AI Memory for
Agentic Teams

Copepod is a secure, multi-tenant memory store built for concurrent multi-agent workflows. Live context sync, knowledge graph inference, and progressive enrichment β€” with enterprise-grade security.

Copepod Dashboard
Total Memories
12,847
↑ 234 today
Active Agents
8
consolidating
Enrichment Rate
94%
fully enriched

Built for multi-agent concurrency

Memory systems for agents aren't like regular databases. Copepod is designed from the ground up for the write patterns of concurrent AI agents.

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Live Context Sync

Queue-Consolidate-Resolve architecture handles concurrent multi-agent writes without conflicts. Every agent sees a consistent, non-contradictory memory state.

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Knowledge Graph Inference

Automatically enriches memory with relationship traversal, co-occurrence analysis, and type-consistent propagation from your existing knowledge graph.

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Progressive LLM Enrichment

Three-tier inference β€” local graph β†’ LLM reasoning β†’ web-grounded β€” each gated by permission. Only pay for what you need.

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Multi-Tenant Security

Row-level PostgreSQL RLS, envelope encryption with Vault transit keys, and mandatory tenant isolation on every operation.

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Schema-Driven Knowledge Structures

Define entity schemas with enrichment hints. Store partial objects, let the worker fill in the gaps based on your KB and permissions.

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MCP-Compatible

Native MCP server implementation for tool integration. Exposes memory ops as tools for any MCP-compatible agent client.

Write to memory. Don't worry about conflicts.

Traditional databases fail when two agents write to the same field simultaneously. Copepod's QCR (Queue-Consolidate-Resolve) architecture handles this automatically β€” every write goes through a queue, conflicts are detected and resolved by policy, and the canonical state is always consistent.

1

Agents write to the queue with status PENDING

2

Consolidation worker polls every 500ms, groups by memory node

3

Conflicts resolved by policy (NEWER_WINS / CONFIDENCE_WINS / DEFER_TO_HUMAN)

4

Committed atomically β€” dirty flag cleared, agents notified via header

Enrichment that respects your permissions.

Knowledge Structures store partial objects. The enrichment worker fills in the gaps β€” but only using engines your plan allows. KG inference is always free. LLM inference requires Pro. Web-grounded inference requires Enterprise.

Free
Knowledge Graph
Relation traversal + co-occurrence
Pro
+ LLM Inference
Structured reasoning over known fields
Enterprise
+ Web Grounding
Search-grounded inference with citations

Simple, usage-based pricing

Start free. Scale when your agents need more memory.

Free

$0/month

For individuals exploring agentic memory

  • 5,000 memories / month
  • 1 tenant
  • KG inference
  • Community support
Start Free
Most Popular

Pro

$49/month

For teams building multi-agent products

  • 100,000 memories / month
  • 5 tenants
  • KG + LLM inference
  • Priority support
  • API access
Start Free Trial

Enterprise

Custom

For organizations at scale

  • Unlimited memories
  • Unlimited tenants
  • Web-grounded inference
  • Dedicated support
  • Custom SLAs
Contact Sales