Lore: MCP Server That Shares AI Agent Session History Across Tools
Lore is an MCP (Model Context Protocol) server that indexes all your AI agent sessions into a single local SQLite database and serves that history over MCP. The result: any agent, in any chat tool, can read the same session history—even if the sessions started fresh with no shared context.
The creator demonstrates with a short clip: two fresh sessions in different tools, no shared context. They ask one agent to pull up the other agent's last session, and it works. Then they flip it and the other agent does the same. No configuration, no shared prompts—just the MCP server providing the index.
How it works
- Runs locally. Nothing phones home.
- Backed by SQLite — every session is indexed.
- Served over MCP — any MCP-compatible agent can query it.
- MIT licensed — free to use, fork, and contribute.
The intended use case is teams (or solo devs) running multiple AI agents on the same project. Instead of manually copying context or restarting sessions, you just ask your agent to fetch details from another agent's session. For example: "Pull up the context from my Cursor agent's last debugging session."
The project is early-stage and the author is actively seeking feedback and PRs. The Reddit post includes a video demo and setup instructions in the comments.
📖 Read the full source: r/openclaw
👀 See Also
Canopy: Terminal Dashboard for Managing Multiple Claude Code Agents
Canopy is an open source terminal UI that provides a single dashboard view for tracking multiple AI coding agents running across git worktrees. It shows agent states (running, idle, waiting for input, done, errored) and lets you jump into sessions or send input without fully switching.
AutoAgents Rust Framework Adds Python Bindings for Prototyping
AutoAgents, a Rust-based multi-agent framework, now has Python bindings that allow developers to prototype in Python while maintaining the same Rust core runtime, provider interfaces, pipeline model, and agent semantics. The bindings enable experimentation with local AI models without external systems.
Agnost AI Launches: Product Analytics for Chat and Voice Agents
Agnost AI reads production conversations to surface behavioral failures like rageprompting, repeated rephrasing, and hidden feature requests.
Krasis: Hybrid CPU/GPU Runtime for Large MoE Models Achieves 3,324 tok/s Prefill on RTX 5080
Krasis is a hybrid CPU/GPU runtime that runs large MoE models by handling prefill on GPU and decode on CPU, achieving 3,324 tokens/second prefill on an RTX 5080 with Qwen3-Coder-Next 80B Q4. It requires ~2.5x model size in system RAM but enables running models too large for VRAM.