[ PERSISTENT MEMORY INFRASTRUCTURE ]
Persistent shared memory for agentic systems. Hivemind recalls, compiles, and routes the right context before every turn.
EVERY MODEL STARTS FROM ZERO.
EVERY AGENT FORGETS WHAT THE LAST ONE LEARNED.
THAT IS NOT INTELLIGENCE.
THAT IS STATELESS AUTOMATION.
Agents write memories as immutable facts with rich metadata.
Hivemind searches the recall field for the most relevant context.
Context is ranked, deduped, and compiled into a task bundle.
The agent acts with full context and clear objectives.
Outcomes are recorded. Memories evolve. Hivemind gets smarter.
Checkout latency spiked during payment provider failover.
Scaled connection pool to 80. Latency p95 improved 37%.
Runbook: Checkout Latency Playbook v3.2.
Operator asked for a reversible mitigation before the next traffic window.
SLO dashboard shows checkout p95 above threshold in us-east-1.
Avoid changes that increase regression risk during active checkout traffic.
+ ranked, deduped, and trimmed before final assembly
Recent failover memory retained because it directly matches the active objective.
Connection-pool receipt retained as a proven reversible mitigation.
Risk constraint preserved so the downstream agent avoids aggressive changes.
Long postmortem detail compressed to fit the active token budget.
Older latency incident removed after semantic duplicate filtering.
Use only the provided context bundle. Prefer reversible mitigations and preserve checkout stability.
Find the lowest-risk path to stabilise checkout latency before peak traffic.
Relevant memories, references, and receipts 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 memories, receipts, references, and holds assembled into a single working boundary."
}
],
"items": [
{ "type": "memory", "id": "MEM-9a31c7d2", "content": "Checkout latency spiked during payment provider failover." },
{ "type": "receipt", "id": "RCPT-0a1162", "content": "Scaled connection pool to 80. Latency p95 improved 37%." },
{ "type": "reference", "id": "DOC-0203", "content": "Runbook: Checkout Latency Playbook v3.2" },
{ "type": "conversation", "id": "TURN-13", "content": "Prefer a reversible mitigation before the next traffic window." },
{ "type": "reference", "id": "DASH-2/1a", "content": "SLO dashboard shows checkout p95 above threshold in us-east-1." },
{ "type": "hold", "id": "HOLD-risk-window", "content": "Avoid changes that increase regression risk during active checkout traffic." }
],
"decisions": [
{ "action": "include", "target": "MEM-9a31c7d2", "reason": "direct objective match" },
{ "action": "include", "target": "RCPT-0a1162", "reason": "recent successful mitigation" },
{ "action": "include", "target": "HOLD-risk-window", "reason": "active policy constraint" },
{ "action": "trim", "target": "TICKET-7749", "reason": "budget control" },
{ "action": "drop", "target": "MEM-114ab88c", "reason": "semantic duplicate" }
],
"policy": {
"prioritise_recent_receipts": true,
"prefer_reversible_actions": true,
"max_context_tokens": 8000
},
"token_count": 6320,
"source_counts": {
"recalled_memory": 6,
"recalled_conversation": 3,
"references": 4
},
"budget_total": 8000,
"budget_available": 1680,
"conversation_tokens": 940,
"hold_tokens": 320,
"trace": {
"session_id": "sess_checkout_882",
"turn_number": 14,
"built_at": "2025-09-19T14:32:11Z",
"event_id": "evt_4831",
"trace_id": "trace_7f3c_9a21",
"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, "references": 4 },
"recalled_conversation_count": 3,
"recalled_memory_count": 6,
"recalled_memory_input_count": 11,
"semantic_memory_duplicates_filtered": 2
}
}Not vector search. Purpose-built recall field that understands time, causality, and operator intent.
MORE SIGNAL. LESS NOISE.
One hive. All agents. Memories are agent-agnostic, deduped, and enriched as the hive learns.
NETWORK EFFECT FOR INTELLIGENCE.
Works with any model, framework, or runtime. Plug in via API. Keep your stack. Add a memory layer.
YOUR MODELS. OUR MEMORY.
Early access for teams building persistent agent systems.
Drop your email. We will be in touch when Hivemind launches.