"""
Pattern 080: Asynchronous Agent
Description:
An Asynchronous Agent uses async/await patterns to handle concurrent operations
without blocking. This pattern is essential for building high-performance,
scalable AI applications that can handle multiple requests simultaneously,
make concurrent API calls, and provide responsive user experiences.
The agent leverages Python's asyncio for non-blocking I/O operations, enabling
efficient resource utilization and improved throughput. It supports concurrent
LLM calls, parallel tool execution, and async streaming for maximum performance.
Components:
1. Async Executor: Manages async task execution
2. Concurrent Runner: Runs multiple tasks concurrently
3. Async LLM Client: Non-blocking LLM interactions
4. Task Scheduler: Schedules and coordinates async tasks
5. Result Collector: Aggregates concurrent results
6. Error Handler: Handles async exceptions
7. Timeout Manager: Enforces timeouts on async operations
8. Resource Pool: Manages async resources and connections
Key Features:
- Non-blocking async/await operations
- Concurrent LLM calls
- Parallel tool execution
- Async streaming
- Task cancellation
- Timeout handling
- Connection pooling
- Backpressure management
- Error propagation
- Graceful shutdown
Use Cases:
- High-throughput API servers
- Real-time chat applications
- Concurrent data processing
- Parallel research queries
- Multi-source information gathering
- Async web scraping
- Real-time analytics
- Concurrent translation services
- Parallel validation
- Multi-agent coordination
LangChain Implementation:
Uses ChatOpenAI with async methods (ainvoke, astream, abatch),
asyncio for concurrency, and async LCEL chains.
"""
import os
import asyncio
import time
from typing import List, Dict, Any, Optional, Coroutine, Callable
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
from dotenv import load_dotenv
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
from langchain_core.runnables import RunnablePassthrough
load_dotenv()
class TaskStatus(Enum):
"""Status of async task"""
PENDING = "pending"
RUNNING = "running"
COMPLETED = "completed"
FAILED = "failed"
CANCELLED = "cancelled"
TIMEOUT = "timeout"
@dataclass
class AsyncTask:
"""An asynchronous task"""
task_id: str
name: str
coroutine: Coroutine
status: TaskStatus = TaskStatus.PENDING
result: Optional[Any] = None
error: Optional[str] = None
started_at: Optional[datetime] = None
completed_at: Optional[datetime] = None
duration: float = 0.0
@dataclass
class AsyncResult:
"""Result of async operation"""
task_id: str
success: bool
result: Optional[Any] = None
error: Optional[str] = None
duration: float = 0.0
metadata: Dict[str, Any] = field(default_factory=dict)
@dataclass
class ConcurrentResult:
"""Result of concurrent operations"""
total_tasks: int
successful: int
failed: int
cancelled: int
results: List[AsyncResult]
total_duration: float
average_duration: float
class AsyncAgent:
"""
Agent that uses async/await for non-blocking operations.
This agent enables high-performance concurrent processing with
async LLM calls, parallel tool execution, and efficient resource usage.
"""
def __init__(self, max_concurrent: int = 10, timeout: float = 30.0):
"""
Initialize the async agent.
Args:
max_concurrent: Maximum concurrent tasks
timeout: Default timeout for operations
"""
self.max_concurrent = max_concurrent
self.timeout = timeout
# Async LLM
self.llm = ChatOpenAI(
model="gpt-3.5-turbo",
temperature=0.7
)
# Task tracking
self.tasks: Dict[str, AsyncTask] = {}
self.semaphore = asyncio.Semaphore(max_concurrent)
async def ainvoke_llm(
self,
prompt: str,
timeout: Optional[float] = None
) -> str:
"""
Invoke LLM asynchronously.
Args:
prompt: Input prompt
timeout: Optional timeout
Returns:
LLM response
"""
timeout = timeout or self.timeout
try:
response = await asyncio.wait_for(
self.llm.ainvoke(prompt),
timeout=timeout
)
return response.content
except asyncio.TimeoutError:
raise TimeoutError(f"LLM call timed out after {timeout}s")
async def astream_llm(
self,
prompt: str,
on_token: Optional[Callable[[str], None]] = None
) -> str:
"""
Stream LLM response asynchronously.
Args:
prompt: Input prompt
on_token: Optional token callback
Returns:
Full response
"""
full_response = ""
async for chunk in self.llm.astream(prompt):
if hasattr(chunk, 'content'):
token = chunk.content
full_response += token
if on_token:
on_token(token)
return full_response
async def run_concurrent_tasks(
self,
tasks: List[Coroutine],
task_names: Optional[List[str]] = None
) -> ConcurrentResult:
"""
Run multiple tasks concurrently.
Args:
tasks: List of coroutines to execute
task_names: Optional names for tasks
Returns:
ConcurrentResult with all results
"""
start_time = time.time()
if task_names is None:
task_names = [f"task_{i}" for i in range(len(tasks))]
# Run tasks concurrently with semaphore
async def run_with_semaphore(coro: Coroutine, name: str) -> AsyncResult:
async with self.semaphore:
task_start = time.time()
task_id = f"{name}_{id(coro)}"
try:
result = await coro
return AsyncResult(
task_id=task_id,
success=True,
result=result,
duration=time.time() - task_start
)
except Exception as e:
return AsyncResult(
task_id=task_id,
success=False,
error=str(e),
duration=time.time() - task_start
)
# Execute all tasks
results = await asyncio.gather(
*[run_with_semaphore(task, name) for task, name in zip(tasks, task_names)],
return_exceptions=True
)
# Process results
async_results = []
successful = 0
failed = 0
cancelled = 0
for result in results:
if isinstance(result, AsyncResult):
async_results.append(result)
if result.success:
successful += 1
else:
failed += 1
elif isinstance(result, asyncio.CancelledError):
cancelled += 1
else:
failed += 1
total_duration = time.time() - start_time
avg_duration = sum(r.duration for r in async_results) / len(async_results) if async_results else 0
return ConcurrentResult(
total_tasks=len(tasks),
successful=successful,
failed=failed,
cancelled=cancelled,
results=async_results,
total_duration=total_duration,
average_duration=avg_duration
)
async def parallel_llm_calls(
self,
prompts: List[str],
timeout: Optional[float] = None
) -> List[str]:
"""
Make multiple LLM calls in parallel.
Args:
prompts: List of prompts
timeout: Optional timeout per call
Returns:
List of responses
"""
tasks = [self.ainvoke_llm(prompt, timeout) for prompt in prompts]
result = await self.run_concurrent_tasks(
tasks,
[f"llm_call_{i}" for i in range(len(prompts))]
)
return [r.result for r in result.results if r.success]
async def concurrent_chain_execution(
self,
inputs: List[Dict[str, str]],
template: str
) -> List[str]:
"""
Execute chain concurrently with different inputs.
Args:
inputs: List of input dictionaries
template: Prompt template
Returns:
List of results
"""
prompt = ChatPromptTemplate.from_template(template)
chain = prompt | self.llm | StrOutputParser()
# Create async tasks for each input
tasks = [chain.ainvoke(input_dict) for input_dict in inputs]
result = await self.run_concurrent_tasks(
tasks,
[f"chain_{i}" for i in range(len(inputs))]
)
return [r.result for r in result.results if r.success]
async def parallel_with_timeout(
self,
tasks: List[Coroutine],
timeout: float
) -> List[Optional[Any]]:
"""
Run tasks in parallel with timeout.
Args:
tasks: List of coroutines
timeout: Timeout for all tasks
Returns:
List of results (None for timed out tasks)
"""
try:
results = await asyncio.wait_for(
asyncio.gather(*tasks, return_exceptions=True),
timeout=timeout
)
return [
r if not isinstance(r, Exception) else None
for r in results
]
except asyncio.TimeoutError:
return [None] * len(tasks)
async def async_map(
self,
func: Callable[[Any], Coroutine],
items: List[Any],
batch_size: Optional[int] = None
) -> List[Any]:
"""
Map an async function over items with optional batching.
Args:
func: Async function to map
items: Items to process
batch_size: Optional batch size for processing
Returns:
List of results
"""
if batch_size is None:
batch_size = self.max_concurrent
results = []
for i in range(0, len(items), batch_size):
batch = items[i:i + batch_size]
batch_tasks = [func(item) for item in batch]
batch_results = await asyncio.gather(*batch_tasks)
results.extend(batch_results)
return results
async def race_tasks(
self,
tasks: List[Coroutine],
return_first: bool = True
) -> Any:
"""
Race multiple tasks, return first to complete.
Args:
tasks: List of coroutines to race
return_first: Return first completed (True) or all (False)
Returns:
First result or all results
"""
if return_first:
done, pending = await asyncio.wait(
tasks,
return_when=asyncio.FIRST_COMPLETED
)
# Cancel remaining tasks
for task in pending:
task.cancel()
# Return first result
return list(done)[0].result()
else:
return await asyncio.gather(*tasks)
async def retry_async(
self,
coro: Coroutine,
max_retries: int = 3,
backoff: float = 1.0
) -> Any:
"""
Retry an async operation with exponential backoff.
Args:
coro: Coroutine to retry
max_retries: Maximum retry attempts
backoff: Initial backoff time
Returns:
Result of successful attempt
"""
last_exception = None
for attempt in range(max_retries):
try:
return await coro
except Exception as e:
last_exception = e
if attempt str:
await asyncio.sleep(0.1) # Simulate work
return await agent.ainvoke_llm(f"Count to {n} and stop.")
tasks = [sample_task(i) for i in range(1, 6)]
task_names = [f"count_to_{i}" for i in range(1, 6)]
print(f"\nRunning {len(tasks)} concurrent tasks...")
print("-" * 80)
result = await agent.run_concurrent_tasks(tasks, task_names)
print(f"✓ All tasks completed!")
print(f"\nStatistics:")
print(f" Total Tasks: {result.total_tasks}")
print(f" Successful: {result.successful}")
print(f" Failed: {result.failed}")
print(f" Total Duration: {result.total_duration:.2f}s")
print(f" Average Duration: {result.average_duration:.2f}s")
# Demo 4: Async Streaming
print("\n" + "=" * 80)
print("DEMO 4: Asynchronous Streaming")
print("=" * 80)
prompt = "Write a haiku about async programming."
print(f"\nPrompt: {prompt}")
print("\nStreaming response asynchronously:")
print("-" * 80)
tokens = []
def on_token(token: str):
tokens.append(token)
print(token, end="", flush=True)
stream_response = await agent.astream_llm(prompt, on_token=on_token)
print("\n" + "-" * 80)
print(f"✓ Streaming complete! ({len(tokens)} tokens)")
# Demo 5: Task Racing
print("\n" + "=" * 80)
print("DEMO 5: Task Racing (First to Complete)")
print("=" * 80)
async def llm_task(question: str, delay: float) -> str:
await asyncio.sleep(delay)
return await agent.ainvoke_llm(question)
race_tasks = [
llm_task("Quick answer: What is AI?", 0.1),
llm_task("Quick answer: What is ML?", 0.2),
llm_task("Quick answer: What is DL?", 0.3)
]
print("\nRacing 3 tasks, returning first to complete...")
print("-" * 80)
race_start = time.time()
first_result = await agent.race_tasks(race_tasks, return_first=True)
race_duration = time.time() - race_start
print(f"✓ First task completed in {race_duration:.2f}s")
print(f"\nResult: {first_result[:150]}...")
# Demo 6: Async vs Sync Performance
print("\n" + "=" * 80)
print("DEMO 6: Async vs Synchronous Performance")
print("=" * 80)
test_prompts = [f"Answer: What is {topic}?" for topic in ["AI", "ML", "DL", "NLP", "CV"]]
print(f"\nProcessing {len(test_prompts)} prompts...")
# Async approach
print("\nAsync Approach:")
async_start = time.time()
async_results = await agent.parallel_llm_calls(test_prompts[:3]) # Limit for demo
async_duration = time.time() - async_start
print(f" Duration: {async_duration:.2f}s")
print(f" Results: {len(async_results)} responses")
# Sync approach (simulated)
print("\nSync Approach (estimated):")
estimated_sync = async_duration * 3 # Rough estimate
print(f" Estimated Duration: {estimated_sync:.2f}s")
speedup = estimated_sync / async_duration if async_duration > 0 else 0
print(f"\nSpeedup: {speedup:.2f}x faster with async")
# Summary
print("\n" + "=" * 80)
print("SUMMARY")
print("=" * 80)
summary = """
The Asynchronous Agent demonstrates non-blocking concurrent operations:
KEY CAPABILITIES:
1. Async LLM Calls: Non-blocking LLM interactions with ainvoke
2. Parallel Execution: Run multiple tasks concurrently
3. Async Streaming: Non-blocking streaming responses
4. Concurrent Chains: Execute chains with different inputs in parallel
5. Task Racing: Return first completed result
6. Timeout Handling: Enforce timeouts on async operations
7. Error Handling: Graceful exception handling in async context
8. Resource Management: Semaphore-based concurrency control
BENEFITS:
- Dramatically improved throughput
- Non-blocking I/O for better resource utilization
- Lower latency for concurrent operations
- Scalable to handle many simultaneous requests
- Efficient handling of I/O-bound workloads
- Better responsiveness in applications
- Reduced total execution time
- Enhanced user experience
USE CASES:
- High-throughput API servers
- Real-time chat applications with multiple users
- Concurrent data processing pipelines
- Parallel research and information gathering
- Multi-source data aggregation
- Async web scraping
- Real-time analytics dashboards
- Concurrent translation services
- Parallel validation and checking
- Multi-agent systems coordination
PRODUCTION CONSIDERATIONS:
1. Concurrency Limits: Use semaphores to control concurrent tasks
2. Timeout Management: Always set timeouts for async operations
3. Error Propagation: Handle exceptions in async context properly
4. Resource Cleanup: Ensure proper cleanup of async resources
5. Backpressure: Handle slow consumers gracefully
6. Connection Pooling: Reuse connections efficiently
7. Monitoring: Track async task performance
8. Testing: Test with asyncio.run() and pytest-asyncio
9. Event Loop: Manage event loop lifecycle carefully
10. Cancellation: Support graceful task cancellation
ADVANCED EXTENSIONS:
- Async context managers for resource management
- Event-driven architectures with async events
- Async queues for producer-consumer patterns
- Async locks and synchronization primitives
- Distributed async processing
- Async retry with circuit breakers
- Priority-based async task scheduling
- Async result caching
- Streaming async aggregation
- Async middleware chains
Async patterns are essential for building high-performance,
scalable AI applications that handle concurrent operations efficiently.
"""
print(summary)
def demonstrate_async_agent_sync():
"""Synchronous wrapper for async demonstration"""
asyncio.run(demonstrate_async_agent())
if __name__ == "__main__":
demonstrate_async_agent_sync()
Agentic Design Pattern: Async Agent
Pattern 080: Async Agent – Line-by-Line Explanation
Overview
This document provides a detailed line-by-line explanation of the Async Agent pattern implementation.
Pattern Description
Pattern 080: Asynchronous Agent
Description: An Asynchronous Agent uses async/await patterns to handle concurrent operations without blocking. This pattern is essential for building high-performance, scalable AI applications that can handle multiple requests simultaneously, make concurrent API calls, and provide responsive user experiences.
The agent leverages Python's asyncio for non-blocking I/O operations, enabling
efficient resource utilization and improved throughput. It supports concurrent
LLM calls, parallel tool execution, and async streaming for maximum performance.
Components: 1. Async Executor: Manages async task execution 2. Concurrent Runner: Runs multiple tasks concurrently 3. Async LLM Client: Non-blocking LLM interactions 4. Task Scheduler: Schedules and coordinates async tasks 5. Result Collector: Aggregates concurrent results 6. Error Handler: Handles async exceptions 7. Timeout Manager: Enforces timeouts on async operations 8. Resource Pool: Manages async resources and connections
Key Features: – Non-blocking async/await operations – Concurrent LLM calls – Parallel tool execution – Async streaming – Task cancellation – Timeout handling – Connection pooling – Backpressure management – Error propagation – Graceful shutdown
Use Cases: – High-throughput API servers – Real-time chat applications – Concurrent data processing – Parallel research queries – Multi-source information gathering – Async web scraping – Real-time analytics – Concurrent translation services – Parallel validation – Multi-agent coordination
LangChain Implementation: Uses ChatOpenAI with async methods (ainvoke, astream, abatch), asyncio for concurrency, and async LCEL chains.
File Statistics
- Total Lines: 682
- Classes: 5
- Functions: 1
- Constants: 0
Module Documentation
"""
Pattern 080: Asynchronous Agent
Description:
An Asynchronous Agent uses async/await patterns to handle concurrent operations
without blocking. This pattern is essential for building high-performance,
scalable AI applications that can handle multiple requests simultaneously,
make concurrent API calls, and provide responsive user experiences.
The agent leverages Python's asyncio for non-blocking I/O operations, enabling
efficient resource utilization and improved throughput. It supports concurrent
LLM calls, parallel tool execution, and async streaming for maximum performance.
Components:
1. Async Executor: Manages async task execution
2. Concurrent Runner: Runs multiple tasks concurrently
3. Async LLM Client: Non-blocking LLM interactions
4. Task Scheduler: Schedules and coordinates async tasks
5. Result Collector: Aggregates concurrent results
6. Error Handler: Handles async exceptions
7. Timeout Manager: Enforces timeouts on async operations
8. Resource Pool: Manages async resources and connections
Key Features:
- Non-blocking async/await operations
- Concurrent LLM calls
- Parallel tool execution
- Async streaming
- Task cancellation
- Timeout handling
- Connection pooling
- Backpressure management
- Error propagation
- Graceful shutdown
Use Cases:
- High-throughput API servers
- Real-time chat applications
- Concurrent data processing
- Parallel research queries
- Multi-source information gathering
- Async web scraping
- Real-time analytics
- Concurrent translation services
- Parallel validation
- Multi-agent coordination
LangChain Implementation:
Uses ChatOpenAI with async methods (ainvoke, astream, abatch),
asyncio for concurrency, and async LCEL chains.
"""
Purpose: This module implements the Async Agent pattern, providing a structured approach to agent design and implementation.
Import Statements
Standard Library Imports
import asyncio
import dataclasses.dataclass
import dataclasses.field
import datetime.datetime
import enum.Enum
import langchain_core.output_parsers.StrOutputParser
import langchain_core.prompts.ChatPromptTemplate
import langchain_core.runnables.RunnablePassthrough
import os
import time
import typing.Any
import typing.Callable
import typing.Coroutine
import typing.Dict
import typing.List
import typing.Optional
Purpose: Core Python functionality:
– asyncio: Asynchronous I/O operations
– dataclasses.dataclass: Simplified class definitions with @dataclass
– dataclasses.field: Simplified class definitions with @dataclass
– datetime.datetime: Date and time handling
– enum.Enum: Enumeration support
– langchain_core.output_parsers.StrOutputParser: Regular expression operations
– langchain_core.prompts.ChatPromptTemplate: Regular expression operations
– langchain_core.runnables.RunnablePassthrough: Regular expression operations
– os: Environment variable access and system operations
– time: Time-related functions
– typing.Any: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Callable: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Coroutine: 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)
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
Purpose: Additional dependencies for specialized functionality.
Classes
Class: TaskStatus
Lines 70-77
Inherits from: Enum
Purpose:
Status of async task
Class: AsyncTask
Lines 81-91
Purpose:
An asynchronous task
Class: AsyncResult
Lines 95-102
Purpose:
Result of async operation
Class: ConcurrentResult
Lines 106-114
Purpose:
Result of concurrent operations
Class: AsyncAgent
Lines 117-444
Purpose:
Agent that uses async/await for non-blocking operations.
This agent enables high-performance concurrent processing with
async LLM calls, parallel tool execution, and efficient resource usage.
Methods:
__init__(self, max_concurrent, timeout)
Lines 125-144
Purpose:
Initialize the async agent.
Args:
max_concurrent: Maximum concurrent tasks
timeout: Default timeout for operations
Parameters:
– max_concurrent: Max concurrent
– timeout: Timeout
Functions
demonstrate_async_agent_sync()
Lines 675-677
Purpose:
Synchronous wrapper for async demonstration
Key Concepts Summary
Pattern Overview
The Async Agent 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 079
- Pattern 081
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 Async Agent 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: 080_async_agent.py
Total Lines: 682
Pattern Category: AI Agent Design Pattern #080
Generated from actual implementation code
For educational and reference purposes
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