Theoretical Foundations
Architecture Overview
OJU is built on a modular architecture that separates concerns between different components:
Agent Core: The central component that handles the execution flow
Provider Interface: Abstract layer for different LLM providers
Prompt Management: System for organizing and retrieving prompts
Concurrency Layer: Manages parallel execution of multiple agents
Design Principles
Extensibility - Provider-agnostic design allows adding new LLM providers with minimal code changes - Plugin architecture for custom components
Performance - Asynchronous I/O for non-blocking operations - Parallel execution of independent agents - Connection pooling and request batching
Usability - Intuitive API design - Comprehensive error handling - Detailed logging and monitoring
Agent System Architecture
+------------------+ +-----------------+ +-----------------+
| User | | Agent Manager | | Agent 1 |
| Application |---->| |---->| Agent 2 |
+------------------+ +--------+--------+ | ... |
| | Agent N |
| +--------+--------+
| |
v v
+------------------+ +-----------------+ +--------+--------+
| OpenAI | | Provider |<-----+ Provider |
| Anthropic |<----| Interface | | Interface |
| Google Gemini | | |<--+ | |
+------------------+ +-----------------+ | +--------------+
|
| +--------------+
+---| Provider |
| Interface |
+--------------+
Concurrency Model
OJU employs a hybrid concurrency model:
Thread-based Parallelism - Each agent runs in its own thread - I/O-bound operations are non-blocking - Thread pool for managing worker threads
Asynchronous Execution - Async/await pattern for I/O operations - Event loop for managing concurrent tasks - Backpressure handling for rate limiting
Prompt Engineering
OJU implements several prompt engineering best practices:
Templating System - Reusable prompt templates - Variable substitution - Conditional logic in prompts
Context Management - Conversation history tracking - Token window management - Context summarization for long conversations
Response Formatting - Structured output generation - JSON schema validation - Streaming responses
Security Considerations
Data Protection - API key management - Secure credential storage - Request encryption
Rate Limiting - Request throttling - Exponential backoff - Circuit breaker pattern
Compliance - Data retention policies - Logging and audit trails - Privacy controls
Performance Optimization
Caching Layer - Response caching - Embedding caching - Vector similarity search
Batch Processing - Request batching - Parallel processing - Pipeline optimization
Resource Management - Connection pooling - Memory management - Garbage collection
Future Directions
Advanced Features - Multi-agent collaboration - Reinforcement learning from human feedback (RLHF) - Fine-tuning support
Scalability - Distributed execution - Load balancing - Auto-scaling
Extensibility - Custom provider plugins - Third-party integrations - Community contributions