Agent Forge 🧪 ALPHA
Self-Compiling Agent Architecture
Revolutionary proof-of-concept combining orchestrator and translator models for optimal performance
Overview
Agent Forge is our comprehensive framework for just-in-time agent architectures, featuring three specialized proof-of-concept implementations. It explores unified Qwen model architectures, reinforcement learning capabilities, and persistent memory systems. This modular approach enables comparison of different JIT strategies and measurable performance improvements over traditional pure LLM systems.
🧠 JIT Architecture
Framework for just-in-time agent compilation using Qwen models for planning, reasoning, and code generation.
⚡ Three POC Implementations
jit-agent-poc (unified), jit-agent-learn (RL), and jit-agent-memory (persistent state) POCs.
🔧 JIT Compilation
Dynamic tool generation and caching for optimized runtime performance.
📊 Benchmarking
Quantifiable performance metrics comparing hybrid vs pure LLM approaches.
Architecture
1. Goal Processing
User goal is processed by the Qwen orchestrator to create an execution plan
2. Concept Generation
Orchestrator generates high-level function concepts for required tools
3. Code Translation
Fine-tuned Qwen models translate concepts into executable Python code
4. Runtime Execution
Compiled tools are cached and executed, with results fed back to orchestrator
Technical Implementation
Base Models
- Qwen2.5-Coder series for code generation
- 4-bit quantization for efficient inference
- Optimized for reasoning and planning tasks
Fine-tuning Strategy
- LORA adapters for specialized tasks
- 4-bit quantization with BitsAndBytesConfig
- Model-specific fine-tuning for each POC
Training Dataset
- High-quality concept-to-code examples
- Structured instruction-output format
- Focus on API integration and tool creation
Runtime Engine
- Dynamic function compilation and caching
- Comprehensive performance benchmarking
- Error handling and fallback mechanisms
Performance Benchmarking
Agent Forge includes a comprehensive benchmarking framework that measures:
- Latency: Total response time for task completion
- Token Efficiency: Number of tokens consumed during execution
- Success Rate: Task completion accuracy and reliability
- Caching Benefits: Performance improvements from tool reuse
Expected Performance Gains
Based on the architecture design, Agent Forge demonstrates significant improvements in efficiency through specialized model usage and intelligent caching mechanisms.