Migrating from AutoGen to Microsoft Agent Framework
The Microsoft Agent Framework was built as the evolution of AutoGen, so the migration preserves your existing agent architecture and patterns. The primary changes are package names, import paths, tool registration patterns, and the addition of new capabilities like state persistence and observability that AutoGen lacked. Most migrations can be completed incrementally, allowing teams to migrate one component at a time rather than doing a full rewrite.
Audit Your Current AutoGen System
Before changing any code, create a complete inventory of your AutoGen implementation. Document every agent with its type, system message, and model configuration. List every tool registration with the function signatures, descriptions, and which agents use them. Map out all conversation patterns, including two-agent chats, sequential chains, and group chats with their speaker selection methods and termination conditions.
Record all external integrations: model provider configurations, code execution environments, database connections, API clients, and any custom middleware or logging. Document the conversation flow for each use case, noting where human-in-the-loop interactions occur and what termination conditions are used. This inventory becomes your migration checklist and ensures nothing is missed during the transition.
Identify any custom AutoGen extensions or monkey patches in your codebase. These areas will require the most attention during migration because they may depend on AutoGen internals that have changed in the Agent Framework. Custom agent types, modified conversation managers, or extended model clients all need careful review.
Update Dependencies and Package Structure
Replace the AutoGen packages in your requirements file with the Microsoft Agent Framework equivalents. The core package autogen-agentchat becomes microsoft-agents (or the specific sub-packages your project uses). The extension package autogen-ext maps to microsoft-agents-ext with the same extra markers for Docker, Azure, and other optional dependencies.
Update all import statements throughout your codebase. The module paths change from autogen_agentchat to microsoft_agents, and many class names remain the same or have direct equivalents. The AssistantAgent, UserProxyAgent, and GroupChat classes exist in both frameworks with compatible interfaces, though some parameter names and defaults have changed.
Run your existing tests after updating imports to identify compilation errors and type mismatches. This gives you a quick list of all the API differences that need attention. Many will be straightforward renames or parameter changes that can be addressed systematically.
Convert Agent Definitions
AutoGen agents defined with ConversableAgent, AssistantAgent, and UserProxyAgent have direct equivalents in the Agent Framework. The system message, model configuration, and basic behavior settings transfer directly. The Agent Framework adds optional parameters for state management, observability, and plugin access that you can add incrementally after the initial migration.
Model client configuration changes from AutoGen's dictionary-based format to the Agent Framework's typed configuration classes. The model name, API key, and endpoint URL are specified through configuration objects rather than plain dictionaries, providing better validation and IDE support. Azure OpenAI configurations gain additional parameters for managed identity authentication and regional deployment selection.
Review and update system messages during migration. The Agent Framework supports the same free-text system messages as AutoGen, but the integration with Semantic Kernel plugins means that tool descriptions are generated automatically from plugin metadata rather than being manually included in the system message. Remove any tool description text that you previously included in system messages and let the framework handle tool presentation to the model.
Migrate Tools to Semantic Kernel Plugins
AutoGen's tool registration pattern, where individual functions are registered with specific agents, converts to Semantic Kernel's plugin pattern, where functions are organized into plugin classes and registered with the kernel. This change provides better organization and reusability because plugins are available to all agents rather than individually registered with each one.
Convert each tool function into a method within a plugin class, adding the KernelFunction decorator and Description annotations. The function's type hints become the parameter schema, and the docstring or description annotations become the tool description that the LLM sees. Group related functions into logical plugin classes, such as a DatabasePlugin for database operations or a FilePlugin for file system access.
Register plugins with the kernel at application startup rather than with individual agents. All agents that use the kernel automatically gain access to all registered plugins, eliminating the need to register the same tool with multiple agents individually. This centralized registration also simplifies testing because plugins can be mocked or stubbed at the kernel level.
Update Conversation Orchestration
AutoGen's conversation patterns (two-agent chat, sequential chat, group chat) have equivalents in the Agent Framework with additional options. The two-agent initiate_chat pattern works similarly, with minor API differences in method names and parameter handling. Group chats gain additional speaker selection strategies and support for sub-group routing.
The Agent Framework introduces new orchestration patterns that AutoGen did not have. The Swarm pattern enables agent handoffs similar to the OpenAI Agents SDK. The Magentic-One pattern provides a sophisticated multi-agent architecture with a lead agent that coordinates specialized sub-agents. Evaluate whether these new patterns better fit your use case than the original AutoGen patterns you were using.
Termination conditions migrate directly, with the Agent Framework supporting the same text-based, function-based, and turn-count-based termination that AutoGen provides. The framework adds richer termination options including timeout-based termination, state-based termination (stop when a specific state key reaches a target value), and composite termination conditions that combine multiple criteria.
Add State Management and Observability
With the core migration complete, take advantage of capabilities the Agent Framework provides that AutoGen lacked. Add state persistence by configuring a state backend (in-memory for development, database-backed for production). Enable checkpointing for long-running workflows so that conversations can resume after interruptions. Configure conversation summarization to manage token costs in extended conversations.
Enable OpenTelemetry tracing by adding the telemetry configuration to your application startup. This provides distributed tracing across agent conversations, tool calls, and model invocations. Export traces to your preferred backend (Azure Monitor, Jaeger, Zipkin, or any OpenTelemetry-compatible collector) for visualization and analysis. Set up dashboards that track key metrics like conversation length, token consumption, error rates, and task completion rates.
These additions represent net-new capabilities rather than migration tasks, so they can be implemented after the core migration is validated. Start with observability first, since tracing data helps identify issues during the migration process itself.
Test and Validate the Migration
Run the migrated system alongside the original AutoGen system with the same inputs and compare the outputs. Because LLM-based systems are non-deterministic, exact output matching is not possible, but you can verify that the migrated system completes the same tasks, uses the correct tools, follows the expected conversation patterns, and produces results of comparable quality.
Create a test suite that covers each use case in your migration inventory. For each use case, define the input, the expected behavior (which agents participate, which tools are called, what kind of output is produced), and quality criteria for the result. Run each test multiple times to account for LLM variability and verify that the migrated system meets your quality bar consistently.
Deploy the migrated system to a staging environment and run it with production-representative traffic before cutting over. Monitor error rates, latency, token consumption, and output quality. If metrics are comparable to the AutoGen system, proceed with the production cutover. Keep the AutoGen system available for rollback during the initial production period until you have confidence in the migrated system's stability.
Migrating from AutoGen to the Microsoft Agent Framework is primarily an API translation exercise because the core concepts map directly between the two frameworks. Start by auditing your current system, update dependencies and imports, convert agents and tools, update orchestration patterns, and then add the new capabilities like state management and observability that motivated the migration.