If you’re trying to figure out which AI agent framework is right for your team in 2025, you’re not alone. There’s a range of options, from no-code platforms like Lindy to developer-first stacks like LangChain and CrewAI. So, any team can find a framework to meet their needs and skill level.
In this article, we’ll cover:
- What AI agent frameworks are
- The top AI agent frameworks and an overview of each
- What to consider when choosing your framework
- How open-source dev stacks compare to no-code tools like Lindy
Let’s now define an AI agent framework.
What is an AI agent framework?
An AI agent framework is a specialized development environment that provides the tools for creating intelligent agents. They aren’t autonomous agents themselves. Instead, they’re the infrastructure and tools developers use to build and customize intelligent agents.
Key tools found in these frameworks include large language models (LLMs), which enable them to understand written inputs. These frameworks have API connections, context management tools, and the ability to execute workflows.
Once built using these frameworks, autonomous agents can make decisions, utilize tools, and perform complex tasks with minimal human intervention.
Unlike chatbot-style LLMs like Claude or Gemini, agents built with these frameworks can be embedded directly into tools and software. They can open programs, create files and content, call tools, and adapt to new inputs across multi-step workflows.
Effective autonomous agents made using these frameworks have many real-world applications. They can execute customer support tasks, retrieve data, or collaborate on multi-agent research. They basically automate complex business workflows with minimal human input.
The Top 10 AI agent frameworks in 2025: TL;DR
- Lindy: Best no-code agent framework for business users
- LangChain: Best for custom LLM workflows
- CrewAI: Best for multi-agent orchestration
- OpenAI Assistants API: Best for GPT-native apps
- AutoGen: Best for conversation-driven agents
- Llama Index: Best offering of prepackaged agents
- LangGraph: Best for DAG-based agents
- Haystack Agents: Best for RAG + LLM
- FastAgency: Best for high-speed inference
- Rasa: Best for chatbots and voice assistants
1. Lindy: Best no-code agent framework for business users
Lindy is a no-code AI agent framework designed for business users who want to build autonomous agents. Unlike developer-centric tools, Lindy handles the technical configuration behind the scenes, letting you manage everything through a visual interface.
Features
- Premade agent templates: Lindy includes a growing library of ready-to-use agent templates designed for tasks like lead qualification, meeting scheduling, and CRM updates. They come pre-configured with tools and workflows, so you can launch agents in minutes.
- Integrations with real-world tools: Out of the box, Lindy natively integrates with apps like Gmail, Slack, HubSpot, Salesforce, and Notion. Through partnerships with Pipedream and Apify, Lindy can connect to over 2,500 native integrations.
- Agent collaboration (Swarms): Build your own agent swarms where multiple agents work together to complete a larger task by dividing responsibilities. For instance, one agent can research contacts, another drafts outreach emails, and a third updates the CRM.
Who’s it for?
Lindy is designed for non-technical users and teams looking to automate tasks without coding. It’s ideal for teams looking for a practical, no-code AI framework to automate high-volume, repetitive tasks across sales, support, recruiting, and operations. It’s built to quickly create AI agents that manage inboxes, conduct research, and even handle phone calls.
Pros
- Accessible agent framework for non-developers: Lindy removes the need for Python, Docker, or dev environments. You can deploy agents using templates and guided UI without any programming.
- Several LLMs to choose from: Lindy allows you to select from a set of foundation models tailored to your workflow needs. Options include GPT-4, GPT-4o, Claude 3 Opus, Mistral, and many others. Choose between LLMs that produce smarter answers but are more expensive, or cheaper ones that may be less detailed.
Cons
- Less flexible than dev-focused alternatives: Lindy isn’t built for full-stack coding or custom framework development. But it does support inline Python and JavaScript scripting, enabling developers to add logic and API calls where needed.
Pricing
Lindy provides a free plan that offers up to 400 monthly actions. Each action is a step in a workflow. Pro pricing starts at $49.99/month and provides up to 5,000 automations per month.
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2. LangChain: Best for custom LLM workflows
LangChain is an open-source framework designed for developers creating complex applications that leverage large language models. It provides the building blocks to assemble sophisticated AI agents that reason, retrieve data, call tools, and interact with external APIs.
Features
- Composable agent workflow architecture: LangChain offers a flexible, component-based framework for chaining together prompts, models, and other components. You can build everything from single-agent tasks to dynamic tool-using agents with stateful memory and conditional logic.
- Retrieval-augmented generation (RAG) and vector store integration: LangChain supports plug-and-play connections to databases, APIs, file systems, and knowledge bases. It integrates with vector stores like Pinecone, Weaviate, and FAISS.
- Broad support for several LLMs: LangChain is not tied to any single LLM provider. So, you can switch between OpenAI, Anthropic, Cohere, Mistral, Hugging Face models, and others.
Who’s it for?
The platform is designed for developers, ML engineers, and data scientists who require complete programmatic control over AI agents and LLM workflows. LangChain is ideal for building multi-step workflows with granular control, document analysis, data agents, or research assistants.
Pros
- Full control over architecture agent behavior: LangChain’s low-level components enable the creation of custom workflows and the integration of LLMs exactly as your application requires.
- Large and active ecosystem: LangChain benefits from a wide developer base, frequent updates, and community-driven components. It offers several tutorials, templates, and integrations that are available across domains.
Cons
- Significant learning curve for developers new to agents: Building agents in LangChain requires knowledge of its abstractions, Python tooling, and the nuances of prompt chaining.
Pricing
LangChain pricing starts at $39/month per user.
3. CrewAI: Best for multi-agent orchestration
CrewAI is an open-source agent framework designed for building collaborative multi-agent systems. It allows you to assign distinct roles, goals, and tools to different agents, then orchestrate them to work together as a coordinated unit.
Features
- Architecture for teamwork and specialization: CrewAI lets you assign agents specific roles, like "researcher" or "writer." Give them goals and descriptions that influence how each agent approaches a task, making multi-agent collaboration more realistic and controllable.
- Natural language task definitions: Define tasks in plain English and have a “manager” agent delegate them. This enables high-level orchestration with minimal code, making it ideal for business-centric automations such as project research or document generation.
- Hierarchical or sequential process models: You can choose whether agents act in a strict order (sequential) or dynamically coordinate under a manager agent (hierarchical).
Who’s it for?
CrewAI is ideal for developers and technical teams at mid-sized businesses or larger who want to create collaborative AI agents that perform multi-step, multi-role tasks. It’s particularly powerful in use cases where specialization, parallel execution, or delegation between agents mirrors real human workflows.
Pros
- Powerful interface for managing multi-agent collaboration: CrewAI abstracts the complexity of agent orchestration into intuitive constructs, such as roles, goals, and tasks.
- Easy to extend with custom tools and APIs: CrewAI supports Python-defined tools and external function calls. You can integrate it with existing systems, internal APIs, or custom workflows without vendor lock-in.
Cons
- Task delegation logic can feel rigid in complex workflows: Fine-tuning complex agent interactions sometimes requires trial and error. Tasks may be misrouted or misinterpreted without clear role-task mappings.
Pricing
Crew AI doesn’t publish pricing. Contact the sales team for more information.
4. OpenAI Assistants API: Best for GPT-native apps
The OpenAI Assistants API is a developer-first framework for building persistent, GPT-powered agents with built-in memory, tool use, and file handling. Assistants maintain thread history and allow tools like code interpreters, retrieval, and custom functions — all accessible via a simple API.
Features
- Deep integration with the OpenAI ecosystem: Assistants are optimized for OpenAI’s latest models, including GPT-4. They integrate natively with OpenAI’s APIs, rate limits, usage dashboards, and API keys.
- Persistent memory and multi-turn thread management: The Assistants API enables developers to store thread-level memory across sessions. This allows agents to “remember” past interactions, documents, and decisions — no external database needed.
- Tool calling with support: Developers can attach tools such as custom functions, OpenAI’s code interpreter, or a retrieval system. These enable assistants to pull relevant information from uploaded files or external documents.
Who’s it for?
The OpenAI Assistants API is ideal for developers and product teams building GPT-native applications. It’s best suited for startups, SMBs, and enterprise innovation teams who want to embed chat-style assistants or tool-using agents into web or mobile apps.
Pros
- Fast way to build GPT agents: You can spin up context-aware agents in minutes, define custom tools, and manage multi-turn threads with just a few API calls
- Optimized for the OpenAI ecosystem: Benefit from OpenAI’s most advanced models and can get access to system-level updates like improved latency, cost efficiency, and native tool support.
Cons
- Usage constraints tied to OpenAI’s rate limits: Your application’s scalability is bound by OpenAI’s system limits. This includes context window, thread count, and function execution time.
Pricing
The OpenAI Assistants API uses pay-as-you-go pricing. It’s based on the underlying model, like GPT-4, GPT-o1. Open AI charges for input, cached input, and output. The platform charges for extra tools like a code interpreter and file search.
Input prices can start as low as $0.10 for GPT-4.1-nano and go as high as $150 for GPT-o1-pro. Output charges can be as low as $0.40 for GPT-4.1-nano high as $600 for GPT-o1-pro.
5. AutoGen: Best for conversation-driven agents
AutoGen is an open-source framework developed by Microsoft. Use it to build multi-agent systems where agents communicate through structured conversations. It’s built for scenarios that involve coordination, negotiation, or multi-turn reasoning among AI agents.
Features
- Conversation-centric multi-agent framework: AutoGen agents communicate using natural language, simulating human-like dialogues. This architecture supports planning, delegation, clarification, and iteration between agents.
- Built-in roles and customizable agent personas: The platform offers built-in agent roles such as AssistantAgent and UserProxyAgent. This lets you define agents’ capabilities and interaction style.
- Tool use and code execution: Agents can invoke Python code execution, APIs, or retrieval systems to answer questions or complete tasks.
Who’s it for?
AutoGen is ideal for technical teams, especially in R&D, analytics, and software development. It’s best suited for midsize to large companies with in-house engineering capacity.
Pros
- Realistic multi-agent dialogues that mirror human collaboration: AutoGen's conversation-first design enables easy simulation of meetings, debates, and peer reviews between AI agents.
- Open-source and customizable, with a strong Python AI framework: AutoGen is built for developers, with clear APIs, extensible agent classes, and Python-native tooling
Cons
- Documentation and stability are still evolving: At the time of writing, AutoGen is actively being rewritten (v0.4) and may introduce breaking changes.
Pricing
AutoGen is fully open-source, licensed under the MIT license, and is available for free use. There are no licensing fees, SaaS charges, or hosting costs from Microsoft. However, users still incur LLM and infrastructure costs.
6. LlamaIndex: Best offering of prepackaged agents
LlamaIndex is an open-source AI agent framework designed to facilitate easy connection of LLMs to both structured and unstructured data sources. It’s particularly strong for teams working with internal knowledge bases, PDFs, databases, and private APIs.
Features
- Prebuilt agents for enterprise use: LlamaIndex offers agents for document Q&A, data exploration, code analysis, and summarization They have built-in workflows, memory, and I/O configurations.
- Powerful data connectors: The core of LlamaIndex lies in its ability to load, chunk, and index data from a wide variety of sources, including PDFs, CSVs, SQL, APIs, Notion, Airtable, and more.
- Integrated observability: Developers can monitor queries, inspect tool calls, and analyze output quality using built-in logging and debug tools.
Who’s it for?
LlamaIndex is ideal for mid-sized and enterprise companies that need to connect LLMs with internal data systems. It’s suitable for teams with moderately technical staff who want fast time-to-value, reusable agent templates, and scalable indexing infrastructure.
Pros
- Prepackaged agents for rapid deployment: LlamaIndex agents come ready to use with prebuilt workflows and RAG pipelines to launch production-quality assistants quickly.
- Handles data ingestion, chunking, and indexing with minimal setup: The platform does all the heavy lifting when it comes to preparing data for LLMs.
Cons
- Prebuilt agents offer limited flexibility: The built-in agents can be difficult to extend or repurpose for highly specialized tasks. Teams needing fully custom architectures may find the abstraction limiting.
Pricing
Pricing starts at $50/month and allows for up to 5 users. The $500/month version lets you onboard up to 10 users.
7. LangGraph: Best for DAG-based agents
LangGraph is a stateful agent framework built on top of LangChain. It’s designed for creating DAG-style (Directed Acyclic Graph) workflows that allow fine-grained control over agent behavior and tool invocation.
Features
- Graph-based orchestration for complex workflows: LangGraph represents each part of an agent’s reasoning as a node and controls transitions between nodes with edges. This enables non-linear, conditional, and looping flows.
- Native integration with LangChain: The platform is an integral part of the LangChain ecosystem. So, it works seamlessly with LangChain agents, prompts, tools, and memory modules.
- Flexible runtime execution and human-AI collaboration: The graph-based model allows developers to insert human verification steps, fallback behaviors, or conditional branching.
Who’s it for?
LangGraph is designed for mid-sized to large enterprises that build stateful, multi-step, and complex LLM workflows. Teams with strong engineering resources who want complete control over how agent decisions, retries, and transitions are handled should shortlist the platform.
Pros
- Fine-grained control over agent behavior using graph-based logic: LangGraph allows developers to model each agent step explicitly. You can introduce decision points, retries, and failovers in a way that’s both transparent and easy to trace.
- Supports long-term memory, pause/resume, and human input: LangGraph workflows can be paused, audited, and resumed — allowing human-in-the-loop interaction or approvals.
Cons
- Assumes a LangChain dependency: Teams looking to move to other orchestration frameworks may find it harder to extract components or reuse logic outside the LangChain ecosystem.
Pricing
The platform requires a LangChain subscription, which starts at $39/month. Pricing for LangGraph costs an additional $0.001 per node.
8. Haystack Agents: Best for RAG + LLM
Haystack Agents, created by the German company deepset, are part of the broader Haystack framework — an open-source toolkit designed to power RAG (retrieval-augmented generation) pipelines with large language models.
Features
- RAG-native architecture: Haystack Agents integrate tightly with dense and sparse retrieval systems and vector databases like FAISS, Weaviate, and Elasticsearch.
- Modular agent design with tool chaining: Agents can be equipped with tools like calculators and document searchers. They can chain these tools together based on task requirements.
- Multi-modal and open model support: Agents in Haystack can be used with open-source models like Mistral and Hugging Face or commercial APIs like OpenAI and Anthropic.
Who’s it for?
Haystack Agents are particularly useful for mid-size to large businesses that need full control over retrieval logic, observability, and model integration. The platform is compatible with engineering teams that want fine-tuned control over both retrieval and generation.
Pros
- Designed for building RAG-native agents: Haystack has a tight integration with retrievers, chunking strategies, and embeddings.
- Supports open-source, self-hosted deployments: Run Haystack anywhere — on-premises, in the cloud, or in a hybrid environment.
Cons
- Lacks a built-in visual workflow builder for agent design: Haystack doesn’t offer no-code design or dashboard-based orchestration — you need to use YAML configs or write Python code to define agents and workflows.
Pricing
Haystack is open-source and free to use under the Apache 2.0 license. You can self-host the framework and plug in open or commercial (paid) LLMs as needed.
9. FastAgency: Best for high-speed inference
FastAgency is a performance-focused AI agent framework designed for up-to-the-second inference and low-latency task execution. It’s built for use cases that demand speed, where delays of even a millisecond impact the user experience.
Features
- Designed for rapid agent execution: FastAgency strips away unnecessary layers and orchestration overhead, enabling agents to respond in near real time, with sub-100ms latency in many common tasks.
- Built-in support for fast LLMs: Agents can be paired with optimized inference models like GPT-4o or Groq-backed endpoints.
- Agent memory and routing designed for concurrency at scale: This allows thousands of agents to process unique tasks without sacrificing speed or leaking context across threads.
Who’s it for?
FastAgency is designed for engineering teams at companies of all sizes who need agents to act within milliseconds. It’s not a tool for prototyping or non-technical teams — it’s optimized for developers building production-ready, latency-sensitive AI features.
Pros
- Lightning-fast agent performance: Built to minimize every millisecond of delay where immediate responses are critical for UX or revenue.
- Supports thousands of concurrent agents: You can spin up thousands of isolated sessions in parallel, enabling support for large user bases or real-time workloads across many tenants.
Cons
- Lacks orchestration features: The platform is not designed for multi-agent teamwork, workflows, or decision branching. It focuses purely on fast single-agent execution and leaves orchestration to the developer.
Pricing
FastAgency offers both open-source tools and commercial solutions. Be sure to check the licensing terms and any infrastructure costs before deploying.
10. Rasa: Best for chatbots and voice assistants
Rasa is an open-source framework for building conversational AI agents — including chatbots, voice assistants, and AI-powered helpdesk tools. It’s suitable for building context-aware, multi-turn conversations that can run on both text and voice channels.
Features
- Custom NLU engine: Rasa includes a flexible machine learning pipeline for understanding user inputs.
- Dialogue management through stories, rules, and LLM integration: Rasa allows you to define agent behavior through structured stories or rules. It also supports hybrid architectures where rule-based logic is combined with LLM-powered fallback actions for ambiguous inputs.
- Open-source deployment: The platform can be deployed on-premise or in your own cloud infrastructure.
Who’s it for?
Rasa is designed for large companies that want to build highly customized, on-brand conversational agents across chat and voice platforms.
Pros
- Full-stack control: Rasa’s open architecture allows teams to own their training data, logic, and hosting.
- Active open-source community with enterprise-grade extensions: Backed by a large developer community and the commercial arm Rasa Pro, the framework offers broad support, templates, and integrations to accelerate bot development and maintenance.
Cons
- Limited to voice and text agents: Rasa is built for conversation flows, not tool-using autonomous agents. You’ll need custom code or third-party integrations to simulate more advanced LLM workflows or document retrieval logic.
Pricing
Pricing starts at $35,000, providing a little under 500,000 conversations.
AI Agent Frameworks: At a glance
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What to Consider When Selecting AI Agent Frameworks
Choosing the best AI agent framework starts with understanding your team’s technical comfort level and automation goals. Here are a few points to consider when selecting an LLM agent framework:
- Match framework to skill level: If you’re a complete beginner, choose a no-code agent framework like Lindy. Those with development experience should opt for Python-based frameworks, such as LangChain or AutoGen.
- Ensure integration with your existing software and data sources: Choose an LLM agent framework that supports native integrations with CRMs and software in your tech stack. Platforms that support APIs can also be integrated with many tools.
- Know your latency, cost, and scalability needs: Be aware of how quickly you need your agents to respond and the associated costs. Additionally, if you're growing, consider how easily the platform can scale with your needs.
- Select based on your use case’s complexity and agent coordination needs: Use a platform like Lindy for creating simple 1 or 2-step workflows. Frameworks like AutoGen or LlamaIndex can be used for complex, multi-step workflows with reasoning and memory.
Choosing the right AI agent framework involves understanding your skill level and identifying practical solutions. Yet, with the sheer number of agentic and conversational AI frameworks, there is a tool designed for your specific needs.
Where Lindy fits in: Agent framework without engineering
Most AI frameworks expect you to be an expert with code and familiar with LLM agent frameworks. Lindy, on the other hand, requires zero coding ability and offers an agent framework built for operators, not engineers.
When compared to common AI agent frameworks, here’s how Lindy stands out:
Lindy is a “framework-as-a-product” for non-dev teams
Unlike Python AI frameworks that require custom coding and orchestration, Lindy is a plug-and-play system. Start your agent creation with a premade template and launch your agent in minutes, without code.
This means that non-technical professionals, such as product managers, sales teams, and analysts, can build and deploy effective agents. For professionals seeking the best AI agent frameworks for beginners, Lindy offers speed, ease, and results without developer bottlenecks.
Agents that work across your tech stack
Lindy integrates with the tools your team already uses: Gmail, Google Calendar, and Slack to CRMs, EMRs, spreadsheets, and APIs. Agents take actions, pass data, and trigger workflows—no manual integrations or developer effort required.
Lindy enables effective agents that route leads, triage tickets, and sync data across apps. This makes Lindy an effective agent for real-world business operations.
Swarms as native multi-agent design
Lindy’s Swarms feature allows multiple agents to handle tasks in parallel within a workflow, with each agent focused on a specific context. However, it doesn’t yet offer full conversational coordination or memory like CrewAI.
For instance, one agent summarizes an email, another updates the CRM, and a third drafts a reply. This reduces hallucination, improves consistency, and scales performance by working within each agent’s context window.
LangChain vs AutoGen vs CrewAI vs Lindy
When comparing LangChain, AutoGPT, CrewAI, and Lindy, the primary difference lies between usability and raw flexibility. Here’s how these 4 platforms compare:
- LangChain: Great for developers, but requires writing code and maintaining custom pipelines
- AutoGen: Built for AI researchers, not everyday business workflows
- CrewAI: Modular and powerful, but requires Python expertise
- Lindy: A production-ready conversational AI framework for cross-functional teams
If you’re a non-developer looking for an AI agent framework, Lindy is an option. Even if you’re a seasoned developer with a working knowledge of LLMs, Lindy is still suitable: The platform allows you to create agents from pre-existing templates that can execute tasks in just a few minutes.
Frequently asked questions
What’s the easiest AI agent framework for beginners?
We built Lindy to be the easiest AI agent framework for beginners. It offers a no-code interface, premade templates, and visual workflow builders. Unlike developer tools like LangChain or AutoGen, Lindy doesn’t require Python or infrastructure setup — just drag, drop, and deploy business-ready agents in minutes.
How do LangChain and AutoGen differ?
LangChain is best for developers building structured, multi-step LLM workflows using prompt chaining, memory, and APIs. It focuses on modular customization.
AutoGen, by contrast, enables multi-agent collaboration through natural language conversations between agents. It’s ideal for research use cases or simulations where dialogue and delegation are key.
What is an agent framework in AI?
An AI agent framework is a toolkit developers use to build autonomous agents. It includes components like LLMs, memory, APIs, and orchestration logic. These frameworks aren’t agents themselves: They’re environments for creating agents that can reason, use tools, and complete tasks with minimal human input.
Do I need to code to use agent frameworks?
No, you don’t. No-code frameworks like Lindy enable you to create agents using visual tools and templates. However, most frameworks, such as LangChain, CrewAI, or AutoGen, require familiarity with Python, APIs, and a development setup. Thus, your need to code depends entirely on the framework you choose and the complexity of your use case.
Is Lindy an AI agent framework?
Yes, Lindy is an AI agent framework designed for non-developers. It replaces technical complexity with a drag-and-drop interface. You can build agents for tasks like replying to emails, qualifying leads, or updating CRMs. It integrates with tools like Gmail, Slack, and Salesforce — without requiring code.
Yet, Lindy includes optional features that cater to developers: Advanced logic tools like conditional branching, variable passing, and scripting.
Try Lindy: Your new AI agent — and way more
Looking for an AI agent framework that doesn’t require coding experience? Try Lindy — it’s the only no-code AI agent development platform on our list. You can create agents in minutes without writing a single line of code. Here are some more reasons to try Lindy:
- AI agents that understand everyday English: Create custom agents that bolster productivity and understand your prompts — no coding required.
- Sales-focused automations: Build one agent to find leads from websites and tools like People Data Labs, and another to email each lead and schedule meetings with your sales team.
- Affordability: Start for free with 400 automated tasks. When you're ready to scale, Lindy's Pro plan gives you up to 5,000 tasks—more generous than many alternatives.
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