Overview Agentic AI Frameworks, January 2025

The agentic AI framework landscape is experiencing rapid growth, presenting developers with a diverse array of choices. Before picking a framework, you should access if you really need a framework as they do come with overhead and over engineering for a lot of use cases. This blog covers the the subject well and its worth reading building effective agents. For those sure on going down the agentic framework route and want to navigate this evolving space in Jan 2025, this post offers a comparative analysis of six prominent frameworks: LangGraph, Pydantic AI Agents, Smol Agents, AutoGen, CrewAI, and LlamaIndex.

Each framework will be evaluated against key criteria crucial for practical application, including: Onboarding Experience, Integrations, Scalability, Adaptability, Documentation, and Additional Capabilities.

1. LangGraph

• Onboarding Experience: LangGraph presents a steeper learning curve due to its requirement for implementing a specific agentic architecture. The comprehensive quick start guide, while thorough, can be initially challenging to grasp. This results in a 2/5 onboarding score.
• Integrations: LangGraph demonstrates strong integration capabilities, seamlessly working with Python and Javascript, and fully integrating with Langchain. Its dedicated monitoring stack, LangSmith, further enhances its ecosystem, earning it a 4/5 for integrations.
• Scalability: Designed with scalability in mind, LangGraph fully supports asynchronous operations and promotes reusable components. This architecture contributes to a robust scalability score of 4/5.
• Adaptability: LangGraph stands out for its exceptional adaptability, offering complete design freedom and the ability to implement diverse architectures, including routers and React agents. Its flexibility scores a high 5/5.
• Documentation: Significant improvements in documentation, including the addition of a dedicated course, have enhanced LangGraph’s accessibility. This earns it a 4/5 documentation rating.
• Additional Capabilities: LangGraph offers a rich feature set, including streaming for messages and tokens, support for human-in-the-loop and time travel functionalities. While available in both Javascript and Python, it currently lacks a low-code environment, resulting in a 4/5 for additional capabilities.
• Target Use Cases: LangGraph is ideally suited for complex tasks and for developers aiming to build sophisticated conversational or voice agents.

2. Pydantic AI Agents

• Onboarding Experience: Pydantic AI Agents excels in ease of entry, allowing agent creation with a single line of code. However, complexity increases when utilizing graph-based architectures. While straightforward to begin with, advanced features introduce a learning curve, leading to a 3/5 onboarding score.
• Integrations: Integration capabilities are focused on major providers such as OpenAI, Anthropic, and Gemini. Currently, it lacks support for Langchain tools or MCP servers, although it features Pydantic log fire integration. This results in a moderate 3/5 integration score.
• Scalability: Pydantic AI Agents provides robust scalability through default asynchronous execution and streaming of structured responses. Its simpler approach maintains feature parity with more complex graph-based methods, achieving a 5/5 scalability rating.
• Adaptability: Offering both high-level and low-level APIs, Pydantic AI Agents provides diverse abstraction levels. Its flexibility allows for simple beginnings and complex scaling, earning it a 4/5 for adaptability.
• Documentation: Documentation is centrally accessible with practical examples and API references. However, its density and syntax-heavy code examples can be challenging, leading to a 3/5 documentation score.
• Additional Capabilities: While supporting streaming, Pydantic AI Agents currently lacks direct human-in-the-loop support, memory, and state persistence. It is also limited to Python, resulting in a 3/5 for additional features.
• Target Use Cases: Pydantic AI is a strong choice for projects prioritizing a high-level API and structured streaming capabilities.

3. Smol Agents

• Onboarding Experience: Smol Agents is designed for ultimate simplicity, enabling agent creation with minimal code. Concise documentation and a limited set of agent types (code, tool, managed) contribute to a very rapid learning curve, scoring a 5/5 for onboarding.
• Integrations: Smol Agents boasts strong integrations with Hugging Face models, Open Telemetry for run visualization, and Langchain-style tools. It supports Hugging Face spaces as tools and integration with MCP servers and e2b for secure code execution. These strong integrations earn a 4/5 rating.
• Scalability: A significant limitation is the lack of asynchronous execution, hindering its suitability for production systems. Scaling code execution and complexity also present challenges, relying on third-party services like e2b and limitations in nested managed agents. This results in a low 1/5 scalability score.
• Adaptability: Offering flexibility in model selection (Hugging Face, local, remote) and supporting planning steps and multi-agent systems, Smol Agents’ simplicity comes at the cost of modification complexity without delving into the framework’s core code. This yields a 3/5 adaptability score.
• Documentation: Excellent documentation — concise, readable, and including practical Colab notebooks and agent-building guidance — earns a top 5/5 rating.
• Additional Capabilities: Currently, Smol Agents lacks streaming, human-in-the-loop, time travel, memory, and low-code development features. It is Python-only and positioned as an open-source development area, resulting in a 1/5 for additional capabilities.
• Target Use Cases: Smol Agents excels in proof-of-concept work, research, and projects utilizing open-source language models.

4. AutoGen 2.0

• Onboarding Experience: AutoGen stands out for its exceptionally fast startup, requiring minimal code (around 6 lines). Its agent and chat-centric approach contributes to its ease of use, earning a 5/5 onboarding score.
• Integrations: Integration limitations exist with non-OpenAI models, although a proxy server setup is available. Some integrations are present with Microsoft products, and a partnership with agent ops provides monitoring capabilities. These factors lead to a 3/5 integration score.
• Scalability: Based on the actor model and supporting asynchronous messaging, AutoGen is designed for scalability and distributed environments. However, the stability of asynchronous features is still under development, resulting in a 3/5 scalability score.
• Adaptability: The actor framework inherently limits AutoGen’s flexibility and modularity. Modifying sections can be challenging, leading to a lower 2/5 adaptability score.
• Documentation: Documentation accessibility is a challenge, with difficulties in locating the correct version. Once found, the documentation is good, but the initial hurdle results in a 3/5 documentation score.
• Additional Capabilities: AutoGen supports streaming, including input and output streaming. Limited human-in-the-loop support and a low-code interface are present, but time travel is not supported. This yields a 2/5 for additional capabilities.
• Target Use Cases: AutoGen is particularly well-suited for projects within .NET or Microsoft infrastructure, or for users prioritizing extremely easy initial setup.

5. CrewAI

• Onboarding Experience: CrewAI introduces more concepts than AutoGen (agents, tools, tasks), but remains relatively fast to get started. YAML-based agent and task definition makes it well-suited for task-centric approaches, resulting in a 3/5 onboarding score.
• Integrations: Strong integrations with Langchain and a wide range of other services, including Google and various SaaS products, are key strengths. Integration with the Open Lit monitoring tool and the ability to replace agents with code further enhance its ecosystem, earning a 4/5 integration score.
• Scalability: Asynchronous task execution and both short-term and long-term memory (using a local SQL database) contribute to scalability. However, memory limitations due to the local database might be a factor, resulting in a 3/5 scalability score.
• Adaptability: As a task-based framework, CrewAI excels in scenarios where problems can be decomposed into tasks. However, it might be less ideal when a task engine is not required. It offers greater flexibility than AutoGen, scoring a 2/5 for adaptability.
• Documentation: CrewAI boasts excellent documentation with a consistent design, conceptual explanations, getting-started guides, and blog updates. Courses through deeplearning.ai further enhance learning resources, earning a 4/5 documentation score.
• Additional Capabilities: Streaming is not currently supported, and human-in-the-loop support is limited. Time travel and a built-in memory system are present. Python-only with an unofficial low-code interface results in a 3/5 for additional capabilities.
• Target Use Cases: CrewAI is recommended for task-based problems requiring fast starts and ease of use, and where strong integrations are beneficial.

6. LlamaIndex

• Onboarding Experience: LlamaIndex Workflows provides a good learning curve with relatively few abstractions. The event flow is intuitive, and documentation is quick to read. However, asynchronous nature can increase debugging and production integration complexity, leading to a 4/5 onboarding score.
• Integrations: Support for third-party observability tools like OpenTelemetry, Langsmith, and Langfuse, combined with the ability to load and use any models within its steps, provides solid integration capabilities, earning a 4/5 integration score.
• Scalability: Its event-driven architecture isolates complexity, enabling individual step scalability. Concurrent execution is supported, contributing to a high level of scalability and a 4/5 rating.
• Adaptability: As an orchestration framework, LlamaIndex Workflows offers flexibility in agent definition and combination with other frameworks, making it suitable for tasks requiring full agent control. This earns it a 4/5 adaptability score.
• Documentation: Documentation is structured into component guides and learning sections, offering a good breakdown of workflow building. However, it lacks high-level application explanations and quickly dives into technical details, resulting in a 3/5 documentation score.
• Additional Capabilities: Streaming of intermediate outputs and checkpointing are supported, but explicit time travel is not. External memory provider support is absent. Human-in-the-loop is supported via explicit events. Python-only, leading to a 3/5 for additional capabilities.
• Target Use Cases: LlamaIndex Workflows is recommended for event-driven frameworks where simple orchestration and control over agent workflows are desired.

Choosing the Right Framework

| Framework   | Onboarding Experience | Integrations | Scalability | Adaptability | Documentation | Additional Capabilities | Key Strengths                                         |
|-------------|-----------------------|--------------|-------------|--------------|---------------|-------------------------|------------------------------------------------------|
| LangGraph   | 3/5                   | 4/5          | 4/5         | 5/5          | 4/5           | 4/5                     | Highly flexible with robust graph-based design       |
| SmolAgents  | 5/5                   | 4/5          | 1/5         | 3/5          | 5/5           | 1/5                     | Fast and easy prototyping                            |
| PydanticAI  | 3/5                   | 3/5          | 5/5         | 4/5          | 3/5           | 3/5                     | Top scalability, flexible architecture               |
| AutoGen     | 5/5                   | 3/5          | 3/5         | 2/5          | 3/5           | 2/5                     | Simple setup, suitable for quick deployment          |
| CrewAI      | 3/5                   | 4/5          | 3/5         | 2/5          | 4/5           | 3/5                     | Versatile for task-based frameworks, great docs      |
| LlamaIndex  | 4/5                   | 4/5          | 4/5         | 4/5          | 3/5           | 3/5                     | Excellent orchestration, great for custom workflows  |

The optimal agentic AI framework selection hinges on your project’s specific needs. LangGraph is the choice for complex tasks requiring maximum flexibility. Pydantic AI Agents shines with its structured approach and streaming capabilities. Smol Agents is excellent for rapid prototyping and model diversity. AutoGen is user-friendly and integrates well within Microsoft environments. CrewAI offers a strong task-based paradigm with rich integrations but lacks core human-in-the-loop features. LlamaIndex is best suited for event-driven orchestration needs.

Ultimately, each framework presents a unique combination of features and trade-offs. Careful consideration of these factors is paramount for making an informed decision aligned with your project goals.