【LLM】Agents
Agents
智能体是一个独立的执行单元，旨在自主行动以实现特定目标。智能体可以执行任务、与用户互动、使用外部工具以及与其他智能体协调。
基于BaseAgent扩展成LlmAgent、WorkflowAgent、CustomAgent
LLM agents
Learn more about LLM Agents…
code_writer_agent = LlmAgent( name="CodeWriterAgent", model=GEMINI_MODEL, # Change 3: Improved instruction instruction="""You are a Python Code Generator. Based *only* on the user's request, write Python code that fulfills the requirement. Output *only* the complete Python code block, enclosed in triple backticks (```python ... ```). Do not add any other text before or after the code block. """, description="Writes initial Python code based on a specification.", output_key="generated_code" # Stores output in state['generated_code'] )Workflow agents
Learn more about Workflow Agents…
Sequential agents
code_pipeline_agent = SequentialAgent( name="CodePipelineAgent", sub_agents=[code_writer_agent, code_reviewer_agent, code_refactorer_agent], description="Executes a sequence of code writing, reviewing, and refactoring.", # The agents will run in the order provided: Writer -> Reviewer -> Refactorer )Loop agents
# Part of agent.py --> Follow https://google.github.io/adk-docs/get-started/quickstart/ to learn the setup import asyncio import os from google.adk.agents import LoopAgent, LlmAgent, BaseAgent, SequentialAgent from google.genai import types from google.adk.runners import InMemoryRunner from google.adk.agents.invocation_context import InvocationContext from google.adk.tools.tool_context import ToolContext from typing import AsyncGenerator, Optional from google.adk.events import Event, EventActions # --- Constants --- APP_NAME = "doc_writing_app_v3" # New App Name USER_ID = "dev_user_01" SESSION_ID_BASE = "loop_exit_tool_session" # New Base Session ID GEMINI_MODEL = "gemini-2.0-flash" STATE_INITIAL_TOPIC = "initial_topic" # --- State Keys --- STATE_CURRENT_DOC = "current_document" STATE_CRITICISM = "criticism" # Define the exact phrase the Critic should use to signal completion COMPLETION_PHRASE = "No major issues found." # --- Tool Definition --- def exit_loop(tool_context: ToolContext): """Call this function ONLY when the critique indicates no further changes are needed, signaling the iterative process should end.""" print(f" [Tool Call] exit_loop triggered by {tool_context.agent_name}") tool_context.actions.escalate = True # Return empty dict as tools should typically return JSON-serializable output return {} # --- Agent Definitions --- # STEP 1: Initial Writer Agent (Runs ONCE at the beginning) initial_writer_agent = LlmAgent( name="InitialWriterAgent", model=GEMINI_MODEL, include_contents='none', # MODIFIED Instruction: Ask for a slightly more developed start instruction=f"""You are a Creative Writing Assistant tasked with starting a story. Write the *first draft* of a short story (aim for 2-4 sentences). Base the content *only* on the topic provided below. Try to introduce a specific element (like a character, a setting detail, or a starting action) to make it engaging. Topic: {{initial_topic}} Output *only* the story/document text. Do not add introductions or explanations. """, description="Writes the initial document draft based on the topic, aiming for some initial substance.", output_key=STATE_CURRENT_DOC ) # STEP 2a: Critic Agent (Inside the Refinement Loop) critic_agent_in_loop = LlmAgent( name="CriticAgent", model=GEMINI_MODEL, include_contents='none', # MODIFIED Instruction: More nuanced completion criteria, look for clear improvement paths. instruction=f"""You are a Constructive Critic AI reviewing a short document draft (typically 2-6 sentences). Your goal is balanced feedback. **Document to Review:** ``` {{current_document}} ``` **Task:** Review the document for clarity, engagement, and basic coherence according to the initial topic (if known). IF you identify 1-2 *clear and actionable* ways the document could be improved to better capture the topic or enhance reader engagement (e.g., "Needs a stronger opening sentence", "Clarify the character's goal"): Provide these specific suggestions concisely. Output *only* the critique text. ELSE IF the document is coherent, addresses the topic adequately for its length, and has no glaring errors or obvious omissions: Respond *exactly* with the phrase "{COMPLETION_PHRASE}" and nothing else. It doesn't need to be perfect, just functionally complete for this stage. Avoid suggesting purely subjective stylistic preferences if the core is sound. Do not add explanations. Output only the critique OR the exact completion phrase. """, description="Reviews the current draft, providing critique if clear improvements are needed, otherwise signals completion.", output_key=STATE_CRITICISM ) # STEP 2b: Refiner/Exiter Agent (Inside the Refinement Loop) refiner_agent_in_loop = LlmAgent( name="RefinerAgent", model=GEMINI_MODEL, # Relies solely on state via placeholders include_contents='none', instruction=f"""You are a Creative Writing Assistant refining a document based on feedback OR exiting the process. **Current Document:** ``` {{current_document}} ``` **Critique/Suggestions:** {{criticism}} **Task:** Analyze the 'Critique/Suggestions'. IF the critique is *exactly* "{COMPLETION_PHRASE}": You MUST call the 'exit_loop' function. Do not output any text. ELSE (the critique contains actionable feedback): Carefully apply the suggestions to improve the 'Current Document'. Output *only* the refined document text. Do not add explanations. Either output the refined document OR call the exit_loop function. """, description="Refines the document based on critique, or calls exit_loop if critique indicates completion.", tools=[exit_loop], # Provide the exit_loop tool output_key=STATE_CURRENT_DOC # Overwrites state['current_document'] with the refined version ) # STEP 2: Refinement Loop Agent refinement_loop = LoopAgent( name="RefinementLoop", # Agent order is crucial: Critique first, then Refine/Exit sub_agents=[ critic_agent_in_loop, refiner_agent_in_loop, ], max_iterations=5 # Limit loops ) # STEP 3: Overall Sequential Pipeline # For ADK tools compatibility, the root agent must be named `root_agent` root_agent = SequentialAgent( name="IterativeWritingPipeline", sub_agents=[ initial_writer_agent, # Run first to create initial doc refinement_loop # Then run the critique/refine loop ], description="Writes an initial document and then iteratively refines it with critique using an exit tool." )