"""
Pattern 008: LLM Compiler
Description:
Optimizes LLM execution by analyzing task dependencies, parallelizing
independent operations, and optimizing the execution plan like a compiler
optimizes code. Combines planning with execution optimization.
Key Concepts:
- Task Decomposition: Break complex tasks into subtasks
- Dependency Analysis: Build dependency graph (DAG)
- Optimization: Identify parallelizable operations
- Scheduling: Optimal execution order
- Resource Management: Efficient LLM call allocation
Compiler Phases:
1. Parsing: Understand and decompose the task
2. Analysis: Build dependency graph
3. Optimization: Find parallelization opportunities
4. Code Generation: Create execution plan
5. Execution: Run optimized plan
Use Cases:
- Complex multi-step workflows
- Research tasks with multiple branches
- Data processing pipelines
- Tasks with many independent subtasks
LangChain Implementation:
Uses dependency graphs and parallel execution with LangChain tools.
"""
import os
from typing import List, Dict, Any, Set, Optional
from dataclasses import dataclass, field
from collections import defaultdict, deque
from concurrent.futures import ThreadPoolExecutor, as_completed
from enum import Enum
from dotenv import load_dotenv
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
# Load environment variables
load_dotenv()
class TaskStatus(Enum):
"""Status of a task in the execution plan."""
PENDING = "pending"
READY = "ready"
RUNNING = "running"
COMPLETED = "completed"
FAILED = "failed"
@dataclass
class Task:
"""Represents a single task in the execution plan."""
id: str
description: str
dependencies: Set[str] = field(default_factory=set)
status: TaskStatus = TaskStatus.PENDING
result: Optional[str] = None
error: Optional[str] = None
parallel_group: Optional[int] = None # For grouping parallel tasks
def can_execute(self, completed_tasks: Set[str]) -> bool:
"""Check if this task can be executed."""
return (self.status == TaskStatus.PENDING and
self.dependencies.issubset(completed_tasks))
@dataclass
class ExecutionPlan:
"""Represents the optimized execution plan."""
goal: str
tasks: Dict[str, Task] = field(default_factory=dict)
dependency_graph: Dict[str, Set[str]] = field(default_factory=lambda: defaultdict(set))
parallel_groups: List[List[str]] = field(default_factory=list)
def add_task(self, task: Task):
"""Add a task to the plan."""
self.tasks[task.id] = task
for dep in task.dependencies:
self.dependency_graph[dep].add(task.id)
def get_ready_tasks(self, completed: Set[str]) -> List[Task]:
"""Get all tasks that are ready to execute."""
ready = []
for task in self.tasks.values():
if task.can_execute(completed):
ready.append(task)
return ready
def topological_sort(self) -> List[str]:
"""Perform topological sort on tasks."""
in_degree = {task_id: len(task.dependencies)
for task_id, task in self.tasks.items()}
queue = deque([task_id for task_id, degree in in_degree.items() if degree == 0])
sorted_tasks = []
while queue:
task_id = queue.popleft()
sorted_tasks.append(task_id)
for dependent in self.dependency_graph[task_id]:
in_degree[dependent] -= 1
if in_degree[dependent] == 0:
queue.append(dependent)
return sorted_tasks
class LLMCompiler:
"""Compiler that optimizes LLM task execution."""
def __init__(self, model_name: str = "gpt-3.5-turbo", max_parallel: int = 3):
"""
Initialize the LLM Compiler.
Args:
model_name: Name of the OpenAI model
max_parallel: Maximum number of parallel tasks
"""
self.llm = ChatOpenAI(
model=model_name,
temperature=0.3,
api_key=os.getenv("OPENAI_API_KEY")
)
self.max_parallel = max_parallel
def parse_task(self, goal: str) -> ExecutionPlan:
"""
Parse the goal and decompose into subtasks.
Args:
goal: The high-level goal
Returns:
Initial execution plan with tasks
"""
prompt = ChatPromptTemplate.from_messages([
("system", """Decompose the goal into specific, actionable subtasks.
For each subtask:
1. Give it a unique ID (T1, T2, T3, etc.)
2. Describe what needs to be done
3. List dependencies (which tasks must complete first)
Format:
T1: [description] | Dependencies: none
T2: [description] | Dependencies: T1
T3: [description] | Dependencies: T1
T4: [description] | Dependencies: T2, T3
...
Be specific and ensure dependencies are correct."""),
("human", "Goal: {goal}\n\nTask Decomposition:")
])
chain = prompt | self.llm | StrOutputParser()
response = chain.invoke({"goal": goal})
# Parse tasks
plan = ExecutionPlan(goal=goal)
for line in response.strip().split('\n'):
line = line.strip()
if not line or ':' not in line:
continue
# Parse: T1: description | Dependencies: T2, T3
parts = line.split('|')
if len(parts) ExecutionPlan:
"""
Analyze dependencies and optimize execution order.
Args:
plan: Initial execution plan
Returns:
Plan with optimized execution order
"""
# Topological sort to get valid execution order
sorted_tasks = plan.topological_sort()
# Identify parallel groups (tasks at same level)
completed = set()
parallel_groups = []
while completed != set(plan.tasks.keys()):
# Get all tasks that can execute now
ready_tasks = []
for task_id in sorted_tasks:
if task_id not in completed:
task = plan.tasks[task_id]
if task.can_execute(completed):
ready_tasks.append(task_id)
if not ready_tasks:
break # Circular dependency or error
parallel_groups.append(ready_tasks)
completed.update(ready_tasks)
# Assign parallel group numbers
for task_id in ready_tasks:
plan.tasks[task_id].parallel_group = len(parallel_groups) - 1
plan.parallel_groups = parallel_groups
return plan
def optimize_plan(self, plan: ExecutionPlan) -> ExecutionPlan:
"""
Apply optimizations to the execution plan.
Args:
plan: Analyzed execution plan
Returns:
Optimized plan
"""
# Optimization strategies:
# 1. Limit parallel groups to max_parallel
# 2. Try to balance load across groups
# 3. Merge small groups if beneficial
optimized_groups = []
for group in plan.parallel_groups:
if len(group) str:
"""
Execute a single task.
Args:
task: The task to execute
plan: The complete plan for context
Returns:
Task result
"""
# Build context from completed dependencies
context_parts = []
for dep_id in task.dependencies:
dep_task = plan.tasks.get(dep_id)
if dep_task and dep_task.result:
context_parts.append(f"{dep_id}: {dep_task.result}")
context = "\n".join(context_parts) if context_parts else "No prior context."
prompt = ChatPromptTemplate.from_messages([
("system", "Execute the task using the provided context from previous steps."),
("human", """Goal: {goal}
Prior Results:
{context}
Task: {task_description}
Result:""")
])
chain = prompt | self.llm | StrOutputParser()
result = chain.invoke({
"goal": plan.goal,
"context": context,
"task_description": task.description
})
return result.strip()
def execute_plan(self, plan: ExecutionPlan) -> Dict[str, Any]:
"""
Execute the optimized plan.
Args:
plan: The execution plan
Returns:
Execution results
"""
print(f"\nGoal: {plan.goal}\n")
print(f"Total Tasks: {len(plan.tasks)}")
print(f"Parallel Groups: {len(plan.parallel_groups)}\n")
# Show execution plan
print("Execution Plan:")
for i, group in enumerate(plan.parallel_groups):
group_tasks = [plan.tasks[tid].description[:50] for tid in group]
print(f" Group {i+1} ({len(group)} tasks in parallel):")
for task_id in group:
deps = list(plan.tasks[task_id].dependencies)
dep_str = f" [depends on: {', '.join(deps)}]" if deps else ""
print(f" - {task_id}: {plan.tasks[task_id].description[:60]}{dep_str}")
print()
completed = set()
total_llm_calls = 0
# Execute groups in sequence
for group_idx, group in enumerate(plan.parallel_groups):
print(f"{'='*60}")
print(f"Executing Group {group_idx + 1} ({len(group)} tasks)")
print('='*60)
# Execute tasks in group in parallel
with ThreadPoolExecutor(max_workers=min(len(group), self.max_parallel)) as executor:
futures = {}
for task_id in group:
task = plan.tasks[task_id]
task.status = TaskStatus.RUNNING
future = executor.submit(self.execute_task, task, plan)
futures[future] = task
# Collect results
for future in as_completed(futures):
task = futures[future]
total_llm_calls += 1
try:
result = future.result()
task.result = result
task.status = TaskStatus.COMPLETED
completed.add(task.id)
print(f"\n✓ {task.id} completed")
print(f" Result: {result[:80]}...")
except Exception as e:
task.status = TaskStatus.FAILED
task.error = str(e)
print(f"\n✗ {task.id} failed: {str(e)}")
# Generate final summary
summary = self._synthesize_results(plan)
return {
"goal": plan.goal,
"total_tasks": len(plan.tasks),
"completed_tasks": len(completed),
"parallel_groups": len(plan.parallel_groups),
"total_llm_calls": total_llm_calls,
"summary": summary
}
def _synthesize_results(self, plan: ExecutionPlan) -> str:
"""Synthesize final summary from all task results."""
results = []
for task in plan.tasks.values():
if task.status == TaskStatus.COMPLETED and task.result:
results.append(f"{task.id}: {task.result}")
if not results:
return "No tasks completed successfully."
prompt = ChatPromptTemplate.from_messages([
("system", "Synthesize the results into a comprehensive summary."),
("human", """Goal: {goal}
Task Results:
{results}
Summary:""")
])
chain = prompt | self.llm | StrOutputParser()
try:
summary = chain.invoke({
"goal": plan.goal,
"results": "\n\n".join(results)
})
return summary.strip()
except:
return "Summary generation failed."
def compile_and_execute(self, goal: str) -> Dict[str, Any]:
"""
Compile and execute a goal (full pipeline).
Args:
goal: The goal to achieve
Returns:
Execution results
"""
print(f"\n{'='*80}")
print("LLM COMPILER: PARSING")
print('='*80)
# Phase 1: Parse and decompose
plan = self.parse_task(goal)
print(f"\nParsed {len(plan.tasks)} tasks")
print(f"\n{'='*80}")
print("LLM COMPILER: ANALYSIS")
print('='*80)
# Phase 2: Analyze dependencies
plan = self.analyze_dependencies(plan)
print(f"\nIdentified {len(plan.parallel_groups)} execution groups")
print(f"\n{'='*80}")
print("LLM COMPILER: OPTIMIZATION")
print('='*80)
# Phase 3: Optimize
plan = self.optimize_plan(plan)
print(f"\nOptimized for {self.max_parallel} max parallel tasks")
print(f"\n{'='*80}")
print("LLM COMPILER: EXECUTION")
print('='*80)
# Phase 4: Execute
result = self.execute_plan(plan)
return result
def demonstrate_llm_compiler():
"""Demonstrates the LLM Compiler pattern."""
print("=" * 80)
print("PATTERN 008: LLM Compiler")
print("=" * 80)
print()
print("LLM Compiler optimizes execution like a code compiler:")
print("1. Parsing: Decompose task into subtasks")
print("2. Analysis: Build dependency graph")
print("3. Optimization: Identify parallelization opportunities")
print("4. Execution: Run optimized plan with parallel execution")
print()
# Create compiler
compiler = LLMCompiler(max_parallel=3)
# Test goals
goals = [
"Research the history of artificial intelligence and create a timeline of major milestones",
"Analyze the advantages and disadvantages of renewable energy sources and compare them"
]
for i, goal in enumerate(goals, 1):
print(f"\n{'='*80}")
print(f"Example {i}")
print('='*80)
try:
result = compiler.compile_and_execute(goal)
print(f"\n\n{'='*80}")
print("COMPILATION SUMMARY")
print('='*80)
print(f"\nGoal: {result['goal']}")
print(f"Total Tasks: {result['total_tasks']}")
print(f"Completed: {result['completed_tasks']}/{result['total_tasks']}")
print(f"Parallel Groups: {result['parallel_groups']}")
print(f"Total LLM Calls: {result['total_llm_calls']}")
print(f"\nFinal Summary:\n{result['summary']}")
except Exception as e:
print(f"\n✗ Error: {str(e)}")
import traceback
traceback.print_exc()
# Summary
print("\n\n" + "=" * 80)
print("LLM COMPILER PATTERN DEMONSTRATION COMPLETE")
print("=" * 80)
print()
print("Key Features Demonstrated:")
print("1. Task Decomposition: Breaking complex goals into subtasks")
print("2. Dependency Analysis: Building execution dependency graph")
print("3. Parallelization: Executing independent tasks concurrently")
print("4. Optimization: Grouping and scheduling for efficiency")
print("5. Resource Management: Limiting parallel execution")
print()
print("Advantages:")
print("- Reduced total execution time through parallelization")
print("- Efficient resource utilization")
print("- Clear dependency management")
print("- Systematic optimization approach")
print()
print("When to use LLM Compiler:")
print("- Complex multi-step workflows")
print("- Tasks with many independent subtasks")
print("- Time-sensitive applications")
print("- Research and analysis tasks")
print()
print("LangChain Components Used:")
print("- ChatPromptTemplate for task decomposition")
print("- Dependency graph (DAG) management")
print("- ThreadPoolExecutor for parallel execution")
print("- Result synthesis chain")
print()
if __name__ == "__main__":
# Check for API key
if not os.getenv("OPENAI_API_KEY"):
print("Error: OPENAI_API_KEY not found in environment variables.")
print("Please set it in your .env file or environment.")
exit(1)
demonstrate_llm_compiler()
Agentic Design Pattern: Llm Compiler
Pattern 008: Llm Compiler – Line-by-Line Explanation
Overview
This document provides a detailed line-by-line explanation of the Llm Compiler pattern implementation.
Pattern Description
Pattern 008: LLM Compiler
Description: Optimizes LLM execution by analyzing task dependencies, parallelizing independent operations, and optimizing the execution plan like a compiler optimizes code. Combines planning with execution optimization.
Key Concepts: – Task Decomposition: Break complex tasks into subtasks – Dependency Analysis: Build dependency graph (DAG) – Optimization: Identify parallelizable operations – Scheduling: Optimal execution order – Resource Management: Efficient LLM call allocation
Compiler Phases: 1. Parsing: Understand and decompose the task 2. Analysis: Build dependency graph 3. Optimization: Find parallelization opportunities 4. Code Generation: Create execution plan 5. Execution: Run optimized plan
Use Cases: – Complex multi-step workflows – Research tasks with multiple branches – Data processing pipelines – Tasks with many independent subtasks
LangChain Implementation: Uses dependency graphs and parallel execution with LangChain tools.
File Statistics
- Total Lines: 553
- Classes: 4
- Functions: 1
- Constants: 0
Module Documentation
"""
Pattern 008: LLM Compiler
Description:
Optimizes LLM execution by analyzing task dependencies, parallelizing
independent operations, and optimizing the execution plan like a compiler
optimizes code. Combines planning with execution optimization.
Key Concepts:
- Task Decomposition: Break complex tasks into subtasks
- Dependency Analysis: Build dependency graph (DAG)
- Optimization: Identify parallelizable operations
- Scheduling: Optimal execution order
- Resource Management: Efficient LLM call allocation
Compiler Phases:
1. Parsing: Understand and decompose the task
2. Analysis: Build dependency graph
3. Optimization: Find parallelization opportunities
4. Code Generation: Create execution plan
5. Execution: Run optimized plan
Use Cases:
- Complex multi-step workflows
- Research tasks with multiple branches
- Data processing pipelines
- Tasks with many independent subtasks
LangChain Implementation:
Uses dependency graphs and parallel execution with LangChain tools.
"""
Purpose: This module implements the Llm Compiler pattern, providing a structured approach to agent design and implementation.
Import Statements
Standard Library Imports
import collections.defaultdict
import collections.deque
import concurrent.futures.ThreadPoolExecutor
import concurrent.futures.as_completed
import dataclasses.dataclass
import dataclasses.field
import enum.Enum
import langchain_core.output_parsers.StrOutputParser
import langchain_core.prompts.ChatPromptTemplate
import os
import typing.Any
import typing.Dict
import typing.List
import typing.Optional
import typing.Set
Purpose: Core Python functionality:
– collections.defaultdict: Specialized container datatypes
– collections.deque: Specialized container datatypes
– concurrent.futures.ThreadPoolExecutor: Regular expression operations
– concurrent.futures.as_completed: Regular expression operations
– dataclasses.dataclass: Simplified class definitions with @dataclass
– dataclasses.field: Simplified class definitions with @dataclass
– enum.Enum: Enumeration support
– langchain_core.output_parsers.StrOutputParser: Regular expression operations
– langchain_core.prompts.ChatPromptTemplate: Regular expression operations
– os: Environment variable access and system operations
– typing.Any: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Dict: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.List: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Optional: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Set: Type hints for better code documentation (List, Dict, Any, Optional)
LangChain Imports
from langchain_openai.ChatOpenAI
Purpose: LangChain framework components: – LLM Interface: Wrapper for OpenAI models – Chains: Composable building blocks for complex workflows
Other Imports
from dotenv.load_dotenv
from traceback
Purpose: Additional dependencies for specialized functionality.
Classes
Class: TaskStatus
Lines 49-55
Inherits from: Enum
Purpose:
Status of a task in the execution plan.
Class: Task
Lines 59-72
Purpose:
Represents a single task in the execution plan.
Methods:
can_execute(self, completed_tasks)
Lines 69-72
Purpose:
Check if this task can be executed.
Parameters:
– completed_tasks: Completed tasks
Returns: bool
Class: ExecutionPlan
Lines 76-114
Purpose:
Represents the optimized execution plan.
Methods:
add_task(self, task)
Lines 83-87
Purpose:
Add a task to the plan.
Parameters:
– task: Task
get_ready_tasks(self, completed)
Lines 89-95
Purpose:
Get all tasks that are ready to execute.
Parameters:
– completed: Completed
Returns: <ast.Subscript object at 0x000001A06D615F10>
topological_sort(self)
Lines 97-114
Purpose:
Perform topological sort on tasks.
Parameters:
Returns: <ast.Subscript object at 0x000001A06D6AA390>
Class: LLMCompiler
Lines 117-464
Purpose:
Compiler that optimizes LLM task execution.
Methods:
__init__(self, model_name, max_parallel)
Lines 120-133
Purpose:
Initialize the LLM Compiler.
Args:
model_name: Name of the OpenAI model
max_parallel: Maximum number of parallel tasks
Parameters:
– model_name: Language model instance
– max_parallel: Max parallel
parse_task(self, goal)
Lines 135-209
Purpose:
Parse the goal and decompose into subtasks.
Args:
goal: The high-level goal
Returns:
Initial execution plan with tasks
Parameters:
– goal: Goal
Returns: ExecutionPlan
analyze_dependencies(self, plan)
Lines 211-249
Purpose:
Analyze dependencies and optimize execution order.
Args:
plan: Initial execution plan
Returns:
Plan with optimized execution order
Parameters:
– plan: Plan
Returns: ExecutionPlan
optimize_plan(self, plan)
Lines 251-277
Purpose:
Apply optimizations to the execution plan.
Args:
plan: Analyzed execution plan
Returns:
Optimized plan
Parameters:
– plan: Plan
Returns: ExecutionPlan
execute_task(self, task, plan)
Lines 279-318
Purpose:
Execute a single task.
Args:
task: The task to execute
plan: The complete plan for context
Returns:
Task result
Parameters:
– task: Task
– plan: Plan
Returns: str
execute_plan(self, plan)
Lines 320-390
Purpose:
Execute the optimized plan.
Args:
plan: The execution plan
Returns:
Execution results
Parameters:
– plan: Plan
Returns: <ast.Subscript object at 0x000001A06D6EB350>
_synthesize_results(self, plan)
Lines 392-421
Purpose:
Synthesize final summary from all task results.
Parameters:
– plan: Plan
Returns: str
compile_and_execute(self, goal)
Lines 423-464
Purpose:
Compile and execute a goal (full pipeline).
Args:
goal: The goal to achieve
Returns:
Execution results
Parameters:
– goal: Goal
Returns: <ast.Subscript object at 0x000001A06D628190>
Functions
demonstrate_llm_compiler()
Lines 467-542
Purpose:
Demonstrates the LLM Compiler pattern.
Key Concepts Summary
Pattern Overview
The Llm Compiler pattern provides a structured approach to building AI agents with specific capabilities and behaviors. This implementation leverages LangChain’s composable components to create a robust and maintainable solution.
Core Components
- Agent Class: Main implementation of the pattern logic
- Prompt Engineering: Carefully crafted prompts to guide LLM behavior
- Tool Integration: Optional tools for extended functionality
- Memory Management: State handling across interactions
- Output Parsing: Structured response processing
When to Use
✅ Good for: – Scenarios requiring this specific pattern’s strengths – Applications needing structured agent behavior – Systems where explainability is important – Production environments requiring reliability
❌ Not ideal for: – Simple single-prompt tasks – Scenarios requiring different pattern characteristics – Resource-constrained environments (if computationally intensive)
Best Practices Demonstrated
1. Environment Configuration
- ✅ Use
.envfiles for API keys - ✅ Never hardcode credentials
- ✅ Validate environment variables on startup
2. Error Handling
- ✅ Graceful degradation on failures
- ✅ Informative error messages
- ✅ Logging for debugging
3. Prompt Engineering
- ✅ Clear, specific instructions
- ✅ Structured output formats
- ✅ Few-shot examples when beneficial
4. Code Organization
- ✅ Separation of concerns (class-based design)
- ✅ Type hints for clarity
- ✅ Docstrings for documentation
5. Performance Optimization
- ✅ Appropriate temperature settings
- ✅ Token budget management
- ✅ Caching where applicable
LangChain Components Used
- ChatOpenAI: OpenAI model interface
- Chains: Sequential component composition
Why LangChain? – Composable abstractions for LLM applications – Rich ecosystem of integrations – Production-ready patterns – Active community and development
Execution Flow
1. Initialize agent with configuration
↓
2. Load environment and dependencies
↓
3. Prepare prompt with input data
↓
4. Invoke LLM through LangChain
↓
5. Parse and validate response
↓
6. Return formatted result
Key Decision Points: – Model selection (temperature, model type) – Prompt structure (few-shot vs zero-shot) – Error handling strategy – Output format validation
Performance Considerations
Computational Cost
- LLM Calls: Primary cost factor (based on tokens)
- Latency: Depends on model and input size
- Memory: Minimal for most patterns
Optimization Strategies
- Prompt Optimization: Minimize tokens while maintaining clarity
- Caching: Cache repeated queries/responses
- Model Selection: Use smaller models when appropriate
- Batch Processing: Group similar requests
- Streaming: For long responses, stream tokens
Cost Estimates (Approximate)
- GPT-3.5-turbo: ~$0.002 per request
- GPT-4: ~$0.03 per request
- GPT-4-turbo: ~$0.01 per request
Actual costs vary based on input/output token counts
Common Pitfalls to Avoid
1. Prompt Design Issues
❌ Vague or ambiguous instructions
✅ Clear, specific, testable prompts
2. Error Handling
❌ Ignoring API errors or rate limits
✅ Implement retry logic and graceful degradation
3. Token Management
❌ Exceeding model token limits
✅ Truncate inputs, chunk processing
4. Security
❌ Exposing API keys in code
✅ Use environment variables
5. Testing
❌ Only testing happy paths
✅ Test edge cases, failures, malformed inputs
6. Cost Management
❌ Unmonitored API usage
✅ Implement budgets, alerts, and monitoring
Further Improvements
Production Enhancements
- Monitoring: Add logging, metrics, tracing
- Validation: Input/output schema validation
- Rate Limiting: Implement request throttling
- Caching: Redis or in-memory cache for responses
- Async Support: Non-blocking operations for scalability
Feature Extensions
- Multi-Model Support: Switch between providers
- Fallback Models: Graceful degradation strategy
- Custom Tools: Domain-specific function calling
- Advanced Memory: Vector stores for long-term memory
- Evaluation: Automated testing and benchmarking
Code Quality
- Testing: Unit tests, integration tests
- Documentation: API docs, usage examples
- Type Safety: Stricter type hints
- Configuration: Externalized config files
Related Patterns
- Pattern 007
- Pattern 009
- Other Reasoning Patterns (001-011)
Pattern Families: – Reasoning Patterns (001-011) – Multi-Agent Patterns (012-019) – Tool Use Patterns (020-025) – Memory Patterns (026-031) – And many more…
Summary
The Llm Compiler pattern provides a powerful approach to building AI agents with specific capabilities. This implementation demonstrates:
✅ Key Strengths: – Clean, maintainable code structure – LangChain integration for composability – Type hints and documentation – Production-ready patterns
🎯 Best Use Cases: – Applications requiring this pattern’s characteristics – Systems needing structured agent behavior – Production environments requiring reliability
📚 Learning Resources: – LangChain Documentation: https://python.langchain.com/ – Pattern Documentation: See related patterns – OpenAI API Reference: https://platform.openai.com/docs/
File: 008_llm_compiler.py
Total Lines: 553
Pattern Category: AI Agent Design Pattern #008
Generated from actual implementation code
For educational and reference purposes
Leave a Reply