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 [UNIVERSALMCPCOMPATIBILITY.md](UNIVERSALMCPCOMPATIBILITY.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

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: tm42
• Source: tm42/mnemograph
• License: MIT

Install and usage instructions live in the source repository linked above.