Any MCP-capable agent can notice what others are doing, inherit what others have learned, and understand complex projects together, with token-optimized context handling and hallucination failsafe.
Apache-2.0 · self-hostable · docker compose up
Markdown memory files are a good primitive, but on their own they become isolated islands. Each agent, IDE, or model vendor keeps its own context, and the project owner turns into the integration layer. Hivemind makes the workspace the owner of memory instead: any MCP-capable agent joins through a scoped token, and the project context persists when the team switches tools.
It starts working like part of a team, it sees what others are doing, picks up where they left off, and shares the same understanding of the project.
An agent changes its behaviour because it's aware of another agent's current work, proposing a better-coordinated approach instead of colliding.
An agent recovers a rule, method, or active project context left by another in the consolidated bank. The handoff lives in the memory layer, not a manual prompt.
An agent explores project concepts through an ontology-backed knowledge graph, entities, typed relations, and conceptual links populated by the collective process.
Hivemind gives your agents one persistent, shared memory instead of cold starts. They write and read from a single structured store, so context survives across runs, tasks, teammates, and gets richer over time.
Observations, decisions, todos, what each agent is doing right now, written fast and visible across the whole space.
An LLM folds raw notes into structured Markdown, rules, project context, guidelines. The working memory other agents inherit from.
Ontology-guided extraction turns the bank into entities and typed relations: a conceptual map, not a search box. Protocol-derived, prevents hallucination.
Just like a mind, Hivemind keeps what just happened close at hand, then settles it into something lasting. Fresh notes from working agents consolidate into a structured bank, then crystallize into a long-term knowledge graph, across short, mid, and long. The important things stay; the noise fades.
Agents append timestamped notes, no friction, no conflicts, cross-visible to the team.
An LLM rewrites notes into structured Markdown against the space's rules, read in one call at startup.
Stable documents feed ontology-guided extraction into named entities and typed relations, a conceptual map in the knowledge graph.
Project Mesh connects multiple teams, open-source contributors, and agent fleets around one logical space. Each side keeps local autonomy while shared memory, provenance, and mutation ordering stay inside the project boundary, synchronized by Mesh Sync.
Mesh Sync is full-mesh all-ACK: shared mutations are serialized through a durable queue, lease, and fencing, no concurrent writes to the bank. A corrupt critical record fails closed until recovery. No hub, no single point of failure.
Several teams run their own Hivemind instances and agent fleets. Each keeps local operational autonomy; shared project memory is synchronized through Project Mesh.
External contributors bring their own agents and tooling via scoped credentials, shared memory, provenance, and membership without forcing one model vendor or runtime.
Agent fleets split work across issues, reviews, docs, tests, and recovery, parallel progress without losing context or re-discovering it from scratch.
The ontology-backed knowledge graph grounds agents in verified entities and typed relations, so long, multi-step work stays anchored to what the project actually knows instead of drifting into hallucination.
Bring S3 + an OpenAI-compatible LLM; the compose stack ships everything else. No vendor lock-in.
SHA-256 bearer tokens, read/write/admin scopes, and a Caddy + Coraza OWASP WAF in front.
Every protocol event is journaled on S3. Provenance, ACKs, and commits are inspectable, not magic.
Apache-2.0, versioned protocol, and a documented contribution workflow driven entirely through GitHub.
Hivemind is publicly available under Apache-2.0. Clone the repository, launch the compose stack, and connect your first agents in minutes.
$ git clone https://github.com/Lesur-ai/hivemind.git