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* Sequential Chat Tutorial (#3861) * Include doc in toctree * Rename title; doc improv * reorder * grammar --------- Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
This commit is contained in:
Gerardo Moreno
@@ -21,6 +21,7 @@ group chat for task decomposition, and reflection for robustness.
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:maxdepth: 1
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concurrent-agents
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sequential-workflow
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group-chat
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handoffs
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mixture-of-agents
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@@ -0,0 +1,391 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Sequential Workflow\n",
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"\n",
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"Sequential Workflow is a multi-agent design pattern where agents respond in a deterministic sequence. Each agent in the workflow performs a specific task by processing a message, generating a response, and then passing it to the next agent. This pattern is useful for creating deterministic workflows where each agent contributes to a pre-specified sub-task."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this example, we demonstrate a sequential workflow where multiple agents collaborate to transform a basic product description into a polished marketing copy.\n",
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"\n",
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"The pipeline consists of four specialized agents:\n",
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"- **Concept Extractor Agent**: Analyzes the initial product description to extract key features, target audience, and unique selling points (USPs). The output is a structured analysis in a single text block.\n",
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"- **Writer Agent**: Crafts compelling marketing copy based on the extracted concepts. This agent transforms the analytical insights into engaging promotional content, delivering a cohesive narrative in a single text block.\n",
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"- **Format & Proof Agent**: Polishes the draft copy by refining grammar, enhancing clarity, and maintaining consistent tone. This agent ensures professional quality and delivers a well-formatted final version.\n",
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"- **User Agent**: Presents the final, refined marketing copy to the user, completing the workflow."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The following diagram illustrates the sequential workflow in this example:\n",
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"\n",
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"\n",
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"\n",
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"We will implement this workflow using publish-subscribe messaging.\n",
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"Please read about [Topic and Subscription](../core-concepts/topic-and-subscription.md) for the core concepts\n",
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"and [Broadcast Messaging](../framework/message-and-communication.ipynb#broadcast) for the the API usage.\n",
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"\n",
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"In this pipeline, agents communicate with each other by publishing their completed work as messages to the topic of the \n",
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"next agent in the sequence. For example, when the `ConceptExtractor` finishes analyzing the product description, it \n",
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"publishes its findings to the `\"WriterAgent\"` topic, which the `WriterAgent` is subscribed to. This pattern continues through \n",
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"each step of the pipeline, with each agent publishing to the topic that the next agent in line subscribed to."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"from dataclasses import dataclass\n",
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"\n",
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"from autogen_core import (\n",
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" MessageContext,\n",
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" RoutedAgent,\n",
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" SingleThreadedAgentRuntime,\n",
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" TopicId,\n",
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" TypeSubscription,\n",
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" message_handler,\n",
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" type_subscription,\n",
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")\n",
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"from autogen_core.components.models import ChatCompletionClient, SystemMessage, UserMessage\n",
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"from autogen_ext.models import OpenAIChatCompletionClient"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Message Protocol\n",
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"\n",
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"The message protocol for this example workflow is a simple text message that agents will use to relay their work."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"@dataclass\n",
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"class Message:\n",
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" content: str"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Topics\n",
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"\n",
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"Each agent in the workflow will be subscribed to a specific topic type. The topic types are named after the agents in the sequence,\n",
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"This allows each agent to publish its work to the next agent in the sequence."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"concept_extractor_topic_type = \"ConceptExtractorAgent\"\n",
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"writer_topic_type = \"WriterAgent\"\n",
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"format_proof_topic_type = \"FormatProofAgent\"\n",
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"user_topic_type = \"User\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Agents\n",
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"\n",
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"Each agent class is defined with a {py:class}`~autogen_core.type_subscription` decorator to specify the topic type it is subscribed to.\n",
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"Alternative to the decorator, you can also use the {py:meth}`~autogen_core.AgentRuntime.add_subscription` method to subscribe to a topic through runtime directly.\n",
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"\n",
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"The concept extractor agent comes up with the initial bullet points for the product description."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"@type_subscription(topic_type=concept_extractor_topic_type)\n",
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"class ConceptExtractorAgent(RoutedAgent):\n",
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" def __init__(self, model_client: ChatCompletionClient) -> None:\n",
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" super().__init__(\"A concept extractor agent.\")\n",
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" self._system_message = SystemMessage(\n",
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" content=(\n",
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" \"You are a marketing analyst. Given a product description, identify:\\n\"\n",
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" \"- Key features\\n\"\n",
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" \"- Target audience\\n\"\n",
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" \"- Unique selling points\\n\\n\"\n",
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" )\n",
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" )\n",
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" self._model_client = model_client\n",
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"\n",
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" @message_handler\n",
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" async def handle_user_description(self, message: Message, ctx: MessageContext) -> None:\n",
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" prompt = f\"Product description: {message.content}\"\n",
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" llm_result = await self._model_client.create(\n",
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" messages=[self._system_message, UserMessage(content=prompt, source=self.id.key)],\n",
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" cancellation_token=ctx.cancellation_token,\n",
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" )\n",
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" response = llm_result.content\n",
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" assert isinstance(response, str)\n",
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" print(f\"{'-'*80}\\n{self.id.type}:\\n{response}\")\n",
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"\n",
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" await self.publish_message(Message(response), topic_id=TopicId(writer_topic_type, source=self.id.key))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The writer agent performs writing."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"@type_subscription(topic_type=writer_topic_type)\n",
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"class WriterAgent(RoutedAgent):\n",
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" def __init__(self, model_client: ChatCompletionClient) -> None:\n",
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" super().__init__(\"A writer agent.\")\n",
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" self._system_message = SystemMessage(\n",
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" content=(\n",
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" \"You are a marketing copywriter. Given a block of text describing features, audience, and USPs, \"\n",
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" \"compose a compelling marketing copy (like a newsletter section) that highlights these points. \"\n",
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" \"Output should be short (around 150 words), output just the copy as a single text block.\"\n",
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" )\n",
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" )\n",
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" self._model_client = model_client\n",
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"\n",
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" @message_handler\n",
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" async def handle_intermediate_text(self, message: Message, ctx: MessageContext) -> None:\n",
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" prompt = f\"Below is the info about the product:\\n\\n{message.content}\"\n",
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"\n",
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" llm_result = await self._model_client.create(\n",
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" messages=[self._system_message, UserMessage(content=prompt, source=self.id.key)],\n",
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" cancellation_token=ctx.cancellation_token,\n",
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" )\n",
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" response = llm_result.content\n",
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" assert isinstance(response, str)\n",
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" print(f\"{'-'*80}\\n{self.id.type}:\\n{response}\")\n",
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"\n",
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" await self.publish_message(Message(response), topic_id=TopicId(format_proof_topic_type, source=self.id.key))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The format proof agent performs the formatting."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"@type_subscription(topic_type=format_proof_topic_type)\n",
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"class FormatProofAgent(RoutedAgent):\n",
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" def __init__(self, model_client: ChatCompletionClient) -> None:\n",
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" super().__init__(\"A format & proof agent.\")\n",
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" self._system_message = SystemMessage(\n",
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" content=(\n",
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" \"You are an editor. Given the draft copy, correct grammar, improve clarity, ensure consistent tone, \"\n",
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" \"give format and make it polished. Output the final improved copy as a single text block.\"\n",
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" )\n",
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" )\n",
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" self._model_client = model_client\n",
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"\n",
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" @message_handler\n",
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" async def handle_intermediate_text(self, message: Message, ctx: MessageContext) -> None:\n",
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" prompt = f\"Draft copy:\\n{message.content}.\"\n",
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" llm_result = await self._model_client.create(\n",
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" messages=[self._system_message, UserMessage(content=prompt, source=self.id.key)],\n",
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" cancellation_token=ctx.cancellation_token,\n",
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" )\n",
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" response = llm_result.content\n",
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" assert isinstance(response, str)\n",
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" print(f\"{'-'*80}\\n{self.id.type}:\\n{response}\")\n",
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"\n",
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" await self.publish_message(Message(response), topic_id=TopicId(user_topic_type, source=self.id.key))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this example, the user agent simply prints the final marketing copy to the console.\n",
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"In a real-world application, this could be replaced by storing the result to a database, sending an email, or any other desired action."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"@type_subscription(topic_type=user_topic_type)\n",
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"class UserAgent(RoutedAgent):\n",
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" def __init__(self) -> None:\n",
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" super().__init__(\"A user agent that outputs the final copy to the user.\")\n",
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"\n",
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" @message_handler\n",
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" async def handle_final_copy(self, message: Message, ctx: MessageContext) -> None:\n",
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" print(f\"\\n{'-'*80}\\n{self.id.type} received final copy:\\n{message.content}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Workflow\n",
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"\n",
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"Now we can register the agents to the runtime.\n",
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"Because we used the {py:class}`~autogen_core.type_subscription` decorator, the runtime will automatically subscribe the agents to the correct topics."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model_client = OpenAIChatCompletionClient(\n",
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" model=\"gpt-4o-mini\",\n",
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" # api_key=\"YOUR_API_KEY\"\n",
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")\n",
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"\n",
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"runtime = SingleThreadedAgentRuntime()\n",
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"\n",
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"await ConceptExtractorAgent.register(\n",
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" runtime, type=concept_extractor_topic_type, factory=lambda: ConceptExtractorAgent(model_client=model_client)\n",
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")\n",
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"\n",
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"await WriterAgent.register(runtime, type=writer_topic_type, factory=lambda: WriterAgent(model_client=model_client))\n",
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"\n",
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"await FormatProofAgent.register(\n",
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" runtime, type=format_proof_topic_type, factory=lambda: FormatProofAgent(model_client=model_client)\n",
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")\n",
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"\n",
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"await UserAgent.register(runtime, type=user_topic_type, factory=lambda: UserAgent())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Run the Workflow\n",
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"\n",
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"Finally, we can run the workflow by publishing a message to the first agent in the sequence."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------------------------------------\n",
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"ConceptExtractorAgent:\n",
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"**Key Features:**\n",
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"- Made from eco-friendly stainless steel\n",
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"- Can keep drinks cold for up to 24 hours\n",
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"- Durable and reusable design\n",
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"- Lightweight and portable\n",
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"- BPA-free and non-toxic materials\n",
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"- Sleek, modern aesthetic available in various colors\n",
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"\n",
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"**Target Audience:**\n",
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"- Environmentally conscious consumers\n",
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"- Health and fitness enthusiasts\n",
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"- Outdoor adventurers (hikers, campers, etc.)\n",
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"- Urban dwellers looking for sustainable alternatives\n",
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"- Individuals seeking stylish and functional drinkware\n",
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"\n",
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"**Unique Selling Points:**\n",
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"- Eco-friendly design minimizes plastic waste and supports sustainability\n",
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"- Superior insulation technology that maintains cold temperatures for a full day\n",
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"- Durable construction ensures long-lasting use, offering a great return on investment\n",
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"- Attractive design that caters to fashion-forward individuals \n",
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"- Versatile use for both everyday hydration and outdoor activities\n",
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"--------------------------------------------------------------------------------\n",
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"WriterAgent:\n",
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"🌍🌿 Stay Hydrated, Stay Sustainable! 🌿🌍 \n",
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"\n",
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"Introducing our eco-friendly stainless steel drinkware, the perfect companion for the environmentally conscious and style-savvy individuals. With superior insulation technology, our bottles keep your beverages cold for an impressive 24 hours—ideal for hiking, camping, or just tackling a busy day in the city. Made from lightweight, BPA-free materials, this durable and reusable design not only helps reduce plastic waste but also ensures you’re making a responsible choice for our planet.\n",
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"\n",
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"Available in a sleek, modern aesthetic with various colors to match your personality, this drinkware isn't just functional—it’s fashionable! Whether you’re hitting the trails or navigating urban life, equip yourself with a stylish hydration solution that supports your active and sustainable lifestyle. Join the movement today and make a positive impact without compromising on style! 🌟🥤\n",
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"--------------------------------------------------------------------------------\n",
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"FormatProofAgent:\n",
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"🌍🌿 Stay Hydrated, Stay Sustainable! 🌿🌍 \n",
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"\n",
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"Introducing our eco-friendly stainless steel drinkware—the perfect companion for environmentally conscious and style-savvy individuals. With superior insulation technology, our bottles keep your beverages cold for an impressive 24 hours, making them ideal for hiking, camping, or simply tackling a busy day in the city. Crafted from lightweight, BPA-free materials, this durable and reusable design not only helps reduce plastic waste but also ensures that you’re making a responsible choice for our planet.\n",
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"\n",
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"Our drinkware features a sleek, modern aesthetic available in a variety of colors to suit your personality. It’s not just functional; it’s also fashionable! Whether you’re exploring the trails or navigating urban life, equip yourself with a stylish hydration solution that supports your active and sustainable lifestyle. Join the movement today and make a positive impact without compromising on style! 🌟🥤\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"User received final copy:\n",
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"🌍🌿 Stay Hydrated, Stay Sustainable! 🌿🌍 \n",
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"\n",
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"Introducing our eco-friendly stainless steel drinkware—the perfect companion for environmentally conscious and style-savvy individuals. With superior insulation technology, our bottles keep your beverages cold for an impressive 24 hours, making them ideal for hiking, camping, or simply tackling a busy day in the city. Crafted from lightweight, BPA-free materials, this durable and reusable design not only helps reduce plastic waste but also ensures that you’re making a responsible choice for our planet.\n",
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"\n",
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"Our drinkware features a sleek, modern aesthetic available in a variety of colors to suit your personality. It’s not just functional; it’s also fashionable! Whether you’re exploring the trails or navigating urban life, equip yourself with a stylish hydration solution that supports your active and sustainable lifestyle. Join the movement today and make a positive impact without compromising on style! 🌟🥤\n"
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]
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}
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],
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"source": [
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"runtime.start()\n",
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"\n",
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"await runtime.publish_message(\n",
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" Message(content=\"An eco-friendly stainless steel water bottle that keeps drinks cold for 24 hours\"),\n",
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" topic_id=TopicId(concept_extractor_topic_type, source=\"default\"),\n",
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")\n",
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"\n",
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"await runtime.stop_when_idle()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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