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## Why are these changes needed? ❗ Before Previously, GraphFlow.__init__() modified the inner_chats and termination_condition for internal execution logic (e.g., constructing _StopAgent or composing OrTerminationCondition). However, these modified values were also used during dump_component(), meaning the serialized config no longer matched the original inputs. As a result: 1. dump_component() → load_component() → dump_component() produced non-idempotent configs. 2. Internal-only constructs like _StopAgent were mistakenly serialized, even though they should only exist in runtime. ⸻ ✅ After This patch changes the behavior to: • Store original inner_chats and termination_condition as-is at initialization. • During to_config(), serialize only the original unmodified versions. • Avoid serializing _StopAgent or other dynamically built agents. • Ensure deserialization (from_config) produces a logically equivalent object without additional nesting or duplication. This ensures that: • GraphFlow.dump_component() → load_component() round-trip produces consistent, minimal configs. • Internal execution logic and serialized component structure are properly separated. <!-- Please give a short summary of the change and the problem this solves. --> ## Related issue number <!-- For example: "Closes #1234" --> Closes #6431 ## Checks - [ ] I've included any doc changes needed for <https://microsoft.github.io/autogen/>. See <https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to build and test documentation locally. - [x] I've added tests (if relevant) corresponding to the changes introduced in this PR. - [x] I've made sure all auto checks have passed.
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.