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## Problem When using GraphFlow with a termination condition, the second task execution would immediately terminate without running any agents. The first task would run successfully, but subsequent tasks would skip all agents and go directly to the stop agent. This was demonstrated by the following issue: ```python # First task runs correctly result1 = await team.run(task="First task") # ✅ Works fine # Second task fails immediately result2 = await team.run(task="Second task") # ❌ Only user + stop messages ``` ## Root Cause The `GraphFlowManager` was not resetting its execution state when termination occurred. After the first task completed: 1. The `_ready` queue was empty (all nodes had been processed) 2. The `_remaining` and `_enqueued_any` tracking structures remained in "completed" state 3. The `_message_thread` retained history from the previous task This left the graph in a "completed" state, causing subsequent tasks to immediately trigger the stop agent instead of executing the workflow. ## Solution Added an override of the `_apply_termination_condition` method in `GraphFlowManager` to automatically reset the graph execution state when termination occurs: ```python async def _apply_termination_condition( self, delta: Sequence[BaseAgentEvent | BaseChatMessage], increment_turn_count: bool = False ) -> bool: # Call the base implementation first terminated = await super()._apply_termination_condition(delta, increment_turn_count) # If terminated, reset the graph execution state and message thread for the next task if terminated: self._remaining = {target: Counter(groups) for target, groups in self._graph.get_remaining_map().items()} self._enqueued_any = {n: {g: False for g in self._enqueued_any[n]} for n in self._enqueued_any} self._ready = deque([n for n in self._graph.get_start_nodes()]) # Clear the message thread to start fresh for the next task self._message_thread.clear() return terminated ``` This ensures that when a task completes (termination condition is met), the graph is automatically reset to its initial state ready for the next task. ## Testing Added a comprehensive test case `test_digraph_group_chat_multiple_task_execution` that validates: - Multiple tasks can be run sequentially without explicit reset calls - All agents are executed the expected number of times - Both tasks produce the correct number of messages - The fix works with various termination conditions (MaxMessageTermination, TextMentionTermination) ## Result GraphFlow now works like SelectorGroupChat where multiple tasks can be run sequentially without explicit resets between them: ```python # Both tasks now work correctly result1 = await team.run(task="First task") # ✅ 5 messages, all agents called result2 = await team.run(task="Second task") # ✅ 5 messages, all agents called again ``` Fixes #6746. > [!WARNING] > > <details> > <summary>Firewall rules blocked me from connecting to one or more addresses</summary> > > #### I tried to connect to the following addresses, but was blocked by firewall rules: > > - `esm.ubuntu.com` > - Triggering command: `/usr/lib/apt/methods/https` (dns block) > > If you need me to access, download, or install something from one of these locations, you can either: > > - Configure [Actions setup steps](https://gh.io/copilot/actions-setup-steps) to set up my environment, which run before the firewall is enabled > - Add the appropriate URLs or hosts to my [firewall allow list](https://gh.io/copilot/firewall-config) > > </details> <!-- START COPILOT CODING AGENT TIPS --> --- 💬 Share your feedback on Copilot coding agent for the chance to win a $200 gift card! Click [here](https://survey.alchemer.com/s3/8343779/Copilot-Coding-agent) to start the survey. --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: ekzhu <320302+ekzhu@users.noreply.github.com> Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
AutoGen AgentChat
AgentChat is a high-level API for building multi-agent applications.
It is built on top of the autogen-core package.
For beginner users, AgentChat is the recommended starting point.
For advanced users, autogen-core's event-driven
programming model provides more flexibility and control over the underlying components.
AgentChat provides intuitive defaults, such as Agents with preset behaviors and Teams with predefined multi-agent design patterns.