并行智能体¶
ADK 中支持Python v0.1.0Typescript v0.2.0Go v0.1.0Java v0.2.0
The ParallelAgent is a workflow agent that executes its sub-agents concurrently. This dramatically speeds up workflows where tasks can be performed independently.
在以下情况下使用 ParallelAgent: 对于优先考虑速度并涉及独立、资源密集型任务的场景,ParallelAgent 有助于高效的并行执行。当子智能体在没有依赖关系的情况下运行时,它们的任务可以并发执行,从而显着减少总处理时间。
与其他 工作流智能体 一样,ParallelAgent 不由 LLM 驱动,因此在执行方式上是确定性的。也就是说,工作流智能体只关心其执行 (即并行执行子智能体),而不关心其内部逻辑;工作流智能体的工具或子智能体可能利用也可能不利用 LLM。
示例¶
This approach is particularly beneficial for operations like multi-source data retrieval or heavy computations, where parallelization yields substantial performance gains. Importantly, this strategy assumes no inherent need for shared state or direct information exchange between the concurrently executing agents.
工作原理¶
When the ParallelAgent's run_async() method is called:
- 并发执行: 它并发启动
sub_agents列表中每个子智能体的run_async()方法。这意味着所有智能体同时 (大约) 开始运行。 - 独立分支: 每个子智能体在自己的执行分支中运行。执行期间,这些分支之间没有自动共享对话历史或状态。
- 结果收集:
ParallelAgent管理并行执行,并且通常提供一种在子智能体完成后访问每个子智能体结果的方法 (例如,通过结果列表或事件)。结果的顺序可能是不确定的。
独立执行和状态管理¶
至关重要的是要理解 ParallelAgent 中的子智能体独立运行。如果您需要这些智能体之间的通信或数据共享,您必须显式实现它。可能的方法包括:
- 共享
InvocationContext: 您可以将共享的InvocationContext对象传递给每个子智能体。此对象可以充当共享数据存储。但是,您需要小心管理对此共享上下文的并发访问 (例如,使用锁) 以避免竞争条件。 - 外部状态管理: 使用外部数据库、消息队列或其他机制来管理共享状态并促进智能体之间的通信。
- 后处理: 收集每个分支的结果,然后实现逻辑以在之后协调数据。
完整示例:并行 Web 研究¶
Imagine researching multiple topics simultaneously:
- Researcher Agent 1: 研究"可再生能源"的
LlmAgent。 - Researcher Agent 2: 研究"电动汽车技术"的
LlmAgent。 -
Researcher Agent 3: 研究"碳捕获方法"的
LlmAgent。
These research tasks are independent. Using a ParallelAgent allows them to run concurrently, potentially reducing the total research time significantly compared to running them sequentially. The results from each agent would be collected separately after they finish.
完整代码
# Part of agent.py --> Follow https://google.github.io/adk-docs/get-started/quickstart/ to learn the setup
# --- 1. Define Researcher Sub-Agents (to run in parallel) ---
# Researcher 1: Renewable Energy
researcher_agent_1 = LlmAgent(
name="RenewableEnergyResearcher",
model=GEMINI_MODEL,
instruction="""You are an AI Research Assistant specializing in energy.
Research the latest advancements in 'renewable energy sources'.
Use the Google Search tool provided.
Summarize your key findings concisely (1-2 sentences).
Output *only* the summary.
""",
description="Researches renewable energy sources.",
tools=[google_search],
# Store result in state for the merger agent
output_key="renewable_energy_result"
)
# Researcher 2: Electric Vehicles
researcher_agent_2 = LlmAgent(
name="EVResearcher",
model=GEMINI_MODEL,
instruction="""You are an AI Research Assistant specializing in transportation.
Research the latest developments in 'electric vehicle technology'.
Use the Google Search tool provided.
Summarize your key findings concisely (1-2 sentences).
Output *only* the summary.
""",
description="Researches electric vehicle technology.",
tools=[google_search],
# Store result in state for the merger agent
output_key="ev_technology_result"
)
# Researcher 3: Carbon Capture
researcher_agent_3 = LlmAgent(
name="CarbonCaptureResearcher",
model=GEMINI_MODEL,
instruction="""You are an AI Research Assistant specializing in climate solutions.
Research the current state of 'carbon capture methods'.
Use the Google Search tool provided.
Summarize your key findings concisely (1-2 sentences).
Output *only* the summary.
""",
description="Researches carbon capture methods.",
tools=[google_search],
# Store result in state for the merger agent
output_key="carbon_capture_result"
)
# --- 2. Create the ParallelAgent (Runs researchers concurrently) ---
# This agent orchestrates the concurrent execution of the researchers.
# It finishes once all researchers have completed and stored their results in state.
parallel_research_agent = ParallelAgent(
name="ParallelWebResearchAgent",
sub_agents=[researcher_agent_1, researcher_agent_2, researcher_agent_3],
description="Runs multiple research agents in parallel to gather information."
)
# --- 3. Define the Merger Agent (Runs *after* the parallel agents) ---
# This agent takes the results stored in the session state by the parallel agents
# and synthesizes them into a single, structured response with attributions.
merger_agent = LlmAgent(
name="SynthesisAgent",
model=GEMINI_MODEL, # Or potentially a more powerful model if needed for synthesis
instruction="""You are an AI Assistant responsible for combining research findings into a structured report.
Your primary task is to synthesize the following research summaries, clearly attributing findings to their source areas. Structure your response using headings for each topic. Ensure the report is coherent and integrates the key points smoothly.
**Crucially: Your entire response MUST be grounded *exclusively* on the information provided in the 'Input Summaries' below. Do NOT add any external knowledge, facts, or details not present in these specific summaries.**
**Input Summaries:**
* **Renewable Energy:**
{renewable_energy_result}
* **Electric Vehicles:**
{ev_technology_result}
* **Carbon Capture:**
{carbon_capture_result}
**Output Format:**
## Summary of Recent Sustainable Technology Advancements
### Renewable Energy Findings
(Based on RenewableEnergyResearcher's findings)
[Synthesize and elaborate *only* on the renewable energy input summary provided above.]
### Electric Vehicle Findings
(Based on EVResearcher's findings)
[Synthesize and elaborate *only* on the EV input summary provided above.]
### Carbon Capture Findings
(Based on CarbonCaptureResearcher's findings)
[Synthesize and elaborate *only* on the carbon capture input summary provided above.]
### Overall Conclusion
[Provide a brief (1-2 sentence) concluding statement that connects *only* the findings presented above.]
Output *only* the structured report following this format. Do not include introductory or concluding phrases outside this structure, and strictly adhere to using only the provided input summary content.
""",
description="Combines research findings from parallel agents into a structured, cited report, strictly grounded on provided inputs.",
# No tools needed for merging
# No output_key needed here, as its direct response is the final output of the sequence
)
# --- 4. Create the SequentialAgent (Orchestrates the overall flow) ---
# This is the main agent that will be run. It first executes the ParallelAgent
# to populate the state, and then executes the MergerAgent to produce the final output.
sequential_pipeline_agent = SequentialAgent(
name="ResearchAndSynthesisPipeline",
# Run parallel research first, then merge
sub_agents=[parallel_research_agent, merger_agent],
description="Coordinates parallel research and synthesizes the results."
)
root_agent = sequential_pipeline_agent
// Part of agent.ts --> Follow https://google.github.io/adk-docs/get-started/quickstart/ to learn the setup
// --- 1. Define Researcher Sub-Agents (to run in parallel) ---
const researchTools = [GOOGLE_SEARCH];
// Researcher 1: Renewable Energy
const researcherAgent1 = new LlmAgent({
name: "RenewableEnergyResearcher",
model: GEMINI_MODEL,
instruction: `You are an AI Research Assistant specializing in energy.
Research the latest advancements in 'renewable energy sources'.
Use the Google Search tool provided.
Summarize your key findings concisely (1-2 sentences).
Output *only* the summary.
`,
description: "Researches renewable energy sources.",
tools: researchTools,
// Store result in state for the merger agent
outputKey: "renewable_energy_result"
});
// Researcher 2: Electric Vehicles
const researcherAgent2 = new LlmAgent({
name: "EVResearcher",
model: GEMINI_MODEL,
instruction: `You are an AI Research Assistant specializing in transportation.
Research the latest developments in 'electric vehicle technology'.
Use the Google Search tool provided.
Summarize your key findings concisely (1-2 sentences).
Output *only* the summary.
`,
description: "Researches electric vehicle technology.",
tools: researchTools,
// Store result in state for the merger agent
outputKey: "ev_technology_result"
});
// Researcher 3: Carbon Capture
const researcherAgent3 = new LlmAgent({
name: "CarbonCaptureResearcher",
model: GEMINI_MODEL,
instruction: `You are an AI Research Assistant specializing in climate solutions.
Research the current state of 'carbon capture methods'.
Use the Google Search tool provided.
Summarize your key findings concisely (1-2 sentences).
Output *only* the summary.
`,
description: "Researches carbon capture methods.",
tools: researchTools,
// Store result in state for the merger agent
outputKey: "carbon_capture_result"
});
// --- 2. Create the ParallelAgent (Runs researchers concurrently) ---
// This agent orchestrates the concurrent execution of the researchers.
// It finishes once all researchers have completed and stored their results in state.
const parallelResearchAgent = new ParallelAgent({
name: "ParallelWebResearchAgent",
subAgents: [researcherAgent1, researcherAgent2, researcherAgent3],
description: "Runs multiple research agents in parallel to gather information."
});
// --- 3. Define the Merger Agent (Runs *after* the parallel agents) ---
// This agent takes the results stored in the session state by the parallel agents
// and synthesizes them into a single, structured response with attributions.
const mergerAgent = new LlmAgent({
name: "SynthesisAgent",
model: GEMINI_MODEL, // Or potentially a more powerful model if needed for synthesis
instruction: `You are an AI Assistant responsible for combining research findings into a structured report.
Your primary task is to synthesize the following research summaries, clearly attributing findings to their source areas. Structure your response using headings for each topic. Ensure the report is coherent and integrates the key points smoothly.
**Crucially: Your entire response MUST be grounded *exclusively* on the information provided in the 'Input Summaries' below. Do NOT add any external knowledge, facts, or details not present in these specific summaries.**
**Input Summaries:**
* **Renewable Energy:**
{renewable_energy_result}
* **Electric Vehicles:**
{ev_technology_result}
* **Carbon Capture:**
{carbon_capture_result}
**Output Format:**
## Summary of Recent Sustainable Technology Advancements
### Renewable Energy Findings
(Based on RenewableEnergyResearcher's findings)
[Synthesize and elaborate *only* on the renewable energy input summary provided above.]
### Electric Vehicle Findings
(Based on EVResearcher's findings)
[Synthesize and elaborate *only* on the EV input summary provided above.]
### Carbon Capture Findings
(Based on CarbonCaptureResearcher's findings)
[Synthesize and elaborate *only* on the carbon capture input summary provided above.]
### Overall Conclusion
[Provide a brief (1-2 sentence) concluding statement that connects *only* the findings presented above.]
Output *only* the structured report following this format. Do not include introductory or concluding phrases outside this structure, and strictly adhere to using only the provided input summary content.
`,
description: "Combines research findings from parallel agents into a structured, cited report, strictly grounded on provided inputs.",
// No tools needed for merging
// No output_key needed here, as its direct response is the final output of the sequence
});
// --- 4. Create the SequentialAgent (Orchestrates the overall flow) ---
// This is the main agent that will be run. It first executes the ParallelAgent
// to populate the state, and then executes the MergerAgent to produce the final output.
const rootAgent = new SequentialAgent({
name: "ResearchAndSynthesisPipeline",
// Run parallel research first, then merge
subAgents: [parallelResearchAgent, mergerAgent],
description: "Coordinates parallel research and synthesizes the results."
});
model, err := gemini.NewModel(ctx, modelName, &genai.ClientConfig{})
if err != nil {
return fmt.Errorf("failed to create model: %v", err)
}
// --- 1. Define Researcher Sub-Agents (to run in parallel) ---
researcher1, err := llmagent.New(llmagent.Config{
Name: "RenewableEnergyResearcher",
Model: model,
Instruction: `You are an AI Research Assistant specializing in energy.
Research the latest advancements in 'renewable energy sources'.
Use the Google Search tool provided.
Summarize your key findings concisely (1-2 sentences).
Output *only* the summary.`,
Description: "Researches renewable energy sources.",
OutputKey: "renewable_energy_result",
})
if err != nil {
return err
}
researcher2, err := llmagent.New(llmagent.Config{
Name: "EVResearcher",
Model: model,
Instruction: `You are an AI Research Assistant specializing in transportation.
Research the latest developments in 'electric vehicle technology'.
Use the Google Search tool provided.
Summarize your key findings concisely (1-2 sentences).
Output *only* the summary.`,
Description: "Researches electric vehicle technology.",
OutputKey: "ev_technology_result",
})
if err != nil {
return err
}
researcher3, err := llmagent.New(llmagent.Config{
Name: "CarbonCaptureResearcher",
Model: model,
Instruction: `You are an AI Research Assistant specializing in climate solutions.
Research the current state of 'carbon capture methods'.
Use the Google Search tool provided.
Summarize your key findings concisely (1-2 sentences).
Output *only* the summary.`,
Description: "Researches carbon capture methods.",
OutputKey: "carbon_capture_result",
})
if err != nil {
return err
}
// --- 2. Create the ParallelAgent (Runs researchers concurrently) ---
parallelResearchAgent, err := parallelagent.New(parallelagent.Config{
AgentConfig: agent.Config{
Name: "ParallelWebResearchAgent",
Description: "Runs multiple research agents in parallel to gather information.",
SubAgents: []agent.Agent{researcher1, researcher2, researcher3},
},
})
if err != nil {
return fmt.Errorf("failed to create parallel agent: %v", err)
}
// --- 3. Define the Merger Agent (Runs *after* the parallel agents) ---
synthesisAgent, err := llmagent.New(llmagent.Config{
Name: "SynthesisAgent",
Model: model,
Instruction: `You are an AI Assistant responsible for combining research findings into a structured report.
Your primary task is to synthesize the following research summaries, clearly attributing findings to their source areas. Structure your response using headings for each topic. Ensure the report is coherent and integrates the key points smoothly.
**Crucially: Your entire response MUST be grounded *exclusively* on the information provided in the 'Input Summaries' below. Do NOT add any external knowledge, facts, or details not present in these specific summaries.**
**Input Summaries:**
* **Renewable Energy:**
{renewable_energy_result}
* **Electric Vehicles:**
{ev_technology_result}
* **Carbon Capture:**
{carbon_capture_result}
**Output Format:**
## Summary of Recent Sustainable Technology Advancements
### Renewable Energy Findings
(Based on RenewableEnergyResearcher's findings)
[Synthesize and elaborate *only* on the renewable energy input summary provided above.]
### Electric Vehicle Findings
(Based on EVResearcher's findings)
[Synthesize and elaborate *only* on the EV input summary provided above.]
### Carbon Capture Findings
(Based on CarbonCaptureResearcher's findings)
[Synthesize and elaborate *only* on the carbon capture input summary provided above.]
### Overall Conclusion
[Provide a brief (1-2 sentence) concluding statement that connects *only* the findings presented above.]
Output *only* the structured report following this format. Do not include introductory or concluding phrases outside this structure, and strictly adhere to using only the provided input summary content.`,
Description: "Combines research findings from parallel agents into a structured, cited report, strictly grounded on provided inputs.",
})
if err != nil {
return fmt.Errorf("failed to create synthesis agent: %v", err)
}
// --- 4. Create the SequentialAgent (Orchestrates the overall flow) ---
pipeline, err := sequentialagent.New(sequentialagent.Config{
AgentConfig: agent.Config{
Name: "ResearchAndSynthesisPipeline",
Description: "Coordinates parallel research and synthesizes the results.",
SubAgents: []agent.Agent{parallelResearchAgent, synthesisAgent},
},
})
if err != nil {
return fmt.Errorf("failed to create sequential agent pipeline: %v", err)
}
import com.google.adk.agents.LlmAgent;
import com.google.adk.agents.ParallelAgent;
import com.google.adk.agents.SequentialAgent;
import com.google.adk.events.Event;
import com.google.adk.runner.InMemoryRunner;
import com.google.adk.sessions.Session;
import com.google.adk.tools.GoogleSearchTool;
import com.google.genai.types.Content;
import com.google.genai.types.Part;
import io.reactivex.rxjava3.core.Flowable;
public class ParallelResearchPipeline {
private static final String APP_NAME = "parallel_research_app";
private static final String USER_ID = "research_user_01";
private static final String GEMINI_MODEL = "gemini-2.0-flash";
// Assume google_search is an instance of the GoogleSearchTool
private static final GoogleSearchTool googleSearchTool = new GoogleSearchTool();
public static void main(String[] args) {
String query = "Summarize recent sustainable tech advancements.";
SequentialAgent sequentialPipelineAgent = initAgent();
runAgent(sequentialPipelineAgent, query);
}
public static SequentialAgent initAgent() {
// --- 1. Define Researcher Sub-Agents (to run in parallel) ---
// Researcher 1: Renewable Energy
LlmAgent researcherAgent1 = LlmAgent.builder()
.name("RenewableEnergyResearcher")
.model(GEMINI_MODEL)
.instruction("""
You are an AI Research Assistant specializing in energy.
Research the latest advancements in 'renewable energy sources'.
Use the Google Search tool provided.
Summarize your key findings concisely (1-2 sentences).
Output *only* the summary.
""")
.description("Researches renewable energy sources.")
.tools(googleSearchTool)
.outputKey("renewable_energy_result") // Store result in state
.build();
// Researcher 2: Electric Vehicles
LlmAgent researcherAgent2 = LlmAgent.builder()
.name("EVResearcher")
.model(GEMINI_MODEL)
.instruction("""
You are an AI Research Assistant specializing in transportation.
Research the latest developments in 'electric vehicle technology'.
Use the Google Search tool provided.
Summarize your key findings concisely (1-2 sentences).
Output *only* the summary.
""")
.description("Researches electric vehicle technology.")
.tools(googleSearchTool)
.outputKey("ev_technology_result") // Store result in state
.build();
// Researcher 3: Carbon Capture
LlmAgent researcherAgent3 = LlmAgent.builder()
.name("CarbonCaptureResearcher")
.model(GEMINI_MODEL)
.instruction("""
You are an AI Research Assistant specializing in climate solutions.
Research the current state of 'carbon capture methods'.
Use the Google Search tool provided.
Summarize your key findings concisely (1-2 sentences).
Output *only* the summary.
""")
.description("Researches carbon capture methods.")
.tools(googleSearchTool)
.outputKey("carbon_capture_result") // Store result in state
.build();
// --- 2. Create the ParallelAgent (Runs researchers concurrently) ---
// This agent orchestrates the concurrent execution of the researchers.
// It finishes once all researchers have completed and stored their results in state.
ParallelAgent parallelResearchAgent =
ParallelAgent.builder()
.name("ParallelWebResearchAgent")
.subAgents(researcherAgent1, researcherAgent2, researcherAgent3)
.description("Runs multiple research agents in parallel to gather information.")
.build();
// --- 3. Define the Merger Agent (Runs *after* the parallel agents) ---
// This agent takes the results stored in the session state by the parallel agents
// and synthesizes them into a single, structured response with attributions.
LlmAgent mergerAgent =
LlmAgent.builder()
.name("SynthesisAgent")
.model(GEMINI_MODEL)
.instruction(
"""
You are an AI Assistant responsible for combining research findings into a structured report.
Your primary task is to synthesize the following research summaries, clearly attributing findings to their source areas. Structure your response using headings for each topic. Ensure the report is coherent and integrates the key points smoothly.
**Crucially: Your entire response MUST be grounded *exclusively* on the information provided in the 'Input Summaries' below. Do NOT add any external knowledge, facts, or details not present in these specific summaries.**
**Input Summaries:**
* **Renewable Energy:**
{renewable_energy_result}
* **Electric Vehicles:**
{ev_technology_result}
* **Carbon Capture:**
{carbon_capture_result}
**Output Format:**
## Summary of Recent Sustainable Technology Advancements
### Renewable Energy Findings
(Based on RenewableEnergyResearcher's findings)
[Synthesize and elaborate *only* on the renewable energy input summary provided above.]
### Electric Vehicle Findings
(Based on EVResearcher's findings)
[Synthesize and elaborate *only* on the EV input summary provided above.]
### Carbon Capture Findings
(Based on CarbonCaptureResearcher's findings)
[Synthesize and elaborate *only* on the carbon capture input summary provided above.]
### Overall Conclusion
[Provide a brief (1-2 sentence) concluding statement that connects *only* the findings presented above.]
Output *only* the structured report following this format. Do not include introductory or concluding phrases outside this structure, and strictly adhere to using only the provided input summary content.
""")
.description(
"Combines research findings from parallel agents into a structured, cited report, strictly grounded on provided inputs.")
// No tools needed for merging
// No output_key needed here, as its direct response is the final output of the sequence
.build();
// --- 4. Create the SequentialAgent (Orchestrates the overall flow) ---
// This is the main agent that will be run. It first executes the ParallelAgent
// to populate the state, and then executes the MergerAgent to produce the final output.
SequentialAgent sequentialPipelineAgent =
SequentialAgent.builder()
.name("ResearchAndSynthesisPipeline")
// Run parallel research first, then merge
.subAgents(parallelResearchAgent, mergerAgent)
.description("Coordinates parallel research and synthesizes the results.")
.build();
return sequentialPipelineAgent;
}
public static void runAgent(SequentialAgent sequentialPipelineAgent, String query) {
// Create an InMemoryRunner
InMemoryRunner runner = new InMemoryRunner(sequentialPipelineAgent, APP_NAME);
// InMemoryRunner automatically creates a session service. Create a session using the service
Session session = runner.sessionService().createSession(APP_NAME, USER_ID).blockingGet();
Content userMessage = Content.fromParts(Part.fromText(query));
// Run the agent
Flowable<Event> eventStream = runner.runAsync(USER_ID, session.id(), userMessage);
// Stream event response
eventStream.blockingForEach(
event -> {
if (event.finalResponse()) {
System.out.printf("Event Author: %s \n Event Response: %s \n\n\n", event.author(), event.stringifyContent());
}
});
}
}