Top 10 Open-Source AI Agent Frameworks for Building Custom Agents in 2026

2025 was the year of autonomous AI agents, or at least the year AI agents became more reliable, observable, and production-ready, and 2026 is expected to bring more advancements and improvements in the field. It is widely predicted that 2026 will bring significant advancements and mainstream adoption of AI agents, just like generative AI. AI agents are just very capable systems that can independently perform complex, multi-step tasks with minimal human intervention. Such capable systems can, in fact, be invaluable for companies and regular people who want to automate tedious tasks.

Although there are several generalist AI agents available in the market that are enough for the majority of people, many businesses and developers want to build their own custom agentic solution. Now, the problem is that although building custom solutions may sound very enticing, building a custom AI agent is not as easy as it may seem. Hence, there is a need for AI agent frameworks to simplify the custom AI agent building process.

In this article, you'll learn what an AI agent framework is and several popular AI agent frameworks that will help you build custom AI agents.

What is an AI agent framework?

An AI agent framework is a software platform that provides pre-built modules and tools to simplify the creation of autonomous AI agents by handling common functionalities such as orchestration, tool integration, and memory management. These frameworks allow developers to build complex AI systems, like simple chatbots and multi-agent workflows, more quickly and efficiently.

Here are the top open-source frameworks to help you build custom AI agents in 2026:

1. LangGraph

LangGraph is for developers who need control. Built on LangChain, it models your agent's workflow as a graph, with nodes as actions and edges as logic. It excels in creating stateful, multi-step applications that retain long-term context. If you need a system that can pause for human approval and resume seamlessly, this is the tool for you.

• Stateful Orchestration: Manages complex, long-running workflows with ease.
• Human-in-the-Loop: Native support for pausing execution to get user feedback or approval.
• Persistence: Provides built-in checkpointing so you can persist and restore agent state across runs.

2. Google ADK (Agent Development Kit)

Google's ADK is a flexible, code-first toolkit designed to make building agents feel like standard software development. While it works incredibly well with Gemini, it's surprisingly model-agnostic, deployment-agnostic, and is built for compatibility with other frameworks. It is a strong contender for developers working within the Google Cloud ecosystem or who need robust multi-modal support.

• Code-first: Define behavior, tools, and orchestration directly in code.
• Multi-language: Official SDKs for Python, Java, and Go.
• Production-ready: One CLI flow from laptop prototypes to cloud deployment, with built-in tracing and dashboards.

3. CrewAI

CrewAI takes a unique role-playing approach to agents. You don't just write code; you define a crew of agents, each with a specific persona, role, and goal (e.g., Senior Researcher or Tech Writer). It orchestrates how these agents collaborate, delegate tasks, and share information, mimicking a real-world human team. It's intuitive and great for breaking down complex problems into manageable pieces.

• Role-Based Design: Assigns specific personas and goals to agents for better focus.
• Collaborative Intelligence: Agents automatically delegate and share tasks among themselves.
• Structured Flows: Combines flexible autonomy with precise, step-by-step process control.

4. OpenAI Agents SDK

The OpenAI Agents SDK is a lightweight, Python-first framework for building multi-agent workflows. It introduces powerful concepts like handoffs, where one agent can transfer a conversation to another specialized agent, and guardrails to ensure your system stays on track. It's clean, fast, and integrates deeply with OpenAI's ecosystem.

• Handoffs: Seamlessly transfer control between specialized agents.
• Guardrails: Built-in safety checks to validate inputs and outputs.
• Tracing: Native observability to debug and visualize agent reasoning.

5. Microsoft Agent Framework

Microsoft Agent Framework is the unified successor to the concepts found in Semantic Kernel and AutoGen. It's a comprehensive toolkit for building, orchestrating, and deploying agents in .NET and Python. It aims to be the enterprise-grade standard, offering a robust foundation that combines the orchestration power of AutoGen with the integration capabilities of Semantic Kernel.

• Unified Foundation: Combines best practices from AutoGen and Semantic Kernel.
• Enterprise Grade: Built for security, scalability, and production deployment.
• Cross-Language: Strong support for both .NET and Python ecosystems.

6. AWS Strands (Strands Agents)

Also known as Strands Agents, this is an open-source SDK from AWS that focuses on a model-driven approach. Instead of writing complex workflow logic, you give the model tools and a goal, and let its reasoning capabilities drive the execution loop. It's designed to be lightweight and integrates natively with Amazon Bedrock and other AWS services.

• Model-Driven Loop: Relies on the LLM's reasoning to plan and execute tasks.
• Native AWS Integration: Seamlessly connects with Bedrock and AWS infrastructure.
• Built-in MCP: Native support for MCP servers, allowing access to thousands of pre-built tools.

7. LlamaIndex

LlamaIndex started out by solving a simple but painful problem, which is connecting LLMs to your own data. Originally known as GPT Index, it grew into a full data framework for LLM applications and now also ships a developer-first agent framework optimized for RAG, knowledge assistants, and custom workflows. If your agent needs to read, understand, and synthesize information from documents, LlamaIndex is one of the most popular options to consider.

• Data-centric toolkit: Tools for ingesting, indexing, and retrieving unstructured data using Retrieval-Augmented Generation (RAG) patterns.
• Query engines: A formal abstraction for composing indexes and retrievers to address complex questions, beyond simple semantic search.
• RAG-powered agents: Support for RAG pipelines and chat engines, enabling the creation of research assistants and Q&A agents that provide accurate answers grounded in documents.

8. IBM BeeAI Framework (Bee Agent Framework)

IBM's BeeAI Framework (successor to the earlier Bee Agent Framework) is an open-source platform for production-grade multi-agent systems in Python and TypeScript. It's used heavily with open models like Llama 3.3 and IBM's Granite family.

• Dual language: Full-featured libraries in TypeScript and Python.
• Multi-agent focus: Tools, memory, RAG, and observability for complex agent teams.
• Open-model friendly: Optimized for IBM Granite and popular open models like Llama 3.x.

9. Hugging Face Smol Agents

Smol Agents by Hugging Face is a minimalist library that focuses on code agents, agents that write code to solve problems rather than just outputting text. It's incredibly lightweight (around 1,000 lines of code) and strips away unnecessary abstractions, giving you a raw, powerful connection to the LLM.

• Code-Centric: Agents solve problems by writing and executing Python code.
• Minimalist: Extremely lightweight with very few dependencies.
• Hub Integration: Seamlessly uses models and tools from the Hugging Face Hub.

10. Agno

Agno is a high-performance multi-agent framework, runtime and control plane that is built for speed, privacy, and scale. It provides a ready-to-use FastAPI app called AgentOS, providing a runtime and control plane for managing agents. It emphasizes memory, knowledge, and privacy, making it ideal for building agents that need to learn and adapt over time without sacrificing performance.

• AgentOS: Provides a complete runtime environment for agent execution.
• Memory & Knowledge: Built-in systems for long-term agent memory.
• High Performance: Optimized for speed and low-latency interactions.

In Conclusion:

The "best" framework doesn't exist, and probably never will; there can, however, be a best one for your specific problem. I

• Control and durability: LangGraph, Strands, Microsoft Agent Framework, Agno.
• Cloud-aligned stacks: Google ADK on Vertex, Strands on AWS, BeeAI with Granite.
• Data spine: LlamaIndex (and similar RAG-first tools) sitting under everything else.
• Lightweight experiments: smolagents and the OpenAI Agents SDK for fast, focused prototypes.

The practical approach is to pick one orchestration stack that matches your language and cloud, then layer on data tools and specialized frameworks. That way, you get the benefits of agentic automation without turning your codebase into a mess of half-finished experiments