Solitaire: Open-Source Identity Infrastructure for AI Agents
Solitaire is an open-source identity infrastructure project for AI agents that addresses how agents improve their working relationship with users over time, rather than just focusing on memory recall. The project has been in production since February 2024, with development shaped by 600+ real user sessions.
Key Features
The system emphasizes local-first architecture with:
- All storage using SQLite + JSONL in your workspace directory
- Zero network requests from the core engine
- No cloud dependency, no telemetry, no external API calls for memory operations
- Automatic rolling backups without any setup
- All data stays on your machine
Core Functionality
Solitaire includes several technical components developed through production use:
- Persona and behavioral identity that compiles from real interaction, not static config
- Retrieval weighting that adjusts based on what actually proved useful
- Self-correcting knowledge graph with contradiction detection, confidence rescoring, and entity relinking
- Tiered boot context so the agent arrives briefed, not blank
- Session residues that carry forward how the work felt, not just what was discussed
- Guided onboarding where new users build a partner through conversation, not a JSON file
Installation and Compatibility
Install via: pip install solitaire-ai (note: NOT install solitaire, which is an unrelated package)
The architecture is model-agnostic and currently runs on:
- Claude Code
- Cowork
- Hermes
- OpenClaw
- Gemini CLI
A Dify plugin is in prototype, with work ongoing for Open LLM integration.
Current Status
The system has processed 600+ sessions with 15,700+ entries in real production use. It's available on PyPI and the MCP Registry. Two research papers from the longitudinal work are currently in review.
License: AGPL-3.0, with commercial licensing available for proprietary embedding.
The developer is seeking feedback on top-requested integrations, areas for improvement in the memory layer, and any missed opportunities.
📖 Read the full source: r/LocalLLaMA
👀 See Also
Codebase Memory MCP: Graph-based code exploration for Claude Code
A developer built an MCP server that indexes codebases into a persistent knowledge graph using Tree-sitter and SQLite, reducing token usage by 20x on average for structural queries like call tracing and dead code detection.
Tokenmeter: Free Windows App to Track Claude Code Token Usage Offline
Tokenmeter is a free, open-source Windows app that reads local Claude Code .jsonl files to show token usage, estimated costs, cache savings, and a 90-day activity heatmap — all offline.
Exasol Releases MCP Server for Database Context in AI Agent Workflows
Exasol has released an MCP Server that enables databases to provide context to AI agents about available data, business rules, and safe interaction methods. The server is read-only by default, supports high-concurrency workflows, and can be deployed on-prem, in cloud, or hybrid environments.
Improving Claude Code Sessions with claude-self-improve
Claude-self-improve is a CLI tool that enhances Claude Code's AI performance by analyzing session data and updating memory files automatically.