🤖 6 AI Agent Development Frameworks & Libraries: Use-Cases, Pros & Cons, LLMs, Tools
AI Agent Frameworks: OpenAI Agent SDK, Langchain Agents, Llamaindex Agents, CrewAI, Google ADK, AWS Strands
The landscape of AI agent development has evolved rapidly, with multiple frameworks emerging to address different use cases and developer needs. This comprehensive analysis compares the leading agent development frameworks across key technical and practical criteria to help you choose the right tool for your project. Comparison of the agent frameworks: OpenAI Agent SDK, LangChain Agents, LlamaIndex Agents, CrewAI, Google Agent Development Kit (ADK), and AWS Strands Agents
1. OpenAI Agent SDK
The OpenAI Agent SDK is a lightweight, Python-first toolkit for building AI agents powered by large language models (LLMs). Whether you’re creating customer support bots, research assistants, or automated workflows, the SDK gives you the tools to orchestrate complex tasks with minimal setup.
The SDK is designed to be simple and fast to learn. It centers around a few intuitive building blocks, Agents, Handoffs, Guardrails, and Sessions, all expressed in plain Python. You can create and run an agent with just a few lines of code, and the SDK handles the orchestration behind the scenes.
An agent is an LLM paired with tools and instructions. The SDK runs an “agent loop” that lets your model:
Reason through multi-step tasks
Call tools
Handle responses and delegate tasks between agents
Built-in guardrails validate inputs/outputs, and sessions manage conversation history across runs, all with minimal overhead.
Built for Real-World Use
The SDK is versatile enough for:
Customer support automation
Research & content generation
Code review
Sales prospecting
Retrieval-Augmented Generation (RAG)
Human-in-the-loop workflows
Whether you’re building a single-agent task bot or a complex multi-agent pipeline, the SDK scales with you.
Works with Any LLM
While it’s built to work seamlessly with OpenAI’s models, the SDK is model-agnostic. You can plug in Anthropic, Mistral, or any chat-style LLM via the same interface. LiteLLM makes switching between providers simple.
Tool & API Integration
You can register any Python function as a tool. The SDK also supports built-in tools like file search and code execution, and integrates easily with external APIs and databases. For advanced setups, it connects to external tool servers via Model Context Protocol (MCP).
Pros
Official OpenAI support and active development
Easy to get started (single pip install)
Pythonic, with low abstraction, great for debugging
Built-in evaluation and tracing tools
Cons
Heavily centered on OpenAI (though extensible)
Smaller ecosystem than LangChain or CrewAI
Fewer prebuilt tools and tutorials
Mostly cloud/API-based, less offline support
2. LangChain Agents
LangChain Agents are a powerful (but complex) way to build smart AI workflows using LLMs. They act as “thinking” programs that reason, use tools, and perform multi-step tasks like answering questions, searching documents, or writing code.
LangChain is feature-rich but complex. You often start with prebuilt agent types (like ReAct), but there are many moving parts, prompts, tools, memory, and callbacks. It’s not beginner-friendly, but great docs and examples help. A newer tool, LangGraph, is making agents easier by turning them into clear graph-based workflows.
Agents follow a simple loop:
Get a prompt
The LLM decides what to do
It runs a tool (like a search or calculator)
The result is fed back into the loop
LangChain supports memory, retrieval (RAG), tool use, and even multimodal inputs. LangGraph lets you wire up agents as state machines or graphs with loops, memory, and conditionals.
What Can You Build?
Almost anything. LangChain agents are used in:
Document Q&A (RAG)
Research assistants
Coding helpers
Scheduling tools
Customer service bots
E-commerce and finance apps
Basically, any task that involves multiple steps, tools, or data sources is a great fit.
Works with Any LLM
LangChain works with dozens of LLMs, including:
OpenAI (GPT)
Anthropic (Claude)
Google (Gemini)
Mistral, Cohere, Hugging Face, and more
You can also run local models. It’s super flexible, just plug in the model API you want to use. LiteLLM makes switching between providers simple.
Tool & API Integration
One of LangChain’s biggest strengths is its huge tool ecosystem. You can connect to:
Databases (SQL, NoSQL)
Vector DBs (Pinecone, Chroma, Weaviate, etc.)
Web search APIs
PDFs, docs, websites
Python code execution
Cloud services (AWS, GCP, Azure)
If your Python code can reach it, LangChain can probably use it.
Pros
Extremely flexible and powerful
Huge ecosystem and community
Works with almost any model or tool
Built-in support for multi-agent systems and workflows
Cons
Steep learning curve for beginners
Many moving parts (older APIs can be confusing)
Debugging takes effort (verbose logs, callbacks)
High API usage can mean higher latency or cost
3. LlamaIndex Agents
LlamaIndex Agents are smart AI agents designed for tasks that involve retrieving and reasoning over data, like answering questions from documents or building research assistants. They're especially good when you need to use a lot of structured or unstructured data (like PDFs, databases, or knowledge bases).
You can start quickly with prebuilt agents for common use cases, or dive deeper with the Workflows API for full control. It’s more approachable than LangChain for some tasks, but understanding its core concepts (like nodes, indices, and events) can still take time if you're building something complex.
LlamaIndex agents use an event-driven workflow. They can:
Break down complex questions
Choose and call tools
Plan steps
Fetch info from document indexes (like PDFs or vector databases)
You can build simple agents (like ReAct-style) or fully custom logic with their event orchestration model.
Best Use Cases
LlamaIndex shines in knowledge-intensive tasks, like:
Research assistants (multi-step document Q&A)
Codebase explorers and coding assistants
Support bots using internal company data
Report generation with multiple agents (e.g., a researcher + writer)
Email/calendar productivity bots
If you need to answer questions based on documents, LlamaIndex is a great fit.
LLM Support
It works with any LLM provider (OpenAI, Mistral, Hugging Face, etc.). You provide the LLM client, and LlamaIndex handles the rest. Local and custom models can also be plugged in easily. LiteLLM makes switching between providers simple.
Tool & API Integration
Works well with vector stores (FAISS, Pinecone, Chroma, Weaviate)
Supports loading data from PDFs, CSVs, JSON, etc.
You can use any Python function as a tool
Fewer built-in APIs than LangChain, but you can add your own via Python
LlamaHub offers a growing set of community tools
Pros
Great for data retrieval and document reasoning
Balanced between quick start and deep customization
Works well with vector DBs and private data
Flexible agent architecture and strong planning features
Cons
Smaller ecosystem and fewer built-in tools vs. LangChain
Less focus on arbitrary tool use or multi-agent workflows
No managed cloud version—you handle deployment yourself
Some learning curve if building complex workflows
4. CrewAI
CrewAI is a user-friendly framework for building multi-agent AI systems where agents work together like a team (“crew”) to complete complex tasks. It’s especially helpful for businesses or any use case where multiple roles (e.g., researcher, writer, planner) need to collaborate using AI.
CrewAI is easy to get started with, especially for non-experts:
You define agents and tasks using simple YAML configuration files
A command-line tool scaffolds everything for you
Good documentation and guided courses help a lot
Setting up a multi-agent team is fast with built-in templates and defaults.
CrewAI is built around two main ideas:
Crews: Groups of agents, each with a role, goal, and tools
Flows: Workflows that control how tasks are sequenced
Agents collaborate by making decisions and passing tasks around. You can control everything through YAML or extend it with Python code. Under the hood, it runs an event loop powered by a language model at each step.
What Can You Build?
Great for enterprise-level workflows and multi-step tasks, such as:
Research reports
Analytics pipelines
Job description generators
Stock market analysis
Travel planning bots
Basically, any system where multiple AI agents need to think, plan, and work together fits CrewAI well.
LLM Support
CrewAI works with:
OpenAI models (default)
Local models (e.g. via Ollama, LM Studio)
Third-party models like Claude, Amazon Bedrock, or Hugging Face
Switching between models is easy via a plug-and-play interface.
Tools & API Integration
CrewAI comes with 40+ built-in tools, covering:
Web search and scraping
Document and file handling
Databases (SQL, vector DBs)
Cloud services (AWS, S3, DALL·E, etc.)
REST API support
You can also write your own tools in Python or use LangChain tools alongside.
Pros
Simple to set up, yet powerful under the hood
Clear structure for multi-agent teamwork (Crews) and control (Flows)
Rich built-in tool library and cloud integration
Supports both OpenAI and non-OpenAI models
Strong docs and learning resources
Cons
Still a newer framework with a smaller community
YAML-based configs may not suit everyone (some prefer pure code)
Advanced features may require paid enterprise plans
Being vendor-backed means future direction might become more proprietary
5. Google’s Agent Development Kit (ADK)
The Google Agent Development Kit (ADK) is a new, flexible framework for building advanced AI agents, especially for enterprise-grade, multi-agent workflows. It’s built for developers who want to combine AI with Google Cloud’s tools and services, like BigQuery, Vertex AI, and Gemini.
ADK is fairly easy to use for developers, especially those familiar with Python and Google Cloud. You define agents using Python classes or simple templates. There’s a local emulator for testing and clear abstractions like “Sequential” or “Parallel” agents. If you’re new to Google Cloud, the learning curve is moderate.
ADK uses two types of agents:
Workflow agents: Run steps in sequence, in parallel, or in loops
LLM agents: Use AI models to decide what to do next
You can connect multiple agents together in hierarchies or graphs, and manage memory/state through built-in Session and State objects. Tools include built-in functions, cloud APIs, or even other agents.
What Can You Build?
ADK is aimed at enterprise AI applications, such as:
Multi-agent teams for customer support or research
AI pipelines that handle data analysis or reporting
Assistants that connect to business tools like Google Workspace
Basically, it’s built for companies that want to deploy smart agents at scale.
LLM Support
ADK works best with Google’s Gemini models and Vertex AI, but is model-agnostic. LiteLLM makes switching between providers simple:
Supports other LLMs like Anthropic, Llama, or Mistral
As long as the model supports chat-style input/output, you can plug it in.
Tool & API Integration
A huge strength of ADK:
Built-in support for over 100 Google Cloud services (BigQuery, Cloud Storage, Docs, Gmail, etc.)
Custom tools and external APIs supported
Works with Google’s Model Garden, MCP, and Application Integration
You can even use other agent frameworks or microservices as tools.
Pros
Seamless integration with Google Cloud services
Supports complex, multi-agent orchestration out of the box
Model-agnostic and highly customizable
Scales easily using Google infrastructure
Great for enterprise AI applications
Cons
Still very new, ecosystem and community are just forming
Best suited to teams using GCP and Gemini
May be too heavy for smaller or non-enterprise projects
Rapid changes expected as the platform evolves
6. AWS Strands Agents
AWS Strands is a new open-source toolkit from Amazon that lets developers build AI agents with just a few lines of code. It’s meant to be lightweight, fast, and flexible, ideal for everything from simple assistants to complex multi-agent systems.
Strands is designed for simplicity:
Define an agent using just a model, a prompt, and some tools.
No complex framework or learning curve.
Tools are just Python functions with a decorator.
It’s great for developers who want to ship fast and focus on logic, not orchestration.
Strands uses a model-driven loop:
The LLM decides what to do next (like calling a tool or stopping).
The framework runs the tool and sends the result back to the LLM.
This loop continues until the task is done.
You can even create multi-agent workflows by making one agent call other agents—they're just tools too.
What Can You Build?
Strands is general-purpose, used for:
Developer assistants (e.g., Amazon Q)
Data analysis or troubleshooting bots
Creative tools like name generators
Coordinated agent systems for solving complex tasks
If you want the LLM to drive the process (reasoning, deciding, calling APIs), this is a great fit.
LLM Compatibility
Strands supports lots of models. LiteLLM makes switching between providers simple:
Amazon Bedrock (Claude, etc.)
OpenAI, Llama, Mistral via LiteLLM
Even local models using Ollama
You can plug in any model that works with prompts and tool calls
This makes it super flexible and future-proof.
Tool & API Integration
Strands has great tool support:
20+ built-in tools (file I/O, HTTP, AWS APIs)
Easily add custom tools using
@tooldecoratorsWorks with MCP (Model Context Protocol), so you can call any published tool server
Includes a retrieval tool for working with large knowledge bases
It’s easy to plug in any function or API you need.
Pros
Super simple and fast to get started
Fully model-driven (lets the LLM handle logic)
Broad model and tool support
Fits both local dev and production use cases
Multi-agent tools already included (workflow, graph, swarm)
Open-source and highly customizable
Cons
Brand new, so the ecosystem is small (for now)
AWS-focused, though technically cloud-agnostic
Model-driven logic can be hard to debug
Advanced features like true agent-to-agent communication are still in development
Heavy LLM usage can increase latency and cost
Comparative Insights
All these frameworks aim to simplify building AI agents, but they have different design trade-offs and target audiences:
OpenAI Agents SDK is ideal if you are already invested in OpenAI’s ecosystem and want an official, lightweight toolkit. It’s great for quickly prototyping agents in Python with OpenAI models and tools. If you need deep customization or must use non-OpenAI models, you can still do so via the generic interfaces, but the main advantage is seamless integration with OpenAI’s API and platform.
LangChain is the most general and battle-tested. Choose LangChain when you need the widest variety of integrations or the largest community. It’s suited for RAG-heavy applications, intricate multi-step reasoning flows, or any complex automation. LangChain’s depth does come with a learning curve, so it’s best for projects that justify its complexity.
LlamaIndex is the go-to for knowledge-first agents, especially over private data. Use it when your agent needs to answer or research using a custom document corpus, knowledge base, or databases. Its workflow abstraction makes it easier to implement RAG pipelines than coding them from scratch.
CrewAI focuses on multi-agent enterprise scenarios with an emphasis on performance and maintainability. It shines when you need a clean separation between autonomous agent behavior (Crews) and procedural control (Flows). It’s a good middle-ground if LangChain feels too heavy but you still want powerful agent orchestration, especially in Python-only stacks.
Google ADK is tailored for organizations on Google Cloud building production-scale agent apps. Its strengths are first-party integrations (Vertex AI, BigQuery, Gemini) and managed deployment (Agent Engine). Use ADK if you require tight cloud integration, built-in evaluation/safety, and if you’re comfortable in Google’s ecosystem. For quick experiments or non-GCP contexts, it may be overkill.
AWS Strands suits developers who want maximum simplicity and who are working in or willing to adopt AWS’s generative AI stack. Its model-driven, minimalistic approach means you can get agents running very quickly. It’s a great choice if you want to leverage Bedrock models and AWS tools, or if you prefer letting the LLM do most of the orchestration. However, for very custom workflows or non-AWS environments, you might prefer a more traditional framework.
Decision Framework
When choosing an agent development framework, consider these key factors:
For Beginners: Start with OpenAI Agent SDK for learning concepts, then graduate to CrewAI for practical applications.
For Knowledge Applications: Choose LlamaIndex Agents for RAG-heavy use cases, LangChain for broader knowledge integration needs.
For Enterprise Applications: Select Google ADK or AWS Strands based on your existing cloud infrastructure and security requirements.
For Complex Workflows: Use CrewAI for role-based scenarios, Google ADK or AWS Strands for maximum flexibility and customization.
For Performance-Critical Applications: Consider OpenAI Agent SDK for simplicity, Google ADK or AWS Strands for managed scalability.
Future Considerations
The agent development landscape continues to evolve rapidly. Key trends to watch include:
Standardization of agent communication protocols
Improved debugging and monitoring tools across all frameworks
Better integration between different framework ecosystems
Enhanced performance optimization for multi-agent scenarios
Growing support for local and open-source model deployment
Conclusion
For general-purpose, off-the-shelf multi-agent systems, LangChain (or its new LangGraph) is a safe choice due to flexibility and community. For enterprise knowledge bots, LlamaIndex is specialized. For rapid, production-ready agents with OpenAI, try the OpenAI Agents SDK. If you prioritize performance and native team coordination, CrewAI stands out. On Google Cloud, ADK is the best fit; on AWS, Strands is the natural option. Each framework has overlapping capabilities, so consider your existing platform, required model providers, and the specific problem domain when choosing.
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