Agently TriggerFlow

Use this skill when the solution clearly needs orchestration semantics rather than one request family.

The user does not need to say TriggerFlow or Agently. Scenario language such as resumable approval flow, branching automation, output-fan-out refactor, mixed sync/async pipeline, process-clarity refactor, or draft-review-revise pipeline should still route here once orchestration is clearly the owner layer.

Native-First Rules

• prefer TriggerFlow for explicit multi-stage quality loops, branching, concurrency, waiting/resume, restart-safe execution, output-fan-out performance refactors, process-clarity refactors, or mixed sync/async orchestration
• treat TriggerFlow as Agently's first-class orchestration substrate: framework features with multi-step loops, approval waits, retry, replan, verification, or resume semantics should be TriggerFlow-backed rather than hidden inside a local executor state machine
• prefer agent.create_task(...) when the app needs one Agent-owned business task loop where the model owns planning, verification, and replan; it returns a task-strategy AgentExecution draft, not a separate public AgentTask handle. Use agent.create_task_loop(...) only when the code should make the task-loop strategy explicit; it still returns an AgentExecution draft. Use TriggerFlow directly when the application owns explicit stages, branching, pause/resume, or restart topology
• for AgentTaskLoop examples, consume the AgentExecution result/stream/meta (result = execution.get_result(), result.get_text(), result.get_data(), result.get_meta(), execution.get_async_generator(), execution.async_get_meta()) and surface task refs or meta.stream_kind=="snapshot" items as compact intermediate state captures; enable options={"agent_task": {"stream_progress": True}} only when the example or host needs natural-language operator updates; omit progress_model_key for template progress with no model requests, or set progress_model_key to run a separate background model that summarizes only existing snapshots/task metadata without adding main-loop fields or latency; prove replan behavior from stream verification/replan events and snapshots rather than hiding the proof in a local Python loop; mocked business systems may supply defective facts or conflicting source data, but must not return pass/fail verdicts or deterministic quality judgments
• default to async-first workflow handlers, execution entrypoints, and runtime stream consumers
• treat sync TriggerFlow APIs as wrappers for scripts or compatibility bridges, not as the default service interface
• prefer explicit execution lifecycle control with close() / async_close() for completion and cleanup
• treat create_execution(concurrency=N) and execution.set_concurrency(N) as an execution-wide handler dispatch budget, including nested dispatch from chunk continuations and data.async_emit(...); use operator-local batch(..., concurrency=...) or for_each(..., concurrency=...) only for local fan-out caps
• use execution.result when services, UIs, stream consumers, or intervention-aware workflows need multiple views of one execution outcome, such as state, compatibility final result, interventions, and metadata; use execution.close() / execution.async_close() for close snapshots
• use runtime intervention for optional supplemental context added while an execution is already running: define explicit .intervention_point(...) boundaries so execution creation can infer planned mode, or create the execution with intervention_mode="auto" for boundary policy insertion; chunks read inserted context with data.get_interventions(...) and explicitly audit usage with data.async_mark_intervention_consumed(...), relying on the chunk-name consumer default unless another consumer identity is clearer
• for human approvals, webhooks, or externally resumed waits, use pause_for(..., resume_to="next" | "self" | {"event": ...}); treat it as a durable graph interrupt, not Python coroutine stack persistence; teach model-decided autonomous interrupts with model-owned pause_for(..., resume_to="self"), where the resumed chunk handles data.is_resume and the default max_resumes=1 prevents unbounded self-replay; teach prearranged approval gates with an explicit pause chunk plus when(...)
• for framework policy approvals, use the global PolicyApproval contract and represent pending approvals as pause_for(type="policy_approval", ...) interrupts that resume through continue_with(...)
• do not put pause_for(...) behind hidden execution sugar such as flow.start() or flow-level runtime stream helpers; create an explicit execution handle and consume get_pending_interrupts() / continue_with(...)
• close waiting executions explicitly: close() / async_close() rejects pending interrupts by default, and pending_interrupts="cancel" must be chosen deliberately when abandoning waits
• when a sub-flow can pause, keep the external API rooted at the parent execution id plus projected root interrupt id; do not require callers to manage child execution ids
• use emit_nowait(...) / async_emit_nowait(...) when a chunk must fan out without blocking the current handler, and rely on execution close to drain registered tasks
• after starting a finite workflow that uses execution-managed nowait fan-out, call await execution.async_close() to drain registered tasks and collect the close snapshot. Do not add while True status polling loops such as execution.result.get_state("status") unless a chunk explicitly owns and updates that status contract
• rely on chunk-internal data.emit(...), data.async_emit(...), data.emit_nowait(...), and data.async_emit_nowait(...) to inherit the current TriggerFlow runtime scope; do not assume unrelated external emits can be paired by when(..., mode="and") unless the host routes them through one scoped flow stage or carries explicit correlation in the payload
• use execution runtime state through get_state(...) / set_state(...) instead of legacy runtime-data helpers in new examples
• treat shared flow data as a risky cross-execution surface and avoid it unless the task explicitly needs shared state
• when discussing restart or distributed pause/resume, describe TriggerFlow as providing foundations for host-managed recovery, not a complete production distributed workflow engine. execution.save() is a versioned top-level execution snapshot with durable TriggerFlow progress, flow definition fingerprint validation, interrupt/resume ledgers, resource requirements, lease metadata, and managed execution environment requirements. TriggerFlow core is process-stateless between save/load; live runtime_resources, clients, callbacks, tasks, semaphores, stateful sessions, and Python coroutine frames are not serialized. runtime_resources is only the process-local mount point for live objects that the host has already created, restored, and validated. If a resource carries state, the external system that owns it must persist a state ref/version/lease or fence token and a resolver/provider must validate it before load is ready. Declare future resources with flow.declare_resource_requirement(...) or execution.declare_resource_requirement(...), inspect or restore with execution.inspect_load(...) / execution.async_load(...), pass a stable resume_request_id to continue_with(...) for external callbacks, and persist through a snapshot store that implements put_snapshot(...) while the production store owns atomic claim, lease enforcement, conflict handling, outbox ordering, and external side-effect idempotency. Fail-closed pending resolvers or pending managed execution environments should be treated as status="pending_resources" and ready=False until async_load(...) resolves and validates live resources. Late callbacks delivered to an expired execution-local lease fail fast before resume acceptance without writing resume ledger entries; the reclaimed worker should load or claim first and then use the same stable resume_request_id. For long-running executions, use execution.set_compaction_policy(...) when snapshot writes should automatically run a host-owned reducer and store large payloads behind provider artifact refs; TriggerFlow records compaction facts, retained anchors, and load read bounds, while Workspace or an enterprise provider owns artifact storage and retention anchor persistence. For framework-level validation, run examples/trigger_flow/durable_recovery.py when available; it proves Workspace-backed snapshot load and duplicate callback idempotency without pretending a production approval transport exists. Use examples/trigger_flow/fastapi_sqlite_exchange_provider.py when service-level provider replacement should be shown: it packages the flow as a module-level TriggerFlow(...) object with top-level .to(...) / .when(...) wiring, stores top-level execution snapshots in SQLite, and routes approval waits through a SQLite ExecutionExchangeProvider exposed by FastAPI without claiming production distributed guarantees. If a snapshot fingerprint is missing or does not match the current flow definition, inspect_load(...) reports invalid_snapshot and load(...) rejects the snapshot
• for service packaging, prefer an importable module that owns the complete flow definition: a module-level TriggerFlow(...) object, module-level chunk handlers, and module-level .to(...) / .when(...) wiring. Service modules should import that flow object and start executions from it. Normal Python imports execute the module body once per process for the same module name; TriggerFlow duplicate-definition protection is only the second line of defense when application code explicitly runs the same wiring again on the same flow object. Create live dependencies in host-owned factories or importable resolvers, and use runtime_resources only as the final process-local attachment point for already-created live objects. Use a build_flow(...) helper only when the application genuinely needs multiple configured flow instances or test isolation, not as the default service shape
• route model-generated or app-submitted DAG data to agently-dynamic-task; TaskDAG is the DAG foundation capability, Dynamic Task is the convenience facade over it, and TriggerFlow is the execution substrate rather than the facade API
• use when(...) + emit_nowait(...) as the native signal-driven pattern for fan-out, loops, side branches, and dependency joins; definition idempotence must not be confused with runtime signal deduplication
• for a developer-owned Todo DAG or other dependency graph represented as stable Python flow code, express multi-dependency joins with flow.when(["task_a_done", "task_b_done"], mode="and").to(...) and have upstream chunks call data.emit_nowait("task_a_done", payload). Do not replace joins with sleeps, polling loops, local completed sets, or pause_for(..., resume_to="self") unless the workflow is genuinely waiting for external input across process time
• when a model-generated Todo List is produced at runtime but the host owns the executor code, compile the returned task list into TriggerFlow definitions immediately after generation: root tasks attach to the execution start boundary with flow.to(handler, name=...), dependent tasks attach with flow.when(dep_signals, mode="and").to(handler), and task handlers emit their own completion signals. Do not implement a generic local scheduler that scans task_status, completed, or dependency lists to decide which task is ready; that hides the DAG from TriggerFlow and defeats graph visibility. Do not pass lambda data: async_task_handler(data, task) to flow.to(...); that registers a sync lambda whose return coroutine may not be awaited. Use a normal factory that returns async def handler(data): ... and register that handler
• when a TriggerFlow + SkillsExecutor example relies on a trusted local Skill bundle to provide declared helper capabilities, pass selector-level auto_allow=True; explicit host configure_skill_capabilities(...) policy still wins and Skill metadata alone is not a capability grant
• keep runtime stream consumers safe by relying on execution close to stop the stream
• keep workflow stages visible instead of hiding nested request loops
• name chunks and stage boundaries so exported flow configs, Mermaid diagrams, and runtime graphs stay readable
• let TriggerFlow definition export and runtime metadata drive visualization instead of maintaining a second manual graph description
• combine with agently-request when one workflow step needs model setup, prompt contracts, structured output, response reuse, session behavior, or retrieval

Python API Shape

When generating or editing Python code, use the actual Agently API shape:

from agently import Agent, Agently, TriggerFlow, TriggerFlowRuntimeData, Workspace

agent = Agent()
factory_agent = Agently.create_agent()
flow = TriggerFlow(name="workflow-name")
factory_flow = Agently.create_trigger_flow("factory-workflow")
shared_workspace = Workspace("./.agently/workflows/shared")

Agent() / Agently.create_agent() and TriggerFlow() / Agently.create_trigger_flow(...) are both valid first-class creation styles. Workspace(...) / Agently.create_workspace(...) are both valid first-class Workspace creation styles. Use one style consistently in an example unless showing the API equivalence.

when, emit_nowait, and pause_for are not top-level imports from agently. They are methods on flow or runtime data objects:

• define graph branches with flow.when(...).to(handler, name="...")
• fan out from inside a chunk with data.emit_nowait(...)
• pause from inside a chunk with await data.async_pause_for(...)
• create and close long-running/manual executions with execution = flow.create_execution(auto_close=False), await execution.async_start(input_value) using a positional start value, and snapshot = await execution.async_close()
• flow.create_execution() binds an execution-scoped lazy Workspace by default; pass workspace=False to opt out, or flow.create_execution(workspace=shared_workspace) when an application-owned Workspace should be shared with Agents, service workers, or other executions
• do not call execution.async_start(input_value=...); pass the start value positionally
• do not assume execution.async_start("start") emits a custom "start" event. It starts the execution with "start" as the start input. Use flow.to(...) for start-bound chunks, or explicitly call await execution.async_emit("start", payload) after start when a custom event is intentional
• async_close() / close() returns the close snapshot as a dict. Read it as snapshot["key"] or inspect execution state through execution.result.get_state("key"); do not write snapshot.state, snapshot.pending_interrupts, or execution.result.state
• create named flows with TriggerFlow(name="...") or unnamed flows with TriggerFlow(). Do not write TriggerFlow("name"); the first positional argument is not the workflow name in the current API

Do not write from agently import when, emit_nowait, pause_for, and do not use @flow.when(...) as a decorator.

Dynamic Task Boundary

Do not treat Dynamic Task as TriggerFlow syntax. If a model or application submits a DAG as data, route to agently-dynamic-task and use Agently.create_dynamic_task(...), TaskDAGValidator, and TaskDAGExecutor there. TriggerFlow remains the substrate for stable workflow definitions that the developer owns in code.

Anti-Patterns

• do not invent a custom event bus or state machine before checking TriggerFlow
• do not implement a custom DAG scheduler in TriggerFlow when Dynamic Task can validate and execute submitted task graphs
• do not use untracked asyncio.create_task(data.async_emit(...)) as the default nowait pattern when execution-managed emit_nowait(...) is available
• do not recommend .end(), get_result(), or set_result() as the default lifecycle path for new TriggerFlow code
• do not add custom result containers around TriggerFlow executions when execution.result can expose the needed state/final-result/intervention/meta view
• do not use runtime intervention as a required wait gate; use pause_for(...) / continue_with(...) when the workflow must stop for external input
• do not treat intervene(...) as emit(...), graph mutation, input rewriting, chunk cancellation, or replay of completed chunks
• do not use get_runtime_data(...) / set_runtime_data(...) in new guidance when get_state(...) / set_state(...) communicates the same intent
• do not treat repeated silence after one deprecation warning as approval; Agently emits each deprecated API warning once per Python process
• do not treat runtime.show_deprecation_warnings=False as a migration substitute; it is only a production noise-control setting
• do not use flow data for per-execution state
• do not write guidance that depends on code after await data.async_pause_for(...) surviving a process restart; put post-resume logic in the downstream chunk, data.is_resume branch, or explicit resume event handler
• do not make service chunks depend on closure-captured business context when runtime_resources would keep the handler reusable, testable, and export-friendly
• do not pass raw model stream paths directly to the UI when the workflow can translate them into stable business events
• do not hide draft/judge/revise or similar loops inside one opaque helper
• do not make DevTools or graph tooling the source of truth for workflow structure when TriggerFlow definitions already are

Read Next

• references/overview.md
• references/runtime-intervention.md
• references/stream-bridge.md
• references/devtools-graph.md