isolated
• Automatic filtering — recall, search, etc. only see current branch + main
• Merge copies branch entities/relations into target branch
• Delete cleans up after merge (or abandons exploratory work)

Branch Naming Conventions

Pattern	Use Case
feature/xyz	Feature-specific knowledge
explore/xyz	Exploratory/experimental work
project/xyz	Project-specific context
user/name	Personal workspace

Entity Types

Type	Purpose	Example
concept	Ideas, patterns, approaches	"Repository pattern", "Event sourcing"
decision	Choices with rationale	"Chose SQLite over Postgres for simplicity"
project	Codebases, systems	"auth-service", "mnemograph"
pattern	Recurring code patterns	"Error handling with Result type"
question	Open unknowns	"Should we add real-time sync?"
learning	Discoveries	"pytest fixtures simplify test setup"
entity	Generic (people, files, etc.)	"Alice", "config.yaml"

Topic Convention

Use topic entities as entry points for browsing related knowledge:

# Create topic entry points
create_entities([
    {"name": "topic/projects", "entityType": "entity"},
    {"name": "topic/decisions", "entityType": "entity"},
    {"name": "topic/patterns", "entityType": "entity"},
])

# Link entities to their topics
create_relations([
    {"from": "auth-service", "to": "topic/projects", "relationType": "part_of"},
    {"from": "Decision: Use Redis", "to": "topic/decisions", "relationType": "part_of"},
])

Standard topics:

• topic/projects — Project entities
• topic/decisions — Architectural decisions
• topic/patterns — Patterns and practices
• topic/learnings — Key discoveries
• topic/questions — Open questions

This makes it easy to query "what decisions have we made?" by exploring topic/decisions.

Development

git clone https://github.com/tm42/mnemograph.git
cd mnemograph
uv sync                    # Install dependencies
uv run pytest --cov        # Run tests with coverage (enforces 75% minimum)
uv run ruff check .        # Lint
uv run mnemograph          # Run MCP server directly

Based On

Mnemograph builds on MCP server-memory — Anthropic's official memory server

License

MIT