Memgentic

Memgentic — Universal AI Memory Layer

Memgentic captures knowledge from all your AI tools and makes it searchable. Every memory carries full provenance: which tool, which session, when it happened.

When to Use

Search memory before doing work, not after:

• Starting a new task — Check for prior decisions, context, or related work.
• Making architectural decisions — Check if this was discussed before.
• Debugging — Search for related past issues and solutions.
• User asks about history — "what did we decide about...", "remember when...", "how did we handle..."
• Encountering unfamiliar code — Check for context on why it was written that way.
• Setting up or configuring something — Check for user preferences and conventions.

When in doubt, search. It costs little and prevents repeated work.

CLI Commands

Semantic Search (primary)

# Search memories by meaning
memgentic search "database migration strategy"

# Compact output — fewer tokens, good for scanning
memgentic search "database migration strategy" --format compact

# Filter by platform
memgentic search "auth implementation" -s claude_code
memgentic search "API design" -s chatgpt

# Filter by content type
memgentic search "why we chose PostgreSQL" -t decision
memgentic search "user coding style" -t preference

Store a Memory

# Save an important fact or decision
memgentic remember "We chose Qdrant over Pinecone for local-first vector storage"

# Memory type is auto-classified from content
memgentic remember "Always use UV for package management"

Other Commands

# See which platforms have memories and how many
memgentic sources

# Generate standalone context file with recent activity
memgentic update-context

# Explore knowledge graph around an entity
memgentic graph "authentication"

# Check system health
memgentic doctor

MCP Tools

If the Memgentic MCP server is running, these tools are available directly:

Tool	Purpose
memgentic_recall	Semantic search with source filtering
memgentic_remember	Store a new memory
memgentic_search	Full-text keyword search
memgentic_recent	Recent memories by timestamp
memgentic_briefing	Cross-agent briefing of recent activity
memgentic_sources	List platforms and memory counts
memgentic_configure_session	Set session-level source filters
memgentic_stats	Memory statistics and analytics
memgentic_forget	Archive (soft-delete) a memory
memgentic_export	Export memories as JSON

MCP Examples

memgentic_recall(query="authentication flow", limit=5)
memgentic_recall(query="auth", sources=["claude_code", "chatgpt"])
memgentic_remember(content="Project uses JWT with refresh tokens", memory_type="decision")
memgentic_search(query="PostgreSQL", content_type="decision")
memgentic_configure_session(exclude_sources=["codex_cli"])
memgentic_briefing(hours=24)

Efficient Retrieval Strategy

Minimize token usage with a progressive approach:

1. Start compact — Use --format compact for an overview of what exists.
2. Narrow down — Add platform filter (-s claude_code) or type filter (-t decision) when you know what you are looking for.
3. Full detail — Drop --format compact only when you need the complete memory content.
4. Limit results — Use --limit to cap the number of results returned.

# Step 1: Quick scan
memgentic search "deployment" --format compact --limit 10

# Step 2: Found relevant results, get details from a specific platform
memgentic search "deployment" -s claude_code --limit 3

Memory Types

Memories are classified into types for filtering:

Type	Contains
decision	Architectural and technical decisions with rationale
learning	Things learned during development
preference	User preferences, conventions, coding style
fact	General knowledge and project context
bug_fix	Bug fixes, root causes, and solutions
conversation_summary	Summaries of complete sessions

Filter by type to get precise results:

memgentic search "database" -t decision    # Only decisions about databases
memgentic search "testing" -t preference   # Only testing preferences
memgentic search "auth" -t bug_fix         # Only auth-related bug fixes

Cross-Platform Context

Memgentic captures from multiple AI tools. Each memory records its source platform:

Platform	Source ID
Claude Code	claude_code
Gemini CLI	gemini_cli
ChatGPT	chatgpt
Aider	aider
Codex CLI	codex_cli
Copilot CLI	copilot_cli
Claude Web/Desktop	claude_web
Antigravity	antigravity

Use source filtering to scope searches:

# What did we discuss in Claude Code about this project?
memgentic search "project architecture" -s claude_code

# What did ChatGPT suggest about this topic?
memgentic search "caching strategy" -s chatgpt

# See all available sources
memgentic sources

Capture Methods

Memories enter the system through:

• auto_daemon — File watcher captures conversations automatically in the background.
• mcp_tool — Stored via MCP tool calls during AI sessions.
• cli — Manually saved via memgentic remember.
• json_import — Bulk imported from conversation exports (ChatGPT, Claude Web).

Setup

# Check prerequisites
memgentic doctor

# Interactive setup (model selection, configuration)
memgentic setup

# Import existing conversations from all detected AI tools
memgentic import-existing

# Start background capture daemon
memgentic daemon

# Start MCP server for AI tool integration
memgentic serve