Agentic AI frameworks enable the creation of autonomous AI agents that can reason, plan, and act to perform complex tasks with minimal human intervention (NVIDIA and Partners Launch Agentic AI Blueprints to Automate Work for Every Enterprise | NVIDIA Blog) Unlike traditional chatbots or single-task AI, these frameworks support multi-step decision-making and orchestrating multiple agents, moving beyond simple Q&A or rule-based interactions (NVIDIA and Partners Launch Agentic AI Blueprints to Automate Work for Every Enterprise | NVIDIA Blog) They provide the tools to manage and coordinate collections of AI agents (an “agentic AI system”) working collaboratively, which is key for tackling sophisticated workflows in enterprise settings (NVIDIA and Partners Launch Agentic AI Blueprints to Automate Work for Every Enterprise | NVIDIA Blog) This report examines some of the latest agentic AI frameworks in the market, covering both open-source libraries and enterprise platforms. We focus on their technical capabilities – key features, scalability, adaptability – and any available performance insights. A comparison table is included to summarize supported architectures, integration ease, programming languages, and real-world applications for each framework.
Several open-source frameworks have emerged to help developers build agentic AI systems. These tools are typically flexible and community-driven, making them adaptable to various use cases:
Microsoft AutoGen is an open-source framework from Microsoft Research for building advanced multi-agent AI applications (Top 7 Frameworks for Building AI Agents in 2025) It provides a layered architecture with a Core programming model for creating a scalable, distributed network of agents, communicating via asynchronous messaging in both request-response and event-driven patterns (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) Built atop the Core, AgentChat offers conversational agent templates (for single or multi-agent dialogues), and an Extensions module allows integration with external libraries and services (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) AutoGen emphasizes modularity (developers can customize agent roles and behaviors) and comes with developer tools like AutoGen Bench for performance benchmarking and AutoGen Studio (a no-code interface for agent design) (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) This framework is designed for scalability, handling complex workflows with multiple specialized agents. Early use cases include coding assistants, data analysis bots, and research agents that collaborate to solve problems (Top 7 Frameworks for Building AI Agents in 2025) (Top 7 Frameworks for Building AI Agents in 2025)
CrewAI is an open-source orchestration framework for multi-agent AI solutions (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) It introduces a role-based architecture treating a group of AI agents as a “crew” working together (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) Each agent is assigned a specific role (defined in natural language, such as analyst, researcher, planner, etc.), and tasks are divided among them accordingly (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) CrewAI supports both sequential task workflows and hierarchical setups where a manager agent oversees others (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) The framework integrates with various large language model backends – Anthropic Claude, Google’s models (e.g. Gemini), Mistral, OpenAI’s GPT series, and even IBM’s Watsonx foundation models (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) It also provides retrieval-augmented generation (RAG) tools for connecting to knowledge sources (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) so agents can search data or documents when reasoning. This makes CrewAI highly adaptable to different domains. An example application is a stock analysis team: one agent analyzes market data, another gathers supporting research, and another formulates a strategy, all working in concert (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) CrewAI’s design emphasizes collaboration and scalability, enabling complex multi-agent workflows to be managed transparently.
LangChain is a popular open-source framework for building applications powered by large language models (LLMs), including chatbots and simple agents (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) It provides a modular architecture where developers chain together components (prompts, models, memory, tools) to create an AI-driven application (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) LangChain supports integration with various LLM providers (OpenAI, Hugging Face, etc.) and external tools, and includes utilities for embedding memory (so agents can remember context) and connecting to vector databases for knowledge retrieval (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) While often used for single-agent conversational AI, LangChain also allows for tool-using agents (via its agent abstractions) that can perform tasks like web searches or calculations. Its design trades some high-level structure for flexibility: it’s ideal for prototyping and fine-tuning agent behaviors. LangChain’s ecosystem (e.g. the LangSmith platform) supports debugging and monitoring performance of these LLM-powered workflows (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) Real-world uses of LangChain include building custom Q&A bots, personal assistants, and other LLM-based services that require integration of multiple steps or data sources.
LangGraph is an open-source orchestration extension within the LangChain ecosystem, aimed at managing complex multi-agent workflows (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) It uses a graph-based architecture: each discrete action or task by an AI agent is represented as a node in a graph, and the possible transitions or dependencies between tasks are the edges (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) This structure allows developers to define branching workflows and looping behaviors more easily than in linear chains. For example, an AI travel assistant built with LangGraph might have nodes for searching flights, selecting options, booking, etc., and can loop back to search with new criteria if needed (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) LangGraph also supports human-in-the-loop steps (manual checkpoints in the workflow) to ensure control at critical junctures (Top 5 Free AI Agent Frameworks) It seamlessly integrates with LangChain and LangSmith tooling (Top 5 Free AI Agent Frameworks) which makes it straightforward to adopt for those already using LangChain. By capturing agent actions in a graph, LangGraph improves state management and error recovery (states can be saved and resumed) for long-running or complex processes (Top 5 Free AI Agent Frameworks) It’s well-suited for scenarios requiring dynamic decision paths and has been used in demos like multi-step assistants and interactive storytelling engines (Top 7 Frameworks for Building AI Agents in 2025) (Top 7 Frameworks for Building AI Agents in 2025)
LlamaIndex (formerly GPT Index) is an open-source data orchestration framework for connecting LLM-based agents with external data sources (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) It streamlines the integration of knowledge into agent workflows, providing pre-built connectors and indices to ingest and query data (documents, databases, APIs, etc.) (Top 5 Free AI Agent Frameworks) LlamaIndex recently introduced a workflow system that supports multi-agent setups (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) It uses an event-driven architecture: an agent’s actions are broken into discrete steps, and events trigger transitions between steps (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) A shared context is maintained across the workflow so that agents (or steps) can store and retrieve information globally (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) Unlike a fixed graph, this event-driven approach allows more flexible execution — agents can dynamically decide to loop back or skip steps as conditions change (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) This design is advantageous for adaptive tasks that might require revisiting earlier steps or handling irregular sequences. LlamaIndex excels at retrieval-augmented generation, enabling an agent to pull in relevant information on the fly. In practice, developers use LlamaIndex to build agents that can do things like deep document analysis or report generation by indexing a knowledge base and then querying it during the agent’s reasoning process (NVIDIA and Partners Launch Agentic AI Blueprints to Automate Work for Every Enterprise | NVIDIA Blog) It’s commonly applied in building research assistants, chatbot knowledge bases, and any AI agent requiring robust data integration.
Semantic Kernel is an open-source SDK from Microsoft aimed at building enterprise-grade AI applications with integrated agents (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) While it is a broader development kit for AI (supporting plugins, memory, etc.), it includes an experimental Agent Framework for creating and orchestrating AI agents (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) Semantic Kernel supports multiple programming languages (C#, Python, Java, and more) (Top 7 Frameworks for Building AI Agents in 2025) making it accessible for developers in different ecosystems. Its agent framework provides abstractions for defining skills and actions, and it includes two built-in agent types: a simple chat completion agent and a more advanced task-oriented assistant agent (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) For more complex workflows, Semantic Kernel offers a Process orchestration model where multiple agents (or skills) can be composed: a process consists of a sequence of steps (tasks) with defined data flow between them (AI Agent Frameworks: Choosing the Right Foundation for Your Business | IBM) This allows for multi-agent collaboration or tool usage in a controlled pipeline. Semantic Kernel’s strength lies in integration: it’s designed to easily embed AI capabilities into existing applications without a complete rewrite (Top 7 Frameworks for Building AI Agents in 2025) Developers can gradually enhance parts of their software with AI agents (for example, adding an AI summarizer or an automated email responder into a larger system) (Top 7 Frameworks for Building AI Agents in 2025) With its multi-language support and plugin architecture, Semantic Kernel can integrate with databases, APIs, or other services as needed. It’s being explored for applications like intelligent assistants in productivity software, and other scenarios where AI agents need to operate within enterprise software environments.
Rasa is a well-established open-source framework for building conversational AI agents (chatbots and voice assistants) (Top 5 Free AI Agent Frameworks) While not originally designed for LLMs, it provides a powerful NLU (Natural Language Understanding) pipeline and dialogue management system. Rasa’s architecture centers on an interpreter that classifies user intents and extracts entities (using machine learning models) (Top 5 Free AI Agent Frameworks) and a dialogue manager that decides the next action based on conversation state (using either rules or a learned policy). It supports context management for multi-turn conversations and can call custom actions (Python code) to integrate with external APIs or databases. This makes Rasa adaptable for task-oriented bots that need to perform transactions or fetch information. Key features include highly customizable pipelines (developers can swap in different NLP components), full open-source transparency (no black-box SaaS dependency), and the ability to run on-premises which appeals to enterprises for data control (Top 5 Free AI Agent Frameworks) Rasa scales to production deployments; for example, using Rasa X or Enterprise, teams can manage training data and models for heavy traffic scenarios. Real-world use cases of Rasa range from customer support chatbots in banking and telecom, to virtual assistants for scheduling or FAQ, often where tight integration with business systems is required and fine-grained control over conversational flows is needed.