Principle:Microsoft Autogen Graph Flow Configuration
| Knowledge Sources | |
|---|---|
| Domains | Multi-Agent Systems, Workflow Configuration, Team Orchestration, Directed Graph Execution |
| Last Updated | 2026-02-11 00:00 GMT |
Overview
Graph flow configuration is the practice of binding a validated directed graph topology to a set of agent participants along with execution constraints to create a runnable agent team.
Description
After designing a graph topology and constructing the agent nodes with their edges, the next step is to configure a graph-directed team: a runnable unit that uses the directed graph to determine which agents execute and in what order.
Graph flow configuration bridges the gap between the static graph definition and the dynamic runtime execution. It binds together three essential components:
- Participants: The concrete agent instances that will execute. Each participant must correspond to a node in the graph by name.
- Graph topology: The validated directed graph that defines execution order, conditional branching, parallel fan-out, and cyclic patterns.
- Execution constraints: Termination conditions and maximum turn limits that bound the execution.
The configuration step performs critical validation:
- Participant-graph consistency: Every node in the graph must have a corresponding participant agent. Participant names must be unique.
- Cycle safety: If the graph contains cycles, either a termination condition or a maximum turn limit must be provided to prevent infinite execution.
- Type verification: All participants must implement the ChatAgent protocol.
The configured team operates as a group chat where the graph serves as the speaker selection policy. Instead of round-robin or selector-based turn-taking, the graph determines which agent(s) speak next based on the current execution state and edge conditions.
Usage
Use graph flow configuration when:
- You have a validated directed graph and a set of matching agent participants, and need to create a runnable team.
- You want graph-directed execution with support for sequential chains, parallel fan-out, conditional branching, and cyclic loops.
- You need to set termination conditions (message count, content match, external signal) to bound execution.
- You want the team to be serializable and reconstructible via component configuration.
- You need to integrate graph-based orchestration into a broader system that expects the standard team interface (run, run_stream, reset, save_state, load_state).
Theoretical Basis
Team as Graph Executor
A graph-directed team can be modeled as a state machine where:
- State: The set of currently active (ready) nodes and the remaining incoming edge counts for each node.
- Transition: When a node completes execution, its outgoing edges are evaluated. For each satisfied edge, the target node's remaining count is decremented. When a target's count reaches zero (for "all" activation) or any edge is satisfied (for "any" activation), the target becomes ready.
- Termination: The team stops when no more nodes are ready (graph naturally completed), a termination condition is met, or the maximum turn count is reached.
Configuration Separation
Separating configuration from execution follows the dependency injection principle:
- The graph defines what happens and in what order.
- The participants define who executes each step.
- The termination condition defines when to stop.
- The runtime (optional) defines where execution occurs.
This separation enables:
- Reusability: The same graph topology can be used with different agent implementations.
- Testability: Agents can be swapped for mock implementations during testing.
- Serialization: The graph and participants can be serialized independently via the component model.
Pseudocode: Graph Flow Configuration
function configure_graph_flow(participants, graph, termination, max_turns):
# Validate participant uniqueness
assert all names are unique in participants
# Validate participant types
for each participant in participants:
assert participant implements ChatAgent
# Validate graph structure
graph.validate()
# Validate cycle safety
if graph.has_cycles():
assert termination is not None or max_turns is not None
# Create the team with graph-directed speaker selection
team = GraphDirectedTeam(
participants=participants,
speaker_policy=GraphSpeakerPolicy(graph),
termination=termination,
max_turns=max_turns,
)
return team