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Module 16 · Page 17 of 20 · 8 sections

Tools and Frameworks for Agentic AI

The framework landscape: LangChain, LlamaIndex, AutoGen, CrewAI and the supporting tools around them.

04Agent Frameworks and Tools08Multi-Agent Systems in Agentic AI17Deployment and Scalability of Agentic AI
16.1

Why Tools and Frameworks are Important?

  • Building an agent from scratch can be complex.
  • Tools and frameworks provide ready-made components for LLM integration, memory, tools, planning and orchestration.
  • They help in faster development, better reliability and production readiness.
  • We can focus on the problem and use these frameworks to handle lower level complexities.
16.2

Popular Frameworks for Agentic AI

1
LangChain
Most popular framework for building LLM applications and agents.
2
LlamaIndex
Useful for RAG and data integration.
3
AutoGen
Multi-agent framework by Microsoft.
4
CrewAI
Easy framework for building role based agents.
5
Haystack
Good for search and RAG applications.
6
Semantic Kernel
Framework by Microsoft for building AI agents.
7
LangGraph
Useful for building complex agent workflows.
Related04Agent Frameworks and Tools
16.3

LangChain (Overview)

  • LangChain provides modular components to build LLM applications and agents.
  • Supports chains, tools, memory, agents and integrations.
  • It can work with different LLMs and external tools.
User Query
AgentLangChain
LLMOpenAI / local
Tools / Integrations
Search API
Database
Vector Store
Custom Tool
LangChain architecture: the agent talks to an LLM and reaches the outside world through tool integrations.
16.4

LlamaIndex (Overview)

  • LlamaIndex is focused on data integration and RAG.
  • It helps in loading, indexing and querying your data from various sources.
  • Useful when working with large documents, PDFs and enterprise data.
DocumentsPDF, DB, API
IndexingChunks + embeddings
Query Engine
Answer
LlamaIndex flow.
Related07Memory in Agentic AI
16.5

AutoGen (Multi-Agent Framework)

  • Developed by Microsoft.
  • Built for creating multiple agents that can communicate with each other.
  • Useful for complex tasks where different agents have different roles.
  • Supports human-in-the-loop and tool integrations.
User Request
Planner AgentBreak task
Research AgentSearch info
Analysis AgentProcess data
Writer AgentGenerate output
Final Result
AutoGen multi-agent architecture.
16.6

CrewAI (Role Based Agents)

  • Simple and easy to use framework.
  • We can define multiple agents with specific roles, goals and tools.
  • Useful for tasks like research, analysis, writing, data processing, etc.
Task
ResearcherFind information
AnalystAnalyze & process
WriterCreate final output
Final Output
CrewAI structure.
16.7

Other Useful Tools

Tool / frameworkUse case
HaystackSearch, RAG and information retrieval
Semantic KernelBuild AI agents with Microsoft ecosystem
LangGraphBuild complex agent workflows and state management
Pinecone / QdrantVector database for memory and RAG
OpenAI Assistants APIBuilt-in tools, function calling and agents
Gradio / StreamlitBuild UI for agent applications
Docker / KubernetesDeployment and scaling of agents
Related17Deployment and Scalability of Agentic AI
16.8

Key Takeaways

  1. 1Different frameworks are useful for different use cases.
  2. 2Choose a framework based on your requirement (RAG, multi-agent, tool use, etc.).
  3. 3These tools make it easier to build, test and deploy agents.
  4. 4LangChain and LlamaIndex are most popular.
  5. 5Proper tools help in faster development and production ready systems.