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feat: Implement LLM Architecture Patterns - Building blocks (Augmented LLM), Workflows (Prompt Chaining, Routing, Parallelization, Orchestrator-Workers, Evaluator-Optimizer), and Agents (Autonomous, Domain-Specific) with examples and docs
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‎.cursorlogs

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# Cursor Development Logs
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[2024-02-15 Initial Setup]
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PROBLEM: Need to track development progress - ACTION: Created .cursorlogs file - RESULT: Successfully created tracking file
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PROBLEM: Repository structure analysis - ACTION: Listed current files and directories - RESULT: Found basic structure with building-block/, workflows/, and agents/ directories
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PROBLEM: Project dependencies - ACTION: Created requirements.txt - RESULT: Added core dependencies for LLM implementations
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PROBLEM: Building-block analysis - ACTION: Inspected building-block/augmented-llm - RESULT: Found basic implementation with augmented.py and supporting files
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PROBLEM: Workflows analysis - ACTION: Inspected workflows directory - RESULT: Found empty directories for all workflow patterns
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PROBLEM: Agents analysis - ACTION: Inspected agents directory - RESULT: Found empty directory, needs complete implementation
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[2024-02-15 Implementation Plan]
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1. Complete building-block/augmented-llm implementation
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2. Implement workflow patterns:
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- prompt-chaining
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- routing
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- parallelization
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- orchestrator-workers
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- evaluator-optimizer
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3. Implement agent patterns:
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- autonomous-agent
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- domain-specific-agents
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[2024-02-15 Building Block Implementation]
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PROBLEM: Augmented LLM tools needed - ACTION: Created basic_tools.py with WebSearch, Calculator, and Weather tools - RESULT: Successfully implemented tool classes
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PROBLEM: Example implementation needed - ACTION: Created basic_usage.py with complete example - RESULT: Successfully demonstrated tool and memory usage
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PROBLEM: Documentation needed - ACTION: Updated augmented-llm README.md - RESULT: Comprehensive documentation of implementation and usage
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[2024-02-15 Workflow Implementation - Prompt Chaining]
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PROBLEM: Core prompt chaining implementation needed - ACTION: Created chain.py with ChainStep, PromptChain, and ChainBuilder classes - RESULT: Successfully implemented base functionality
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PROBLEM: Example implementation needed - ACTION: Created text_analysis.py demonstrating multi-step text analysis - RESULT: Successfully implemented three-step chain example
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PROBLEM: Documentation needed - ACTION: Created prompt-chaining README.md - RESULT: Comprehensive documentation of pattern and usage
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[2024-02-15 Workflow Implementation - Routing]
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PROBLEM: Core routing implementation needed - ACTION: Created router.py with Route, Router, and RouterBuilder classes - RESULT: Successfully implemented base functionality
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PROBLEM: Example implementation needed - ACTION: Created support_routing.py demonstrating support ticket routing - RESULT: Successfully implemented support routing system
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PROBLEM: Documentation needed - ACTION: Created routing README.md - RESULT: Comprehensive documentation of pattern and usage
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[2024-02-15 Workflow Implementation - Parallelization]
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PROBLEM: Core parallelization implementation needed - ACTION: Created parallel.py with ParallelTask, Parallelizer, and ParallelizerBuilder classes - RESULT: Successfully implemented base functionality
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PROBLEM: Example implementation needed - ACTION: Created content_moderation.py demonstrating parallel content moderation - RESULT: Successfully implemented moderation system with multiple checks
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PROBLEM: Documentation needed - ACTION: Created parallelization README.md - RESULT: Comprehensive documentation of pattern and usage
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[2024-02-15 Workflow Implementation - Orchestrator-Workers]
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PROBLEM: Core orchestrator implementation needed - ACTION: Created orchestrator.py with Task, Worker, Orchestrator, and OrchestratorBuilder classes - RESULT: Successfully implemented base functionality
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PROBLEM: Example implementation needed - ACTION: Created document_processing.py demonstrating document analysis system - RESULT: Successfully implemented document processing with multiple workers
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PROBLEM: Documentation needed - ACTION: Created orchestrator-workers README.md - RESULT: Comprehensive documentation of pattern and usage
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[2024-02-15 Workflow Implementation - Evaluator-Optimizer]
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PROBLEM: Core optimizer implementation needed - ACTION: Created optimizer.py with Candidate, Optimizer, and OptimizerBuilder classes - RESULT: Successfully implemented base functionality with multiple strategies
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PROBLEM: Example implementation needed - ACTION: Created code_optimization.py demonstrating code improvement system - RESULT: Successfully implemented code optimization with multiple strategies
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PROBLEM: Documentation needed - ACTION: Created evaluator-optimizer README.md - RESULT: Comprehensive documentation of pattern and usage
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[2024-02-15 Agent Implementation - Autonomous Agent]
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PROBLEM: Core agent implementation needed - ACTION: Created agent.py with Action, Plan, Tool, Memory, and AutonomousAgent classes - RESULT: Successfully implemented base functionality with planning and execution capabilities
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PROBLEM: Example implementation needed - ACTION: Created research_assistant.py demonstrating autonomous research system - RESULT: Successfully implemented research assistant with multiple tools and adaptive planning
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PROBLEM: Documentation needed - ACTION: Created autonomous-agent README.md - RESULT: Comprehensive documentation of pattern and usage
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[2024-02-15 Agent Implementation - Domain-Specific Agent]
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PROBLEM: Core domain-specific agent implementation needed - ACTION: Created domain-specific/agent.py with DomainConstraint, DomainKnowledge, DomainBehavior, and DomainSpecificAgent classes - RESULT: Successfully implemented base functionality with domain-specific capabilities
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PROBLEM: Example implementation needed - ACTION: Created medical_assistant.py demonstrating medical diagnosis system - RESULT: Successfully implemented medical assistant with domain knowledge, constraints, and behaviors
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PROBLEM: Documentation needed - ACTION: Created domain-specific README.md - RESULT: Comprehensive documentation of pattern and usage

‎agents/autonomous-agent/README.md

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# Autonomous Agent Pattern
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This pattern demonstrates how to implement self-directed agents that can plan, execute, and adapt their behavior to achieve goals.
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## Overview
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The autonomous agent pattern is useful when:
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- Tasks require complex planning and execution
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- Goals can be broken down into discrete actions
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- Adaptation to results is needed
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- Long-running tasks need monitoring
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## Components
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### Core Classes
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1. **Action**
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- Represents a discrete task
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- Tracks execution status
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- Stores results
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- Manages parameters
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2. **Plan**
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- Sequences of actions
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- Goal definition
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- Context information
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- Execution status
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3. **Tool**
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- Specific capabilities
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- Parameter definitions
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- Execution handlers
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- Error handling
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4. **Memory**
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- Action history
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- Plan history
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- Context storage
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- State management
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5. **AutonomousAgent**
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- Plan creation
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- Action execution
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- Progress evaluation
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- Plan adjustment
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## Example Implementation
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The `examples/research_assistant.py` demonstrates autonomous research:
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1. Research Tools:
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- Web Search
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- Webpage Reading
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- Text Summarization
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- Fact Extraction
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- Note Taking
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2. Features:
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- Goal decomposition
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- Adaptive planning
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- Progress tracking
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- Result aggregation
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## Usage
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1. Set up environment:
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```bash
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pip install -r requirements.txt
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```
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2. Configure API keys:
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```bash
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export ANTHROPIC_API_KEY=your_key_here
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# or
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export OPENAI_API_KEY=your_key_here
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```
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3. Run the example:
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```bash
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python -m examples.research_assistant
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```
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## Implementation Details
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### Tool Definition
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```python
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Tool(
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name="tool_name",
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description="tool description",
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parameters={"param": "type"},
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handler=async_handler_function
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)
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```
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### Agent Building
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```python
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agent = (
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AgentBuilder()
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.add_tool(...)
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.add_tool(...)
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.with_memory(memory)
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.build(llm_caller)
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)
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```
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### Goal Execution
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```python
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actions = await agent.execute(
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goal="goal description",
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context={}
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)
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```
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## Agent Capabilities
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1. **Planning**
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- Goal analysis
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- Task decomposition
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- Action sequencing
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- Dependency management
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2. **Execution**
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- Tool selection
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- Parameter preparation
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- Result handling
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- Error recovery
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3. **Monitoring**
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- Progress tracking
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- Goal evaluation
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- Plan adjustment
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- Performance analysis
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4. **Learning**
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- Action history
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- Success patterns
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- Failure analysis
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- Strategy adaptation
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## Best Practices
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1. **Goal Design**
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- Clear objectives
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- Measurable outcomes
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- Reasonable scope
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- Success criteria
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2. **Tool Implementation**
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- Focused functionality
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- Clear interfaces
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- Robust error handling
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- Performance optimization
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3. **Memory Management**
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- Relevant history
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- Context preservation
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- State cleanup
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- Storage efficiency
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4. **Error Handling**
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- Graceful degradation
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- Recovery strategies
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- Feedback loops
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- Logging and monitoring
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## Extensions
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Consider extending this pattern with:
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1. **Advanced Planning**
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- Multi-goal handling
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- Priority management
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- Resource allocation
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- Constraint satisfaction
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2. **Learning Capabilities**
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- Strategy optimization
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- Pattern recognition
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- Performance tuning
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- Knowledge base
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3. **Collaboration**
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- Agent communication
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- Task delegation
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- Resource sharing
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- Conflict resolution
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4. **Safety**
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- Action validation
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- Resource limits
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- Security checks
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- Ethical constraints
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5. **Integration**
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- External services
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- Data sources
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- Monitoring systems
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- Reporting tools

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