ag2-network-workflow

name	ag2-network-workflow
description	Build an AG2 network `workflow` channel — the orchestrated N-party adapter driven by a declarative `TransitionGraph`. Use when the user needs conditional handoffs, multi-step pipelines, context-aware routing, feedback loops, or is migrating from the classic `GroupChat` + `Agent.handoffs` pattern. Covers `TransitionGraph`; factories `TransitionGraph.sequence(...)` / `.round_robin(...)`; built-in targets (`AgentTarget`, `RoundRobinTarget`, `StayTarget`, `RevertToInitiatorTarget`, `TerminateTarget`); built-in conditions (`Always`, `FromSpeaker`, `ToolCalled`, `ContextEquals`); the typed `Handoff` return for dynamic routing; channel-scoped context via `set_context`; `register_target` / `register_condition` for custom subclasses; the packet execution model and idempotent-tool requirement; eight cookbook patterns (pipeline, hierarchical, escalation, feedback loop, triage, and more); and side-by-side migration from classic `GroupChat`. Load this after `ag2-network-quickstart`.
license	Apache-2.0

AG2 Network — Workflow Adapter

workflow is the orchestrated multi-party adapter. A declarative TransitionGraph describes who speaks first, what conditions fire on each turn, and when the channel terminates. It's the modern replacement for the classic GroupChat + Agent.handoffs pattern — turn-taking lives in the hub, not in an in-process speaker selector.

Prerequisite: read ag2-network-quickstart first. This skill assumes you know Hub.open, HubClient.register, the channel lifecycle, and the basic agent_client.open(...) / channel.send(...) flow.

When to use

Load this skill when the user asks for any of:

• "Two/three/N agents with conditional handoffs"
• "Pipeline / sequence of agents (researcher → writer → editor)"
• "Triage agent routes to specialists"
• "Drafter / reviewer feedback loop"
• "Coordinator + specialists / hierarchical orchestration"
• "Migrate from GroupChat / ConversableAgent.handoffs / ReplyResult(target=...)"
• "I want context-driven routing (OnContextCondition, StringContextCondition)"

Don't use workflow for:

• Strict 1Q1R between two agents → consulting (see ag2-network-quickstart).
• Free-form 2-party chat with no turn order → conversation (see ag2-network-quickstart).
• Fixed round-robin with no conditions → discussion (see ag2-network-discussion).

If the user is unsure, the rule of thumb: need any condition more complex than "next speaker in a fixed list"? Use workflow.

Shape

Participants	2+
Turn order	Whatever TransitionGraph says
Auto-close	Yes — when graph emits TerminateTarget or max_turns is hit
Default view	WindowedSummary(recent_n=N*2)
Default expectations	turn_within(120s, warn), turn_within(600s, auto_close)
Required knob	{"graph": <TransitionGraph.to_dict()>}

The expectation is strict: a stuck speaker auto-closes the channel after 10 minutes. Tune via ag2-network-governance.

Workflow agents and the NetworkPlugin

HubClient.register(...) attaches NetworkPlugin by default. The plugin adds only the identity-level cross-cutting tools — peers / channels / tasks / context / delegate. Channel-specific tools (notably say, plus your workflow handoff @tools) come from adapter.tools_for(client, metadata, state, participant_id), resolved per turn by the default handler and merged into agent.ask(tools=...) — and the workflow adapter offers none: WorkflowAdapter.tools_for(...) returns []. So a workflow agent never sees say; routing is entirely your @tool-decorated handoff functions (returning Handoff, mutating context vars, or calling channel.close()).

Consequence: the old "mid-turn say races the round-end EV_PACKET" hazard can't happen on a workflow channel — you do not need attach_plugin=False for correctness. Choose by capability instead:

• Keep the plugin (default) if the agent should also delegate to a peer, look up peers / channels / tasks, or read/write channel context from outside the graph. (delegate / channels(action="open") spin up separate channels — they never emit on the workflow channel, so they don't disturb its turn machinery.)
• attach_plugin=False for a genuinely bare agent — only the domain @tools you wrote. Tidy for pure pipeline workers and most tests.

# Bare pipeline worker — no cross-cutting network tools:
worker = await wc.register(agent, Passport(name="worker"), Resume(), attach_plugin=False)

The default handler wraps the LLM's plain-text reply into the workflow EV_PACKET either way. The examples below pass attach_plugin=False because they're pure pipeline workers; drop it if your agents need delegate / peers / etc. Full detail: ag2-network-tools-and-views → "Adapter-owned tools (tools_for)".

Smallest example — pipeline via sequence

import asyncio

from autogen.beta import Agent
from autogen.beta.config import AnthropicConfig
from autogen.beta.knowledge import MemoryKnowledgeStore
from autogen.beta.network import (
    EV_CHANNEL_CLOSED,
    Hub,
    HubClient,
    LocalLink,
    Passport,
    Resume,
    TransitionGraph,
)


async def main() -> None:
    config = AnthropicConfig(model="claude-sonnet-4-6")
    hub = await Hub.open(MemoryKnowledgeStore(), ttl_sweep_interval=0)
    link = LocalLink(hub)

    rhc, whc, ehc = (HubClient(link, hub=hub) for _ in range(3))

    researcher = await rhc.register(
        Agent("researcher", prompt="Bullet-point three facts on the topic.", config=config),
        Passport(name="researcher"), Resume(), attach_plugin=False,
    )
    writer = await whc.register(
        Agent("writer", prompt="Turn the bullets into one paragraph.", config=config),
        Passport(name="writer"), Resume(), attach_plugin=False,
    )
    editor = await ehc.register(
        Agent("editor", prompt="Tighten the paragraph. Reply with the final text.", config=config),
        Passport(name="editor"), Resume(), attach_plugin=False,
    )

    graph = TransitionGraph.sequence([
        researcher.agent_id, writer.agent_id, editor.agent_id,
    ])

    channel = await researcher.open(
        type="workflow",
        target=[writer.agent_id, editor.agent_id],
        knobs={"graph": graph.to_dict()},
    )
    await channel.send("Topic: how does HTTPS work?")

    close_env = await researcher.wait_for_channel_event(
        channel_id=channel.channel_id,
        predicate=lambda e: e.event_type == EV_CHANNEL_CLOSED,
        timeout=180.0,
    )
    print(f"closed: {close_env.event_data.get('reason')!r}")  # 'sequence_complete'

    for hc in (rhc, whc, ehc):
        await hc.close()
    await hub.close()


asyncio.run(main())

TransitionGraph.sequence([a, b, c]) builds three FromSpeaker(x) → AgentTarget(next) transitions plus a TerminateTarget("sequence_complete") default. After c speaks no transition matches, the default fires, and the channel auto-closes.

TransitionGraph anatomy

@dataclass(slots=True)
class TransitionGraph:
    initial_speaker: str            # agent_id of the first speaker (must be a participant)
    transitions: list[Transition]   # ordered list, walked in priority order on every fold
    default_target: TransitionTarget  # consulted if no transition matches
    max_turns: int | None = None    # hard turn cap; force-terminates on reaching


@dataclass(slots=True)
class Transition:
    when: TransitionCondition       # evaluated against the just-accepted envelope + state
    then: TransitionTarget          # if when() is True, this resolves the next speaker
    priority: int = 0               # LOWER runs first; ties break by insertion order

On every accepted substantive envelope (text or packet), the adapter walks the transitions list. The first matching condition's target resolves the next speaker. If none match, default_target is consulted. A TerminateTarget resolves with next_speaker=None, which makes the adapter return AdapterResult(next_state=CLOSING, ...) and the hub posts EV_CHANNEL_CLOSED with that target's reason. max_turns is a separate, harder cap: once turn_count reaches it the adapter closes the channel immediately with reason "max_turns" — it does not fall through to default_target (so a default_target=TerminateTarget("…") reason only appears when an actual turn produced no matching transition).

priority sorts ascending — a lower number is checked first. The adapter does sorted(transitions, key=lambda t: t.priority) then walks the result, so priority=0 (the default) beats priority=100. If you want a rule consulted before the others, either put it at the top of the list (the sort is stable, so equal priorities keep list order) or give it a negative priority. A common mistake: writing priority=100 on a ContextEquals(...) → TerminateTarget(...) rule expecting it to win — it gets pushed to the end and a FromSpeaker(...) fallback fires first instead, so the channel never terminates.

Gotcha — the kickoff channel.send() is the initial_speaker's first turn

When you do agent.open(type="workflow", ...) then await channel.send(text), that first send is folded as initial_speaker's turn — it's an envelope from that agent, and expected_next_speaker starts equal to initial_speaker, so validate_send accepts it and the graph immediately routes onward. The initial_speaker's Agent.ask never runs for that turn — the text you sent is its contribution.

So if your design wants the first agent that should actually think (the drafter, the researcher, the planner) to respond to the kickoff prompt, that agent must not be initial_speaker and must not be the one calling channel.send(). The clean answer is a HumanClient as the seeder — a non-LLM participant (no Agent, no plugin) whose only job is to open the channel and post the brief. Make it initial_speaker and route FromSpeaker(user) → AgentTarget(drafter):

from autogen.beta.network import HumanClient, Passport  # plus the usual imports

user_hc = HubClient(link, hub=hub)
user = await user_hc.register_human(Passport(name="user", kind="human"))   # ← register_human, not register
drafter  = await drf_hc.register(drafter_agent,  Passport(name="drafter"),  Resume(), attach_plugin=False)
reviewer = await rev_hc.register(reviewer_agent, Passport(name="reviewer"), Resume(), attach_plugin=False)

graph = TransitionGraph(
    initial_speaker=user.agent_id,
    transitions=[
        Transition(when=ContextEquals("approved", value=True), then=TerminateTarget("approved")),
        Transition(when=FromSpeaker(user.agent_id),     then=AgentTarget(drafter.agent_id)),    # brief → drafter
        Transition(when=FromSpeaker(drafter.agent_id),  then=AgentTarget(reviewer.agent_id)),
        Transition(when=FromSpeaker(reviewer.agent_id), then=AgentTarget(drafter.agent_id)),    # revise loop
    ],
    default_target=TerminateTarget("max_revisions"),
    max_turns=12,
)

channel = await user.open(type="workflow", target=[drafter.agent_id, reviewer.agent_id], knobs={"graph": graph.to_dict()})
await channel.send("Brief: …")   # consumed as `user`'s turn → routes to `drafter`, who drafts
# Later, when the channel closes: await user.next_envelope(predicate=lambda e: e.event_type == EV_CHANNEL_CLOSED, timeout=300)

register_human(passport, *, resume=None, rule=None, auto_ack_invites=True) returns a HumanClient (HubClient.register(...) now rejects kind="human" and points you here). It auto-acks invites so the quorum handshake completes without UI round-trips; pass auto_ack_invites=False to gate joins by hand. Drive it with on_envelope(callback) (push) or next_envelope(...) / envelopes() (pull) — see ag2-network-quickstart → "HumanClient".

If you'd rather not introduce a HumanClient, the older workaround still works: a throwaway "intake" Agent as initial_speaker whose Agent.ask is never invoked (no transition routes back to it) — but a HumanClient is cheaper (no model config, no plugin) and is what the framework's own smoke tests use. Either way, the same applies to TransitionGraph.sequence([a, b, c]): channel.send(...) is a's turn, so if a is supposed to produce the first artifact (not just relay a prompt), prepend a seeder — sequence([user, a, b, c]).

(Symptom when you get this wrong: the second agent in the chain receives the bare kickoff prompt as if it were the first agent's output, "responds" to something that isn't there, and the pipeline produces nonsense — often hallucinating that an artifact exists.)

Built-in targets

Target	Decision
AgentTarget(agent_id)	Hand off to a specific named agent.
RoundRobinTarget()	Advance through the participant order.
StayTarget()	Same speaker continues (rare; "let me elaborate" patterns).
RevertToInitiatorTarget()	Hand back to whoever opened the channel.
TerminateTarget(reason="…")	End the channel; reason flows on EV_CHANNEL_CLOSED.event_data["reason"].

Built-in conditions

Condition	Fires when
Always()	Every accepted envelope.
FromSpeaker(agent_id)	The just-accepted envelope was sent by this agent.
ToolCalled(tool_name)	The previous turn called this tool by name (matched via routing.tool on the packet).
ContextEquals(key, value)	Channel-scoped context_vars[key] equals value. Missing keys compare as None.

For combinations beyond what these compose to, see "Custom targets / conditions" below.

Convenience factories

# Pipeline: a → b → c → terminate.
graph = TransitionGraph.sequence([alice.agent_id, bob.agent_id, carol.agent_id])

# Round-robin with a cap: a → b → c → a → b → c → terminate after 6 turns.
graph = TransitionGraph.round_robin(
    participants=[alice.agent_id, bob.agent_id, carol.agent_id],
    max_turns=6,
)

round_robin uses Always() → RoundRobinTarget() and a TerminateTarget("round_robin_complete") default — but since Always() matches every turn, the default is unreachable and the channel actually closes with reason "max_turns" once the cap is hit (set max_turns or it cycles forever). sequence uses FromSpeaker(steps[i]) → AgentTarget(steps[i+1]) chains with a TerminateTarget("sequence_complete") default — that one is reachable: after the last step speaks, no FromSpeaker rule matches, so the default fires and you get "sequence_complete".

Conditional handoff (the most common manual graph)

A triage agent inspects each request and routes to a specialist via a tool call:

from autogen.beta.network import (
    AgentTarget, FromSpeaker, RevertToInitiatorTarget,
    TerminateTarget, ToolCalled, Transition, TransitionGraph,
)

graph = TransitionGraph(
    initial_speaker=triage.agent_id,
    transitions=[
        # Triage's `escalate` tool fired → route to security.
        Transition(when=ToolCalled("escalate"),
                   then=AgentTarget(security.agent_id)),
        # Once security has spoken, hand back to whoever opened the channel.
        Transition(when=FromSpeaker(security.agent_id),
                   then=RevertToInitiatorTarget()),
        # Otherwise triage's reply goes to the generic responder.
        Transition(when=FromSpeaker(triage.agent_id),
                   then=AgentTarget(general.agent_id)),
    ],
    default_target=TerminateTarget(reason="triage_complete"),
    max_turns=20,
)

For each ToolCalled(name) → AgentTarget(agent) transition, attach an @tool-decorated function on the speaker's Agent. The simplest form returns a typed Handoff — the framework reads it from the tool's result and routes:

from autogen.beta.network import Handoff


# `.tool` lives on `Agent`. The `AgentClient` returned by `hc.register(...)`
# is the network handle (used for `.open()`, `.send()`, `.agent_id`) and does
# NOT expose `.tool`. So: build the Agent, attach tools, *then* register.
triage_agent = Agent("triage", prompt="…", config=config)


@triage_agent.tool
async def escalate(reason: str = "") -> Handoff:
    """Escalate this ticket to the security reviewer."""
    return Handoff(target=security.agent_id, reason=reason)


triage = await triage_hc.register(
    triage_agent, Passport(name="triage"), Resume(), attach_plugin=False,
)

The typed Handoff return supersedes the matching ToolCalled rule when both are configured — useful when the target depends on runtime state. The graph's ToolCalled rule remains useful as documentation and as a fallback.

Context variables — the read/write loop

Channel-scoped mutable state lives on WorkflowState.context_vars: dict[str, Any]. It's the modern equivalent of classic ContextVariables from autogen.agentchat.group, scoped to one workflow channel and persisted as EV_CONTEXT_SET envelopes on the WAL.

Writing context (from a tool)

from autogen.beta.network import ChannelInject, EV_CONTEXT_SET
from autogen.beta.network.workflow_helpers import set_context


async def set_route(route: str, channel: ChannelInject) -> str:
    """Record the routing decision for this channel."""
    if channel is None:
        return "no active channel"
    await set_context(channel, "route", route)
    return f"route set to {route!r}"

set_context(channel, key, value) is a thin wrapper that emits EV_CONTEXT_SET with audience=[] (state-only; no participant is notified). Equivalent raw form:

await channel.send(
    "",
    event_type=EV_CONTEXT_SET,
    event_data={"set": {"route": route}},
    audience=[],
)

The full event_data shape supports both set and delete: {"set": {...}, "delete": [...]}. Either is optional; within one envelope delete runs before set.

Reading context (in a transition)

Transition(
    when=ContextEquals(key="route", value="security"),
    then=AgentTarget(security.agent_id),
)

ContextEquals(key, value=None) fires when the key is unset or explicitly deleted. The mutation is folded before substantive turn checks, so a ContextEquals rule on the same fold sees the just-set value.

Reading context (in a tool)

from autogen.beta.network import ChannelStateInject


async def increment_counter(channel: ChannelInject, state: ChannelStateInject) -> str:
    if state is None or channel is None:
        return "no active channel"
    current = state.context_vars.get("counter", 0)
    await set_context(channel, "counter", current + 1)
    return f"counter now {current + 1}"

Initial values

channel = await alice.open(
    type="workflow",
    target=[bob.agent_id, carol.agent_id],
    knobs={
        "graph": graph.to_dict(),
        "context_vars": {"escalation_level": 0, "ticket_id": ticket_id},
    },
)

The stuck-routing trap

ContextEquals is sticky — once a key is set, every subsequent fold re-evaluates it. If you have ContextEquals("route", "security") → AgentTarget(security) near the top of the list, you'll bounce back to security forever (or until max_turns). Two fixes:

1. List terminate rules before context-conditions: FromSpeaker(security) → TerminateTarget(...) placed earlier short-circuits the loop after security speaks.
2. Have the routed agent clear the key: the security agent's tool emits EV_CONTEXT_SET with {"delete": ["route"]} when done.

Fix #1 is the more common pattern.

Feedback loop — context-driven termination

graph = TransitionGraph(
    initial_speaker=intake.agent_id,
    transitions=[
        # Approve sets done=True → terminate.
        Transition(when=ContextEquals("done", value=True),
                   then=TerminateTarget("approved")),
        # intake → drafter.
        Transition(when=FromSpeaker(intake.agent_id),
                   then=AgentTarget(drafter.agent_id)),
        # Alternate drafter ↔ reviewer.
        Transition(when=FromSpeaker(drafter.agent_id),
                   then=AgentTarget(reviewer.agent_id)),
        Transition(when=FromSpeaker(reviewer.agent_id),
                   then=AgentTarget(drafter.agent_id)),
    ],
    default_target=TerminateTarget("max_iterations"),
    max_turns=10,
)


# `reviewer_agent` is the `Agent`; `reviewer` is the `AgentClient` returned
# by `register()` and used in the graph above (for `reviewer.agent_id`).
# Tools must be attached to the Agent before registration.
@reviewer_agent.tool
async def approve(reason: str, channel: ChannelInject) -> str:
    """Mark the draft approved; the graph terminates with reason='approved'."""
    if channel is None:
        return "no channel"
    await set_context(channel, "done", True)
    return f"approved: {reason}"

The reviewer's approve call writes done=True; the reviewer's reply text lands on the same packet fold; ContextEquals("done", True) matches and the channel terminates with "approved". Without approve, the drafter/reviewer alternation continues until turn_count hits max_turns and the channel closes with reason "max_turns". (The default_target=TerminateTarget("max_iterations") above only fires if some turn produced no matching transition — here one of the FromSpeaker rules always matches, so in this graph it's effectively dead; keep it as a sane fallback for graphs where it can be reached.)

The eight cookbook patterns

The AG2 docs ship a Pattern Cookbook with runnable examples. One-line summaries — each one is a workflow channel with a specific graph:

Pattern	Graph	When
Pipeline	TransitionGraph.sequence([a, b, c])	Linear A→B→C→terminate (research → write → edit).
Hierarchical	Coordinator + FromSpeaker(specialist) → RevertToInitiatorTarget()	Coordinator dispatches; specialist returns to coordinator.
Star	Hub queries N spokes; ToolCalled("synthesise") → TerminateTarget	Multi-source aggregation, parallel data gathering.
Escalation	Tiered Handoff + ToolCalled("resolve") → TerminateTarget("resolved")	Support tiers, multi-level approval.
Redundant	sequence with N proposers + evaluator	Niche; novelty/ideation with comparison.
Feedback loop	ContextEquals("done", True) → TerminateTarget + max_turns	Drafter ↔ reviewer until approved.
Context-aware routing	Triage sets context_vars["category"]; ContextEquals(category, X) → AgentTarget(specialist_X)	Support triage, skill-based dispatch.
Triage with tasks	TransitionGraph.sequence + knobs["context_vars"] initial values	Triage produces plan; sequence executes the plan.

Pipeline, escalation, context-aware routing, and feedback loop together cover most real-world needs. The cookbook page has runnable end-to-end code for each.

Custom targets and conditions

When the built-ins don't fit, implement the Protocol and register the class so it round-trips through TransitionGraph.to_dict():

from typing import ClassVar
from dataclasses import dataclass, field

from autogen.beta.network import (
    Envelope,
    TransitionDecision,
    TransitionTarget,
    register_target,
)


@dataclass(slots=True)
class HighestRankedReviewer(TransitionTarget):
    name: ClassVar[str] = "highest_ranked_reviewer"
    role_priority: list[str] = field(default_factory=list)

    def resolve(self, state, envelope: Envelope) -> TransitionDecision:
        # ... look up the next reviewer based on domain logic ...
        return TransitionDecision(next_speaker=chosen_id)


register_target(HighestRankedReviewer)

Same pattern for conditions — implement evaluate(state, envelope) -> bool, set a ClassVar[str] name, call register_condition(MyCondition). Both customisations persist via to_dict(): the name field is the key, dataclass fields become the args dict reconstituted by loads(...).

The packet execution model and idempotent tools

Each Agent.ask round on a workflow channel commits to the WAL atomically as a single EV_PACKET envelope. The packet carries the agent's routing decision (routing.tool matched against ToolCalled rules, or a pre-resolved routing.target from a typed Handoff), the round's body text, and a reserved context_updates slot. EV_CONTEXT_SET envelopes from tool calls land before the packet, so a ContextEquals rule on the same fold sees the just-set value.

Atomicity has a consequence: if the agent crashes mid-packet, the channel reverts to its pre-packet state and the input is re-dispatched. Tool calls in that packet execute again on retry.

Tools that touch external systems must be idempotent under retry:

• Use the external service's idempotency-key feature where available (Stripe, S3). Derive a stable key from (channel_id, round_counter, tool_name).
• For database writes, use upsert (INSERT ... ON CONFLICT) rather than blind insert.
• For tools that genuinely cannot be idempotent, gate them behind HITL or run them in single-tool rounds so the packet boundary is tighter.

Migration from classic GroupChat

The translation is mostly mechanical. The two systems share the vocabulary — speakers, conditions, targets, terminations — but the new one is data-first (a TransitionGraph is JSON-serialisable and survives Hub.hydrate()).

Concept mapping

Classic	Beta network	Notes
GroupChat(agents=[...])	workflow channel with participants	The hub plays GroupChatManager's role.
GroupChatManager	WorkflowAdapter + Hub	Turn-taking moves into the hub.
Agent.handoffs	TransitionGraph (per-channel, not per-agent)	Handoffs described once at channel level.
AgentTarget(agent)	AgentTarget(agent_id)	Takes id, not reference.
RevertToInitiator()	RevertToInitiatorTarget()	Same semantics.
Stay()	StayTarget()	Same.
Terminate()	TerminateTarget(reason="…")	Now carries a reason.
OnContextCondition(...)	ContextEquals or custom TransitionCondition	Register via register_condition.
OnCondition(...)	Custom TransitionCondition	Same.
ReplyResult(target=...) from a tool	Typed Handoff return	Handoff(target=..., reason=...).
FunctionTarget(fn)	Custom TransitionTarget	Implement resolve(), register_target(...).
max_round=N	max_turns=N on the graph	Same hard cap.

Side-by-side: round-robin

# Classic
groupchat = GroupChat(agents=[alice, bob, carol],
                     speaker_selection_method="round_robin", max_round=6)
manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config)
alice.initiate_chat(manager, message="Topic: …")

# Beta network
graph = TransitionGraph.round_robin(
    participants=[alice.agent_id, bob.agent_id, carol.agent_id],
    max_turns=6,
)
channel = await alice.open(
    type="workflow",
    target=[bob.agent_id, carol.agent_id],
    knobs={"graph": graph.to_dict()},
)
await channel.send("Topic: …")

Side-by-side: conditional handoff (ReplyResult → Handoff)

# Classic
@triage.register_for_llm(description="Escalate to security review.")
def escalate(reason: str) -> ReplyResult:
    return ReplyResult(target=AgentTarget(security_reviewer), message=...)

# Beta network — attach to the Agent *before* registering with the hub
@triage_agent.tool
async def escalate(reason: str = "") -> Handoff:
    return Handoff(target=security_reviewer.agent_id, reason=reason)

# Plus the matching transition in the graph (as documentation/fallback):
Transition(when=ToolCalled("escalate"), then=AgentTarget(security_reviewer.agent_id))

Migration checklist

1. Stand up a hub (Hub.open(MemoryKnowledgeStore())).
2. Wrap each existing Agent via hc.register(...) with a Passport and Resume.
3. Translate Handoffs config into a TransitionGraph. Use sequence / round_robin factories where they fit; build manually for conditional logic.
4. Replace initiate_chat(...) with alice.open(type="workflow", target=[...], knobs={"graph": graph.to_dict()}) followed by channel.send(text).
5. Wait for EV_CHANNEL_CLOSED via wait_for_channel_event(...).
6. Inspect via hub.read_wal(channel_id) and hub._audit_log.read_all().

State object

@dataclass(slots=True)
class WorkflowState:
    participant_order: list[str]
    expected_next_speaker: str | None
    last_speaker_id: str | None = None
    last_envelope_id: str | None = None
    turn_count: int = 0
    pending_close_reason: str = ""
    creator_id: str = ""
    graph_data: dict = field(default_factory=dict)
    context_vars: dict[str, Any] = field(default_factory=dict)

expected_next_speaker = None signals "channel should terminate" — the adapter's on_accepted reads this and returns AdapterResult(next_state=CLOSING, ...). graph_data is the serialised graph; the adapter rebuilds TransitionGraph on every fold so there's no mutable graph state in memory.

Quick reference — imports

from autogen.beta.network import (
    # Graph and transitions
    TransitionGraph,
    Transition,
    TransitionTarget,
    TransitionCondition,
    TransitionDecision,
    register_target,
    register_condition,
    # Built-in targets
    AgentTarget,
    RoundRobinTarget,
    StayTarget,
    RevertToInitiatorTarget,
    TerminateTarget,
    # Built-in conditions
    Always,
    FromSpeaker,
    ToolCalled,
    ContextEquals,
    # Typed handoff return for tools
    Handoff,
    # Context variables
    EV_CONTEXT_SET,
    ChannelInject,
    ChannelStateInject,
    # Channel types
    WORKFLOW_TYPE,
    # Seeding a workflow (kickoff participant)
    HumanClient,  # via HubClient.register_human(Passport(name=..., kind="human"))
)
from autogen.beta.network.workflow_helpers import set_context, delete_context