A complete multi-agent AI architecture built from the ground up
Overview
The Vision
SAIOS represents a fundamental shift in how AI systems are built. Rather than relying on a single general-purpose model, SAIOS deploys multiple specialized agents that collaborate to solve complex problems.
Each agent has a defined role, specific capabilities, access to tools, and persistent memory. The system orchestrates these agents to handle tasks that would be impossible for any single model.
Project Details
TypeAI Platform
StackRust, Axum, PostgreSQL
AI BackendOllama (Local LLM)
Vector DBpgvector
StatusProduction
Architecture
The Seven Agents
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Atlas
Knowledge management and retrieval. Maintains organizational memory, indexes documents, and provides context to other agents.
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Nova
Research and information gathering. Explores topics, synthesizes findings from multiple sources, and validates information.
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Echo
Content creation and writing. Drafts documents, creates reports, helps with communication across different formats.
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Pulse
Scheduling and time management. Coordinates calendars, manages deadlines, handles reminders and time-sensitive tasks.
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Beam
Analytics and data insights. Processes data, generates visualizations, identifies patterns and trends.
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Cipher
Security and compliance. Handles sensitive data, enforces policies, manages permissions and access control.
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Spark
Communication and notifications. Manages messaging, coordinates with external systems, handles alerts.
Technical Deep Dive
Core Components
Agent Worker System
services/agent_worker.rs
Task execution engine with approval workflows. Agents receive tasks, execute them using available tools, and return results. High-stakes actions require human approval before execution.
Agent Tools
services/agent_tools.rs
8 integrated tools: search, generate, create_doc, analyze_data, schedule, send_notification, query_knowledge, and execute_action. Extensible architecture for custom tools.
Memory System
services/agent_memory.rs
Conversation persistence using PostgreSQL with pgvector. Semantic search for relevant context. Agents learn and improve from past interactions.
Unified Chat Interface
handlers/enhanced_chat.rs
Single endpoint that routes to appropriate agents automatically. Aggregates responses from multiple agents for complex queries.
Implementation
Key Technical Decisions
Why Rust + Axum?
Performance and reliability are critical for AI workloads. Rust's zero-cost abstractions provide the speed of C++ with memory safety guarantees. Axum integrates seamlessly with the Tokio async runtime, enabling efficient concurrent agent execution.
Why Local LLM (Ollama)?
Privacy, cost control, and reliability. Sensitive business data stays on-premises. No per-token API costs. No dependency on external service availability. Fine-tuning capabilities for domain-specific performance.
Why pgvector?
Semantic search without a separate vector database. PostgreSQL's reliability with vector similarity capabilities. Simpler infrastructure, easier backups, unified query language for both relational and vector operations.
Results
Capabilities Achieved
7
Specialized Agents
8
Integrated Tools
<100ms
Agent Routing
8
Conversation Memory
Interested in multi-agent AI for your business?
Let's discuss how SAIOS architecture can be adapted for your needs.