Top 8 AI Agent Frameworks in 2025

AI agents are no longer experimental: they’re quickly becoming core to how modern software systems think, act, and automate. From inbox-sorting AI assistants to decision-making agents in enterprise workflows, the shift toward intelligent, task-driven systems is accelerating. Behind every smart agent is a framework, or increasingly, a platform, that powers its logic and collaboration.

As LLM-powered applications go mainstream, demand for scalable, reliable AI frameworks has surged. The rise of large language models (LLMs) has opened new possibilities, but it also reveals serious gaps in orchestration, monitoring, and cross-team collaboration.

Traditional DevOps tools weren’t built for the LLM agent framework lifecycle. Teams struggle to manage agent architecture, ensure performance, and involve non-developers meaningfully in the build process.

In this article, we break down the top AI agent frameworks and platforms, like Orq.ai, that teams should explore in 2025 to build and orchestrate AI agents at scale.

Let’s dive in.

From Frameworks to Platforms: Bridging the Gap in AI Agent Development

Although AI agent development has evolved, there remains a significant divide between traditional open-source AI agent frameworks and the comprehensive platforms needed to operate at scale. While many teams rely on AI agent building frameworks to prototype and experiment with agentic AI and autonomous agents, these frameworks often lack the collaborative and operational tools essential for production environments.

Most AI agent development frameworks focus heavily on building the core agent logic and chaining LLM calls, but they don’t address the broader challenges of deployment, monitoring, and scaling in complex systems. Traditional DevOps tools, designed for classical software, fall short when applied to the dynamic, stateful workflows required by agentic AI systems.

Key challenges teams face include:

• Collaboration gaps: Many frameworks are developer-centric, limiting involvement from non-technical stakeholders.

• Operational complexity: Monitoring and versioning autonomous agents in production remains difficult.

• Scalability issues: Existing tools often struggle to support multi-agent orchestration and evolving workflows.

• Lack of end-to-end lifecycle support: Most frameworks don’t cover deployment, evaluation, or iterative improvement processes.

The rapid rise of agentic AI, where autonomous agents make decisions, collaborate, and learn, demands tooling that supports full lifecycle management. This includes:

• Versioning and continuous evaluation of agents

• Multi-agent orchestration and role management

• Cross-functional collaboration between developers, data scientists, and business teams

To meet these needs, the industry is shifting from pure frameworks toward integrated platforms that unify development, collaboration, and operations. While the best AI framework depends on your specific use case, it’s clear that scalable, production-ready autonomous agents require more than just code libraries: they need a full-stack approach.

Top 8 AI Agent Frameworks & Platforms

1. Orq.ai

Orq.ai Platform Screenshot

Orq.ai is an end-to-end platform built to streamline the development, deployment, and optimization of AI agents across teams. Unlike traditional AI agent building frameworks that focus primarily on code-level experimentation, Orq.ai provides the infrastructure and interfaces needed to manage the full lifecycle of AI agents from early prototyping to production-grade orchestration. It brings technical and non-technical contributors together in a shared environment, helping teams accelerate iteration, ensure compliance, and scale intelligently as use cases grow more complex.

Key Features

• Agent lifecycle management: Go from prototype to production with support for staging, deployment, and performance monitoring all in one place.

• Code/AI Studio: Empower both developers and non-developers to design and configure agents, streamlining cross-functional collaboration.

• Multi-agent orchestration: Coordinate multiagent systems with flexible workflows like step-by-step execution to event-driven task hand-offs.

• Agent role definition: Assign distinct roles, behaviors, and decision-making scopes to each agent for controlled, intelligent delegation.

• Tool & API integration: Equip agents with custom tools, allowing them to retrieve data, take action, and interface directly with your stack.

• Human-in-the-loop controls: Enable agents to escalate decisions or collaborate with humans when needed, enhancing oversight and trust.

• Integrated evaluation and observability: Use built-in tools to test prompt performance, run A/B tests, and monitor cost, latency, and output quality at scale.

• Secure, compliant infrastructure: Orq.ai is SOC2-certified and aligned with GDPR and the EU AI Act, making it enterprise-ready for teams with strict data privacy requirements.

• Model-agnostic architecture: Through our Generative AI Gateway, Orq.ai supports integration with over 150 leading AI models, enabling flexible deployment strategies across diverse use cases like chatbots, task management, and workflow automation.

Teams seeking a platform that brings structure, agility, and visibility to the agent development process, whether you’re deploying intelligent chatbots, coordinating autonomous agents, or scaling cross-functional task orchestration across complex environments.

Sign up for free or book a demo with our team to get a personalized walkthrough of our platform.

2. LangChain

Credits: Langchain

LangChain is one of the most widely adopted open-source AI agent frameworks, known for enabling developers to build sophisticated chains and autonomous agents using large language models. Positioned as a framework for applying AI in the enterprise, it offers a highly modular system that supports integration with a variety of tools, APIs, vector databases, and language models.

Strengths

• Modular and extensible: LangChain makes it easy to compose logic with reusable building blocks tailored for natural language processing tasks, tool use, and memory handling.

• Strong ecosystem: A vibrant open-source community and library of integrations support rapid experimentation and prototyping.

• LangGraph support: LangChain now offers LangGraph, a specialized library for building agents with a graph architecture, allowing for structured, state-aware workflows.

Challenges

• Steep learning curve: The modularity comes at the cost of complexity. New users, especially those without an engineering background, may find it difficult to get started.

• Engineering-heavy collaboration: While powerful, LangChain often requires developer involvement to maintain and scale agent workflows, which can limit cross-functional participation.

• Limited observability at scale: Out-of-the-box tools for monitoring agent behavior, debugging failures, or tracking performance in production are still evolving.

• Environmental integration and deployment often need to be custom-built, making enterprise adoption slower without supporting infrastructure.

• While flexible, LangChain does not natively enforce a role-based architecture, which may be required in more structured or regulated workflows.

LangChain remains a strong choice for teams with the technical capacity to invest in customization and low-level control. When paired with complementary tools like LangGraph (for stateful agent workflows) and LangSmith (for debugging and evaluation), LangChain becomes a more complete solution for building experimental or production-ready natural language processing applications.

3. CrewAI

Credits: Crew AI

CrewAI is an emerging open-source framework designed to manage multiple autonomous agents working together as a structured “crew.” Inspired by real-world team dynamics, it focuses on assigning roles and coordinating tasks among agents to simulate collaborative workflows. This makes it a valuable option for teams exploring agent collaboration in areas like content generation, research synthesis, or customer support.

Strengths

• Role-based task assignment: Each agent in CrewAI can be assigned a specific function or domain expertise, enabling structured division of labor.

• Simulates multi-agent workflows: Particularly useful for mimicking how human teams operate, making it easier to design and prototype collaborative systems.

• Flexible agent configuration: CrewAI allows users to customize each agent’s capabilities, goals, and responsibilities within the crew hierarchy.

Challenges

• Early-stage development: As an actively evolving project, CrewAI often introduces breaking changes and lacks long-term version stability.

• Limited evaluation tools: Systematic tracking of multi-agent output quality and performance optimization is still an area of growth.

• Basic communication protocols: While agents can exchange information, advanced messaging, negotiation, or memory-sharing features are still limited.

Despite these constraints, CrewAI is a solid tool for experimenting with agent collaboration in RAG, complex task execution, and multi-role interactions. Its simplicity is appealing to developers who want to model crew-based logic without heavy architectural overhead.

4. AutoGPT

Credits: Autogen

AutoGPT is one of the most popular AI agent frameworks that helped bring the concept of autonomous agents into mainstream awareness. Built on top of OpenAI's APIs and developed using Python, it was among the first projects to showcase what happens when you give a large language model a high-level goal, access to tools, and the ability to recursively reason and act.

Strengths

• Pioneering architecture: AutoGPT introduced the idea of chaining reasoning steps using function calling, file storage, web access, and memory, all without direct user intervention.

• Good for experimentation: As an open-source prototype, it helped inspire a wave of newer AI multi-agent frameworks and continues to be a sandbox for testing autonomous behaviors.

• Community-driven innovation: AutoGPT's ecosystem is vibrant, with forks and spin-offs (like AutoGen) that extend its core architecture into more stable and customizable formats.

Challenges

• Limited production readiness: While useful for demos and ideation, AutoGPT lacks the reliability, error handling, and failover logic required for real-world deployment.

• Security and sandboxing: Granting agents tools like web browsing and file access requires manual controls and poses potential risks, especially for enterprise use.

• Developer-intensive: Configuration, prompt tuning, and error management all require hands-on coding experience, making it less accessible to broader teams compared to more mature Python AI frameworks.

AutoGPT remains a key reference point in the evolution of agentic AI, especially when paired with more stable toolkits like AutoGen, which builds on its foundation to offer improved modularity, system-level design, and orchestration. It’s best suited for those exploring cutting-edge concepts in machine learning models and autonomous decision-making logic.

5. Semantic Kernel

Credits: Semantic Kernel

Semantic Kernel (SK) is an open-source software development kit (SDK) from Microsoft designed to help developers seamlessly embed large language models and other AI capabilities into their applications. Rather than functioning as a standalone agent runtime, SK focuses on agent orchestration, skill composition, and memory management, making it a powerful foundation for custom LLM-powered workflows.

Strengths

• Composable architecture: Semantic Kernel introduces “skills” as reusable components, allowing developers to assemble agents of change with clearly defined capabilities.

• Native .NET and Python support: Ideal for enterprise developers already building in Microsoft ecosystems.

• Integration-ready: SK is designed to plug into enterprise apps, APIs, and vector databases with minimal friction.

• Planner module: Offers basic task planning and execution, making it easier to define multi-step workflows for LLMs.

Challenges

• Not opinionated about agent design: Unlike many AI multi-agent frameworks, Semantic Kernel doesn’t prescribe how to structure autonomous agents, leaving teams to architect behaviors from the ground up.

• Limited out-of-the-box tooling: Advanced capabilities like agent memory sharing, long-term state management, or human-in-the-loop workflows require additional implementation.

• Still maturing: While supported by a strong backer in Microsoft, SK is evolving rapidly and may introduce breaking changes as it scales toward broader adoption.

Semantic Kernel stands out for teams looking to build deeply integrated AI features in enterprise-grade applications. It may not offer all the turnkey agent management capabilities found in the best AI agent frameworks, but for engineering-driven teams prioritizing flexibility and app integration, it provides a powerful foundation to orchestrate intelligent behaviors at scale.

6. Lyzer
   
   

Credits: Lyzer

Lyzer is a fast-evolving platform that focuses on AI-powered knowledge agents, helping companies build internal tools that can automate research, summarization, and decision support. It has carved out a niche among companies that want more than just chatbots. Lyzer aims to provide context-aware agents capable of reasoning across enterprise data.

Strengths

• Knowledge-centric agents: Lyzer is purpose-built for use cases like research assistants, knowledge synthesis, and internal copilots that draw from company-specific documents and databases.

• Seamless integration: The platform supports integration with various enterprise systems such as CRMs, data lakes, and communication tools to ground its agents in relevant context.

• Task orchestration features: Lyzer supports lightweight workflows that allow agents to manage multi-step queries, synthesize responses, and deliver insights.

• Beyond chatbots: While Lyzer includes chatbot interfaces, it emphasizes deeper reasoning and persistent memory features that help it stand out from simple prompt-based tools.

Challenges

• Not fully open-source: Lyzer is a managed platform, which may limit flexibility for teams looking to deeply customize agent logic or deployment environments.

• Less suited for complex agent collaboration: Compared to some other AI multi-agent frameworks, Lyzer’s coordination across multiple agents is more limited, making it best suited for task-specific or single-agent use cases.

• Emerging platform: While promising, Lyzer is still early in its product lifecycle and evolving fast. Stability and long-term support should be considered for enterprise adoption.

Lyzer is a compelling choice for teams looking to move beyond generic chatbots and toward AI agents that can handle nuanced, document-rich workflows. With strong integration capabilities and a focus on enterprise productivity, it supports a growing category of think agent support systems designed to augment human expertise in real time.

7. Hugging Face Transformer Agents

Credits: Huggingface

Transformer Agents from Hugging Face offer a modular way to build AI agents using pretrained machine learning models from the Hugging Face ecosystem. Focused on extensibility and transparency, this toolkit allows agents to leverage tools like web search, Python execution, image generation, and more, wrapped in a flexible agent interface.

Strengths

• Plug-and-play capabilities: Agents can call external tools like WolframAlpha, Wikipedia, or code interpreters using Hugging Face’s function calling system.

• Tight ecosystem integration: Built on top of the Transformers library, it gives direct access to hundreds of state-of-the-art models for text, vision, and multimodal tasks.

• Developer-friendly: Ideal for researchers, builders, or hobbyists looking to experiment with tool-using agents in a lightweight and transparent environment.

• Support for AutoGen-style agents: Though not officially part of AutoGen, the approach aligns well with modular agent behavior powered by open models and accessible infrastructure.

Challenges

• No orchestration layer: Unlike more robust AI multi agent frameworks, Hugging Face Transformer Agents lack native support for task sequencing, agent communication, or long-term memory.

• Manual configuration: Setting up agent tools and task logic can be developer-intensive, especially when moving beyond simple use cases.

• Limited enterprise integrations: While excellent for experimentation, the platform doesn’t currently offer the type of production-ready integration or observability features found in full-stack platforms.

Transformer Agents is best suited for individual developers, research teams, or AI hobbyists looking to test the boundaries of autonomous agents in a flexible, model-rich environment. While not among the best AI agent frameworks for large-scale deployment, it’s a great sandbox for building agents that interact with tools, APIs, and multi-modal inputs.

8. Atomic Agents

Credits: Atomic Corp

Atomic Agents is an open-source framework focused on creating lightweight, modular autonomous agents designed for scalability and flexibility. It emphasizes simple building blocks, or “atoms,” that developers can combine to define agent behaviors, communication protocols, and decision-making logic.

Strengths

• Modular design: The framework breaks down agent capabilities into reusable components, allowing for easy customization and extension.

• Support for multi-agent systems: Atomic Agents facilitates coordination among multiple agents, enabling complex workflows and interactions.

• Lightweight and efficient: Its minimalist architecture makes it suitable for projects where performance and low overhead are priorities.

• Flexible communication protocols: Built-in support for messaging and event-driven coordination enhances agent collaboration.

Challenges

• Limited out-of-the-box tooling: Unlike full-stack platforms, Atomic Agents requires significant development effort to build complete AI agents with task orchestration and monitoring.

• Smaller community and ecosystem: The framework is less widely adopted compared to other AI multi agent frameworks, which may impact access to ready-made integrations and support.

• Focus on developers: Non-technical stakeholders may find it difficult to participate in the development lifecycle due to limited no-code or low-code options.

Atomic Agents is well-suited for developers seeking a best AI agent frameworks option that prioritizes modularity and control. It provides a strong foundation for building scalable, agentic AI systems, particularly when custom communication and collaboration patterns are needed.

AI Agents Framework: Key Takeaways

As the landscape of AI continues to evolve, choosing the right framework or platform for building and orchestrating AI agents is critical for success. The surge in large language models and agentic AI has opened exciting possibilities but also introduced complex challenges around scalability, collaboration, and integration.

Here are the essential points to consider:

• End-to-end platforms like Orq.ai offer comprehensive workflows that bridge development and operational needs, enabling teams to streamline collaboration between technical and non-technical roles while managing the full AI agent lifecycle.

• Open-source frameworks such as AutoGPT and Atomic Agents are excellent for experimentation and custom development but often require substantial developer expertise to ensure production readiness and secure deployments.

• SDKs like Semantic Kernel provide powerful building blocks for embedding AI into enterprise applications, ideal for teams seeking flexible, integration-friendly tools without a full turnkey solution.

• Specialized platforms like Lyzer cater to knowledge workflows and task-specific chatbots, focusing on contextual understanding and enterprise integration rather than broad autonomous agent orchestration.

• Toolkits like Hugging Face Transformer Agents enable modular agent creation with access to vast pretrained models, great for research and prototyping but less focused on end-to-end management or deployment.

Ultimately, your choice depends on your team’s goals, technical expertise, and the complexity of your AI agent workflows. Whether assembling simple assistants or designing multi-agent systems with advanced task orchestration and workflow automation, understanding these options empowers you to build smarter, scalable AI-driven solutions.