title: Index
Atlas Architecture Overview
Atlas is a comprehensive framework for building intelligent agents with knowledge retrieval capabilities. It combines large language models with vector-based retrieval in a flexible, modular architecture that supports both simple queries and complex multi-agent workflows.
Key Architectural Components
Atlas is built around several core architectural components that work together to provide a complete agent framework:
1. Agent System
The Agent System provides the primary interface for user interactions and coordinates the other components. Key features include:
- Unified Agent Interface: Common API across all agent types
- Multi-Provider Support: Compatible with Anthropic, OpenAI, and Ollama
- Controller-Worker Pattern: For parallel processing and specialization
- Conversation Management: Maintains chat history and context
2. Knowledge System
The Knowledge System manages document storage, retrieval, and processing. Key features include:
- ChromaDB Integration: Vector database for semantic search
- Document Processing: Chunking and embedding for efficient retrieval
- Metadata Management: Source tracking and document categorization
- Relevance Ranking: Scoring and filtering of search results
3. Provider System
The Provider System handles interactions with language model APIs. Key features include:
- Provider Abstraction: Unified interface across LLM providers
- Provider Options: Configuration framework with capability-based model selection
- Streaming Support: Real-time token-by-token responses
- Error Handling: Robust error recovery and retry logic
- Auto-detection: Automatic provider selection from model name
4. Graph Workflow System
The Graph Workflow System enables complex agent interactions and workflows. Key features include:
- LangGraph Integration: Graph-based workflow definition
- Conditional Routing: Dynamic decision-making and branching
- State Management: Tracking of workflow state and progress
- Parallel Processing: Concurrent execution of agent tasks
5. Core Infrastructure
Core Infrastructure provides foundational capabilities used by all components. Key features include:
- Configuration Management: Environment variables and defaults
- Schema Validation: Comprehensive data validation using Marshmallow schemas
- Telemetry: Performance monitoring and tracing
- Error Handling: Standardized error types and recovery
- CLI Interface: Command-line tools for all operations
Architectural Principles
Atlas follows several key architectural principles:
- Modularity: Components are decoupled and independently usable
- Extensibility: Easy to add new providers, workflows, and capabilities
- Robustness: Comprehensive error handling and recovery
- Transparency: Clear visibility into operations and decisions
- Efficiency: Optimal resource usage for cost-effective operation
Deployment Model
Atlas can be deployed in several different configurations:
- Standalone Application: Run as a command-line tool with local database
- Client Library: Embedded in other Python applications
- Multi-Agent System: Distributed across multiple processes or machines
- Specialized Worker: Focused on specific tasks in a larger system
Next Steps
- See Components for detailed descriptions of each component
- See Data Flow for information on how data moves through Atlas
- See Module Interaction for details on how modules integrate with each other
- See Schema Validation for information on the schema validation architecture