10 Best AutoGen Alternatives for Conversational Agents

Conversational AI is evolving from basic chatbots to agents with reasoning, tool use, contextual understanding, and collaboration on complex tasks. AutoGen is a popular framework to build such agent-based applications, but it might not fit every project’s architecture, integrations or deployment needs. In this article, I will walk through the best AutoGen alternatives for conversational agents, comparing their key capabilities, multi-agent support, model compatibility, memory, integrations, deployment options, and practical use cases to help developers understand which framework fits their needs.

Why Look for AutoGen Alternatives?

Different Agent Architectures: Developers might favor frameworks that support graph-based, workflow-driven, or role-based agent architectures that align better with their needs for conversational applications, development style, and orchestration.

Production Scalability: Some projects need robust production capabilities to manage growing workloads, reliable agent execution, monitoring, deployment and scaling across enterprise conversational applications and environments.

Model Flexibility: Teams may want more support for using AI models from different providers so that they can compare performance, costs, and capabilities, and not be too reliant on a single ecosystem.

Enhanced Developer Experience: Additional frameworks could offer easier-to-use APIs, visual development tools, more intelligible abstractions, improved documentation or debugging tools that simplify the development of conversational agent projects.

Advanced Memory Management: The conversational applications often involve persistent memory, session management, context handling, and retrieval capabilities that are very different between agent frameworks and affect the quality of the application.

More Integrations: Businesses may look for platforms that offer more integrations for databases, APIs, enterprise apps, knowledge bases, communication channels and the external tools their conversational workflows need.

Security and governance: When evaluating alternatives, framework capabilities can be an important consideration, as enterprise deployments may require stronger controls for authentication, permissions, data protection, observability, guardrails, and governance.

Comparison Table

AutoGen Alternative	Type	Primary Language / Ecosystem	Multi-Agent Support	Conversation / State	Key Strength for Conversational Agents	Open Source
Microsoft Agent Framework	Multi-agent orchestration framework	Python, .NET	Yes	Graph-based workflows, agent orchestration	AutoGen successor with multi-agent orchestration, MCP and A2A support	Yes
LangGraph	Agent runtime & orchestration framework	Python, TypeScript	Yes	Stateful workflows, memory, persistence	Fine-grained control over multi-turn and complex agent workflows	Yes
CrewAI	Multi-agent orchestration framework	Python	Yes	Agent tasks, crews and workflows	Role-based agent collaboration and delegation	Yes
OpenAI Agents SDK	Agent workflow SDK	Python, TypeScript	Yes	Agent handoffs and workflow state	Lightweight agent creation, tool use and multi-agent delegation	Yes
LlamaIndex Workflows	Agent workflow framework	Python, TypeScript	Yes	Event-driven workflows	Data- and document-centric conversational agent workflows	Yes
Google ADK	Agent development framework	Python, TypeScript, Java	Yes	Agent workflows and sessions	Agent development within the Google/Gemini ecosystem	Yes
Mastra	AI agent application framework	TypeScript	Yes	Workflows, memory and state	Production-oriented agents for TypeScript applications	Partial
PydanticAI	Python agent framework	Python	Yes	Structured agent execution and dependencies	Type-safe agent development with structured outputs and tool integration	Yes
Semantic Kernel	AI orchestration SDK	C#, Python, Java	Yes	Agents, processes and orchestration	Enterprise application integration and Microsoft ecosystem support	Yes
AG2	Multi-agent framework	Python	Yes	Conversational multi-agent workflows	Community-driven continuation of the AutoGen-style multi-agent approach	Ye

1. Microsoft Agent Framework

Microsoft Agent Framework is Microsoft’s new framework for building and orchestrating AI agents and is positioned as the direction for new development after AutoGen’s maintenance mode. Supports Python and .NET with agent orchestration, workflows, tools, and multi-agent collaboration Its architecture has state and workflow control as important components.

Human-in-the-loop patterns can be used for approval-based processes. This works quite well with Microsoft oriented environments and Enterprise Application Integration for production use, especially when Azure and Microsoft services are already in the technology stack.

Why it is an alternative to AutoGen?

1. Microsoft has positioned it as the next direction to take for new agent development after AutoGen.
2. Supports single-agent and multi-agent application patterns.
3. Offers structured workflows for complex agent applications.
4. Supports tools, agent collaboration and external integrations.
5. Designed for organizations already using Microsoft and

Microsoft Agent Framework Feature

Feature	Details
Agent Orchestration	Supports structured agent and workflow orchestration for multi-step and multi-agent applications.
Multi-Agent Collaboration	Designed for coordinating multiple specialized agents within workflows.
State Management	Supports workflow-oriented state handling for maintaining execution context.
Human-in-the-Loop	Supports workflows where human interaction or approval can be incorporated.
Tool Integration	Agents can work with external tools and application capabilities.
MCP Support	Supports Model Context Protocol-based tool and agent integrations.
A2A Support	Supports Agent2Agent interoperability patterns.
Enterprise Integration	Designed for integration with Microsoft and enterprise application environments.
Deployment	Can be used as part of production-oriented agent applications and workflows.
AutoGen Relationship	Microsoft positions Agent Framework as the direction for new development following AutoGen’s maintenance mode.

2. LongGraph

LangGraph is a low-level agent orchestration framework designed for stateful, controllable agent workflows and supports Python and TypeScript. Its persistence layer uses checkpoints for thread-level state and stores for longer-term application data, allowing conversations to continue across interactions.

Human-in-the-loop workflows can pause and resume through persisted state, which is useful for approval processes. LangGraph can also support single-agent, multi-agent, and hierarchical architectures. For production applications, its runtime-oriented design provides control over execution, persistence, recovery, and workflow observability.

LangGraph Feature

Feature	Details
Graph-Based Architecture	Represents agent workflows as graphs containing nodes and execution paths.
Stateful Execution	Designed for applications that need persistent workflow state.
Checkpointing	Can save execution state at workflow checkpoints.
Memory	Supports persistence and storage mechanisms for conversational and application information.
Human-in-the-Loop	Workflows can pause and resume around human decisions.
Multi-Agent Workflows	Supports different agents operating within coordinated graph workflows.
Conditional Routing	Workflow execution can follow different paths based on state or results.
Persistence	Enables applications to maintain state across workflow runs.
Languages	Supports Python and JavaScript/TypeScript ecosystems.
Production Control	Provides detailed control over agent execution, state, persistence, and workflow behavior.

3. CrewAI

CrewAI is a python based framework for agents, crews, tasks and workflows and is perfect for apps where multiple specialized agents collaborate. Its architecture allows the developers to cluster the agents according to their defined responsibilities and synchronize their execution.

CrewAI offers its AMP platform for deploying, monitoring and scaling agent workflows, as well as managed infrastructure, API access, execution traces, logs and webhook streaming for production deployment.

In this way, enterprise integration can go beyond the open-source framework into managed deployment. Where applications need review or intervention, human interaction and workflow controls can be built in.

CrewAI Feature

Feature	Details
Agents	Developers can create specialized agents with defined roles, goals, and capabilities.
Crews	Multiple agents can collaborate as a coordinated crew.
Tasks	Work can be divided into specific tasks assigned to agents.
Flows	Provides structured workflow orchestration for agent applications.
Delegation	Agents can delegate work to other agents where configured.
Memory	Supports memory capabilities for maintaining relevant information across agent interactions.
Tools	Agents can be equipped with tools for external actions.
Observability	Production tooling provides monitoring and execution visibility.
Deployment	CrewAI provides deployment and managed infrastructure options through its platform.
Enterprise Features	Enterprise capabilities extend agent development into deployment, monitoring, scaling, and organizational workflows.

4. OpenAI Agents SDK

With the OpenAI Agents SDK you get Python and TypeScript SDKs to build agents that use tools, have memory, and collaborate via handoffs. Its runtime supports agent turns, sessions, tools, guardrails, and delegation between multiple agents.

Human approval is a known capability. A tool call can stop execution, return an interruption with a resumable state, then resume after approval or rejection.

The SDK also has built-in tracing for model generations, tool calls, handoffs, guardrails, and workflow events. Deployment is still controlled by the application. Teams control their infrastructure, storage, tools, approval logic.

OpenAI Agents SDK Feature

Feature	Details
Agents	Agents combine models, instructions, tools, and runtime behavior.
Handoffs	An agent can delegate a task or conversation to another specialized agent.
Agents as Tools	Agents can also be exposed as tools for other agents.
Guardrails	Provides input, output, and tool-related validation mechanisms.
Function Tools	Python and TypeScript functions can be exposed as agent tools with schemas.
MCP	Supports calling tools exposed through MCP servers.
Sessions	Provides persistent working context for agent loops.
Human-in-the-Loop	Supports human participation during agent execution.
Tracing	Built-in tracing records model generations, tool calls, handoffs, guardrails, and workflow events.
Voice & Sandbox	Supports voice agents and sandbox agents with isolated workspaces in supported SDK environments.

5. Pipelines in LlamaIndex

LlamaIndex Workflows is a event-driven approach to workflows for building agentic applications, especially around data, documents, retrieval, and tool-based processing. State can be passed through workflow execution.

LlamaIndex’s larger ecosystem supports memory and retrieval patterns for conversational applications. Instead of treating the agent as a standalone chatbot, developers are able to link workflows to models, data sources, tools, and application services.

Production deployment can be integrated as part of the application’s own infrastructure, which is relevant for teams building data-intensive conversational agents that need retrieval, structured workflows, and integration with external systems.

LlamaIndex Workflows Feature

Feature	Details
Event-Driven Workflows	Uses events to pass information between workflow steps.
Workflow Steps	Individual functions can represent separate stages of an application process.
Agent Integration	Workflows can coordinate agent-based tasks and tool operations.
Data Integration	Designed to work closely with documents, indexes, retrieval, and external data.
RAG Support	Can be used to construct retrieval-augmented agent workflows.
Tool Integration	External tools and application functions can participate in workflows.
Async Execution	Supports asynchronous workflow execution for agentic applications.
State Handling	Workflow applications can maintain information required between execution steps.
Multi-Step Processing	Supports complex sequences rather than limiting applications to single model calls.
Conversational Applications	Particularly useful for agents that need external knowledge, retrieval, and structured workflow execution.

6. Google ADK

Google Agent Development Kit (ADK) supports Python, TypeScript, Go, Java and Kotlin. Agents, sessions, state, memory, tools, multi-agent orchestration. Its context model separates the current conversation session and temporary state from the searchable long-term memory, which is useful for multi-turn conversational applications.

You can use Google Cloud options like Agent Runtime, Cloud Run, and GKE for production deployment. Google’s deployment tooling also includes Cloud Trace observability, and enterprise publishing can link deployed agents with Gemini Enterprise. Approval gates are also supported by ADK development patterns.

Google ADK Feature

Feature	Details
Agent Development	Provides components for creating and orchestrating AI agents.
Multi-Agent Systems	Supports applications composed of multiple cooperating agents.
Sessions	Sessions provide conversation and execution context.
State	Agent applications can maintain state associated with sessions.
Memory	Supports mechanisms for making longer-term information available to agents.
Tools	Agents can use functions and external tools.
Workflow Orchestration	Supports structured agent workflows and different agent coordination patterns.
Deployment	Supports Google Cloud deployment options for production applications.
Observability	Google Cloud integrations can provide tracing and operational visibility.
Enterprise Integration	Fits applications built around Google Cloud and the Gemini ecosystem.

7. Mastra

Mastra is a typescript AI app framework for agents, workflows, tools, memory and app integration. Its workflow engine supports explicit multi-step execution with sequential, branching and parallel control flow. Mastra has memory features for conversation context, such as Observational Memory to remember useful information across long conversations and sessions.

Human-in-the-loop workflows can pause execution and wait for user input or approval before continuing. Storage retains the state of execution. It can be embedded in Node.js, React, and Next.js apps and deployed on managed or app-controlled infrastructure.

Mastra Feature

Feature	Details
Agents	Provides abstractions for building AI agents with models, instructions, tools, and memory.
Workflows	Uses a graph-based workflow engine for multi-step processes.
Branching	Workflows can select different execution paths.
Parallel Execution	Independent workflow operations can run in parallel.
Memory	Supports conversation history, retrieval, and longer-term agent context.
Human-in-the-Loop	Workflows can suspend for user input or approval and resume later.
State Persistence	Storage preserves execution state so paused workflows can continue.
Model Providers	Supports models from providers including OpenAI, Anthropic, and Google.
TypeScript	Built primarily for the modern TypeScript/JavaScript application ecosystem.
Application Integration	Can embed agents and workflows into web and server-side applications.

8. PydanticAI

PydanticAI is a Python framework for building typed AI agents that focuses on structured outputs, tool integrations, dependencies and type-safe application development. Conversation storage and durable execution are different things. An app can store conversation info, but durable execution is about keeping long running executions alive across failures and restarts.

Human-in-the-loop workflows: PydanticAI provides durable execution integrations with engines such as Temporal, DBOS, Prefect, Restate, and AWS Lambda. This makes it suitable for production systems that need reliable execution, recoverability, structured agent behavior, and integration with existing Python infrastructure.

PydanticAI Feature

Feature	Details
Python Framework	Designed specifically for building AI applications with Python.
Type Safety	Uses Python typing and Pydantic validation for structured agent applications.
Structured Output	Agents can produce outputs validated against defined schemas.
Tools	Python functions can be exposed as tools for agents.
Dependencies	Applications can provide typed dependencies and external services to agents.
Model Flexibility	Supports integration with multiple model providers.
Durable Execution	Supports integrations for long-running and recoverable agent execution.
Testing	Typed structures and application-level testing approaches help validate agent behavior.
Production Workflows	Can be integrated with durable-execution systems for persistent workloads.
Structured Agents	Particularly suited to applications where reliable schemas and validated outputs are important.

9. Semantic Kernel

Semantic Kernel is Microsoft’s AI SDK ecosystem that supports C#, Python and Java and has concepts such as kernels, plugins, memory, processes and agents. Its agent architecture supports multi-agent collaboration and orchestration, and some patterns support human-in-the-loop participation when human judgment is needed.

“Enterprise integration is a big part of the ecosystem, and Microsoft’s docs cover observability, security, filters, plugins, and process capabilities.” Its language support and Microsoft ecosystem connections make it a good choice for organizations building AI agents into existing enterprise applications, rather than treating conversational agents as standalone chatbot projects.

Semantic Kernel Feature

Feature	Details
Agent Framework	Provides agent abstractions within the Semantic Kernel ecosystem.
Languages	Supports C#, Python, and Java.
Plugins	Plugins allow agents to access application functions and external capabilities.
Function Calling	Agents can dynamically use registered functions and plugins.
Multi-Agent Orchestration	Supports concurrent, sequential, handoff, group-chat, and Magentic orchestration patterns.
Process Framework	Provides event-driven process capabilities for business workflows.
Human-Agent Collaboration	Supports workflows where humans and agents work together.
Observability	Microsoft documents observability as an enterprise component of the ecosystem.
Enterprise Integration	Designed to integrate AI capabilities into existing applications and business processes.
Extensibility	Kernel services, plugins, processes, and agents can be combined into larger AI applications.

10. AG2

AG2 is a community-driven evolution of the AutoGen style multi-agent framework. Microsoft’s agent resources document ongoing development. The main focus remains on multi-agent application development and conversational coordination, which is especially helpful for developers looking for an architecture similar to the original AutoGen approach.

AG2 is community-driven, so you should compare the language ecosystem and production capabilities of AG2 to the exact version you are adopting. Before migrating an existing AutoGen application, teams should consider its available memory, state persistence, human-in-the-loop mechanisms, observability integrations, deployment model, and enterprise requirements.

AG2 Feature

Feature	Details
Multi-Agent Conversations	Focuses on conversational coordination between multiple agents.
Agent Tools	Tools can connect agents to APIs, databases, calculations, code, and other external capabilities.
Human-in-the-Loop	Provides mechanisms for incorporating human interaction into agent execution.
Async Runtime	Supports asynchronous agent and tool execution.
Persistent State	History, storage, and stream state can be externalized to persistent backends.
Scalability	Externalized state allows applications to design horizontally scalable agent deployments.
OpenTelemetry	Provides OpenTelemetry tracing for agent execution and observability.
Structured Output	Supports structured-output approaches for agent responses.
Middleware	Supports composable concerns such as retries, rate limits, token budgets, and history compaction.
Production Support	Provides production-oriented primitives including testing utilities, persistent backends, tracing, and concurrent execution.

How to Choose the Best AutoGen Alternative

Agent Architecture Choose a framework with an architecture that fits your application — graph-based workflows, linear agents, role-based collaboration, autonomous agents, or structured conversational interactions.

Multi-Agent Support: Assess the system’s capability to facilitate collaboration among multiple agents, task allocation, knowledge sharing, workflow collaboration, and handling complex conversational processes without introducing excessive complexity.

Model Compatibility: Confirm compatibility with the AI models and providers you intend to utilize. Flexible model compatibility can make it easier to experiment, reduce vendor dependence and improve cost management.

Memory & context: Look into session handling, conversation history, persistent memory and context management. Strong capabilities enable agents to retain relevant information over longer and more complex user interactions.

Tools & Integrations: Review supported APIs, databases, search engines, external tools and business applications; Conversational agents are highly integrable, thus more usable in real world workflows.

Deployment & Scalability: Decide on cloud, self-hosted, or on-premises deployment choices and the scalability, monitoring, reliability, and operational needs to determine if a framework will work for your production environment.

Pricing and Development Experience: Compare licensing, usage costs, documentation, SDK quality, debugging tools, community support and learning curve to get an idea of the overall development and ownership costs of the platform.

Conclusion

Whether an AutoGen alternative is suitable depends on the conversational agent’s type, the orchestration model, and production needs. There are frameworks like LangGraph, CrewAI, Semantic Kernel, LlamaIndex, Haystack, OpenAI Agents SDK, Google ADK, AWS Strands Agents, Dify, and Botpress, which provide a variety of options for agent creation, memory, tool integration, model support, and deployment. Some focus on multi-agent collaboration, others RAG, enterprise integration, visual workflows or scalable production environments.

Compare architecture, model compatibility, memory, integrations, deployment options, security, developer experience, and total costs before selecting a platform. The best option ultimately depends on the exact technical requirements and complexity of your app’s workflow.

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