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
Loom: A Local Execution Harness for Complex AI Tasks
Loom is an open-source local execution harness designed to manage complex tasks by providing a structured process with around 50 tools, a custom package plugin system for repeatable workflows, and both CLI and MCP server interfaces.
Nit: A Git Replacement in Zig Optimized for AI Agent Token Efficiency
Nit is a native Git replacement written in Zig that reduces token usage by 35-87% on common commands like status, diff, log, and show. It achieves this through compact output defaults and direct libgit2 integration, eliminating subprocess overhead.
Qwen3.6-27B as a Local Reasoning Layer: 2-Week Multi-Agent Test Results
A developer replaced Claude with local Qwen3.6-27B in a multi-agent orchestrator for 2 weeks across 47 workflows. Results: strong reasoning, 12% tool-call format errors, and 12k token context limit.
CAL: Open-Source Context Optimization Layer for LLM Agents
CAL (Context Assembly Layer) is a Python library that reduces Claude API token usage by 83% through intelligent context selection and compression. It's available via pip install and MIT licensed.