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Python: Default DevUI workflows to string input when start node is auto-wrapped agent (#1143)

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* DevUI workflows default to string input when start node is AgentExecutor

* Remove unnecessary sample

* Fix lint issue
This commit is contained in:
Evan Mattson
2025-10-03 17:11:38 +09:00
committed by GitHub
Unverified
parent 5a208ab1e1
commit c543dbaa92
5 changed files with 230 additions and 20 deletions
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python/packages/devui/agent_framework_devui/_executor.py
+61 -9
View File
@@ -7,7 +7,7 @@ import logging
import os
import uuid
from collections.abc import AsyncGenerator
from typing import Any
from typing import Any, get_origin
from agent_framework import AgentThread
@@ -638,6 +638,54 @@ class AgentFrameworkExecutor:
logger.warning(f"Error parsing workflow input: {e}")
return raw_input
def _get_start_executor_message_types(self, workflow: Any) -> tuple[Any | None, list[Any]]:
"""Return start executor and its declared input types."""
try:
start_executor = workflow.get_start_executor()
except Exception as exc: # pragma: no cover - defensive logging path
logger.debug(f"Unable to access workflow start executor: {exc}")
return None, []
if not start_executor:
return None, []
message_types: list[Any] = []
try:
input_types = getattr(start_executor, "input_types", None)
except Exception as exc: # pragma: no cover - defensive logging path
logger.debug(f"Failed to read executor input_types: {exc}")
else:
if input_types:
message_types = list(input_types)
if not message_types and hasattr(start_executor, "_handlers"):
try:
handlers = start_executor._handlers
if isinstance(handlers, dict):
message_types = list(handlers.keys())
except Exception as exc: # pragma: no cover - defensive logging path
logger.debug(f"Failed to read executor handlers: {exc}")
return start_executor, message_types
def _select_primary_input_type(self, message_types: list[Any]) -> Any | None:
"""Choose the most user-friendly input type for workflow kick-off."""
if not message_types:
return None
preferred = (str, dict)
for candidate in preferred:
for message_type in message_types:
if message_type is candidate:
return candidate
origin = get_origin(message_type)
if origin is candidate:
return candidate
return message_types[0]
def _parse_structured_workflow_input(self, workflow: Any, input_data: dict[str, Any]) -> Any:
"""Parse structured input data for workflow execution.
@@ -650,18 +698,20 @@ class AgentFrameworkExecutor:
"""
try:
# Get the start executor and its input type
start_executor = workflow.get_start_executor()
if not start_executor or not hasattr(start_executor, "_handlers"):
start_executor, message_types = self._get_start_executor_message_types(workflow)
if not start_executor:
logger.debug("Cannot determine input type for workflow - using raw dict")
return input_data
message_types = list(start_executor._handlers.keys())
if not message_types:
logger.debug("No message types found for start executor - using raw dict")
return input_data
# Get the first (primary) input type
input_type = message_types[0]
input_type = self._select_primary_input_type(message_types)
if input_type is None:
logger.debug("Could not select primary input type for workflow - using raw dict")
return input_data
# If input type is dict, return as-is
if input_type is dict:
@@ -715,18 +765,20 @@ class AgentFrameworkExecutor:
"""
try:
# Get the start executor and its input type
start_executor = workflow.get_start_executor()
if not start_executor or not hasattr(start_executor, "_handlers"):
start_executor, message_types = self._get_start_executor_message_types(workflow)
if not start_executor:
logger.debug("Cannot determine input type for workflow - using raw string")
return raw_input
message_types = list(start_executor._handlers.keys())
if not message_types:
logger.debug("No message types found for start executor - using raw string")
return raw_input
# Get the first (primary) input type
input_type = message_types[0]
input_type = self._select_primary_input_type(message_types)
if input_type is None:
logger.debug("Could not select primary input type for workflow - using raw string")
return raw_input
# If input type is str, return as-is
if input_type is str:
python/packages/devui/agent_framework_devui/_server.py
+64 -10
View File
@@ -7,7 +7,7 @@ import json
import logging
from collections.abc import AsyncGenerator
from contextlib import asynccontextmanager
from typing import Any
from typing import Any, get_origin
from fastapi import FastAPI, HTTPException, Request
from fastapi.middleware.cors import CORSMiddleware
@@ -23,6 +23,47 @@ from .models._discovery_models import DiscoveryResponse, EntityInfo
logger = logging.getLogger(__name__)
def _extract_executor_message_types(executor: Any) -> list[Any]:
"""Return declared input types for the given executor."""
message_types: list[Any] = []
try:
input_types = getattr(executor, "input_types", None)
except Exception as exc: # pragma: no cover - defensive logging path
logger.debug(f"Failed to access executor input_types: {exc}")
else:
if input_types:
message_types = list(input_types)
if not message_types and hasattr(executor, "_handlers"):
try:
handlers = executor._handlers
if isinstance(handlers, dict):
message_types = list(handlers.keys())
except Exception as exc: # pragma: no cover - defensive logging path
logger.debug(f"Failed to read executor handlers: {exc}")
return message_types
def _select_primary_input_type(message_types: list[Any]) -> Any | None:
"""Choose the most user-friendly input type for rendering workflow inputs."""
if not message_types:
return None
preferred = (str, dict)
for candidate in preferred:
for message_type in message_types:
if message_type is candidate:
return candidate
origin = get_origin(message_type)
if origin is candidate:
return candidate
return message_types[0]
class DevServer:
"""Development Server - OpenAI compatible API server for debugging agents."""
@@ -223,23 +264,36 @@ class DevServer:
try:
start_executor = entity_obj.get_start_executor()
if start_executor and hasattr(start_executor, "_handlers"):
message_types = list(start_executor._handlers.keys())
if message_types:
input_type = message_types[0]
except Exception as e:
logger.debug(f"Could not extract input info for workflow {entity_id}: {e}")
else:
if start_executor:
start_executor_id = getattr(start_executor, "executor_id", "") or getattr(
start_executor, "id", ""
)
message_types = _extract_executor_message_types(start_executor)
input_type = _select_primary_input_type(message_types)
if input_type:
input_type_name = getattr(input_type, "__name__", str(input_type))
# Basic schema generation for common types
if input_type is str:
input_schema = {"type": "string"}
elif input_type is dict:
input_schema = {"type": "object"}
elif hasattr(input_type, "model_json_schema"):
input_schema = input_type.model_json_schema()
try:
input_schema = input_type.model_json_schema()
except Exception as exc: # pragma: no cover - defensive path
logger.debug(f"model_json_schema() failed for workflow {entity_id}: {exc}")
elif hasattr(input_type, "__annotations__"):
input_schema = {"type": "object"}
start_executor_id = getattr(start_executor, "executor_id", "")
except Exception as e:
logger.debug(f"Could not extract input info for workflow {entity_id}: {e}")
if not input_schema:
input_schema = {"type": "string"}
if input_type_name == "Unknown":
input_type_name = "string"
# Get executor list
executor_list = []