With the rapid development of AI agents, businesses these days continue to leverage intelligent automation to improve their operational efficiency. AI agents are transforming industries by automating tasks and delivering custom outputs at scale, yet the foundation of a comprehensive AI system lies in the right framework—it provides the right tools, libraries, and pre-built components that make developing intelligent systems faster, more efficient, and much more sustainable for future scalability and advancements.
To learn more about AI agents and their impact on modern enterprises, check out our comprehensive guide for a deeper dive into their capabilities and real-world applications.
A robust AI framework streamlines agent development with the essential components that facilitate the creation of sophisticated, interactive systems to help businesses achieve tangible goals such as improved customer satisfaction and accelerated business growth.
A well-designed AI framework typically includes:
Agent Architecture: Sophisticated decision-making engines with persistent memory management systems and advanced interaction protocols.
Environmental Integration Layer: APIs for real-world system integration, virtual environment adapters and robust security and access controls with performance monitoring interfaces.
Task Orchestration Framework: Automated workflow management with priority-based execution systems and resource allocation controls. Error handling and recovery mechanisms for emergencies.
Communication Infrastructure: Human-AI interaction protocols, API integration capabilities, data exchange systems, and inter-agent communication channels to facilitate internal collaborations.
Performance Optimization: Machine learning models with continuous learning capabilities and iteration frameworks. Audit trail capabilities and system health diagnostics for future optimization.
In this guide, we explore the top 9 AI agent frameworks you can use to create powerful AI solutions tailored to your business needs. Each of these frameworks has its own set of powerful AI capabilities designed to meet the goals and technical needs of the business as well as scales. While there is no one-size-fits-all AI framework for every system, we hope this guide helps you identify the most suitable framework that aligns with your business’s unique needs and technical requirements.
LangChain has emerged as a go-to framework for developers building LLM-powered applications, simplifying the handling of complex workflows with its modular tools and robust abstractions. The core strength of LangChain is its ability to build applications involving LLMs and complex workflows. Its companion library, LangGraph (now at v1.2.7), provides a standalone graph-based runtime for building stateful, multi-actor agents — and no longer requires other langchain-* packages. Together, LangChain and LangGraph can be easily integrated with APIs, databases, and external tools, making the ecosystem highly flexible for different applications.
This is particularly beneficial for use cases like building conversational assistants, automated document analysis and summarization, personalized recommendation systems, and research assistants across various domains. We'd recommend this framework for both mature corporations and beginner startups. It's particularly well-suited for mature companies, especially those with large-scale natural language processing (NLP) use cases, as well as startups developing AI-powered products.
However, building and running applications in LangChain, especially those involving large language models and external integrations, can be resource-heavy. LangChain also relies on several external dependencies and integrations, which may require constant updates or troubleshooting. Managing these dependencies can sometimes be cumbersome, especially when dealing with rapid changes in the AI landscape.
To accelerate the development of large-scale projects, we recommend utilizing platforms such as Shakudo to provide a fully managed environment for building and deploying AI applications. By integrating LangChain with Shakudo, teams can focus more on innovation and less on managing resources, resulting in faster and more efficient project execution.
Kaji is Shakudo’s autonomous AI agent platform purpose-built for enterprise environments where security, compliance, and scale are non-negotiable. Unlike traditional agent frameworks that require developers to wire up tools and orchestration logic manually, Kaji runs as a full computing environment with its own file system, CPU, and memory — all deployed inside your private cloud or on-prem infrastructure so sensitive data never leaves your perimeter. It connects to 200+ production data sources out of the box and executes complex, multi-step workflows end to end with minimal human intervention.
The platform excels in scenarios that demand real autonomy rather than simple prompt-and-response loops. Think compliance audits that query databases, cross-reference access logs, and generate filing-ready reports, or ops workflows that scaffold a live monitoring dashboard, deploy it to an internal Kubernetes cluster, and configure alerting in Teams or Slack. Kaji plans with frontier models, delegates simpler subtasks to lightweight models to control token costs, and stops to request human approval whenever it encounters a high-risk action — all governed by your organization’s RBAC policies and escalation rules.
For teams managing a growing ecosystem of internal tools and MCP servers, the Shakudo AI Gateway acts as the unified control plane sitting in front of Kaji. It aggregates every internal tool into a single secure endpoint, enforces parameter-level governance, and automatically strips PII from responses before they reach any external model. A permanent, identity-linked audit trail satisfies SOC 2 and HIPAA requirements without bolting on additional services.
We have found Kaji well suited to regulated industries such as financial services, healthcare, energy, and logistics where autonomous execution must coexist with strict compliance guardrails. The main trade-off is that Kaji is designed for production-grade enterprise deployments, so very small teams looking for a lightweight pip-install experimentation tool may prefer a simpler starting point. For organizations ready to move past prototypes and into reliable, auditable agentic AI at scale, Kaji shortens that path considerably.
AutoGen was originally developed by Microsoft to facilitate the creation of AI-powered multi-agent applications. However, in October 2025, Microsoft placed the original AutoGen into maintenance mode — no new features are being added. The project has since split into two successor paths: AG2, a community-driven fork of AutoGen v0.2 maintained by several of the original creators who left Microsoft, and Microsoft Agent Framework (MAF), the official Microsoft successor that reached v1.0 GA on April 2, 2026 by combining Semantic Kernel and AutoGen into a unified platform.
AG2 continues the spirit of the original AutoGen — automating the process of generating AI agents and making it easy for developers to create tailored agents without deep AI expertise. It remains actively developed with community contributions, and its user-friendly design makes it accessible to a wide range of developers. For teams already using AutoGen v0.2, AG2 offers the most straightforward migration path with ongoing feature development.
For organizations deeply invested in the Microsoft ecosystem, the recommended path forward is MAF, which unifies Semantic Kernel's enterprise integration capabilities with AutoGen's multi-agent orchestration patterns. We recommend evaluating AG2 for open-source flexibility or MAF for enterprise Microsoft deployments — the original AutoGen codebase should be treated as legacy for new projects.
Semantic Kernel is a framework developed by Microsoft that integrates AI capabilities into traditional software development. The core strength of Semantic Kernel lies in its ability to integrate AI-driven components seamlessly into existing applications, allowing for advanced functionalities such as natural language understanding, dynamic decision-making, and task automation. Note: Semantic Kernel has been absorbed into Microsoft Agent Framework (MAF) as its core orchestration layer. While standalone packages continue to receive updates, MAF is now the recommended path for new enterprise projects within the Microsoft ecosystem.
Semantic Kernel offers enterprise-grade language flexibility through its comprehensive support for Python, C#, and Java development environments. This cross-language compatibility, combined with robust security protocols for legacy system integration and sophisticated workflow orchestration capabilities, positions it as a strategic choice for organizations building production-ready AI applications at scale.
We recommend Semantic Kernel to organizations already invested in the Microsoft stack seeking advanced solutions across a variety of applications such as enterprise chatbots and virtual assistants, intelligent process automation, and AI-enhanced productivity tools. Teams starting greenfield projects may want to evaluate MAF directly, while existing Semantic Kernel users can continue with the standalone packages and migrate to MAF at their own pace.
Google ADK 2.0 is a major agent framework from Google, announced and updated at Google I/O 2026. With its shift from a hierarchical executor to a graph-based execution engine (conceptually similar to LangGraph), ADK 2.0 supports sophisticated multi-agent orchestration including coordinator agents, sub-agent delegation, and fan-out/fan-in patterns. It ships with built-in human-in-the-loop primitives and state persistence, making it production-ready out of the box. Install it with pip install google-adk, and choose from Python, TypeScript, Go, Java, or Kotlin for your implementation language.
ADK 2.0 is part of a broader Google ecosystem that includes the Managed Agents API for hosted deployments and the Agent2Agent (A2A) protocol for cross-framework interoperability. This makes it particularly appealing for organizations already leveraging Google Cloud, Vertex AI, or Gemini models, as the integration is seamless. We recommend Google ADK for teams building complex, multi-agent systems that need first-class Google Cloud support and the flexibility to orchestrate agents across multiple languages and runtimes.
CrewAI (now at v1.15.1) specializes in creating intelligent agents capable of collaborating, sharing tasks, and optimizing actions through real-time communication and decision-making. This framework effectively manages multiple agents in a shared environment, ideal for applications requiring teamwork between autonomous systems. Recent releases have introduced pluggable backends for memory, knowledge, and RAG, along with Snowflake Cortex support — expanding its integration surface considerably.
While CrewAI's niche focus on multi-agent collaboration may limit its applicability compared to general-purpose frameworks, its growing ecosystem and active community have significantly matured since its early releases. The framework now offers stronger documentation, more integrations, and a more stable API surface.
CrewAI is particularly well-suited for teams building collaborative AI systems that require multiple agents interacting or working together. Use CrewAI when building systems that require human-AI or multi-agent cooperation, such as virtual assistants, fraud detection, or personalized learning platforms where seamless collaboration and coordination are essential.
RASA (latest: Rasa Pro 3.17.0) is a framework built for developing conversational AI and chatbots. The platform has undergone a significant architectural shift from its traditional intent-based NLU pipeline to CALM (Conversational AI with Language Models), an LLM-native approach that replaces rigid intent classification with flexible, language-model-driven dialogue management. CALM allows developers to define business logic through natural language descriptions rather than training data, dramatically reducing the effort needed to build and maintain conversational flows.
RASA still supports both machine learning and rule-based methods for teams with existing deployments, but CALM is the recommended path for new projects. The framework can be difficult to learn for beginners, especially those unfamiliar with machine learning or natural language processing — its advanced features often require significant configuration and setup.
Running RASA, especially with machine learning-based configurations, can be resource-intensive, requiring substantial computational power for training and operation. We recommend this framework for businesses with dedicated technical resources and a need for highly customizable, scalable conversational solutions that benefit from CALM's LLM-native approach.
Hugging Face has rebranded its Transformers Agents framework to smolagents (v1.26.0), reflecting a deliberate shift toward minimalism and code-first agent development. Unlike most agent frameworks that rely on JSON-based tool calls, smolagents takes a fundamentally different approach: agents write and execute Python code directly to interact with tools, giving them the full expressiveness of a programming language for complex reasoning and multi-step workflows. The core library is intentionally minimalist at roughly 1,000 lines of code, making it easy to understand, audit, and extend.
smolagents supports dynamic model orchestration across the Hugging Face ecosystem, enabling engineering teams to leverage different transformer architectures based on specific task requirements. The framework's model flexibility supports customization through fine-tuning, allowing organizations to optimize performance for industry-specific use cases and proprietary datasets.
We recommend smolagents to teams that value simplicity and transparency in their agent stack — particularly research institutions, startups, and businesses in sectors like e-commerce and healthcare that want lightweight, auditable agents powered by open-source models without heavy framework overhead.
The OpenAI Agents SDK is OpenAI's production-ready agent framework and the official successor to the experimental Swarm project (now archived). Built around a small set of powerful primitives, the SDK provides Agents (LLMs configured with instructions and tools), Handoffs (structured task delegation between agents), Guardrails (input and output validation layers), and Sandbox Agents (isolated code execution environments). This design philosophy keeps the framework lightweight while offering the building blocks for sophisticated multi-agent systems.
The SDK offers configurable max_turns for controlling agent execution depth, improved tool execution concurrency for faster workflows, and first-class support for OpenAI's latest models. It is particularly well-suited for teams already using the OpenAI API who want to add agentic capabilities without introducing a separate orchestration layer. The SDK's opinionated but extensible design means faster time-to-production for common patterns like customer support agents, research assistants, and code generation workflows.
We recommend the OpenAI Agents SDK for teams building production agent systems on OpenAI models who want an officially supported, well-documented path from prototype to deployment with built-in safety guardrails and multi-agent coordination.
Shakudo
The right AI framework provides the foundation for scalable, intelligent systems, but building it can be difficult. Shakudo simplifies the complexities of managing AI frameworks by providing an end-to-end platform that integrates data processing, model orchestration, and real-time deployment. Unlike traditional approaches that require extensive engineering effort, Shakudo streamlines AI implementation, reducing time to production and operational costs. Its flexible architecture supports a wide range of AI frameworks, making it ideal for businesses aiming to scale AI applications without being locked into a specific ecosystem.
With built-in automation, security, and performance monitoring, Shakudo empowers organizations to focus on innovation while ensuring their AI systems remain robust, adaptable, and future-proof. To explore how Shakudo can help your company grow, contact our experts for a quick demo.
An AI agent framework provides tools, libraries, and pre-built components that make developing intelligent systems faster and more scalable, providing core features like agent architecture, task orchestration, communication infrastructure, and performance optimization capabilities.
smolagents has the lowest learning curve for code-based scripting due to its minimalist ~1,000-line core, while Kaji offers the easiest path to deploying secure, production-grade agents without writing complex custom integration code. The OpenAI Agents SDK is also beginner-friendly for teams already using OpenAI models.
Kaji runs as a full computing environment with its own file system, CPU, and memory inside your private cloud or on-prem infrastructure, ensuring sensitive data never leaves your perimeter. It connects to 200+ production data sources out of the box and executes multi-step workflows with minimal human intervention.
LangChain (with LangGraph) works best for complex LLM-powered applications requiring extensive customization and modular integrations. The original AutoGen is now in maintenance mode — for Microsoft ecosystem projects, evaluate Microsoft Agent Framework (MAF) or AG2 for open-source flexibility.
RASA has a high learning curve and requires substantial computational power for training and operation. Its new CALM (Conversational AI with Language Models) approach reduces the training data burden, but the framework is still better suited for businesses with dedicated technical resources.
The Shakudo AI Gateway acts as a unified control plane that aggregates internal tools into a single secure endpoint, enforces parameter-level governance, automatically strips PII from responses, and maintains an identity-linked audit trail for SOC 2 and HIPAA compliance.
CrewAI specializes in multi-agent collaboration and is designed for systems requiring teamwork between autonomous agents. For single-agent applications, frameworks like LangChain or the OpenAI Agents SDK offer more general-purpose capabilities.
Google ADK 2.0 uses a graph-based execution engine conceptually similar to LangGraph, but with first-class Google Cloud and Gemini integration, multi-language support (Python, TypeScript, Go, Java, Kotlin), and the Agent2Agent (A2A) interoperability protocol. It is ideal for teams already in the Google Cloud ecosystem.
