Enables multi-agent systems to share memories, synchronize knowledge, and collaborate by managing memory registration, sharing, and insight generation across agents.
Audit Agent-to-Agent communications to detect delegation loops, privilege escalation, and data leakage in multi-agent systems using protocols like A2A, CrewAI, LangGraph, and AutoGen.
Retrieve statistics about the shared memory system to monitor context deduplication, state sharing, and data transmission efficiency for AI agent coordination.
Provides a shared context layer for AI agent teams to improve token efficiency through context deduplication and incremental state sharing. It enables multiple agents to coordinate tasks, share real-time discoveries, and manage dependencies while significantly reducing redundant data transmission.
An MCP server that allows users to run and visualize systems models using the lethain:systems library, including capabilities to run model specifications and load systems documentation into the context window.
A knowledge graph memory server using SQLite to provide persistent, isolated contexts for organizing information into searchable categories like work and personal projects. It features unique ID-based operations and a token-efficient serialization format designed to optimize interactions with LLMs.