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AI agent frameworks are reshaping development by enabling systems to reason, plan, and act autonomously. These tools go beyond traditional AI libraries, offering features like multi-agent collaboration and state management to handle complex workflows. Open-source options dominate the field, with 80% of teams relying on them for cost-effectiveness, transparency, and community-driven support. For example, LangGraph has helped companies like Klarna reduce resolution times by 80% for 85 million users.
This guide explores 11 open-source frameworks, from LangGraph's stateful orchestration to Smolagents' lightweight code-first approach. Each offers unique strengths for building scalable, intelligent systems. Whether you're prototyping or deploying at scale, these frameworks - and tools like Latenode - simplify development, making advanced AI accessible to teams of all skill levels.
LangGraph is an open-source framework designed for managing complex workflows through multi-agent systems. Instead of relying on linear processes, it uses computational graphs to model interactions, offering a more dynamic way to orchestrate tasks.
LangGraph stands out by maintaining a persistent state across agent interactions, unlike traditional systems that treat each request independently. This allows agents to remember past decisions, monitor multi-step progress, and seamlessly coordinate with other agents. Its graph-based design assigns each agent or function to a node, while the connections between nodes dictate the flow of information and control.
The framework is equipped with tools that make it adaptable to various scenarios:
These features ensure smooth operation even in demanding workflows, making the framework reliable and efficient.
LangGraph is particularly effective in automating workflows that require multiple agents to collaborate on intricate tasks. It finds application in fields like literature reviews, financial transactions, and manufacturing processes, where real-time data handling and coordinated decision-making are crucial. Its advanced orchestration capabilities also pave the way for visual development approaches.
However, not all teams have the Python expertise needed to fully leverage LangGraph's potential. According to Latenode specialists, about 70% of teams favor visual tools for quicker prototyping. Latenode addresses this gap by offering a visual interface that integrates seamlessly with leading open-source libraries. This approach simplifies access to LangGraph's powerful features, enabling more teams to build and refine workflows without being limited by programming knowledge.
AutoGen is an advanced framework designed to streamline the development of AI agents. Its multi-tiered architecture caters to both researchers and developers, offering flexibility to suit varying skill levels and project demands. Unlike rigid frameworks that enforce a single development method, AutoGen provides a dynamic environment adaptable to diverse needs.
At its core, AutoGen is built to support both quick experimentation and in-depth customization. A standout feature is AgentChat, a high-level API that simplifies managing complex multi-agent conversations. This eliminates the need for extensive setup, allowing researchers to focus on testing agent behavior and interaction patterns rather than grappling with infrastructure code.
By structuring agent interactions as organized conversations, AutoGen makes it easier to analyze communication patterns, debug multi-agent behaviors, and refine configurations. For added convenience, AutoGen Studio introduces a visual layer that further accelerates prototyping, making it ideal for research-focused projects.
AutoGen Studio is a web-based interface designed for teams requiring quick iteration cycles. Its visual tools, such as the Team Builder and live playground, remove coding barriers, enabling even non-technical team members to design workflows, monitor communication streams, and track interaction flows effortlessly [2][3]. The Gallery feature further enhances collaboration by allowing teams to discover and integrate community-built components.
One of AutoGen's key strengths is its ability to support adaptive workflows that respond to changing contexts. This feature is particularly valuable for research applications where the best agent configuration isn’t immediately clear. Teams can experiment with different strategies, evaluate their performance, and refine them based on practical outcomes.
While AutoGen excels in offering research-oriented flexibility, Latenode complements this by providing a visual interface that integrates seamlessly with open-source AI frameworks like AutoGen. By combining clear code capabilities with intuitive visual tools, Latenode enables teams to harness AutoGen’s conversational AI strengths through visual nodes while retaining the option to incorporate custom code. This hybrid approach bridges technical and non-technical workflows, making advanced AI development more accessible.
CrewAI transforms how multiple AI agents collaborate by assigning them specialized roles. This structured approach simplifies complex projects by breaking them into smaller, manageable tasks that align with each agent's unique capabilities.
At the heart of CrewAI is its ability to create clear hierarchies and define responsibilities within a team of agents. Unlike models where all agents are treated the same, CrewAI allows developers to assign specific roles such as researcher, writer, analyst, or reviewer. Each agent operates within its designated role, understanding both its individual tasks and its contribution to the larger team goal.
This setup mirrors real-world teamwork, where members bring distinct skills to the table. For instance, a research agent might excel at collecting and analyzing data, while a content agent focuses on transforming that data into compelling narratives. CrewAI ensures these agents work together seamlessly, facilitating smooth transitions between tasks and maintaining consistent context throughout the project.
CrewAI's role-based design makes task delegation efficient and precise. Tasks are automatically routed to the most suitable agent, with the framework tracking dependencies, supporting simultaneous task processing, and handling error recovery. By monitoring the status of each agent, CrewAI prevents bottlenecks and ensures optimal use of resources.
Once a task is completed, the system assigns the next step, creating a structured and efficient workflow. This orchestration significantly reduces the challenges of managing multiple AI agents in complex production environments.
One of CrewAI's standout features is its ability to facilitate collaborative decision-making among agents. Instead of relying on a single agent for critical decisions, the framework enables multiple agents to engage in discussions, challenge assumptions, and reach consensus. This process often results in more well-rounded and reliable solutions.
CrewAI supports these interactions through structured debates, voting mechanisms for group decisions, and escalation procedures to address disagreements. When combined with Latenode's visual workflow design, this capability becomes even more dynamic, offering teams the flexibility to create and manage agent interactions seamlessly.
Latenode enhances CrewAI's functionality by providing an intuitive visual interface for designing agent hierarchies and task flows. Teams can use visual nodes to map out workflows while retaining the option to incorporate custom code for advanced orchestration needs. This blend of visual design and code-based configuration makes CrewAI a powerful tool for managing multi-agent collaboration efficiently.
The OpenAI Agents SDK is a powerful tool designed to create production-ready AI agents using GPT models. It seamlessly integrates with OpenAI's ecosystem while offering the flexibility of open-source development.
The SDK stands out by integrating directly with GPT models, unlike other frameworks that treat them as external services. This approach minimizes latency and simplifies authentication, enabling agents to fully utilize advanced GPT capabilities such as function calling, structured outputs, and multi-turn conversations - all without the added complexity of third-party wrappers.
Its architecture is built specifically to align with OpenAI API patterns, offering features like streaming responses, token usage tracking, and automatic retry mechanisms. Developers can choose from a variety of GPT models, such as GPT-3.5 Turbo for cost-efficient tasks or GPT-4 for more demanding reasoning processes. The SDK ensures a consistent interface, automatically optimizing for the selected model.
This deep integration leads to faster response times, particularly in complex workflows requiring multiple interactions between models, making it highly effective for real-world applications.
The OpenAI Agents SDK provides a robust system for enabling agents to interact with external systems. Through its advanced function-calling architecture, agents can perform real-world actions, query databases, call APIs, and manipulate data with high reliability.
Function definitions are validated using JSON schema, ensuring parameters are correctly formatted and reducing the risk of integration errors. The SDK supports both synchronous and asynchronous function calls, allowing agents to execute multiple tasks simultaneously.
Beyond basic API interactions, the SDK's tool system supports advanced workflows, such as file processing, data analysis, and multi-step business logic. Agents can chain function calls, use outputs from one operation to inform the next, and maintain context across extended interactions. This makes it an excellent choice for building agents capable of handling complex, multi-step processes.
One of the SDK's standout features is its flexibility in designing custom workflows. It provides the necessary building blocks for teams to create tailored agent behaviors without being constrained by rigid frameworks. Developers can implement conversation flows, decision trees, and state management systems while leveraging OpenAI's advanced language capabilities.
The SDK supports custom prompt engineering, allowing precise control over agent behavior through system directives and context-specific guidance. Features like conversation memory, user preference tracking, and dynamic prompt adjustments enable agents to deliver personalized and context-aware experiences.
Integration with external systems is streamlined through webhook support and an event-driven architecture. Agents can respond to external triggers, process batch tasks, and integrate seamlessly with existing business workflows.
When paired with Latenode's visual development environment, the OpenAI Agents SDK becomes even more accessible. Latenode's intuitive visual nodes simplify the orchestration of agent interactions, enabling teams to configure functions, manage conversations, and integrate with business systems without requiring deep Python expertise. This combination allows teams to leverage OpenAI's advanced models through user-friendly visual workflows, while still offering the option to incorporate custom code for specialized needs.
Smolagents is an open-source framework designed for flexibility and speed, catering to the dynamic demands of AI projects in 2025. Unlike many other frameworks, it enables agents to write and execute Python code directly, bypassing the need to convert intentions into JSON structures.
Created by Hugging Face, Smolagents operates on a simple yet powerful concept: when an agent encounters a task, it writes and runs Python code immediately. This approach eliminates the extra steps of translating intentions into structured data formats, which are common in other frameworks [4][5].
Smolagents’ lightweight design allows developers to build functional AI agents with minimal setup. Instead of relying on a collection of pre-built features, it provides only the core tools, letting developers introduce complexity as their projects demand.
At its core, Smolagents offers two primary agent types. The first, CodeAgent, focuses on generating and executing Python code to tackle problems. The second, ToolCallingAgent, supports JSON-based interactions, but the emphasis remains on the code-first method. This streamlined architecture promotes a direct, code-driven approach to problem-solving.
What sets Smolagents apart from other AI agent frameworks is its direct interaction with tasks. Instead of mapping inputs and outputs through JSON schemas, Smolagents agents write Python code to achieve their objectives.
For instance, if an agent is tasked with analyzing a dataset, it can write Python code using libraries like pandas or numpy to perform the analysis directly. By leveraging Python’s rich ecosystem, agents avoid the inefficiencies of intermediary data translation.
This code-first approach not only speeds up prototyping but also integrates smoothly with various large language models (LLMs). Smolagents supports a range of LLMs, including Hugging Face models, OpenAI APIs, Azure OpenAI, and LiteLLM connections [4][5]. Developers can skip defining extensive tool catalogs, managing complex workflows, or writing integration wrappers - agents are ready to solve problems with just a few lines of setup code.
While Smolagents excels at code-driven development, pairing it with tools like Latenode can enhance collaboration and monitoring. Latenode’s user-friendly interface allows teams to oversee agent execution, adjust workflows without coding, and integrate Smolagents-powered agents into business systems using drag-and-drop nodes. This combination retains the flexibility of direct code execution while adding a layer of accessibility and teamwork support, making it ideal for growing teams.
Microsoft's Semantic Kernel is an advanced AI framework that powers Microsoft 365 Copilot and Bing. This lightweight SDK redefines how businesses integrate AI agents into their existing systems, leveraging a unique kernel-based architecture for seamless functionality.
Semantic Kernel is purpose-built for stable, production-ready enterprise applications [6]. Unlike many frameworks designed for experimentation, it focuses on delivering reliability and scalability from the outset.
Its adoption highlights its appeal among enterprise users. By February 2025, Semantic Kernel had received 22,900 stars on GitHub and reached 2.6 million downloads - doubling from 1 million in April 2024 [7][10]. While its numbers are smaller compared to LangChain's 99,600 stars and 27 million monthly downloads, Semantic Kernel’s targeted approach attracts teams working on critical, high-stakes applications.
Microsoft's trust in this framework is evident, as it underpins flagship products like Microsoft 365 Copilot. The SDK supports three programming languages - C#, Python, and Java - with C# offering the most extensive features [10].
The framework’s plugin-based design allows developers to assemble reusable "skills" with minimal coding, enabling flexible orchestration of tasks [6]. This modularity treats text understanding as a set of reusable components tailored for business workflows.
Semantic Kernel also includes a built-in planner capable of generating Directed Acyclic Graphs (DAGs) for complex workflows without requiring developers to define every sequence explicitly [7]. This capability is particularly useful in dynamic scenarios where workflows are influenced by runtime conditions, though setting it up may require some initial effort.
For creating AI agents, the Semantic Kernel Agent Framework allows seamless interaction through messaging, model-generated responses, and human inputs [8]. These agents are well-suited for collaborative environments, supporting multi-agent workflows often required in enterprise settings. This modular design ensures consistent and efficient performance across various business processes.
Semantic Kernel’s integration with Azure services and the .NET environment enhances its reliability and compatibility within the Microsoft ecosystem [10]. While this deep integration benefits organizations already invested in Microsoft technologies, it may pose limitations for those relying on alternative platforms.
The framework emphasizes workflow automation, prioritizing reliability and predictable outcomes over flexibility. Its native planner is particularly effective for managing complex, production-grade business processes, making it a dependable choice for enterprise workflows.
Microsoft plans to unify Semantic Kernel and AutoGen by early 2025, offering a seamless transition from AutoGen’s experimental capabilities to Semantic Kernel’s enterprise-grade features [9]. This roadmap reflects Microsoft's commitment to providing a comprehensive development lifecycle for AI agents.
Moreover, Semantic Kernel integrates effortlessly with existing enterprise systems. Many teams have also found that pairing Semantic Kernel with Latenode’s visual development environment speeds up deployment and collaboration. Latenode’s intuitive drag-and-drop interface enables non-technical users to interact with Semantic Kernel-powered agents, while developers can leverage the robust enterprise features that make Semantic Kernel a strong choice for production systems.
LlamaIndex Agents take a focused approach to multi-agent workflows by prioritizing real-time data integration. This framework transforms how developers create knowledge-driven AI systems by specializing in retrieval-augmented generation (RAG) architectures. These architectures connect language models with external data sources in real time, enabling more dynamic and informed interactions.
Unlike frameworks that treat data retrieval as a secondary concern, LlamaIndex Agents build their foundation around RAG. This makes them particularly effective for developing agents capable of querying, analyzing, and reasoning over large knowledge bases, all while aiming for a high degree of factual accuracy.
The framework supports advanced reasoning patterns like ReAct (Reasoning + Acting), function calling, and multi-step query decomposition. These capabilities allow agents to break down complex questions into smaller, actionable sub-queries. They can then retrieve relevant data from multiple sources and combine it to deliver well-rounded answers.
LlamaIndex also stands out for its ability to integrate with an array of data sources, including databases, APIs, document repositories, and cloud storage systems. This makes it a valuable tool for enterprises where agents often need access to proprietary knowledge bases and up-to-date business data.
LlamaIndex's query engine is designed to adapt to the complexity of each request. It uses a mix of semantic search, keyword matching, and hybrid methods, automatically selecting the most effective strategy for retrieving information.
In multi-agent systems, LlamaIndex supports hierarchical agent structures. For example, a financial analysis agent might oversee specialized sub-agents dedicated to market data, regulatory updates, and company filings. This layered approach ensures that each domain is handled with precision and efficiency.
The framework also offers seamless interaction with various tools and platforms. Agents can connect to SQL databases, vector stores, graph databases, and even custom business applications through a single interface. This capability allows developers to bridge organizational data silos and create agents that function cohesively across different systems.
LlamaIndex tackles key challenges in production environments, such as ensuring data is up-to-date, retrieval processes are accurate, and responses remain consistent. Features like built-in caching, incremental indexing, and real-time synchronization ensure agents operate with current and reliable information.
Its evaluation framework provides detailed metrics for assessing RAG performance. Developers can measure factors like retrieval relevance, answer accuracy, and how well the context is utilized. These insights help fine-tune agent performance and maintain high standards in live deployments.
For enterprise use, security is a priority. LlamaIndex includes features such as document-level access controls, query filtering, and audit logging. These tools ensure that agents retrieve only the information users are authorized to access, making it a secure choice for handling sensitive data.
For teams looking to streamline the deployment of knowledge-driven agents, pairing LlamaIndex with Latenode offers a powerful solution. Latenode’s visual development environment complements LlamaIndex’s RAG capabilities, providing an intuitive way to configure data sources, design workflows, and monitor agent performance - all without requiring extensive Python expertise. This combination simplifies the process of bringing sophisticated, knowledge-based agents into production.
Strands Agents focuses on solving complex problems by breaking them into smaller, manageable tasks and executing multi-step solutions with precision. Its expertise lies in advanced planning and multi-step reasoning, making it ideal for tackling intricate challenges.
At the heart of Strands Agents is its hierarchical planning system, which allows agents to break down large objectives into a series of sequential sub-tasks. This structure is especially useful in scenarios where multiple dependencies need to be managed, conditional logic must be applied, and strategies require adjustment based on intermediate results.
The system employs a goal-oriented engine that works backward from the desired outcome. This enables agents to systematically analyze challenges and develop iterative solutions, even in research-heavy contexts. Additionally, it supports dynamic replanning, allowing agents to adapt their strategies as conditions evolve. This adaptability is particularly valuable in areas like automated trading systems or real-time data analysis, where circumstances can shift rapidly.
These planning capabilities lend themselves to a wide range of applications, demonstrating the versatility of Strands Agents.
In academic research, Strands Agents shines by conducting literature reviews and generating hypotheses. It systematically navigates databases, extracts key findings, and synthesizes new research directions, streamlining the research process.
For business automation, the framework handles workflows with complex decision points, conditional branches, and time-sensitive schedules. This is particularly useful for tasks like approvals, compliance checks, and coordinating among multiple stakeholders. In project management, it ensures that dependencies are managed effectively, ensuring that certain activities are completed before others begin.
Strands Agents is designed to integrate seamlessly with external systems through robust APIs and advanced state management. This enables agents to process data according to complex rules, coordinate actions across platforms, and maintain consistency throughout lengthy operations.
Its state management features track the progress of long-running tasks, ensuring that agents can pick up where they left off after interruptions or delays. This is crucial for multi-day or multi-week projects where maintaining context is essential. Additionally, built-in error handling allows agents to switch to alternative methods or escalate issues when problems arise, ensuring reliability and reducing downtime.
This ability to integrate with and manage complex systems makes it an excellent choice for real-world applications.
In production settings, Strands Agents provides tools for monitoring and debugging, helping teams understand agent decisions and ensuring transparency. Its scalable architecture supports both horizontal and vertical growth, while built-in security features, like role-based controls and audit trails, ensure compliance with industry regulations. These capabilities are especially important for organizations in regulated industries, where accountability is a priority.
While Strands Agents offers powerful tools for advanced reasoning, combining it with Latenode's visual development environment can simplify deployment and reduce maintenance. Latenode’s user-friendly interface allows developers to configure Strands Agents' advanced planning features through visual workflows. This approach makes it easier for teams without deep technical expertise to leverage complex AI capabilities, accelerating project timelines and lowering barriers to entry.
Pydantic AI Agents brings type-safe validation into AI agent development, addressing the unpredictability of large language model (LLM) outputs. Created by the team behind the well-known Pydantic validation library, this framework transforms unstructured AI responses into reliable, structured data that applications can depend on.
The standout feature of Pydantic AI Agents is its ability to enforce structured, validated responses from language models. Instead of relying on chance for properly formatted outputs like JSON, this framework uses schemas to ensure consistency. Developers can define output formats using Pydantic models, dataclasses, TypedDicts, or even simple scalar types, giving them flexibility without compromising validation standards [11].
When an output schema is defined, Pydantic AI automatically generates JSON schemas that guide the language model's responses. This makes it ideal for tasks like extracting customer details, processing financial records, or working with complex nested data. Additionally, its type coercion feature can convert strings like '123' into integers seamlessly [13].
Pydantic AI offers three output modes tailored to different scenarios:
All three modes uphold Pydantic's validation guarantees, ensuring consistent data handling regardless of the approach [11]. This adaptability is especially useful when switching between various language model providers or toggling between local and cloud-based models. Developers can maintain the same validation logic while the framework manages the technical variations.
Pydantic AI's custom validation system goes beyond simple type checks. Using the @agent.output_validator decorator, developers can implement advanced business logic validations, including asynchronous tasks or external API calls. If a validation fails, the framework triggers a ModelRetry to generate a corrected response automatically [11].
This self-correction mechanism also applies to tool argument validation. When an agent calls a function with incorrect parameters, Pydantic AI compares the arguments against the function signature and provides corrective prompts to the model. This feedback loop enhances accuracy over multiple iterations [15].
For applications requiring real-time responses, Pydantic AI supports partial validation during streaming outputs. This feature validates data incrementally as it streams, allowing validated portions to be displayed immediately. This reduces latency and improves user experience, particularly in chat interfaces or live data processing [11][12].
Partial validation is especially effective for handling complex nested structures. Early fields can be validated and processed while the remaining data is still being generated. This approach not only reduces perceived delays but also enables more responsive AI-driven applications.
To ensure reliability in production environments, Pydantic AI includes usage limits and safety controls. The UsageLimits feature restricts the number of requests and tokens an agent can consume, preventing runaway token usage or infinite loops. This is critical for controlling costs and maintaining system stability in automated workflows [14].
The framework also manages optional fields and default values effectively, making agents more resilient to inconsistent model outputs. It supports complex nested structures, lists, and recursive data types, enabling sophisticated workflows while maintaining validation integrity across multiple steps [13]. These safeguards make it easier to integrate Pydantic AI with visual tools.
Pydantic AI Agents excels at ensuring data reliability through its rigorous validation processes. However, implementing these capabilities often requires advanced Python knowledge. Visual development platforms like Latenode simplify this by integrating Pydantic AI's strengths into an intuitive drag-and-drop interface. This combination allows teams to maintain strict data standards while empowering non-technical members to build and deploy advanced AI workflows quickly. The result is faster implementation and reduced maintenance effort, making complex AI capabilities more accessible across teams.
Atomic Agents represents a step forward in deploying artificial intelligence for enterprise use. By focusing on a decentralized model, it offers a resilient and autonomous system that operates independently across distributed environments. Unlike traditional frameworks that rely on centralized control, Atomic Agents empowers individual components - referred to as agents - to make decisions and coordinate with minimal oversight. This approach is particularly useful for enterprises that require high availability and fault tolerance in their operations.
At its core, Atomic Agents emphasizes autonomous decision-making. Each agent functions independently, equipped with its own knowledge base, decision-making logic, and communication protocols. This setup eliminates single points of failure and allows deployment across multiple locations, whether data centers, cloud regions, or edge devices.
For global organizations managing operations across various time zones and regulatory frameworks, this decentralized model is a game-changer. Instead of relying on a central hub to process all decisions, local agents can handle region-specific tasks while staying aligned with the larger system. Distributed consensus mechanisms ensure that the system remains consistent, even without constant communication between agents.
Atomic Agents enables the creation of self-organizing networks, where agents dynamically discover and collaborate with one another. Through peer-to-peer communication, these agents form temporary teams to tackle tasks and redistribute responsibilities as needed. This adaptability ensures the system remains robust, even in critical scenarios where downtime is unacceptable.
Agents continuously assess the performance and availability of their peers, rerouting tasks to the most capable resources available. This self-healing capability enhances reliability, particularly for applications where uninterrupted operation is essential.
The lightweight design of Atomic Agents makes it a natural fit for edge computing environments. It can operate efficiently on IoT devices, industrial sensors, and mobile platforms, enabling real-time decision-making directly at the data source. This eliminates the need for constant connectivity to cloud services.
For industrial automation, this means that sensors and control systems can continue functioning intelligently even during network disruptions. Agents maintain localized decision-making capabilities and synchronize with the larger system once connectivity is restored, ensuring smooth and reliable operations.
Atomic Agents incorporates Byzantine fault-tolerant consensus algorithms, ensuring system integrity even if up to one-third of the agents fail. To handle disputes, the framework uses mechanisms like weighted voting, reputation systems, and evidence-based evaluations. These tools automatically resolve conflicts, reducing the need for manual intervention.
This approach minimizes operational overhead while maintaining reliability and accuracy, making it ideal for environments where data consistency and decision precision are critical.
While Atomic Agents offers powerful capabilities for distributed AI, setting up and managing decentralized networks requires significant technical expertise. This is where Latenode steps in, providing intuitive visual tools that simplify the design and monitoring of agent workflows. Teams can easily configure agent behaviors, track network health, and fine-tune coordination without needing extensive coding skills.
Botpress is an AI agent development platform designed to simplify the creation of intelligent agents while maintaining flexibility for advanced customizations. Unlike traditional frameworks that demand extensive coding knowledge, Botpress combines a visual-first interface with full code access, making it approachable for users of varying expertise. It stands out with its enterprise-ready features and omnichannel capabilities, enabling agents to perform reasoning, goal-setting, and memory retention using LLMs, integrated tools, and persistent memory [16].
At the core of Botpress is its custom inference engine, LLMz. This engine handles critical tasks like interpreting instructions, managing memory, selecting tools, executing JavaScript in a secure sandbox, and generating structured responses. Its isolated, versioned runtime ensures reliable execution of even the most complex multi-step tasks, maintaining consistency and security [17].
Botpress supports a variety of language models, including GPT-4o, Claude, and Mistral, allowing organizations to choose models based on their specific needs - whether optimizing for cost, performance, or unique capabilities. Its tool-calling framework further enhances functionality, enabling agents to access live data, trigger workflows, and execute intricate processes seamlessly [16].
For enterprise users, Botpress offers both on-premise and cloud deployment options. Features such as role-based access control, compliance tools, detailed observability, and staging environments make it a robust solution for businesses. Developers can inject custom code into lifecycle events, monitor agent actions, and manage agents programmatically through API endpoints, providing a high degree of control and adaptability [17].
Botpress agents can be deployed across multiple platforms, including web, mobile apps, and messaging services, ensuring a seamless omnichannel experience. Its architecture is built to handle increasing workloads, allowing organizations to scale their digital assistants without compromising performance [16].
Botpress not only simplifies the development of AI agents with its visual tools but also enables rapid prototyping and efficient business process automation. By integrating Latenode, teams can leverage pre-built AI workflow templates to further streamline development, combining the strengths of open-source technology with the ease of no-code solutions.
Selecting the best open-source AI agent framework hinges on your team's expertise and the specific needs of your project. Each of the 11 frameworks detailed here brings unique strengths to the table, catering to different scenarios and goals.
The table below highlights key differences in multi-agent capabilities, ease of use, extensibility, ideal use cases, and the level of community support:
| Framework | Multi-Agent Support | Ease of Use | Extensibility | Ideal Use Cases | Community Support |
|---|---|---|---|---|---|
| LangGraph | ★★★★★ | ★★★☆☆ | ★★★★★ | Complex workflows, state management, enterprise applications | Discord community |
| AutoGen | ★★★★★ | ★★☆☆☆ | ★★★★☆ | Multi-agent conversations, research tasks, collaborative AI | Community support |
| CrewAI | ★★★★★ | ★★★★☆ | ★★★★☆ | Role-based teamwork, streamlined automation | Community |
| OpenAI Agents SDK | ★★★☆☆ | ★★★★★ | ★★★☆☆ | OpenAI-centric apps, quick prototypes, GPT integrations | Official OpenAI support |
| Smolagents | ★★★☆☆ | ★★★★★ | ★★★☆☆ | Lightweight agents, educational projects, simple automation | HuggingFace ecosystem |
| Semantic Kernel | ★★★☆☆ | ★★★☆☆ | ★★★★★ | Enterprise integration, .NET/Python applications | Enterprise support |
| LlamaIndex Agents | ★★★☆☆ | ★★★★☆ | ★★★★☆ | Retrieval augmented generation applications, document processing, knowledge systems | Community |
| Strands Agents | ★★☆☆☆ | ★★★★☆ | ★★★☆☆ | Financial services, data analysis, specialized domains | Niche community |
| Pydantic AI Agents | ★★☆☆☆ | ★★★★★ | ★★★★☆ | Type-safe development, data validation, Python-first teams | Adoption |
| Atomic Agents | ★★★☆☆ | ★★★★☆ | ★★★★☆ | Modular architectures, microservices, component reuse | Development |
| Botpress | ★★★☆☆ | ★★★★★ | ★★★★☆ | Chatbots, conversational AI, omnichannel deployment | Visual interface |
For teams focused on enterprise-grade solutions with minimal coding, Semantic Kernel and Botpress provide a balanced approach. On the other hand, if your project demands maximum flexibility for complex multi-agent systems, LangGraph and AutoGen are excellent, though they come with steeper learning curves.
When time is of the essence and rapid prototyping is key, tools like Latenode offer a visual interface that simplifies deployment and collaboration. This makes it an excellent companion to popular open-source libraries, streamlining workflows without sacrificing functionality.
The best choice ultimately depends on your team's technical expertise and project deadlines. Next, explore code examples and practical implementations to see these frameworks in action.
To better understand the features of various frameworks, let's explore some practical examples. Each framework has its distinct coding style and approach, and these samples provide a glimpse into how they can be applied in real scenarios.
Here’s how you can build a research assistant that coordinates multiple specialized agents using LangGraph:
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from typing import TypedDict, List
class ResearchState(TypedDict):
query: str
research_data: List[str]
summary: str
def researcher_node(state: ResearchState):
llm = ChatOpenAI(model="gpt-4")
response = llm.invoke(f"Research: {state['query']}")
return {"research_data": [response.content]}
def summarizer_node(state: ResearchState):
llm = ChatOpenAI(model="gpt-4")
data = "".join(state["research_data"])
summary = llm.invoke(f"Summarize: {data}")
return {"summary": summary.content}
workflow = StateGraph(ResearchState)
workflow.add_node("researcher", researcher_node)
workflow.add_node("summarizer", summarizer_node)
workflow.add_edge("researcher", "summarizer")
workflow.add_edge("summarizer", END)
workflow.set_entry_point("researcher")
app = workflow.compile()
result = app.invoke({"query": "Latest AI developments"})
LangGraph’s state-driven design is particularly suited for enterprise applications where data persistence and complex routing are priorities.
Next, let’s look at how AutoGen facilitates conversational dynamics in multi-agent systems.
AutoGen is tailored for creating interactive systems where AI agents engage in natural conversations. Here’s an example of setting up a code review system with specialized roles:
import autogen
config_list = [{"model": "gpt-4", "api_key": "your-key"}]
developer = autogen.AssistantAgent(
name="Developer",
system_message="You write Python code based on requirements.",
llm_config={"config_list": config_list}
)
reviewer = autogen.AssistantAgent(
name="CodeReviewer",
system_message="You review code for bugs and improvements.",
llm_config={"config_list": config_list}
)
user_proxy = autogen.UserProxyAgent(
name="ProductManager",
human_input_mode="NEVER",
code_execution_config={"work_dir": "coding"}
)
user_proxy.initiate_chat(
developer,
message="Create a function to validate email addresses"
)
AutoGen excels in dynamic systems where agents interact organically, clarifying and refining responses without rigid workflows.
For a structured teamwork approach, CrewAI offers a different perspective.
CrewAI focuses on structured teamwork, with clearly defined roles and tasks. Here’s an example of coordinating a team for market research and content creation:
from crewai import Agent, Task, Crew
from crewai_tools import SerperDevTool
search_tool = SerperDevTool()
market_researcher = Agent(
role='Market Research Analyst',
goal='Analyze market trends and competitor data',
backstory='Expert in market analysis with 10 years experience',
tools=[search_tool],
verbose=True
)
content_creator = Agent(
role='Content Strategist',
goal='Create compelling marketing content',
backstory='Creative writer specializing in tech marketing',
verbose=True
)
research_task = Task(
description='Research the latest trends in AI development tools',
agent=market_researcher
)
content_task = Task(
description='Create a blog post based on research findings',
agent=content_creator
)
crew = Crew(
agents=[market_researcher, content_creator],
tasks=[research_task, content_task],
verbose=2
)
result = crew.kickoff()
This framework is ideal for projects requiring well-defined roles, such as marketing campaigns or business process automation.
Now, let’s explore the OpenAI Agents SDK, which provides a direct path to integrating GPT models.
The OpenAI Agents SDK simplifies GPT-powered application development. Here’s a quick example:
from openai import OpenAI
import json
client = OpenAI()
def get_weather(location: str) -> str:
# Mock weather function
return f"Weather in {location}: 72°F, sunny"
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current weather",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"}
}
}
}
}
]
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "What's the weather in San Francisco?"}],
tools=tools,
tool_choice="auto"
)
if response.choices[0].message.tool_calls:
tool_call = response.choices[0].message.tool_calls[0]
function_args = json.loads(tool_call.function.arguments)
weather_result = get_weather(**function_args)
print(weather_result)
This SDK is perfect for developers seeking a streamlined way to integrate GPT into their applications, though it lacks multi-agent coordination features.
Finally, let’s look at Semantic Kernel, which focuses on enterprise-grade integrations.
Microsoft’s Semantic Kernel is designed for robust integrations and compatibility across platforms. Here’s an example of chaining multiple skills:
import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
kernel = sk.Kernel()
kernel.add_chat_service("chat", OpenAIChatCompletion("gpt-4", "your-key"))
email_plugin = kernel.import_semantic_skill_from_directory(
"./plugins", "EmailPlugin"
)
calendar_plugin = kernel.import_semantic_skill_from_directory(
"./plugins", "CalendarPlugin"
)
# Chain multiple skills
context = kernel.create_new_context()
context["input"] = "Schedule meeting with John about Q4 planning"
result = await kernel.run_async(
calendar_plugin["ScheduleMeeting"],
email_plugin["SendConfirmation"],
input_context=context
)
This framework is particularly appealing for organizations already using Microsoft tools or requiring extensive third-party integrations.
The complexity of implementation varies across these frameworks. Smolagents and Pydantic AI keep things simple, making them ideal for quick prototypes. On the other hand, LangGraph and AutoGen offer advanced coordination capabilities, which are worth the additional effort for production-level systems.
Botpress bridges technical and non-technical teams with its visual flow builders. Meanwhile, LlamaIndex Agents are strong contenders for document-heavy applications, thanks to their built-in retrieval features.
Latenode simplifies the use of open-source AI frameworks by turning them into visual workflows, making it easier for teams to prototype without needing extensive Python coding. By replacing complex programming with intuitive drag-and-drop actions, Latenode enables a wider audience to work with advanced AI tools. This approach not only speeds up development but also fosters collaboration among team members with varying skill levels.
Latenode's visual workflow editor revolutionizes how teams interact with open-source AI agent frameworks. Instead of spending weeks learning the intricacies of each framework, users can connect pre-built nodes that represent agent actions, data sources, and logic flows. This method has proven to cut prototyping time by 50–70% and allows over 70% of users to contribute without requiring prior Python knowledge[18].
Latenode combines a visual, node-based design interface with the option to add custom Python code, creating a seamless path from initial prototypes to production-ready solutions. For instance, a customer support workflow might visually link a CrewAI research agent to a CRM database, set escalation rules using drag-and-drop tools, and integrate custom Python scripts for sentiment analysis. This hybrid approach enables non-technical team members to actively participate in the design process while giving developers the freedom to extend functionality as needed.
To tackle the coordination challenges of multi-agent projects, Latenode provides features like shared workspaces, version control, and permission management. These tools allow multiple users to co-design workflows, review changes, and comment on specific nodes or logic blocks. Teams have reported a 60% reduction in onboarding time and noted that business stakeholders can now directly contribute to agent design[18]. Additionally, robust security and collaboration features ensure that large teams can manage complex projects effectively and securely.
Latenode keeps pace with the evolving needs of enterprise environments by offering continuous updates and customization options. The platform frequently updates its node library to stay compatible with the latest open-source frameworks. It also supports customization through code injection and open API access, allowing teams to adapt workflows to their specific needs. This balance between ease of use and technical depth ensures that organizations can tailor solutions while maintaining flexibility.
Latenode simplifies the transition from prototype to production with tools designed for importing existing code and integrating with enterprise systems. Built-in features like monitoring, error handling, and deployment tools facilitate smooth production across both cloud and on-premises environments. Additionally, visual debugging and monitoring tools help teams track agent behavior, identify bottlenecks, and optimize workflows - all without requiring deep coding expertise.
Experts highlight that Latenode's hybrid model "preserves the flexibility of open source frameworks while democratizing access to advanced AI tools."
Selecting the right open-source AI agent framework depends on your project's complexity, your team's expertise, and the integration needs of your existing systems. Each framework has its own strengths, as outlined in the comparison below, and understanding these will help you align the right tool with your project requirements.
| Framework | Best For | Learning Curve | Team Size | Integration Strength | Notable Success |
|---|---|---|---|---|---|
| LangGraph | Enterprise workflows, stateful agents | Moderate | 5+ developers | Strong | Klarna (85M users, 80% faster resolution) |
| AutoGen | Multi-agent dialogue, R&D projects | High | 3+ Python experts | Good | Analytics and software development |
| CrewAI | Collaborative agent teams | High | 2-4 developers | Medium | Virtual assistants, fraud detection |
| LlamaIndex | Data-heavy applications | Moderate | 2-5 developers | Strong | Knowledge management systems |
| Semantic Kernel | Microsoft ecosystem integration | Medium | 3+ developers | Strong | Enterprise .NET applications |
| Smolagents | Lightweight, simple agents | Low | 1-3 developers | Basic | Quick prototypes, MVPs |
For simple chatbots, frameworks like Smolagents or basic LlamaIndex are well-suited. On the other hand, LangGraph is ideal for more complex, multi-agent systems. LangGraph's popularity, evidenced by its 11,700+ GitHub stars and comprehensive documentation, makes it a solid choice for intermediate developers and enterprise-scale projects alike [1].
Here’s a practical three-phase plan to guide your framework selection and implementation process:
Phase 1: Environment Setup and Testing
Begin by setting up your environment. Install Python 3.8+ and the framework of your choice. Test the setup by running a basic example. Frameworks like AutoGen provide starter templates with predefined agent personas, while LlamaIndex offers step-by-step tutorials to connect with data sources.
Phase 2: Prototype Development
Develop a prototype that addresses a specific problem in your domain. Focus on delivering core functionality and seek help from active community forums or Discord channels to resolve challenges quickly.
Phase 3: Production Preparation
Prepare your system for production by implementing robust error handling and monitoring. Frameworks like LangGraph integrate seamlessly with monitoring tools such as Langfuse, making it easier to track performance in live environments.
When choosing a framework, it’s easy to fall into certain traps. Here’s how to sidestep them:
Once you’ve built a foundation, consider transitioning to a hybrid workflow. Start with open-source frameworks to create a proof of concept, then move to visual development platforms like Latenode for production and team collaboration. This approach combines technical flexibility with accessibility, making AI agent development manageable for both technical and non-technical team members.
With Latenode, you can import agent logic, integrate multiple frameworks, and add custom code without heavy development work. Many teams report 50-70% faster prototyping times and smoother collaboration when adopting this strategy.
The transition typically involves three steps: exporting your functional agent logic, recreating workflows visually, and enhancing them with drag-and-drop components for data sources, monitoring, and team collaboration. This method bridges the gap between code-based development and visual tools, helping you advance your AI projects efficiently.
Open-source AI agent frameworks offer a combination of clarity, adaptability, and affordability, which makes them a go-to option for many developers. Unlike closed proprietary systems, these frameworks allow complete customization, giving you the freedom to shape the tool to fit your exact requirements.
Another key advantage is the backing of engaged developer communities. These communities provide a wealth of shared expertise, regular updates, and opportunities for collaborative troubleshooting. On top of that, open-source frameworks are free to use, helping to cut down on development expenses without compromising on performance quality.
Latenode streamlines the process of working with open-source AI agent frameworks through its intuitive visual interface. With a simple drag-and-drop design, it removes the heavy reliance on extensive coding, allowing developers to prototype and deploy AI solutions more efficiently while minimizing potential errors.
By blending visual workflows with the option to incorporate custom code, Latenode bridges the gap between technical and non-technical teams. This approach brings advanced frameworks like LangChain and AutoGen within reach, fostering collaboration, accelerating development timelines, and simplifying the management and implementation of powerful AI tools.
When choosing an AI agent framework, it's important to weigh several critical considerations to ensure it aligns with your team's needs and project goals:
Taking the time to evaluate these factors can help you select a framework that supports both your immediate technical needs and your long-term objectives.
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