System Architecture Overview

OctoLLM implements a five-layer architecture inspired by octopus neurobiology, combining distributed intelligence with centralized governance.

Architecture Layers

Layer 1: Ingress (API Gateway + Reflex)

Purpose: Fast preprocessing and caching before expensive LLM processing.

Technology: NGINX/Traefik + Rust Latency Target: <10ms cache hits, <50ms reflex decisions Current Status: ✅ COMPLETE (Sprint 1.1, v1.1.0)

Key Features:

• Redis caching with <5ms latency (2x better than target)
• Pattern matching and PII detection <8ms (6x better than target)
• Request routing based on complexity
• Rate limiting and input validation

Details: Reflex Layer Component

Layer 2: Orchestration (The Brain)

Purpose: Strategic planning, task decomposition, and arm coordination.

Technology: Python + FastAPI, LangChain/LlamaIndex Model: GPT-4 or Claude Opus Current Status: ✅ COMPLETE (Sprint 1.2, v1.2.0)

Main Loop:

1. Cache check (via Reflex Layer)
2. Plan generation (task decomposition)
3. Step execution (arm delegation)
4. Result integration (combining outputs)
5. Validation (quality assurance)

Details: Orchestrator Component

Layer 3: Execution (The Arms)

Purpose: Domain-specific execution with local decision-making.

Arms Implemented:

• ✅ Reflex Layer (v1.1.0) - Pattern matching, caching
• ✅ Orchestrator (v1.2.0) - Coordination, planning
• 🚧 Planner Arm (Planned Sprint 1.3) - Task decomposition
• ⏳ Tool Executor - Sandboxed command execution
• ⏳ Retriever - Knowledge base search
• ⏳ Coder - Code generation/debugging
• ⏳ Judge - Output validation
• ⏳ Safety Guardian - PII detection, filtering

Details: Arms Overview

Layer 4: Persistence

Purpose: Global memory, caching, and vector stores.

Components:

• PostgreSQL: Global semantic memory (tasks, decisions, provenance)
• Redis: High-speed caching (responses, embeddings)
• Qdrant/Weaviate: Vector stores for semantic search

Current Status: ✅ PostgreSQL + Redis operational (Sprint 1.2)

Layer 5: Observability

Purpose: Monitoring, logging, and tracing for debugging and optimization.

Stack:

• Prometheus: Metrics collection (latency, throughput, errors)
• Loki: Centralized logging
• Jaeger: Distributed tracing
• Grafana: Dashboards and alerting

Current Status: ⏳ Planned (Phase 3)

Data Flow

User Request
    ↓
[API Gateway] → Reflex Layer (cache check, pattern match)
    ↓
[Orchestrator] (task decomposition, planning)
    ↓
[Arms] (parallel execution, specialized processing)
    ↓
[Orchestrator] (result aggregation, validation)
    ↓
[API Gateway] → User Response

Detailed flow: Data Flow Documentation

Key Design Principles

1. Modular Specialization: Each component excels at one thing
2. Distributed Autonomy with Centralized Governance: Arms decide locally, brain coordinates globally
3. Defense in Depth: Multiple security layers (reflex, capability isolation, PII sanitization)
4. Hierarchical Processing: Expensive resources reserved for complex problems
5. Active Inference: System proactively reduces uncertainty

Details: Architecture Principles

Performance Metrics

See Also

• Layer Architecture Details
• Data Structures
• Swarm Decision Making
• Architecture Decision Records