10 Best Open-souce Frameworks To Build Multi-Agent AI Applications

Multiple AI agents working together can solve problems that a single model could not handle. But what happens when these AI Agents start making quick decisions on their own? 

A Multi-Agent AI application is a system in which multiple intelligent agents interact within a shared environment, perceiving, learning, and acting autonomously to achieve individual or collective objectives. 

As time goes on, tasks like real-time strategy planning and large-scale data analysis will become more difficult. This will require solutions that can grow with the needs of the business, change quickly, and have built-in backups to avoid single points of failure. Teams working on AI applications need Multi-Agent systems so that specialized agents can work together, work is distributed flexibly, and the system as a whole is more resilient. 

In this article, we will look at frameworks to build a Multi-Agent AI application.

1. LangChain

LangChain is an open-source system for building apps that use large language models (LLMs). It speeds up every step of the development process.

The graph-based approach of LangGraph, a module within LangChain, allows developers to define single, multi-agent, and hierarchical workflows.

This multi-agent setup lets you give each agent specific tasks, separate prompts, and either shared or private state, which makes things more reliable and transparent.

LangChain is great at building chatbots, RAG pipelines, document summarization tools, API automation agents, and systems that understand code.

Stand-Out Features

• Graph-based multi-agent orchestration: Develop complex agent workflows using hierarchical control flows, multi-agent control flows, or single agents. 
• Enhanced context-aware prompting: Support the development of grounded responses by connecting LLMs to external data, such as prompts, examples, and documents. 
• Memory modules: Use memory classes like ConversationBufferMemory to keep the discussion and program state between calls. 
• Rich integrations: Support for over 50 document types, cloud storage, databases, APIs, and vector repositories such as Milvus and Weaviate. 
• Chain and agent abstractions: Easily connect LLM calls, tools, and custom logic into reusable chains and agents. 
• Moderation and human-in-the-loop: Implement moderation loops, quality reviews, and approval steps to direct agent actions. 
• Production deployment: Use LangServe for hosting nodes and LangGraph for agent-based workloads to launch apps at a large scale. 
• Community-driven and open-source: Supported by a dynamic community that provides tutorials, examples, and active contributions on GitHub.

2. LangGraph

LangGraph is a module in the LangChain ecosystem that substitutes a graph-based method for single linear chains in organizing interactions among several AI agents.

It allows developers to divide complicated tasks into nodes that are linked by edges. These nodes handle state changes and cyclic processes for advanced agent runtimes.

LangGraph is used by companies as Replit, Uber, LinkedIn, and GitLab to create modular, controlled AI systems fit for their current workflows.

Typical uses are RAG pipelines, document summarizers, autonomous chatbots, and LLM-backed research assistants.

Stand-Out Features

• Graph-based orchestration: Set up nodes and edges to organize agent processes, which can handle both linear and circular task flows.
• Multi-agent coordination: Assign specialized agents to individual graph nodes, allowing for the execution of complex tasks in a parallel or sequential manner.
• Stateful memory management: Built-in memory modules let you keep shared or private context between runs so that results are always the same.
• Human-in-the-loop controls: Integrate approval or moderation steps into the graph to direct or halt agent actions as necessary.
• Integrate LangChain seamlessly: Use data connectors, toolkits, and LLM chains that are already in place inside LangGraph processes.
• Production-ready deployments: Deployments that are ready for production: use LangSmith for testing and LangServe to run agent graphs at scale with little setup.
• Open-source community: Discover tutorials, examples, and contributions on GitHub, Academy, and LangChain.
• Use cases: powerful chatbots, automatic data processing, retrieval-enhanced tools, and personalized LLM agents in enterprise and research settings.

3. AutoGen

AutoGen is a programming framework that is available as an open-source download. It helps with the development of multi-agent AI applications that can operate simultaneously with humans or independently.

It has high-level APIs, such as AgentChat, for making agents that can talk to each other, and a Core event-driven model for setting up scalable, predictable, or changing processes.

It’s used by developers to automate tasks like distributed services powering web applications, code generation, execution, and debugging.

AutoGen Studio adds a no-code interface to prototype, debug, and test multi-agent processes, making it easier for people who aren’t coders to use.

Stand-Out Features

Conversable Agents: Any agent can interact with another agent via chat to work together on tasks.  

• Custom Workflows: Combine tools, human inputs, and language models to match various tasks.  
• Event-Driven Core: Allows both dynamic and deterministic flows for research and business workflows.
• AgentChat API: It gives agents and teams easy-to-understand settings that speed up development.
• No-Code Studio: It provides a Python API and a drag-and-drop interface that enables you to create and evaluate workflows without the need to write code.
• Tool Use Support: Includes adapters for external APIs and LangChain tools to enhance the capabilities of the agent.
• Multi-Model & Human Modes: Operate agents autonomously or with human supervision as required.
• Design Scalability: Capable of supporting long-running, distributed agent systems for enterprise applications.
• Open Source: MIT-licensed with active community support and contributions.
• Extensible Architecture: Integration of custom extensions to accommodate specialized features and new models.

4. Semantic Kernel

Microsoft’s model-agnostic SDK Semantic Kernel enables developers to build and operate enterprise-grade, dependable AI agents and multi-agent systems.

It lets you mix your own code, data storage, and plugins with language models to create solutions ranging from basic chatbots to totally automated process workflows.

The platform’s agent structure and process orchestration features allow for both simple chatbots and complex multi-agent workflows.

This means it can be used for tasks like customer service bots, automatic document processing, and corporate knowledge mining.

Stand-Out Features

• Model Flexibility: Connect to any LLM that includes built-in support for OpenAI, Azure OpenAI, Hugging Face, NVIDIA, and other technologies.
• Agent Framework: Develop AI agents that are modular in nature and have access to memory, planning capabilities, and tools/plugins.
• Multi-Agent Systems: Collaborate with specialist agents to orchestrate complex workflows.
• Plugin Ecosystem: Enhance capabilities with native code functions, prompt templates, OpenAPI specifications, or Model Context Protocol (MCP).
• Support for vector databases: Easy connection with Azure AI Search, Elasticsearch, Chroma, and more for memory and retrieval based on embedding.
• Multimodal Support: Enhance AI interactions by processing text, vision, and audio inputs.
• Process Framework: The use of a structured workflow approach to model and automate business processes.
• Local Deployment: For low-latency or inactive scenarios, run on-premises with Ollama, LMStudio, or ONNX.
• Enterprise-Grade Features: These features include robust APIs, telemetry support, and security triggers to ensure production readiness.
• SDKs in multiple languages: C#, Python, and Java are available to fit the needs of a wide range of developers and present codebases.

5. OpenAI Agents SDK

The OpenAI Agents SDK is a Python framework that is open-source and enables the development of multi-agent workflows. It allows developers to orchestrate the actions of multiple AI agents within a single application.

It supports the OpenAI Responses and Chat Completions APIs, as well as over 100 other large language models, and is provider-agnostic.

Clear instructions, access to equipment, safety guardrails, and handoffs allow agents to work autonomously or in a set sequence.

It is used by Box and InfoQ teams for visible, scalable AI-driven processes and by developers to build language triage bots, bespoke function-driven assistants for weather lookup or translation, and automated document processors.

Stand-Out Features

• Provider-agnostic model support: Maximize your flexibility by connecting to OpenAI Responses, Chat Completions, and over 100 other LLMs.
• Agent abstraction: Define modular agents with names, instructions, tools, guardrails, and handoff rules to encapsulate distinct roles via agent abstraction.
• Handoffs: Customized logic or condition-based specialized tool calls allow you to move control across agents.
• Guardrails: Set up input and exit validations to make sure data security and safety checks are carried out.
• Tracing and observability: Automated run tracing with extensible support for external processors such as Logfire and AgentOps to optimize and debug operations.
• Function Tools: Customize Python functions to provide agents with access to custom capabilities, such as interacting with databases or getting live data.
• Flexible agent loop: Support dynamic, iterative operations, detect structured outputs, and control execution with parameters such as max_turns.
• Advanced reasoning and multimedia support: Use the latest model features to do complicated reasoning tasks and deal with inputs from text, vision, and audio.
• Integrations with business systems: Box uses it to make content-aware bots, and enterprise teams use it to make AI processes that can be trusted and seen in action.

6. SuperAGI

SuperAGI is a framework for autonomous AI agents that is open-source and designed with developers in mind, providing a simple transition from prototype to production.

A community of toolkits lets you add to the abilities of multiple agents at once, and you can use ReAct-style processes that iterate and improve results on each run to guide them through difficult tasks.

Its multiple-agent setup works well for organizing specialized agents, with each one working on a different subtask, and then putting their results together to make a final answer.

Document processing and OCR automation, virtual employee service desks, insurance underwriting, medication discovery, and customer assistance are common use cases.

Stand-Out Features

• Agent Spawn & Deploy: Generate autonomous agents that are production-ready and scalable with a single command.
• Toolkits Marketplace: Explore and attach pre-built integrations, including web scraping, email, social media, and file management, to any agent.
• Graphical User Interface: Use an intuitive web interface to monitor and regulate workflows by interacting with agents.
• Action Console: Give agents real-time inputs and permissions in the middle of a run to enable supervised autonomy.
• Multi-Vector DB Support: Connect to Chroma, Pinecone, Elasticsearch, and more to drive memory and retrieval activities.
• Performance Telemetry: Monitor agent runs’ metrics, including token usage and time per step, to identify and resolve bottlenecks.
• Optimized Token Usage: Set up prompts and processes to lower API costs without lowering the quality of the results.
• Agent Memory Storage: Enable continuous learning by preserving past interactions and outcomes for future reference.
• Custom Models: Integrate proprietary or fine-tuned LLMs to satisfy business-specific needs.
• Predefined ReAct Workflows: Use the built-in step-by-step patterns to expedite development and minimize errors for common tasks.

7. Crew AI

Crew AI is the top multi-agent platform that optimizes workflows by employing potent AI agents.

It enables users to create and deploy automated procedures using any cloud provider and a large language model.

Its multi-agent architecture helps developers assign tasks, build custom agents with roles and goals, and coordinate them in “crews” to handle challenging procedures.

Crew AI is used by businesses for things like AI-powered cloud solutions, predictive marketing campaigns, improving healthcare data, automating financial reports, and streamlining the supply chain.

Stand-Out Features

• Build Quickly: Use Crew AI’s framework or UI Studio to rapidly establish multi-agent automations using either code or no-code templates.
• Deploy with Trust: Use the autogenerated UI and built-in deployment tools to seamlessly transition personnel into production.
• Track All Your Crews: Monitor the performance and progress of agents on both basic and complex duties by tracking all of your crews.
• Perfection Through Iteration: Use training and testing tools to enhance the efficiency of the crew and the quality of the output.
• Works from the Cloud, Self-Hosted, or Locally: Install for complete control over your infrastructure or chosen cloud service.
• Easily integrates all apps: Connect to current systems without coding to simplify team procedures.
• Interface for Management: A straightforward interface that facilitates human supervision and allows for the management of AI agents.
• Gives Full Visibility: Use detailed data to keep track of agent quality, speed, and return on investment (ROI) to support Investments in automation.
• Proven at Scale: It runs more than 60 million agent jobs every month, showing that it works reliably in the real world.
• Open-Source SDK: Supported by an active developer community on GitHub, it is accessible through the pip install crewai command.

8. Atomic Agents

Atomic Agents aligns input and output schemas to allow chaining agents and tools easy to use, promoting modularity and reuse in AI systems.

You can add context providers, like search results, date providers, or custom data sources, to system messages so they always have the most up-to-date information.

The system contains a RAG chatbot for document-based Q&A, a web-search agent that creates inquiries and gives complete responses, and an orchestration agent that picks calculators or search engines based on user input.

Atomic Agents are adaptable for automated document processing, semantic search, and data extraction since advanced templates encompass deep research assistants with multi-step reasoning and multimodal agents that extract structured data from images using GPT-4 Vision. 

Stand-Out Features

• Schema-Driven Chaining: Enable transparent composition and shifting by aligning the input/output schemas of the agent and tool.
• Context Providers: To get better answers, add dynamic data to the questions, like search results, times, or custom information.
• Atomic Forge Toolkits: To enhance functionality without imposing significant dependencies, download standalone tools, including semantic search and YouTube Transcript Scraper.
• RAG Chatbot Example: A pre-built retrieval-augmented generation example that utilizes ChromaDB vector storage and document chunking.
• Web Search Agent: An intelligent agent that generates queries and conducts SearxNG-powered searches with the ability to ask follow-up questions.
• Orchestration Agent: Develops an agent that prioritizes tools (e.g., search versus calculator) according to real-time input.
• Deep Research Agent: A framework for multi-agent research workflows that involve the synthesis of answers, query generation, and choice.
• Multimodal Support: GPT-4 Vision pipeline for image analysis and structured text extraction (dietary labeling).
• Modular Design: Components live in your coding, which gives you full control over how the tool works and what it depends on.
• Python SDK & CLI: Use a simple Python API and CLI to add tools, execute agents, and scaffold projects with minimal boilerplate.

9. Dify

Dify is an open-source tool for making LLM-powered apps. It combines backend-as-a-service and LLMOps to help teams make AI solutions quickly that are ready for production.

It has built-in RAG engines, visual prompt orchestration, and an easy, low-code interface that helps to ease data-driven pipelines and chatbot creation.

You can set up, organize, and handle specialist agents in complex flows with Dify’s AI Agent framework, which allows multi-agent workflows.

Dify is used by companies for purposes that include customizing GPT assistants, automating document processing, driving RAG-based knowledge search, and creating multimodal pipelines combining text and vision models.

Stand-Out Features

• Open-Source LLMOps Stack: Allows the rapid development of AI applications by integrating backend-as-a-service and LLMOps.
• Low-Code & Visual IDE: The drag-and-drop builder and Prompt IDE simplify the process of defining AI workflows for non-technical users.
• High-Quality RAG Engine: Vector databases are supported by the built-in retrieval-augmented generation.
• AI Agent Framework: Allows multi-agent workflows by utilizing ReAct and Function Calling strategies that are accessible through the marketplace.
• Marketplace Plugin Ecosystem: Provides modules for custom components, data sources, and utilities.
• Multimodal Workflows: The integration of vision, audio, and text in collaborative pipelines with DeepSeek R1.
• Observability and Model Management: Use the built-in dashboards to monitor and manage model usage, agent performance, and logs.
• Enterprise Security & Compliance: Safe APIs and data controls; SOC2 Type I and II certified, ISO 27001 certified.  
• Flexible Deployment: Easily deploy on any cloud or on-premise infrastructure with scalable options through Docker, Kubernetes, or managed services.
• Wide Model Compatibility: Uses module support to integrate with self-hosted models such as Llama, Anthropic, Hugging Face, and OpenAI.

10. Google Agent Development Kit (ADK)

Google’s Agent Development Kit (ADK) is an open-source Python framework that is both extensible and modular.

It is designed to facilitate the development and deployment of AI agents, including complex multi-agent systems and basic bots.

It focuses on the code first, letting you describe agent logic, tools, and coordination in Python. This makes sure that tests are strong and that you can keep track of versions.

ADK’s multi-agent features let you combine greeter, task executor, and evaluator agents into scalable hierarchies that handle user requests.

ADK is used for production-scale customer care assistants, automated document pipelines, and financial reporting handles on Cloud Run or Vertex AI Agent Engine.

Stand-Out Features

• Rich Tool Ecosystem: Integrate securely with Google services and extend agent capabilities by utilizing pre-built tools, custom functions, and OpenAPI specifications.
• Code-First Development: Use Python to establish agent logic, tools, and orchestration, which improves version control, testability, and flexibility.
• Modular Multi-Agent Systems: To handle complicated processes, group together a number of specialized agents into flexible structures.
• Built-In Evaluation: Performs a systematic evaluation of the performance and execution trajectories of agents in comparison to predefined test cases.
• Development UI: Use an integrated web UI to talk to bots and test, fix, and show off processes in real time.
• A2A Protocol Support: Provide secure agent-to-agent communication for distributed systems by using the Agent-to-Agent protocol.
• Model & Deployment Agnostic: Use any LLM and deployment platform, while taking advantage of optimized support for Gemini and Google Cloud integrations.
• Sessions & Memory: For richer, stateful talks, use session state and memory services to keep the conversation in the same context across interactions.
• Callback Patterns: Customize workflows by applying callback hooks to events, artifacts, and runtime control to extend agent behavior.
• Open Source & Community: Apache 2.0 licensed, active community, thorough manuals, and example agent repositories for quick starts.

Conclusion

These tools are flexible, easy to use, and ready for production.

No-code platforms like AutoGen Studio and Dify speed up prototyping, and code-first toolkits like the Google ADK and OpenAI Agents SDK give devs more control and built-in observability.

Semantic Kernel and Atomic Agents structure complicated multi-agent handles with modular architecture and schema-driven chaining.

Scalable and real-time performance monitoring platforms like SuperAGI and Crew AI use telemetry dashboards and action consoles to track agent runs.

Enterprise-level solutions like Google ADK, Dify, and Semantic Kernel make it easy to connect to cloud services and come with review tools and compliance features that help with deployments in production.

Dify’s visual Prompt IDE and AutoGen Studio’s drag-and-drop interface help non-technical users build multi-agent pipelines without coding.

To meet project goals, choose a framework based on deployment environment, model compatibility, development experience, and performance needs.