synapt 0.3.0

Persistent conversational memory for AI coding assistants

synapt

Persistent conversational memory for AI coding assistants. Synapt indexes your past coding sessions and makes them searchable — so your AI assistant remembers what you worked on, decisions you made, and patterns you established.

Works as an MCP server for Claude Code and other MCP-compatible tools.

Install

pip install synapt

# With MCP server support (recommended)
pip install 'synapt[mcp]'

Quick start

1. Build the index

Synapt discovers Claude Code transcripts automatically:

synapt recall build

2. Search past sessions

synapt recall search "how did we fix the auth bug"

3. Use as an MCP server

Add to your Claude Code config (.mcp.json):

{
  "mcpServers": {
    "synapt": {
      "type": "stdio",
      "command": "synapt",
      "args": ["server"]
    }
  }
}

This gives your AI assistant 13 tools for searching past sessions, managing a journal, setting reminders, and building a durable knowledge base.

Features

• Transcript indexing — BM25 full-text search over past coding sessions
• Topic clustering — Jaccard token-overlap clustering groups related chunks
• Knowledge consolidation — Extracts durable knowledge from session journals
• Session journal — Rich entries with focus, decisions, done items, and next steps
• Reminders — Cross-session sticky reminders that surface at session start
• Timeline — Chronological work arcs showing project narrative
• LLM enrichment — Optional LLM-powered summaries and cluster upgrades
• Working memory — Frequency-boosted search results for active topics
• Plugin system — Extend with additional tools via entry-point discovery

MCP tools

Tool	Description
recall_search	Search past sessions by query
recall_context	Get context for the current session
recall_files	Find sessions that touched specific files
recall_sessions	List indexed sessions
recall_timeline	View chronological work arcs
recall_build	Build or rebuild the transcript index
recall_setup	Auto-configure hooks and MCP integration
recall_stats	Index statistics
recall_journal	Write rich session journal entries
recall_remind	Set cross-session reminders
recall_enrich	LLM-powered chunk summarization
recall_consolidate	Extract knowledge from journals
recall_contradict	Flag contradictions in knowledge

CLI reference

synapt recall build              # Build index (discovers transcripts automatically)
synapt recall build --incremental # Skip already-indexed files
synapt recall search "query"     # Search past sessions
synapt recall stats              # Show index statistics
synapt recall journal --write    # Write a session journal entry
synapt recall setup              # Auto-configure hooks
synapt server                    # Start MCP server

Optional backends

Synapt uses local LLMs for enrichment and summarization. Install optional backends:

# MLX (Apple Silicon)
pip install mlx-lm

# Ollama
# Install from https://ollama.com, then:
ollama pull qwen2.5:3b

# Transformers (GPU/CPU)
pip install 'synapt[transformers]'

Plugins

Synapt discovers plugins via Python entry points. To create a plugin:

1. Create a module with a register_tools(mcp) function
2. Register it in your pyproject.toml:

[project.entry-points."synapt.plugins"]
my_plugin = "my_package.server"

The MCP server automatically discovers and loads plugins at startup.

Development

git clone https://github.com/laynepenney/synapt.git
cd synapt
pip install -e ".[test]"
pytest tests/ -v

License

MIT