This article will cover several Open-Source Agentic AI Frameworks in Python. These frameworks allow software developers to incorporate software agent technology in their applications, including memory, planning and scheduling, and other advanced generative capabilities.
These agentic AI frameworks are free and available to all, including large corporations, small businesses, and academic researchers. By providing an architecture comparison and assessment of each of these frameworks, I hope this will help you make a decision on which of these frameworks to adopt for your project, if any, for the year 2026.
What Are Open-Source Agentic AI Frameworks?
An Open-source agentic AI framework is a software development kit that allows software engineers to develop software agents using large language models (LLMs). These frameworks provide developers with the necessary components to create software agents that can make decisions, form plans and act upon them. Most of these frameworks are written in the Python computer programming language.
Some of these software frameworks include components such as RAG (Retrieval-augmented Generation), memory, and state components. Almost all of these frameworks allow developers to integrate various external models and frameworks (i.e. OpenAI, Anthropic, and Hugging Face).
Since these are open-source projects, they are completely free to use for commercial and non-commercial purposes. Using these frameworks, businesses and research institutions can develop and integrate customized software agents for various purposes without relying on other organizations for these customizations.
How to Choose an Agentic AI Framework
Defining Your Requirements
Identify the nature of your use case. Is it heavyweight enterprise use case, coding or retrieval automation, or light-weight prototyping?
Analyzing Architecture
Find out what type of architecture the framework employs. Some examples of architecture are graph architecture, chain architecture, the actor model, and plugin architecture.
Assessing Python Integration
How well integrated is the framework with the Python ecosystem?
Managing Memory and State
Look for frameworks which provide persistence for memory and/or state.
Utilizing Different Models
Are different LLMs provided by the framework?
Retrieving Information
Can the frameworks perform retrieval-augmented generation? Can the framework integrate with various data sources?
Distributing Work
For large scale enterprise use cases would the framework provide distributed architecture?
Considering Cost
Are there any costs associated with using the framework? Are there any licenses associated with the framework?
Quick Comparison Table
| Framework | Style | Best For | GitHub Stars (2026) | License |
|---|---|---|---|---|
| LangGraph | Graph-based orchestration | Complex stateful agents, enterprise workflows | ~16K | MIT |
| CrewAI | Role-based multi-agent | Team collaboration, fast prototyping | ~28K | MIT |
| AutoGen (AG2) | Conversational, actor model | Enterprise multi-agent, distributed execution | ~40K | Apache 2.0 |
| LangChain | Chain-based RAG + tools | Retrieval-augmented generation, tool pipelines | ~95K | MIT |
| LlamaIndex Agents | Data-centric orchestration | Knowledge graph + retrieval agents | ~30K | Apache 2.0 |
| Semantic Kernel | Plugin-based orchestration | Microsoft ecosystem, hybrid workflows | ~22K | MIT |
| OpenHands | Autonomous coding agents | Code generation, dev tasks | ~39K | Apache 2.0 |
| Dify | Low-code/no-code | Non-technical teams, agent builders | ~87K | Apache 2.0 |
| smolagents | Minimal scripting | Lightweight agent prototyping | ~15K | MIT |
| AgentScope | Actor model | Distributed agent systems | ~7K | Apache 2.0 |
1. LangGraph
LangGraph is an example of a framework that uses directed graphs for agent state and transformation to construct stateful agent workflows. Being fully written in Python facilitates integration with other ML components.
Memory and state management components are provided out-of-the-box. Agents can be configured with different LLMs. RAG integration is provided out-of-the-box, and therefore, LangGraph is suitable for retrieval-based tasks.
Last, but not least, LangGraph is licensed under MIT, and therefore, can be used in production by enterprises free of charge. Overall, LangGraph is a good framework to construct production-quality agents with branching workflows.
Key Capabilities: Graph-based framework, checkpointing, and observability.
Best Use Case: Enterprise level workflows and branching.
Weaknesses: More difficult to learn and use compared to other frameworks. Requires knowledge of and experience with graphs.
Strengths: State management. Framework is licensed under the MIT license.
Other Notes: Compared to other frameworks LANGGRAPH is less beginner friendly and has a smaller user base.
LangGraph Pros & Cons
| Pros | Cons |
|---|---|
| Graph-based orchestration for complex workflows | Steeper learning curve for beginners |
| Strong memory and state management | Smaller ecosystem compared to LangChain |
| Checkpointing and observability built-in | Requires graph knowledge for advanced use |
| Supports multiple LLMs | Less suited for quick prototyping |
| MIT license, free for commercial use | Documentation less beginner-friendly |
| Enterprise-ready scalability | Limited community tutorials |
| Seamless RAG integration | Higher setup complexity |
| Strong debugging tools | Fewer plug-and-play templates |
| Robust production reliability | Less adoption outside enterprise use cases |
2. CrewAI
CrewAI implements a architecture where agents are assigned to roles to construct a team. Agents work collaboratively to achieve a goal. Being fully implemented in Python makes integration and prototyping simple. Like other frameworks in this list,
CrewAI provides means to configure agents with long and short term memory. CrewAI supports integration with various LLMs and other external APIs. Like other frameworks, CrewAI supports RAG integrations. CrewAI is MIT licensed, and thus has no pricing. Given the nature of the framework, CrewAI is best suited to construct simple agent based systems.
Key Capabilities: Memory persistence, multi-agent framework, and easy to use APIs.
Best Use Cases: Creating a team of collaborative agents.
Weaknesses: Not well suited for creating and managing systems with a lot of complex branching.
Strengths: MIT license. Easy to use and learn.
Other Notes: Great framework for creating less complex systems. A lot of potential for this framework.
CrewAI Pros & Cons
| Pros | Cons |
|---|---|
| Role-based multi-agent collaboration | Less suited for highly complex branching workflows |
| Intuitive Python APIs | Limited scalability for enterprise systems |
| Strong memory persistence | Smaller ecosystem than LangChain |
| Easy prototyping for startups | Fewer advanced orchestration features |
| MIT license, free use | Less focus on distributed execution |
| Active community support | Limited enterprise-grade observability |
| Supports multiple LLMs | Less emphasis on RAG pipelines |
| Great for teamwork scenarios | Not optimized for fault tolerance |
| Beginner-friendly design | Less modular than LangGraph |
3. AutoGen (AG2)
AutoGen (AG2), developed by Microsoft, utilizes an actor-model architecture for distributed multi-agent systems. It provides a set of enterprise APIs, primarily in Python. With AutoGen, agents can persist their state and memory. This enables agents to retain context across distributed systems. AG2 is able to execute LLMs and can do local as well as cloud execution.
AG2 uses RAG for integration with various systems. These integrations include APIs and various databases as well as enterprise knowledge systems. AutoGen is open source, and with its focus on enterprise, it is optimized for scalability. AutoGen is designed for high availability and fault tolerance. This makes it well-suited for large-scale distributed agent systems.
Key Capabilities: Memory persistence, actor model framework, and distributed computing.
Best Use Cases: Large scale systems with a lot of agents.
Weaknesses: More difficult to set up and use.
Strengths: Fault tolerance and the Apache 2.0 license.
Other Notes: Compared to other frameworks this one is more difficult to use and more geared for large enterprise systems.
AutoGen (AG2) Pros & Cons
| Pros | Cons |
|---|---|
| Actor-model architecture for distributed agents | Complex setup and infrastructure needs |
| Enterprise-grade memory and state | Higher technical barrier |
| Hybrid cloud + local execution | Less beginner-friendly |
| Strong RAG integration | Documentation geared toward enterprises |
| Apache 2.0 license, free | Smaller open-source community |
| Fault-tolerant design | Requires more resources |
| Supports multiple LLMs | Less suited for lightweight prototyping |
| Enterprise scalability | Limited plug-and-play templates |
| Microsoft-backed reliability | Narrower adoption outside enterprise |
4. LangChain
LangChain is designed for RAG and tool integrations, and provides a chain orchestration layer. Built mostly in Python, there are thousands of community integrations and modules.
It provides different memory types (short-/long-term and episodic) and allows agents to retain memory across several chain invocations. LangChain is LLM agnostic and provides RAG out of the box.
It provides vector database integration and supports several commercial vector databases. It provides a generous MIT License and is free to use. It is the most widely adopted framework for developing RAG-based applications.
Key Capabilities: Orchestration, Advanced RAG pipelines, and other framework integrations.
Best Use Cases: Chatbots, knowledge and retrieval based assistants.
Weaknesses: Can sometimes over-engineer a solution.
Strengths: Large ecosystem, and the MIT license.
Other Notes: Very flexible framework with a large number of integrations. Can be over-engineered for simpler tasks.
LangChain Pros & Cons
| Pros | Cons |
|---|---|
| Chain-based orchestration | Can be over-engineered for simple tasks |
| Advanced RAG pipelines | Performance overhead in large deployments |
| Huge ecosystem and integrations | Complexity for new users |
| Strong memory modules | Documentation fragmented |
| MIT license, free | Requires optimization for speed |
| Supports nearly all LLMs | Less efficient for distributed systems |
| Vector DB integrations (Pinecone, FAISS, etc.) | Can be resource-heavy |
| Large community support | Risk of dependency bloat |
| Flexible tool pipelines | Less specialized for coding agents |
5. LlamaIndex Agents
LlamaIndex Agents follow a data-centric architecture for query processing and knowledge graphs. For Python, LlamaIndex provides good embedding and indexing APIs and query interfaces. Agents use memory structures for data and are hence able to reason over huge datasets..
LlamaIndex provides out of the box integrations with multiple LLMs. It is extremely useful in developing RAG pipelines. The open source code and the Apache 2.0 license make it an attractive option for both commercial and research usage. LlamaIndex Agents work best for data intensive and enterprise level uses cases including search and knowledge graph reasoning.
Key Capabilities: Data-centric orchestration, knowledge graph queries, structured retrieval.
Best Use Cases: Enterprise search, data-heavy workflows, graph reasoning.
Limitations: Less suited for lightweight prototyping.
Strengths: Strong database connectors, Apache 2.0 license.
Weaknesses: Smaller ecosystem, higher dependency on structured data.
LlamaIndex Agents Pros & Cons
| Pros | Cons |
|---|---|
| Data-centric orchestration | Less suited for lightweight prototyping |
| Strong knowledge graph queries | Smaller ecosystem |
| Structured retrieval support | Higher dependency on structured data |
| Python APIs for embedding/indexing | Less beginner-friendly |
| Apache 2.0 license | Limited plug-and-play templates |
| Seamless RAG integration | Narrower focus than LangChain |
| Works with multiple LLMs | Less emphasis on multi-agent collaboration |
| Ideal for enterprise search | Documentation more technical |
| Strong database connectors | Limited adoption outside data-heavy use cases |
6. Semantic Kernel
Microsoft’s Semantic Kernel, which runs in plugin mode, uses LLMs and symbolic AI together. It has complete Syntax for Python and Semantics for C# and JS. Memory is modular, and persistence is supported. It can integrate with various storage systems.
It can use multiple LLMs, and has companions for tools in the MSFT Cognitive Services suite. The Hybrid (RAG) workflows are provided by plugins. It is free to use, and licensed by MSFT. Developers who build solutions in the MSFT ecosystem get a generative AI toolkit integrated with symbolic AI to provide flexible enterprise workspace solutions.
Key Capabilities: Plugin-based orchestration, hybrid symbolic + LLM workflows.
Best Use Cases: Microsoft ecosystem, enterprise hybrid AI workflows.
Limitations: Best value only within Microsoft stack.
Strengths: Multi-language support (Python, C#, JS), MIT license.
Weaknesses: Smaller open-source community outside Microsoft users.
Semantic Kernel Pros & Cons
| Pros | Cons |
|---|---|
| Plugin-based orchestration | Best value only within Microsoft stack |
| Hybrid symbolic + LLM workflows | Smaller open-source community |
| Multi-language support (Python, C#, JS) | Less adoption outside enterprise |
| Modular memory handling | Limited beginner tutorials |
| MIT license, free | Narrower ecosystem than LangChain |
| Azure Cognitive Services integration | Heavier dependency on Microsoft tools |
| Enterprise-ready workflows | Less suited for lightweight prototyping |
| Supports multiple LLMs | Documentation enterprise-focused |
| Flexible plugin system | Less emphasis on RAG pipelines |
7. OpenHands
OpenHands is a coding agent framework that is designed for software development and related tasks. It is completely implemented in Python and offers APIs for automated code generation and execution as well as debugging. The memory of the agents if focused on persisting code context, that is, agents can remember code they wrote previously.
The framework integrates RAG. As a result, agents can retrieve documentation and software code by interacting with the agents. The framework is free and available for commercial use. The authors of the framework recommend using the framework to develop software code writing assistants.
Key Capabilities: Autonomous coding agents, debugging, execution context memory.
Best Use Cases: Developer assistants, code automation, software prototyping.
Limitations: Narrow focus on coding tasks.
Strengths: Optimized for coding LLMs, Apache 2.0 license.
Weaknesses: Limited general-purpose agent use cases.
OpenHands Pros & Cons
| Pros | Cons |
|---|---|
| Autonomous coding agent design | Narrow focus on coding tasks |
| Optimized for coding LLMs | Limited general-purpose use |
| Strong debugging and execution context | Smaller ecosystem |
| Python-based APIs | Less suited for enterprise workflows |
| Apache 2.0 license | Limited RAG support |
| Memory persistence for code context | Documentation still growing |
| Free for commercial use | Less adoption outside developer community |
| Great for developer assistants | Not ideal for multi-agent orchestration |
| Automates repetitive coding tasks | Limited scalability |
8. Dify
Dify is a low-code/no-code agent framework that lets non-technical users build complex tasks through agents. With Dify, more experienced users can still define tasks using Python SDKs. Dify uses session persistence to provide agents with memory and integrates with other apps using connectors. Dify’s free plan is licensed under the Apache 2.0 license.
Agents can access multiple LLMs and use RAG Pipelines to perform analysis. Difty charges for agent hosting and provides businesses with a way to quickly develop agents. Agents built with Dify can access products and define tasks without the assistance of IT or developers.
Key Capabilities: Low-code/no-code orchestration, drag-and-drop workflows, Python SDK.
Best Use Cases: Non-technical teams, startups, quick deployment.
Limitations: Less flexible for highly technical workflows.
Strengths: Democratizes agent building, Apache 2.0 license.
Weaknesses: Limited scalability for complex enterprise systems.
Dify Pros & Cons
| Pros | Cons |
|---|---|
| Low-code/no-code orchestration | Less flexible for technical workflows |
| Drag-and-drop workflow builder | Limited scalability for complex systems |
| Python SDK for developers | Smaller ecosystem |
| Apache 2.0 license | Less suited for enterprise-grade reliability |
| Democratizes agent building | Limited advanced orchestration features |
| Accessible to non-technical teams | Less emphasis on distributed execution |
| Free for commercial use | Documentation geared toward beginners |
| Supports multiple LLMs | Limited RAG connectors compared to LangChain |
| Quick deployment for startups | Less modular design |
9. smolagents
smolagents is a Python-based framework for rapidly creating agent prototypes. The framework consists of small Python scripts to define agent behaviors. Although memory constrained, the framework can perform basic state and perspective tracking for shorter interactions. The framework can work with various Large Language Models (LLMs), but is optimized for smaller models.
As such, Rough-, Aussie-, and other System-X integrations are not included out-of-the-box, but can be added using Python. smolagents is distributed under the MIT license, and is entirely free for personal use. The framework is intended for agent prototyping and experiments, and is not intended for production use. Because of its simplicity and focus on prototyping, smolagents is a great framework for developers to quickly implement agent ideas.
Key Capabilities: Minimal scripting, lightweight prototyping, basic memory.
Best Use Cases: Quick experiments, educational use, small-scale agents.
Limitations: Limited RAG support, minimal ecosystem.
Strengths: Simple, fast, MIT license.
Weaknesses: Not suitable for enterprise or complex workflows.
smolagents Pros & Cons
| Pros | Cons |
|---|---|
| Minimal scripting framework | Limited RAG support |
| Lightweight prototyping | Not suitable for enterprise |
| Basic memory persistence | Smaller ecosystem |
| Python-based simplicity | Less emphasis on scalability |
| MIT license, free | Limited integrations |
| Fast experimentation | Documentation minimal |
| Great for educational use | Narrower focus |
| Free for commercial use | Limited observability tools |
| Easy to learn | Less adoption in production |
10. AgentScope
AgentScope uses an actor model for distributing agent systems. The framework is strong for Python. Agents and message passing can be defined using Python. For persistence across machines, AgentScope uses distributed state management.
This framework integrates with multiple LLMs. AgentScope supports RAG pipelines. However, the developers focus more on LangChain and LlamAIndex. The framework is open source and distributed under the Apache 2.0 license. It is suitable for research. The framework is ideal for projects that require distributing agent systems across multiple machines.
Key Capabilities: Actor-model architecture, distributed state management, scalability.
Best Use Cases: Research projects, distributed agent systems.
Limitations: Less emphasis on RAG compared to LangChain/LlamaIndex.
Strengths: Scalable, Apache 2.0 license, strong distributed design.
Weaknesses: Smaller ecosystem, less beginner-friendly.
AgentScope Pros & Cons
| Pros | Cons |
|---|---|
| Actor-model architecture | Less emphasis on RAG |
| Distributed state management | Smaller ecosystem |
| Scalable across multiple machines | Less beginner-friendly |
| Python APIs for agents | Limited plug-and-play templates |
| Apache 2.0 license | Documentation still growing |
| Supports multiple LLMs | Narrower adoption |
| Ideal for research projects | Less suited for quick prototyping |
| Free for commercial use | Limited enterprise-grade integrations |
| Strong distributed design | Less community support |
Conclusion
The Python-based open-source agentic AI frameworks have evolved and become more abundant. These frameworks are applicable at all levels in an organization. LangGraph and CrewAI provide stateful orchestration and collaborative multi-agent systems. AutoGen (AG2) and Semantic Kernel provide multi-tenant systems. For data and RAG-based systems and workflows,
LangChain and LlamaIndex Agents are great options. OpenHands is a great choice for multi-agent systems for coding tasks. For enterprise systems integration, AutoGen (AG2) and Semantic Kernel, coupled with the Microsoft ecosystem, would be a great choice. Dify and smolagents offer frameworks for agent development and rapid prototyping, respectively. Finally, AgentScope offers an actor-based model for system development.
FAQ
What is LangGraph?
LangGraph is a graph-based orchestration framework for building stateful agents. It supports Python, multiple LLMs, memory persistence, and advanced RAG pipelines. Free under MIT license.
How does CrewAI differ from LangGraph?
CrewAI uses a role-based multi-agent system, making collaboration easier. LangGraph focuses on complex stateful workflows. Both support Python and memory, but CrewAI is simpler for beginners.
What is AutoGen (AG2)?
AutoGen (AG2) is Microsoft’s actor-model framework for distributed multi-agent systems. It supports Python, enterprise-grade memory, hybrid cloud execution, and RAG integration. Licensed under Apache 2.0.
Why is LangChain still popular?
LangChain remains the most widely adopted due to its chain-based architecture, extensive Python libraries, strong RAG integrations, and huge community ecosystem.
What are LlamaIndex Agents best for?
LlamaIndex Agents excel in data-centric workflows, knowledge graphs, and structured retrieval. They integrate with SQL/NoSQL databases and support Python seamlessly.