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Enable multiple AI agents to share and coordinate memory.

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Use Case

A team of AI agents working on a project:
  • Research agent finds information
  • Coding agent implements features
  • Review agent checks code quality
  • All share context via MemoClaw

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Implementation

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Share Findings Between Agents

# Research agent stores findings
memoclaw store "Found that pgvector HNSW index performs better than IVFFlat for small datasets" \
  --importance 0.85 \
  --agent research-001 \
  --namespace project-backend \
  --type decision

# Coding agent recalls research findings
memoclaw recall "vector database performance research" \
  --namespace project-backend \
  --limit 5

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Filter by Agent

memoclaw list --agent research-001 --namespace project-backend

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Create Relations Between Agents’ Memories

# Store research finding
memoclaw store "HNSW is better for our use case" \
  --agent research-001 --namespace project-backend
# Note the returned ID, e.g. <research-id>

# Store implementation note
memoclaw store "Implemented HNSW index for vector search" \
  --agent coding-001 --namespace project-backend
# Note the returned ID, e.g. <impl-id>

# Link them
memoclaw relate <impl-id> <research-id> --type derived_from

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Memory Isolation

  • Same wallet = same user identity
  • Different agent_ids = different agent perspectives on same memory store
  • Namespaces = completely separate memory pools

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Best Practices

  1. Use consistent agent_id naming: {role}-{number} or {name}
  2. Use namespace to separate projects
  3. Use relations to link cross-agent dependencies
  4. Use memory_type to distinguish findings vs implementations vs reviews