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Merge pull request #185 from UiPath/feat/agent-framework-orchestration-samples
feat: multi-agent graph, streaming refactor, and orchestration samples
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‎packages/uipath-agent-framework/pyproject.toml‎

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[project]
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name = "uipath-agent-framework"
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version = "0.0.2"
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version = "0.0.3"
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description = "Python SDK that enables developers to build and deploy Microsoft Agent Framework agents to the UiPath Cloud Platform"
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readme = "README.md"
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requires-python = ">=3.11"

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

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|--------|-------------|
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| [quickstart-agent](./quickstart-agent/) | Single agent with tool calling: fetches live weather data for any location |
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| [multi-agent](./multi-agent/) | Multi-agent coordinator: delegates research and code execution to specialist sub-agents via `as_tool()` |
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| [group-chat](./group-chat/) | Group chat orchestration: researcher, critic, and writer discuss a topic with an orchestrator picking speakers |
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| [concurrent](./concurrent/) | Concurrent orchestration: sentiment, topic extraction, and summarization agents analyze text in parallel |
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| [handoff](./handoff/) | Handoff orchestration: customer support agents transfer control to specialists with explicit routing rules |

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

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# Concurrent
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Three specialist agents analyze the same input text in parallel: sentiment analysis, topic extraction, and summarization. Results are aggregated automatically.
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## Agent Graph
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```mermaid
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flowchart TB
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__start__(__start__)
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concurrent_analysis(concurrent_analysis)
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__end__(__end__)
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__start__ --> |input|concurrent_analysis
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concurrent_analysis --> |output|__end__
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```
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Internally, the concurrent orchestration fans out to:
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- **sentiment** — analyzes positive/negative/neutral sentiment
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- **topic_extractor** — identifies topics, entities, and themes
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- **summarizer** — writes a concise summary
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All three run simultaneously, results are collected and returned together.
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## Run
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```
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uipath run agent '{"messages": [{"contentParts": [{"data": {"inline": "Apple announced a new Vision Pro headset today, priced at $3,499. The mixed-reality device features a custom M4 chip and will be available in 10 countries. Analysts are divided on whether the high price point will limit adoption."}}], "role": "user"}]}'
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```
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## Debug
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```
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uipath dev web
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```

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

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flowchart TB
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__start__(__start__)
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__end__(__end__)
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dispatcher(dispatcher)
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sentiment(sentiment)
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topic_extractor(topic_extractor)
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summarizer(summarizer)
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aggregator(aggregator)
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__start__ --> |input|dispatcher
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dispatcher --> sentiment
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dispatcher --> topic_extractor
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dispatcher --> summarizer
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sentiment --> aggregator
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topic_extractor --> aggregator
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summarizer --> aggregator
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aggregator --> |output|__end__

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

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{
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"agents": {
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"agent": "main.py:agent"
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}
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}

‎packages/uipath-agent-framework/samples/concurrent/main.py‎

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from agent_framework.orchestrations import ConcurrentBuilder
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from uipath_agent_framework.chat import UiPathOpenAIChatClient
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client = UiPathOpenAIChatClient(model="gpt-5-mini-2025-08-07")
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sentiment_agent = client.as_agent(
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name="sentiment",
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description="Analyzes text sentiment.",
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instructions=(
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"You are a sentiment analyst. Analyze the sentiment of the given text. "
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"Return a short assessment: positive/negative/neutral with a confidence "
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"percentage and a one-sentence explanation."
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),
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)
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topic_agent = client.as_agent(
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name="topic_extractor",
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description="Extracts main topics and entities from text.",
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instructions=(
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"You are a topic extraction specialist. Identify the main topics, "
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"entities (people, organizations, places), and key themes in the "
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"given text. Return a structured list."
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),
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)
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summary_agent = client.as_agent(
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name="summarizer",
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description="Writes concise summaries.",
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instructions=(
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"You are a summarization expert. Write a concise one-paragraph "
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"summary of the given text, capturing the key points."
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),
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)
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workflow = ConcurrentBuilder(
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participants=[sentiment_agent, topic_agent, summary_agent],
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).build()
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agent = workflow.as_agent(name="concurrent_analysis")

‎packages/uipath-agent-framework/samples/concurrent/pyproject.toml‎

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[project]
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name = "concurrent"
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version = "0.0.1"
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description = "Concurrent: multiple agents analyze the same input in parallel"
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authors = [{ name = "John Doe" }]
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readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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"uipath",
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"uipath-agent-framework",
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"agent-framework-core>=1.0.0b260212",
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"agent-framework-orchestrations>=1.0.0b260212",
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]
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[dependency-groups]
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dev = [
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"uipath-dev",
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]
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[tool.uv]
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prerelease = "allow"
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[tool.uv.sources]
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uipath-dev = { path = "../../../../../uipath-dev-python", editable = true }
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uipath-agent-framework = { path = "../../", editable = true }

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

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

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

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# Group Chat
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A group discussion where multiple agents (researcher, critic, writer) take turns in a shared conversation. An orchestrator agent selects who speaks next based on the discussion flow.
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## Agent Graph
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```mermaid
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flowchart TB
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__start__(__start__)
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group_chat(group_chat)
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__end__(__end__)
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__start__ --> |input|group_chat
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group_chat --> |output|__end__
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```
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Internally, the group chat orchestration manages:
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- **researcher** — finds facts via Wikipedia
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- **critic** — challenges claims and asks probing questions
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- **writer** — synthesizes the discussion into clear prose
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- **orchestrator** — picks the next speaker each round
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## Run
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```
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uipath run agent '{"messages": [{"contentParts": [{"data": {"inline": "What are the environmental impacts of lithium mining?"}}], "role": "user"}]}'
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```
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## Debug
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```
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uipath dev web
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```

‎packages/uipath-agent-framework/samples/group-chat/agent_framework.json‎

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{
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"agents": {
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"agent": "main.py:agent"
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}
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}

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