🤖 Agents & Orchestration¶

Frameworks for building autonomous agents, multi-agent systems, and orchestrating complex AI workflows.

📋 Table of Contents¶

Overview
Tools List
Selection Guide
Quick Start

Overview¶

Agent frameworks enable building autonomous AI systems that can reason, plan, and execute complex tasks. This collection includes: - Agent Frameworks: LangChain, LlamaIndex, AutoGen, CrewAI - Memory Systems: Persistent context and knowledge management - Orchestration: Multi-agent coordination and workflow management

Tools List¶

Repo	Description	Stars
langchain-ai/langchain	Most popular framework for building LLM applications
run-llama/llama_index	Data framework for LLM applications with RAG focus
microsoft/autogen	Multi-agent conversation framework from Microsoft
joaomdmoura/crewAI	Role-based multi-agent framework for complex workflows
mem0ai/mem0	Intelligent memory layer for AI agents
princeton-nlp/SWE-agent	Autonomous software engineering agent
geekan/MetaGPT	Multi-agent framework simulating software company
Significant-Gravitas/AutoGPT	Autonomous GPT-4 agent for complex tasks

Selection Guide¶

By Use Case¶

📚 RAG & Knowledge Management - LlamaIndex - Best for RAG, data ingestion, indexing - LangChain - Comprehensive, most integrations - Mem0 - Persistent memory across sessions

🤝 Multi-Agent Systems - AutoGen - Flexible multi-agent conversations - CrewAI - Role-based teams with task delegation - MetaGPT - Software development simulation

⚡ Production Applications - LangChain - Most mature, extensive ecosystem - LlamaIndex - Best RAG performance - AutoGen - Enterprise multi-agent workflows

🛠️ Autonomous Agents - AutoGPT - Full autonomy, general tasks - SWE-agent - Software engineering specific - CrewAI - Task-oriented agent teams

By Experience Level¶

Beginner - LangChain - Excellent docs, large community - LlamaIndex - Simple RAG setup

Intermediate - AutoGen - Multi-agent patterns - CrewAI - Agent orchestration

Advanced - MetaGPT - Complex multi-agent systems - SWE-agent - Autonomous development

Quick Start¶

LangChain - Basic Agent¶

from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI

# Define tools
tools = [
    Tool(
        name="Calculator",
        func=lambda x: eval(x),
        description="Useful for math calculations"
    )
]

# Create agent
llm = OpenAI(temperature=0)
agent = initialize_agent(
    tools,
    llm,
    agent="zero-shot-react-description",
    verbose=True
)

# Run
agent.run("What is 25 * 4 + 10?")

LlamaIndex - RAG Agent¶

from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index.agent import OpenAIAgent

# Load documents
documents = SimpleDirectoryReader('data').load_data()
index = VectorStoreIndex.from_documents(documents)

# Create RAG agent
agent = OpenAIAgent.from_tools(
    [index.as_query_engine()],
    verbose=True
)

# Query
response = agent.chat("What does the document say about AI safety?")

AutoGen - Multi-Agent System¶

from autogen import AssistantAgent, UserProxyAgent

# Configure agents
assistant = AssistantAgent(
    "assistant",
    llm_config={"model": "gpt-4"}
)

user_proxy = UserProxyAgent(
    "user_proxy",
    human_input_mode="NEVER",
    code_execution_config={"work_dir": "coding"}
)

# Start conversation
user_proxy.initiate_chat(
    assistant,
    message="Write a Python script to analyze CSV data"
)

CrewAI - Task-Based Agents¶

from crewai import Agent, Task, Crew

# Define agents
researcher = Agent(
    role='Researcher',
    goal='Research AI trends',
    backstory='Expert in AI research',
    verbose=True
)

writer = Agent(
    role='Writer',
    goal='Write engaging content',
    backstory='Technical content writer',
    verbose=True
)

# Define tasks
research_task = Task(
    description='Research latest AI developments',
    agent=researcher
)

write_task = Task(
    description='Write article based on research',
    agent=writer
)

# Create crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    verbose=True
)

# Execute
result = crew.kickoff()

Mem0 - Persistent Memory¶

from mem0 import Memory

# Initialize memory
memory = Memory()

# Add memories
memory.add(
    "User prefers Python over JavaScript",
    user_id="user123"
)

memory.add(
    "User is working on a RAG project",
    user_id="user123"
)

# Retrieve relevant memories
memories = memory.get_all(user_id="user123")
print(memories)

# Search memories
results = memory.search(
    "What programming language does user prefer?",
    user_id="user123"
)

Framework Comparison¶

Feature Matrix¶

Framework	RAG	Multi-Agent	Memory	Production Ready	Learning Curve
LangChain	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐⭐	Medium
LlamaIndex	⭐⭐⭐⭐⭐	⭐⭐	⭐⭐⭐	⭐⭐⭐⭐	Low
AutoGen	⭐⭐	⭐⭐⭐⭐⭐	⭐⭐	⭐⭐⭐⭐	High
CrewAI	⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐	Medium
Mem0	N/A	N/A	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	Low

Performance Benchmarks¶

RAG Query Speed (1000 docs)

Framework	Query Time	Indexing Time	Memory Usage
LlamaIndex	150ms	5s	512MB
LangChain	220ms	8s	768MB
Basic Vector Search	80ms	3s	256MB

Multi-Agent Task Completion

Framework	Simple Task	Complex Task	Agents Supported
AutoGen	30s	5min	Unlimited
CrewAI	45s	8min	10+ recommended
MetaGPT	60s	12min	Specialized roles

Architecture Patterns¶

1. Autonomous Agent Loop¶

Plan → Execute → Observe → Reflect → Repeat

Best for: AutoGPT, SWE-agent

2. Multi-Agent Collaboration¶

Manager → Delegates Tasks → Agents → Aggregate Results

Best for: AutoGen, CrewAI, MetaGPT

3. RAG + Agent¶

Query → Retrieve Context → Agent Reasoning → Response

Best for: LangChain, LlamaIndex

4. Memory-Augmented Agent¶

Input → Retrieve Memories → Agent Processing → Update Memories → Output

Best for: Mem0 + any agent framework

Best Practices¶

1. Agent Design¶

Define clear roles and responsibilities
Limit tool access to prevent unsafe actions
Implement human-in-the-loop for critical decisions
Use structured output formats

2. Multi-Agent Systems¶

Start with 2-3 agents, scale gradually
Implement clear communication protocols
Monitor token usage and costs
Use async execution where possible

3. Memory Management¶

Implement memory pruning strategies
Use vector search for relevant context
Separate short-term and long-term memory
Validate memory accuracy periodically

4. Production Deployment¶

Implement rate limiting and retries
Log all agent actions and decisions
Use observability tools (see Observability category)
Test with safety guardrails

Common Use Cases¶

Customer Support Agent¶

Stack: LangChain + Mem0 + Vector DB - Persistent customer context - RAG over documentation - Multi-turn conversations

Software Development Team¶

Stack: AutoGen + SWE-agent - Code generation and review - Testing automation - Documentation writing

Research Assistant¶

Stack: LlamaIndex + GPT-4 - Paper analysis and summarization - Citation management - Hypothesis generation

Content Creation Team¶

Stack: CrewAI - Research, writing, editing roles - SEO optimization - Multi-format output

Foundation Models - Choose models for agents
Developer Tools - Build and debug agents
Observability - Monitor agent behavior
Vector Databases - Memory and RAG backends

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