Persistent experience for AI
Every model starts cold. Hivemind gives it memory that persists, context that rebuilds, and recall that reacts — before the model even has to think.
A model's weights are frozen at training. Everything after that — every conversation, every decision, every correction — is lost the moment the session ends.
Your agent has the same conversation for the hundredth time. It doesn't remember what it learned yesterday, what it decided last week, or what you corrected an hour ago. Every turn is a blank slate.
Traditional RAG waits to be asked. It searches when prompted, retrieves by vector distance, and treats a six-month-old fragment the same as something said five minutes ago. No judgement. No priority.
Each agent carries its own context, its own history, its own limited view. Nothing is shared. Nothing compounds. Scale the swarm and you multiply the amnesia.
Every approach treats memory as a retrieval problem.
It's a continuity problem.
A persistent memory substrate that any model connects to. One shared mind, reactive recall, dynamic context — rebuilt every turn.
The model doesn't search its memory — the memory comes to it. Every turn, Hivemind evaluates the incoming context and injects the most semantically relevant memories before the model generates a response. No tool call. No delay. Reactive.
Every turn, the context window is reconstructed from scratch: current conversation, reactively recalled memories, held artifacts, operator briefings — compiled into a single working context within a token budget. Nothing is carried over blindly. Everything is re-evaluated, re-ranked, and re-assembled.
Every agent connected to the Hivemind reads and writes to the same memory pool. What one agent learns, all agents know. A swarm of models operating off a single shared mental model — individually lightweight, collectively deep. Validated at 50 concurrent agents.
Not all memories are equal. Hivemind ranks by semantic similarity, recall frequency, and contextual relevance — a gravitational model where what matters gains pull and what doesn't decays over time. The system curates itself. No manual cleanup. No stale data bloat.
The model is the neural substrate — the processing power. Hivemind is the experience that runs on top. Swap the model, keep the memory. Upgrade the weights, keep the scars. Any model that connects to the Hivemind becomes a long-lived, context-rich agent without retraining, fine-tuning, or ballooning prompts. Unlimited dynamic context for any model.
Every turn, Hivemind compiles a working context from these layers — within a token budget you control.
Three explicit boundary types. Every record has a kind, a storage mode, and a recall mode — inspectable at the payload level.
The core unit. Notes, decisions, corrections, conversation — anything the agent learns becomes a memory. Stored inline or chunked, recalled semantically or temporally. Long memories are split at natural boundaries and reassembled on recall. No overlap. No fragmentation.
Documents stay where they are. Hivemind doesn't ingest your files into a vector store — it links related memories to external artifacts through referential metadata. Source ID, URI, SHA256, version, title. The lineage is preserved without duplicating the body. Your memory pool stays clean.
Every operation produces a diagnostic receipt — stored inside the Hivemind as a first-class record. What was stored, what was recalled, what was compiled, what was dropped. Full causal chain. Excluded from ordinary recall so they don't contaminate the agent's memory. Available when you need to audit.
Semantic search for meaning. Metadata filters for structure. Scope recall by session, document, tag, partition — or let gravitational ranking surface what matters. Both mechanisms, same query surface.
Conversations are first-class memory. Session-scoped, turn-indexed, exchange-paired. Recency-first recall without embedding overhead. Long exchanges are chunked and reassembled transparently.
Each tenant gets their own memory space. Agents within a tenant share the collective. Operator-level auth for destructive operations. Tenant plans enforce rate limits, storage caps, and access controls.
Stateless service boundary. Any agent framework, any model, any language. Store, recall, compile — three operations that turn a stateless model into a persistent agent.
Validated at 50 concurrent agents. Hardened service boundary. Preparing for launch.
Register interest for early access.