This gives Claude persistent memory with causal tracking, which means it can recall not just what happened but why decisions were made and how events connect. You'd reach for this when building agents that need to learn from past interactions, maintain context across sessions, or trace reasoning chains over time. The semantic recall layer lets you query memories by meaning rather than exact matches, while the skills component appears to store learned capabilities. It's essentially a graph database optimized for agent cognition, turning Claude from stateless to stateful without you managing the infrastructure yourself.
MADB_DATA_DIRDatabase storage directory
MADB_TENANT_IDDefault tenant ID for multi-tenant isolation
io.github.ericm1018/skillfm-llm-cost-optimizer-openai-anthropic-usage
io.github.mikerawsonnz/llm-orchestration-agent
io.github.mikerawsonnz/authenticated-llm-agent
labforgedev/copilot-memory-mcp
csoai-org/agent-prompt-injection-firewall-mcp
io.github.mikerawsonnz/authenticated-multi-llm-agent