Hivemind is a cloud-backed shared memory system for AI agents, enabling persistent learning and knowledge sharing across a team.
Source: README View on GitHub →Hivemind is gaining attention for addressing the pain point of knowledge silos in AI agent teams. It fills the gap by providing a persistent, shared memory that captures and codifies patterns, allowing agents to learn from each other. The unique technical choice of a hybrid lexical + semantic retrieval system stands out, as does its support for multiple AI assistants.
Source: Synthesis of README and project traitsHivemind captures and stores session traces, codifies patterns into reusable skills, and allows real-time propagation of capabilities across agents.
Source: READMEIt uses a combination of lexical and semantic retrieval for efficient searching and accessing of stored knowledge.
Source: READMEHivemind supports integration with various AI assistants like Claude Code, OpenClaw, Codex, Cursor, Hermes, and pi, providing a unified learning experience.
Source: READMEThe architecture is modular, with distinct components for capturing, storing, retrieving, and propagating knowledge. It leverages a virtual filesystem backed by SQL for intercepting file operations and a background worker for session summarization. The design patterns include dependency injection and the use of hooks for integration with various assistants.
Source: Code tree + dependency filesCenter: project; inner ring: core feature modules; outer ring: key dependencies. Auto-generated from core_features and tech_stack.key_deps.
@anthropic-ai/sdk@modelcontextprotocol/sdkdeeplakejs-yamljust-bashyargs-parserzodHivemind is suitable for teams using AI agents in scenarios such as collaborative coding, data analysis, and research. It helps solve problems like knowledge silos, inefficient knowledge sharing, and repetitive tasks.
Source: READMEv0.7.89 (2026-06-11): fix(mcp): treat missing memory/sessions tables as empty memory
Source: GitHub ReleasesHivemind is a promising project for teams looking to enhance the collaborative capabilities of their AI agents. Its focus on shared memory and knowledge propagation makes it a valuable tool for fostering a more efficient and knowledgeable AI workforce.
Hivemind is a cloud-backed shared memory system for AI agents, enabling persistent learning and knowledge sharing across a team.
hivemind's core features include: Persistent Shared Memory, Hybrid Retrieval System, Integration with Multiple Assistants.
Hivemind is gaining attention for addressing the pain point of knowledge silos in AI agent teams.
Hivemind is suitable for teams using AI agents in scenarios such as collaborative coding, data analysis, and research. It helps solve problems like knowledge silos, inefficient knowledge sharing, and repetitive tasks.