[ PROBLEM ]

EVERY MODEL STARTS FROM ZERO.

EVERY AGENT FORGETS WHAT THE LAST ONE LEARNED.

THAT IS NOT INTELLIGENCE.
THAT IS STATELESS AUTOMATION.

[ HIVEMIND STACK ]

01
STORE

Agents write memories as immutable facts with rich metadata.
02
RECALL

Hivemind searches the recall field for the most relevant context.
03
COMPILE

Context is ranked, deduped, and compiled into a task bundle.
04
ACT

The agent acts with full context and clear objectives.
05
RECEIPT

Outcomes are recorded. Memories evolve. Hivemind gets smarter.

[ WORKING CONTEXT ]
REPRESENTATIVE OUTPUT

WORKING CONTEXT BUNDLE / CONTEXT.COMPILE BUNDLE ID bundle_001

TRACE

Session ID: session_001
Turn Number: 14
Built At: 2025-09-19T14:32:11Z
Event ID: event_001
Trace ID: trace_001
User Input: Find the lowest-risk path to stabilise checkout latency before peak traffic.
Context Messages: 3

ITEMS (TOP 5)

MEMORYitem_001

Checkout latency spiked during payment provider failover.
BRIEFINGoperator

Prefer reversible mitigations and preserve checkout stability during active traffic.
ARTIFACTSactive context

Runbook: Checkout Latency Playbook v3.2; SLO dashboard shows p95 above threshold in us-east-1.
CONVERSATIONitem_004

Operator asked for a reversible mitigation before the next traffic window.
HOLDpolicy

Avoid changes that increase regression risk during active checkout traffic.

+ ranked, deduped, and trimmed before final assembly

ACCOUNTING (API DATA)

POLICYactive

Prefer reversible actions within the configured context budget.
COUNTSsources

Source counts separate recalled memory, conversation, holds, artifacts, and briefing.
ADMISSIONdiagnostic

Assembler records explain included, dropped, and trimmed candidates. They are not prompt content.
TRACEsnapshot

Compiler diagnostics remain inspectable alongside the assembled bundle.

MESSAGES (COMPILED 3)

SYSTEMcompiled

Use only the provided context bundle. Prefer reversible mitigations and preserve checkout stability.
USERactive input

Find the lowest-risk path to stabilise checkout latency before peak traffic.
CONTEXTassembled

Relevant memory, briefing, artifacts, conversation, and holds assembled into a single working boundary.

{
  "messages": [
    {
      "role": "system",
      "content": "Use only the provided context bundle. Prefer reversible mitigations and preserve checkout stability."
    },
    {
      "role": "user",
      "content": "Find the lowest-risk path to stabilise checkout latency before peak traffic."
    },
    {
      "role": "system",
      "name": "working_context",
      "content": "Relevant memory, briefing, artifacts, conversation, and holds assembled into a single working boundary."
    }
  ],
  "items": [
    { "type": "recalled_memory", "id": "item_001", "content": "Checkout latency spiked during payment provider failover." },
    { "type": "operator_briefing", "id": "item_002", "content": "Prefer reversible mitigations and preserve checkout stability during active traffic." },
    { "type": "artifacts_context", "id": "item_003", "content": "Runbook: Checkout Latency Playbook v3.2; SLO dashboard shows checkout p95 above threshold in us-east-1." },
    { "type": "recalled_conversation", "id": "item_004", "content": "Prefer a reversible mitigation before the next traffic window." },
    { "type": "hold", "id": "item_005", "content": "Avoid changes that increase regression risk during active checkout traffic." }
  ],
  "decisions": [
    { "status": "included", "item_id": "item_001", "reason": "direct objective match", "tokens": 210 },
    { "status": "included", "item_id": "item_002", "reason": "operator briefing", "tokens": 64 },
    { "status": "included", "item_id": "item_003", "reason": "active artifacts context", "tokens": 180 },
    { "status": "included", "item_id": "item_004", "reason": "recent recalled conversation", "tokens": 46 },
    { "status": "included", "item_id": "item_005", "reason": "active hold", "tokens": 38 },
    { "status": "dropped_budget", "item_id": "item_006", "reason": "lower priority after dedupe", "tokens": 720 }
  ],
  "policy": {
    "prefer_reversible_actions": true,
    "max_context_tokens": 8000
  },
  "token_count": 6320,
  "source_counts": {
    "recalled_memory": 6,
    "recalled_conversation": 3,
    "holds": 1,
    "artifacts_context": 1,
    "operator_briefing": 1
  },
  "budget_total": 8000,
  "budget_available": 1680,
  "conversation_tokens": 940,
  "hold_tokens": 320,
  "trace": {
    "session_id": "session_001",
    "turn_number": 14,
    "built_at": "2025-09-19T14:32:11Z",
    "event_id": "event_001",
    "trace_id": "trace_001",
    "receipt_id": "receipt_001",
    "user_input": "Find the lowest-risk path to stabilise checkout latency before peak traffic.",
    "budget_total": 8000,
    "budget_used": 6320,
    "budget_available": 1680,
    "conversation_tokens": 940,
    "hold_tokens": 320,
    "context_message_count": 3,
    "source_counts": { "recalled_memory": 6, "recalled_conversation": 3, "holds": 1, "artifacts_context": 1, "operator_briefing": 1 },
    "recalled_conversation_count": 3,
    "recalled_memory_count": 6,
    "recalled_memory_input_count": 11,
    "semantic_memory_duplicates_filtered": 2,
    "decisions": [
      { "status": "included", "item_id": "item_001", "reason": "direct objective match", "tokens": 210 },
      { "status": "dropped_budget", "item_id": "item_006", "reason": "lower priority after dedupe", "tokens": 720 }
    ]
  }
}

[ WHY HIVEMIND ]
THE DIFFERENCE

REACTIVE RECALL

Not vector search. Purpose-built recall field that understands time, causality, and operator intent.

MORE SIGNAL. LESS NOISE.

SHARED MEMORY
ACROSS AGENTS

One hive. All agents. Memories are agent-agnostic, deduped, and enriched as the hive learns.

NETWORK EFFECT FOR INTELLIGENCE.

MODEL-AGNOSTIC
INTEGRATION

Works with any model, framework, or runtime. Plug in via API. Keep your stack. Add a memory layer.

YOUR MODELS. OUR MEMORY.

[ Access Hivemind ]

Build with Hivemind

Early access for teams building persistent agent systems.

WEIGHTS AREINSTINCT.HIVEMIND ISEXPERIENCE.

STORE

RECALL

COMPILE

ACT

RECEIPT