Whats New

AutoGen Studio Declarative Configuration

• in #5172, you can now build your agents in python and export to a json format that works in autogen studio

AutoGen studio now used the same declarative configuration interface as the rest of the AutoGen library. This means you can create your agent teams in python and then dump_component() it into a JSON spec that can be directly used in AutoGen Studio! This eliminates compatibility (or feature inconsistency) errors between AGS/AgentChat Python as the exact same specs can be used across.

See a video tutorial on AutoGen Studio v0.4 (02/25) -

Here's an example of an agent team and how it is converted to a JSON file:

from autogen_agentchat.agents import AssistantAgent from autogen_agentchat.teams import RoundRobinGroupChat from autogen_ext.models.openai import OpenAIChatCompletionClient from autogen_agentchat.conditions import TextMentionTermination agent = AssistantAgent( name="weather_agent", model_client=OpenAIChatCompletionClient( model="gpt-4o-mini", ), ) agent_team = RoundRobinGroupChat([agent], termination_condition=TextMentionTermination("TERMINATE")) config = agent_team.dump_component() print(config.model_dump_json()) { "provider": "autogen_agentchat.teams.RoundRobinGroupChat", "component_type": "team", "version": 1, "component_version": 1, "description": "A team that runs a group chat with participants taking turns in a round-robin fashion\n to publish a message to all.", "label": "RoundRobinGroupChat", "config": { "participants": [ { "provider": "autogen_agentchat.agents.AssistantAgent", "component_type": "agent", "version": 1, "component_version": 1, "description": "An agent that provides assistance with tool use.", "label": "AssistantAgent", "config": { "name": "weather_agent", "model_client": { "provider": "autogen_ext.models.openai.OpenAIChatCompletionClient", "component_type": "model", "version": 1, "component_version": 1, "description": "Chat completion client for OpenAI hosted models.", "label": "OpenAIChatCompletionClient", "config": { "model": "gpt-4o-mini" } }, "tools": [], "handoffs": [], "model_context": { "provider": "autogen_core.model_context.UnboundedChatCompletionContext", "component_type": "chat_completion_context", "version": 1, "component_version": 1, "description": "An unbounded chat completion context that keeps a view of the all the messages.", "label": "UnboundedChatCompletionContext", "config": {} }, "description": "An agent that provides assistance with ability to use tools.", "system_message": "You are a helpful AI assistant. Solve tasks using your tools. Reply with TERMINATE when the task has been completed.", "model_client_stream": false, "reflect_on_tool_use": false, "tool_call_summary_format": "{result}" } } ], "termination_condition": { "provider": "autogen_agentchat.conditions.TextMentionTermination", "component_type": "termination", "version": 1, "component_version": 1, "description": "Terminate the conversation if a specific text is mentioned.", "label": "TextMentionTermination", "config": { "text": "TERMINATE" } } } }

Note: If you are building custom agents and want to use them in AGS, you will need to inherit from the AgentChat BaseChat agent and Component class.

Note: This is a breaking change in AutoGen Studio. You will need to update your AGS specs for any teams created with version autogenstudio <0.4.1

Ability to Test Teams in Team Builder

• in #5392, you can now test your teams as you build them. No need to switch between team builder and playground sessions to test.

You can now test teams directly as you build them in the team builder UI. As you edit your team (either via drag and drop or by editing the JSON spec)

New Default Agents in Gallery (Web Agent Team, Deep Research Team)

• in #5416, adds an implementation of a Web Agent Team and Deep Research Team in the default gallery.

The default gallery now has two additional default agents that you can build on and test:

• Web Agent Team - A team with 3 agents - a Web Surfer agent that can browse the web, a Verification Assistant that verifies and summarizes information, and a User Proxy that provides human feedback when needed.
• Deep Research Team - A team with 3 agents - a Research Assistant that performs web searches and analyzes information, a Verifier that ensures research quality and completeness, and a Summary Agent that provides a detailed markdown summary of the research as a report to the user.

Other Improvements

Older features that are currently possible in v0.4.1

• Real-time agent updates streaming to the frontend
• Run control: You can now stop agents mid-execution if they're heading in the wrong direction, adjust the team, and continue
• Interactive feedback: Add a UserProxyAgent to get human input through the UI during team runs
• Message flow visualization: See how agents communicate with each other
• Ability to import specifications from external galleries
• Ability to wrap agent teams into an API using the AutoGen Studio CLI

To update to the latest version:

pip install -U autogenstudio

Overall roadmap for AutoGen Studion is here #4006 . Contributions welcome!