DevTools

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agently-devtools is an optional companion package. It consumes Agently observation events and provides local observation, evaluation, and interactive UI workflows. It is not the source of truth for workflow structure; TriggerFlow definitions and executions remain the source of truth.

Install and listener

pip install -U agently agently-devtools
agently-devtools start

Default local endpoints from examples/devtools/README.md:

Surface	Default
DevTools console	`http://127.0.0.1:15596/`
Observation ingest	`http://127.0.0.1:15596/observation/ingest`
Interactive wrapper UI	`http://127.0.0.1:15365/`

ObservationBridge

The example path is examples/devtools/01_observation_bridge_local.py. It creates the bridge through Agently’s LazyImport helper, watches the global runtime, runs a TriggerFlow, then unregisters the bridge:

from agently import Agently

bridge = Agently.create_observation_bridge(
    app_id="agently-main-examples",
    group_id="devtools-local-demo",
)
bridge.watch(Agently)

try:
    ...
finally:
    bridge.unregister()

Use Agently.create_observation_bridge(target, ...) or bridge.watch(target) when only selected objects should be uploaded; see 02_observation_bridge_selective_watch.py. bridge.watch(...) accepts the global Agently object, agents, model requests/responses, TriggerFlows, TriggerFlow executions, Dynamic Task / TaskDAG selectors, Skill executions, tool functions, or {"target_type": ..., "target_name": ...} mappings.

For a minimal example that keeps agently_devtools behind Agently’s LazyImport facade, see 07_agently_observe_lazy_bridge.py.

The lower-level DevTools package can also bind at construction time:

from agently import Agently
from agently_devtools import ObservationBridge

bridge = ObservationBridge(Agently)
bridge.watch(agent)

The older bridge = ObservationBridge(...); bridge.register(Agently) form remains compatible and emits a deprecation warning.

ObservationBridge uploads from a background queue and coalesces high-frequency observation events such as model.streaming before sending them to the listener. This keeps passive observation off the request/output path. For short scripts that exit immediately after a run, call await bridge.flush() before process exit if you need all buffered events uploaded.

EvaluationBridge

03_scenario_evaluations.py builds a small TriggerFlow, binds it with EvaluationBinding, and runs EvaluationRunner across multiple EvaluationCase inputs. Use this path for repeatable scenario checks, not for request-time validation inside application code.

InteractiveWrapper

InteractiveWrapper can wrap:

a plain callable or generator: 04_interactive_wrapper_basic.py
an Agently Agent: 05_interactive_wrapper_agent.py
a TriggerFlow that streams stage updates: 06_interactive_wrapper_trigger_flow.py

For TriggerFlow, stream progress through data.async_put_into_stream(...); the wrapper consumes the runtime stream and then shows the close snapshot.

Compatibility boundary

DevTools consumers should fail open:

ignore unknown observation event types and unknown payload fields
prefer run lineage fields over parsing message
treat TriggerFlow graphs as derived from flow definitions and runtime metadata, not as a separate manual graph

For release management, package versions are only part of the story. Unreleased branch work uses a runtime protocol declared by Agently in agently/compatibility.py; DevTools should compare that protocol first, then use package version ranges as upgrade guidance. Historical published manifests live under the Agently source registry at compatibility/releases/<framework_version>.json. For legacy published releases 4.1.0.2 through 4.1.1, DevTools may fall back to the built-in legacy protocol mapping because those builds predate the manifest. Outside that legacy window, missing manifest support should be treated as unverified compatibility.

See Event Center for the observation event schema and alias rules.