feat: structured output support for schema inference and runtime extraction

packages/uipath-agent-framework/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "uipath-agent-framework"
-version = "0.0.7"
+version = "0.0.8"
 description = "Python SDK that enables developers to build and deploy Microsoft Agent Framework agents to the UiPath Cloud Platform"
 readme = "README.md"
 requires-python = ">=3.11"

packages/uipath-agent-framework/samples/README.md

@@ -7,7 +7,11 @@ Sample agents built with [Agent Framework](https://github.com/microsoft/agent-fr
 | Sample | Description |
 |--------|-------------|
 | [quickstart-workflow](./quickstart-workflow/) | Single workflow agent with tool calling: fetches live weather data for any location |
-| [group-chat](./group-chat/) | Group chat orchestration: researcher, critic, and writer discuss a topic with an orchestrator picking speakers |
+| [structured-output](./structured-output/) | Structured output workflow: extracts city information and returns it as a typed Pydantic model |
+| [sequential-structured-output](./sequential-structured-output/) | Sequential pipeline with structured output: researcher and editor agents produce a typed Pydantic city profile |
+| [hitl-workflow](./hitl-workflow/) | Human-in-the-loop workflow: customer support with approval-gated billing and refund operations |
+| [sequential](./sequential/) | Sequential pipeline: writer, reviewer, and editor agents process a task one after another |
 | [concurrent](./concurrent/) | Concurrent orchestration: sentiment, topic extraction, and summarization agents analyze text in parallel |
 | [handoff](./handoff/) | Handoff orchestration: customer support agents transfer control to specialists with explicit routing rules |
-| [hitl-workflow](./hitl-workflow/) | Human-in-the-loop workflow: customer support with approval-gated billing and refund operations |
+| [group-chat](./group-chat/) | Group chat orchestration: researcher, critic, and writer discuss a topic with an orchestrator picking speakers |
+| [magentic](./magentic/) | Magentic-One orchestration: a manager dynamically coordinates researcher and analyst agents based on task progress |

packages/uipath-agent-framework/samples/hitl-workflow/README.md

@@ -57,3 +57,4 @@ uipath run agent '{"messages": [{"contentParts": [{"data": {"inline": "Transfer
 ```
 uipath dev web
 ```
+

packages/uipath-agent-framework/samples/magentic/agent_framework.json

@@ -0,0 +1,5 @@
+{
+  "agents": {
+    "agent": "main.py:agent"
+  }
+}

packages/uipath-agent-framework/samples/sequential-structured-output/README.md

@@ -0,0 +1,46 @@
+# Sequential + Structured Output
+
+A sequential pipeline that combines multi-agent processing with structured output. A researcher gathers facts about a city, then an editor organizes them into a well-defined Pydantic model (`CityInfo`). The final output is a typed JSON object — not free-form text.
+
+## Agent Graph
+
+```mermaid
+flowchart TB
+  __start__(__start__)
+  __end__(__end__)
+  input-conversation(input-conversation)
+  researcher(researcher)
+  editor(editor)
+  end_(end)
+  __start__ --> |input|input-conversation
+  input-conversation --> researcher
+  researcher --> editor
+  editor --> end_
+  end_ --> |output|__end__
+```
+
+Internally, the sequential orchestration chains:
+- **researcher** — gathers key facts about the city (country, population, landmarks, cultural significance)
+- **editor** — organizes the research into a structured `CityInfo` schema with `response_format`
+
+Each agent sees the full conversation history from previous agents. The last agent's `response_format` determines the output schema.
+
+## Prerequisites
+
+Authenticate with UiPath to configure your `.env` file:
+
+```bash
+uipath auth
+```
+
+## Run
+
+```
+uipath run agent '{"messages": [{"contentParts": [{"data": {"inline": "Tell me about Tokyo"}}], "role": "user"}]}'
+```
+
+## Debug
+
+```
+uipath dev web
+```

packages/uipath-agent-framework/samples/sequential-structured-output/agent.mermaid

@@ -0,0 +1,12 @@
+flowchart TB
+  __start__(__start__)
+  __end__(__end__)
+  input-conversation(input-conversation)
+  researcher(researcher)
+  editor(editor)
+  end(end)
+  __start__ --> |input|input-conversation
+  input-conversation --> researcher
+  researcher --> editor
+  editor --> end
+  end --> |output|__end__

packages/uipath-agent-framework/samples/sequential-structured-output/agent_framework.json

@@ -0,0 +1,5 @@
+{
+  "agents": {
+    "agent": "main.py:agent"
+  }
+}

packages/uipath-agent-framework/samples/sequential-structured-output/main.py

@@ -0,0 +1,44 @@
+from agent_framework.orchestrations import SequentialBuilder
+from pydantic import BaseModel
+
+from uipath_agent_framework.chat import UiPathOpenAIChatClient
+
+
+class CityInfo(BaseModel):
+    """Structured output for city information."""
+
+    city: str
+    country: str
+    description: str
+    population_estimate: str
+    famous_for: list[str]
+
+
+client = UiPathOpenAIChatClient(model="gpt-5-mini-2025-08-07")
+
+researcher = client.as_agent(
+    name="researcher",
+    description="Researches factual information about a city.",
+    instructions=(
+        "You are a thorough researcher. Given a city name, gather key facts "
+        "including its country, population, notable landmarks, cultural "
+        "significance, and what it is famous for. Present your findings clearly."
+    ),
+)
+
+editor = client.as_agent(
+    name="editor",
+    description="Edits research into a structured city profile.",
+    instructions=(
+        "You are a precise editor. Take the researcher's findings and organize "
+        "them into a well-structured city profile. Ensure all facts are accurate "
+        "and the description is concise and informative."
+    ),
+    default_options={"response_format": CityInfo},
+)
+
+workflow = SequentialBuilder(
+    participants=[researcher, editor],
+).build()
+
+agent = workflow.as_agent(name="sequential_structured_output_workflow")

packages/uipath-agent-framework/samples/sequential-structured-output/pyproject.toml

@@ -0,0 +1,25 @@
+[project]
+name = "sequential-structured-output"
+version = "0.0.1"
+description = "Sequential + structured output: agents process a task in a pipeline, the last agent returns data in a well-defined Pydantic schema"
+authors = [{ name = "John Doe" }]
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "uipath",
+    "uipath-agent-framework",
+    "agent-framework-core>=1.0.0rc1",
+    "agent-framework-orchestrations>=1.0.0b260219",
+]
+
+[dependency-groups]
+dev = [
+    "uipath-dev",
+]
+
+[tool.uv]
+prerelease = "allow"
+
+[tool.uv.sources]
+uipath-dev = { path = "../../../../../uipath-dev-python", editable = true }
+uipath-agent-framework = { path = "../../", editable = true }

packages/uipath-agent-framework/samples/sequential-structured-output/uipath.json

@@ -0,0 +1,14 @@
+{
+  "$schema": "https://cloud.uipath.com/draft/2024-12/uipath",
+  "runtimeOptions": {
+    "isConversational": false
+  },
+  "packOptions": {
+    "fileExtensionsIncluded": [],
+    "filesIncluded": [],
+    "filesExcluded": [],
+    "directoriesExcluded": [],
+    "includeUvLock": true
+  },
+  "functions": {}
+}

packages/uipath-agent-framework/samples/sequential/agent.mermaid

@@ -0,0 +1,14 @@
+flowchart TB
+  __start__(__start__)
+  __end__(__end__)
+  input-conversation(input-conversation)
+  writer(writer)
+  reviewer(reviewer)
+  editor(editor)
+  end(end)
+  __start__ --> |input|input-conversation
+  input-conversation --> writer
+  writer --> reviewer
+  reviewer --> editor
+  editor --> end
+  end --> |output|__end__

packages/uipath-agent-framework/samples/sequential/agent_framework.json

@@ -0,0 +1,5 @@
+{
+  "agents": {
+    "agent": "main.py:agent"
+  }
+}

packages/uipath-agent-framework/samples/sequential/uipath.json

@@ -0,0 +1,14 @@
+{
+  "$schema": "https://cloud.uipath.com/draft/2024-12/uipath",
+  "runtimeOptions": {
+    "isConversational": false
+  },
+  "packOptions": {
+    "fileExtensionsIncluded": [],
+    "filesIncluded": [],
+    "filesExcluded": [],
+    "directoriesExcluded": [],
+    "includeUvLock": true
+  },
+  "functions": {}
+}

packages/uipath-agent-framework/samples/structured-output/agent.mermaid

@@ -0,0 +1,6 @@
+flowchart TB
+  __start__(__start__)
+  __end__(__end__)
+  city_agent(city_agent)
+  __start__ --> |input|city_agent
+  city_agent --> |output|__end__

packages/uipath-agent-framework/samples/structured-output/agent_framework.json

@@ -0,0 +1,5 @@
+{
+  "agents": {
+    "agent": "main.py:agent"
+  }
+}

packages/uipath-agent-framework/samples/structured-output/main.py

@@ -22,7 +22,7 @@ class CityInfo(BaseModel):
         "Given a city name, provide the city name, country, a brief description, "
         "an estimated population, and a list of things the city is famous for."
     ),
-    response_format=CityInfo,
+    default_options={"response_format": CityInfo},
 )
 
 workflow = WorkflowBuilder(start_executor=city_agent).build()

packages/uipath-agent-framework/samples/structured-output/uipath.json

@@ -0,0 +1,14 @@
+{
+  "$schema": "https://cloud.uipath.com/draft/2024-12/uipath",
+  "runtimeOptions": {
+    "isConversational": false
+  },
+  "packOptions": {
+    "fileExtensionsIncluded": [],
+    "filesIncluded": [],
+    "filesExcluded": [],
+    "directoriesExcluded": [],
+    "includeUvLock": true
+  },
+  "functions": {}
+}