AI Agent Frameworks (2026 Edition)

An AI agent framework is a library or toolkit that streamlines the design and implementation of agents. Orchestration logic, tool management, and memory management that would require hundreds of lines to implement from scratch can be written concisely using the APIs that frameworks provide.

Target audience: Beginner-to-intermediate developers who understand the basic concepts of AI agents and want to actually build one.

Estimated learning time: 25 minutes to read

Prerequisites: What Is an AI Agent?

Why Use a Framework?

Building from Scratch vs. Using a Framework

Building an agent from scratch requires you to implement everything yourself:

The ReAct loop (managing the Reason → Act → Observe cycle)
Tool definitions, invocations, and error handling
Context management (length control, summarization)
Information passing between agents
Human-in-the-loop approval flows
Execution logs and debugging

Frameworks provide all of this out of the box, so developers can focus on task-specific logic.

Aspect	From Scratch	Using a Framework
Flexibility	Maximum	Within the framework’s scope
Development speed	Slow	Fast
Learning cost	No framework to learn	Must learn the framework
Maintenance	Self-managed	Follow framework updates
Recommended when	Strong custom requirements	Building standard agents

Notable Frameworks in 2026

1. LangGraph

LangGraph is a graph-based agent framework developed as part of the LangChain ecosystem. Processing steps are defined as “nodes” and transition conditions as “edges,” expressing agent flow as a directed graph (DAG).

# Python - Minimal LangGraph example (conceptual)
from langgraph.graph import StateGraph, END
from typing import TypedDict

class AgentState(TypedDict):
    messages: list
    next_step: str

# Define the graph
workflow = StateGraph(AgentState)

# Add nodes (processing steps)
workflow.add_node("reason", reason_node)     # Thinking step
workflow.add_node("tool_use", tool_node)     # Tool execution
workflow.add_node("observe", observe_node)   # Result observation

# Define edges (transitions)
workflow.add_edge("reason", "tool_use")
workflow.add_conditional_edges(
    "observe",
    should_continue,  # condition function
    {"continue": "reason", "finish": END}
)

# Compile and run the graph
app = workflow.compile()
result = app.invoke({"messages": [("user", "Please execute the research task")]})

StateGraph is a mechanism that manages workflow by type-defining the agent’s state (State) and having each node update that state. It’s well-suited for agents with complex conditional branching and state management.

Best suited for

Agents that require complex state management
Workflows with many conditional branches and loops
Cases where you’re already using the LangChain ecosystem

2. CrewAI

CrewAI is a framework that defines multiple agents as a “crew” (team), assigning each agent a Role and Goal. Its design philosophy — mimicking human teamwork in AI — is its defining characteristic.

# Python - Minimal CrewAI example (conceptual)
from crewai import Agent, Task, Crew

# Define agents (specify role, goal, and backstory)
researcher = Agent(
    role="Research Analyst",
    goal="Comprehensively research the latest EV market trends",
    backstory="An analyst with 10 years of market research experience who values data accuracy.",
    tools=[web_search_tool, data_analysis_tool],
    verbose=True
)

writer = Agent(
    role="Technical Writer",
    goal="Compile research findings into a clear report",
    backstory="A technical documentation expert skilled at communicating complex information clearly.",
    tools=[file_write_tool]
)

# Define tasks
research_task = Task(
    description="Collect and analyze the latest 2026 EV market data",
    agent=researcher,
    expected_output="Analysis report including market share, growth rate, and key players"
)

writing_task = Task(
    description="Create a readable report based on the research findings",
    agent=writer,
    expected_output="A market trends report of approximately 1,500 words",
    context=[research_task]  # Use results from research_task as input
)

# Assemble and run the crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    verbose=True
)
result = crew.kickoff()

Best suited for

Multi-agent systems with clear role divisions (research → writing → review, etc.)
Automation of business processes
Content generation pipelines

3. AutoGen (Microsoft)

AutoGen is a framework developed by Microsoft Research, with the ConversableAgent (conversational agent) as its basic unit. Agents solve tasks by “talking” to each other — a design that’s distinctive and widely used for research purposes.

# Python - Minimal AutoGen example (conceptual)
import autogen

# LLM configuration
llm_config = {"model": "gpt-4o", "api_key": "YOUR_API_KEY"}

# Define agents
assistant = autogen.AssistantAgent(
    name="CodingAssistant",
    llm_config=llm_config,
    system_message="An agent that writes Python code to solve problems."
)

# Human proxy agent (implements Human-in-the-loop)
user_proxy = autogen.UserProxyAgent(
    name="UserProxy",
    human_input_mode="TERMINATE",  # Ask the human when termination condition is met
    code_execution_config={"work_dir": "workspace"}
)

# Start the conversation (agents solve the task through dialogue)
user_proxy.initiate_chat(
    assistant,
    message="Write a Python script that calculates the Fibonacci sequence and test it"
)

HumanProxyAgent is a special agent that inserts human intervention points into agent conversations. Setting human_input_mode lets you choose between always confirming, confirming only at termination, or fully autonomous.

Best suited for

Interactive task solving
Automation of code generation, execution, and debugging
Multi-agent experiments for research purposes

4. Mastra

Mastra is a TypeScript-first agent framework that has been gaining attention since 2025. It provides a unified API for managing workflows, tools, memory, and LLM calls, with a design that emphasizes integration into web applications.

// TypeScript - Minimal Mastra example (conceptual)
import { Mastra, Agent } from "@mastra/core";

// Define the agent
const researchAgent = new Agent({
  name: "ResearchAgent",
  instructions: "A specialized agent that uses web search to gather the latest information.",
  model: {
    provider: "ANTHROPIC",
    name: "claude-sonnet-4-6",
  },
  tools: {
    webSearch: webSearchTool,
    fileWrite: fileWriteTool,
  },
});

// Create the Mastra instance
const mastra = new Mastra({
  agents: { researchAgent },
});

// Run the agent
const agent = mastra.getAgent("researchAgent");
const result = await agent.generate(
  "Please research the AI agent market size in 2026"
);

console.log(result.text);

Best suited for

Integration into web frameworks like Next.js or Nuxt
Agent development in TypeScript projects
Cases where you want tight coupling between the frontend and agents

5. OpenAI Agents SDK (formerly Swarm)

The OpenAI Agents SDK is OpenAI’s official agent SDK. An evolution of the former Swarm framework, it centers on the concept of handoffs — the delegation of control from one agent to another.

# Python - Minimal OpenAI Agents SDK example (conceptual)
from openai import OpenAI
from agents import Agent, Runner, handoff

client = OpenAI()

# Define each specialist agent
triage_agent = Agent(
    name="TriageAgent",
    instructions="Analyze the user's question and route it to the appropriate specialist agent.",
    model="gpt-4o"
)

coding_agent = Agent(
    name="CodingAgent",
    instructions="Handle questions about code.",
    model="gpt-4o",
    handoffs=[handoff(triage_agent)]  # Return to triage after completion
)

research_agent = Agent(
    name="ResearchAgent",
    instructions="Handle information gathering and research tasks.",
    model="gpt-4o",
    handoffs=[handoff(triage_agent)]
)

# Set handoff targets on the triage agent
triage_agent.handoffs = [
    handoff(coding_agent),
    handoff(research_agent)
]

# Run
result = Runner.run_sync(triage_agent, "Please implement a sorting algorithm in Python")

A handoff is a mechanism for delegating control from one agent to another. Think of it like transferring a customer call to the right department — the appropriate agent takes over based on its specialty.

Best suited for

Projects centered on the OpenAI API
Simple multi-agent handoff implementations
Customer support and triage systems

6. Claude Code (Anthropic)

Claude Code is a coding-specialized AI agent provided by Anthropic. It runs directly from the command line, understands the entire codebase, and autonomously performs refactoring, debugging, and feature additions.

Its defining features are parallel execution of sub-agents and deep integration with MCP (Model Context Protocol), making it capable of large-scale software development tasks.

# Claude Code basic usage (command line)

# Execute a single task
claude -p "Investigate and fix the bug in src/auth.ts"

# Parallel tasks using sub-agents (conceptual example)
claude -p "Please execute the following in parallel:
  1. Write unit tests for the frontend
  2. Generate API documentation for the backend
  3. Optimize the CI configuration file"

# Operations integrated with an MCP server
# (when MCP servers are configured in .claude/mcp.json)
claude -p "Reference GitHub issue #123 and fix the corresponding code"

Using the Claude Code SDK, you can also call Claude as an agent from your own application.

Best suited for

Software development and codebase operations
Large-scale refactoring and automated test generation
Integration with external services via MCP

Framework Comparison Table

Framework	Language	Key Feature	Learning Cost	Best For
LangGraph	Python	Graph-based, state management	Medium	Complex workflows
CrewAI	Python	Role/goal-based design	Low–Medium	Role-divided multi-agent
AutoGen	Python	Conversational agents	Medium	Research, code execution automation
Mastra	TypeScript	Web integration, type safety	Medium	Web app integration
OpenAI Agents SDK	Python	Handoffs, simplicity	Low	OpenAI-centered multi-agent
Claude Code	CLI/Python/TS	Coding-specialized, MCP integration	Low	Software development

Framework Selection Flowchart

graph TD
    Start["Framework Selection"] --> Lang{"Primary language?"}

    Lang -->|"TypeScript"| TS["Mastra\n(TypeScript-first)"]
    Lang -->|"Python"| Python{"Primary use case?"}

    Python -->|"Coding / development assistance"| Claude["Claude Code\n(coding-specialized)"]
    Python -->|"Want to use OpenAI API"| OpenAI["OpenAI Agents SDK\n(simple multi-agent)"]
    Python -->|"Complex workflow / state management"| LangGraph["LangGraph\n(graph-based)"]
    Python -->|"Clear role-divided team structure"| CrewAI["CrewAI\n(role/goal-based)"]
    Python -->|"Research / conversational"| AutoGen["AutoGen\n(conversational agents)"]

Summary

Frameworks let you avoid implementing the ReAct loop, tool management, and inter-agent communication yourself
Each of the six frameworks has its own design philosophy and strengths
For TypeScript projects, use Mastra; for coding assistance, use Claude Code; for complex workflows, use LangGraph; for role-divided agents, use CrewAI
I recommend trying a small prototype before committing to production use

Frequently Asked Questions

Q: Which framework is easiest for beginners?

A: CrewAI or the OpenAI Agents SDK are most beginner-friendly. CrewAI is especially intuitive — you can build a multi-agent system just by defining roles and goals in natural language. LangGraph is feature-rich but takes time to get comfortable with the graph concept.

Q: What’s the difference between LangChain and LangGraph?

A: LangChain is a general-purpose framework for LLM applications. LangGraph is an agent-specialized framework built on top of LangChain, centered on flow control through graph structures. For building agents, LangGraph is recommended.

Q: Can multiple frameworks be used together?

A: Technically possible, but it gets complex and I don’t recommend it. First check whether a single framework can meet your requirements; only consider combining them if there’s truly no other way.

Q: Is there a way to call the Anthropic API directly without a framework?

A: Yes. Using the Anthropic Python SDK (anthropic package), you can build agents without a framework. This approach is appropriate for simple agents or cases with strong custom requirements. See AI Agents and MCP for details.

References

Next step: AI Agents and MCP