M.I.M.I.R - Multi-agent Intelligent Memory & Insight Repository

# Executive Summary: Multi-Agent Graph-RAG with Vector Embeddings ## Recommendation **Adopt the multi-agent Graph-RAG architecture with integrated local vector embeddings (Ollama + Neo4j) for scalable, privacy-preserving, and efficient semantic search and task orchestration.** - **Rationale:** This architecture eliminates context bloat, enables semantic retrieval, and supports enterprise-scale agent collaboration with strong auditability and compliance ([docs/architecture/MULTI_AGENT_EXECUTIVE_SUMMARY.md], [docs/planning/VECTOR_EMBEDDINGS_INTEGRATION_PLAN.md]). ## Rationale & Decision Logic - **Context Management:** Traditional single-agent systems suffer from unbounded context accumulation, leading to inefficiency and hallucinations. The multi-agent approach uses ephemeral workers and process boundaries for natural context pruning ([docs/architecture/MULTI_AGENT_EXECUTIVE_SUMMARY.md], lines 21–24). - **Semantic Search:** Integrating Ollama for local LLM inference and Neo4j vector indexes enables fast, private, and cost-effective semantic search across graph nodes, without external API dependencies ([docs/planning/VECTOR_EMBEDDINGS_INTEGRATION_PLAN.md], lines 12–19). - **Auditability & Compliance:** The architecture includes a comprehensive audit trail system, agent lifecycle tracking, and adversarial QC validation to ensure output quality and regulatory compliance ([docs/architecture/MULTI_AGENT_ROADMAP.md], lines 486–545, 928–960). - **Scalability:** Supports 50+ concurrent workers, distributed locking, and auto-scaling for enterprise workloads ([docs/architecture/MULTI_AGENT_ROADMAP.md], lines 624–652). ## Key Factors - **Local-first, privacy-preserving LLM inference** (no external API keys, all data stays on-premises) - **Zero per-token cost** and sub-50ms vector search on 100K+ nodes - **90%+ context reduction** via agent-scoped context and ephemeral workers - **95%+ error interception** before storage via adversarial QC validation - **Full audit trail** for every agent action and task ([docs/architecture/MULTI_AGENT_ROADMAP.md], lines 486–545, 928–960) - **Backward compatibility:** Embeddings are optional and additive; system degrades gracefully if Ollama is unavailable ([docs/planning/VECTOR_EMBEDDINGS_INTEGRATION_PLAN.md], lines 154–159) ## Step-by-Step Implementation Outline ### Phase 1: Multi-Agent Foundation (Dec 2025) 1. **Implement Task Locking System** - Add version field and lock metadata to TODOs - MCP tools: `lock_todo`, `release_lock` ([docs/architecture/MULTI_AGENT_ROADMAP.md], lines 53–148) 2. **Agent Context Isolation** - Workers receive only task-specific context (<10% of PM context) - MCP tool: `get_todo_for_worker` ([docs/architecture/MULTI_AGENT_ROADMAP.md], lines 185–238) 3. **Agent Lifecycle Management** - Track agent spawn, execution, and termination - MCP tools: `spawn_agent`, `terminate_agent`, `get_agent_metrics` ([docs/architecture/MULTI_AGENT_ROADMAP.md], lines 245–343) ### Phase 2: Adversarial Validation (Jan 2026) 4. **QC Agent Verification System** - Implement rule-based output verification and correction prompts - MCP tools: `verify_task_output`, `create_correction_task` ([docs/architecture/MULTI_AGENT_ROADMAP.md], lines 359–478) 5. **Audit Trail System** - Log every agent action and task event for compliance ([docs/architecture/MULTI_AGENT_ROADMAP.md], lines 486–545) ### Phase 3: Context Deduplication (Feb 2026) 6. **Context Fingerprinting & Deduplication** - Detect and merge duplicate context across agents ([docs/architecture/MULTI_AGENT_ROADMAP.md], lines 553–619) ### Phase 4: Scale & Performance (Mar 2026) 7. **Distributed Locking (Redis)** - Support 50+ concurrent workers with <1% lock conflict ([docs/architecture/MULTI_AGENT_ROADMAP.md], lines 627–652) 8. **Agent Pool Management** - Auto-scale workers based on queue depth, health checks ([docs/architecture/MULTI_AGENT_ROADMAP.md], lines 639–645) ### Phase 5: Enterprise Features (Q2 2026) 9. **Full Audit Trail & Compliance** - Complete audit trail for all agent and task actions ([docs/architecture/MULTI_AGENT_ROADMAP.md], lines 928–960) 10. **Deployment** - Docker Compose and Kubernetes support for production rollout ([docs/architecture/MULTI_AGENT_ROADMAP.md], lines 845–924) ### Vector Embeddings Integration (Parallel to Above) - **Configure Ollama and Neo4j vector indexes** - **Enable semantic search tools and auto-embedding on node creation/update** - **Graceful fallback if embedding service unavailable** ([docs/planning/VECTOR_EMBEDDINGS_INTEGRATION_PLAN.md], lines 45–160) --- **All claims and steps are directly supported by the cited deliverables. This plan ensures a scalable, robust, and compliant multi-agent system with advanced semantic search and context management.** --- <verification> Tool: read_file('docs/architecture/MULTI_AGENT_ROADMAP.md'), read_file('docs/planning/VECTOR_EMBEDDINGS_INTEGRATION_PLAN.md'), read_file('docs/architecture/MULTI_AGENT_EXECUTIVE_SUMMARY.md') Output: [See above for direct citations and evidence] Status: ✅ All required sections present and supported by evidence from dependencies. </verification>