ricoeur — a local-first archive & search engine for your LLM history
Description: Archive, search, and understand your LLM conversation history. Local-first CLI with full-text search, topic discovery, semantic search, and MCP integration.

v0.3.3 — PyPI release

# Archive. Search. Understand.

A local-first archive, search, and intelligence engine
for your LLM conversation history.

Get Started
GitHub
`$ pip install ricoeur`
Features

## Everything you need to understand your AI history

Import, search, analyze, and explore your conversations across platforms — entirely on your machine.

### Local-First

Your data stays on your machine. SQLite database, no cloud required. Full ownership of your conversation archive.

### Multi-Platform

Import from ChatGPT, Claude, and more. One unified archive across all your LLM conversations.

### Full-Text Search

FTS5-powered instant search across all messages. Filter by platform, language, model, date, or role.

### Topic Discovery

BERTopic-powered topic modeling. Discover themes and patterns in your conversation history automatically.

### Semantic Search

Embedding-powered similarity search using sentence-transformers or Ollama. Find conversations by meaning, not keywords.

### MCP Integration

Expose your archive as a Model Context Protocol server. Let your current AI access your past conversations.

Quick Start

## Up and running in minutes

Import your data, search your history, discover patterns.

    # Install ricoeur
    pip install ricoeur

    # Initialize database
    ricoeur init
    Initialized ricoeur at ~/.ricoeur

    # Import your ChatGPT export
    ricoeur import chatgpt ~/Downloads/export.zip
    Import from ChatGPT
      Parsed:       3,756 conversations (42,618 messages)
      New:          3,756

    # Import Claude conversations too
    ricoeur import claude ~/Downloads/claude-export.json

    # Check your archive stats
    ricoeur stats

    # Hybrid search (keyword + semantic, default)
    ricoeur search "deployment strategies"
    Found 18 results for "deployment strategies" (hybrid)

    # Semantic search — find by meaning, not keywords
    ricoeur search "how to containerize apps" --semantic

    # Keyword search (FTS5)
    ricoeur search "neural networks" --keyword

    # Filter by platform and language
    ricoeur search "API design" --platform chatgpt --lang en

    # Search only in code blocks
    ricoeur search "async def" --code --limit 10

    # Export results as JSON
    ricoeur search "Python" --format json

CLI Reference

## 10 commands, one archive

A focused, composable interface for your conversation history.

ricoeur init
Initialize a new ricoeur database and config at ~/.ricoeur
ricoeur import
Import from ChatGPT or Claude exports (.json or .zip)
ricoeur search
Hybrid, semantic, or keyword search with filters for platform, language, model, and date
ricoeur show
Display a full conversation in rich, markdown, or JSON format
ricoeur stats
Analytics dashboard with counts by platform, model, and language
ricoeur topics
Discover themes with BERTopic-powered topic modeling
ricoeur index
Build embeddings and summaries for semantic search
ricoeur mcp
Start a Model Context Protocol server for AI-to-archive access
ricoeur tui
Launch the interactive terminal UI for browsing conversations
ricoeur serve
Start a FastAPI web server with REST endpoints
“

> To understand oneself is to understand oneself in front of the text.

Paul Ricoeur