agent-patterns

# Agent Patterns ## Creating Agents with LLMConfig Always use the `LLMConfig` context manager. Agents created inside its scope inherit the configuration automatically. ```python from ag2 import LLMConfig from ag2.agentchat import AssistantAgent, UserProxyAgent with LLMConfig(api_type="openai", model="gpt-4o"): planner = AssistantAgent( name="planner", system_message="You are a project planner. Break tasks into steps.", ) reviewer = AssistantAgent( name="reviewer", system_message="You review plans for completeness and correctness.", ) executor = UserProxyAgent( name="executor", human_input_mode="NEVER", code_execution_config={"work_dir": "workspace"}, ) ``` Agents that do not need an LLM (e.g., pure code executors) should be created outside the context manager. ## System Message Best Practices 1. **Be specific about the role.** State what the agent does, not what it is. 2. **Define boundaries.** Tell the agent what it should NOT do. 3. **Keep it short.** Aim for 2-4 sentences. Long prompts dilute focus. 4. **Use second person.** Write "You analyze data" not "This agent analyzes data". ```python # Good system_message = ( "You are a SQL analyst. Write SQL queries to answer user questions. " "Only use SELECT statements. Never modify data." ) # Bad -- too vague system_message = "You are a helpful assistant." ``` ## Agent Naming Conventions - Use lowercase snake_case: `data_analyst`, `code_reviewer`. - Names must be unique within a group chat. - Names appear in conversation logs, so make them descriptive. - Avoid generic names like `assistant` or `agent1`. ## Tool Registration Register tools on the agents that should be able to call and execute them: ```python from ag2.tools import tool @tool def search_database(query: str, limit: int = 10) -> str: """Search the database and return matching rows.""" # implementation return results analyst = AssistantAgent(name="analyst", system_message="...") executor = UserProxyAgent(name="executor", human_input_mode="NEVER") # Register: analyst decides when to call, executor runs the function analyst.register_tool(search_database, caller=analyst, executor=executor) ``` ## Human Input Modes - `"ALWAYS"` -- Ask for human input on every turn. - `"TERMINATE"` -- Ask only when the agent wants to terminate. - `"NEVER"` -- Fully autonomous, no human input. ```python # Autonomous executor executor = UserProxyAgent(name="executor", human_input_mode="NEVER") # Human-in-the-loop user = UserProxyAgent(name="user", human_input_mode="TERMINATE") ``` ## Termination Agents stop when a reply contains `"TERMINATE"`. Configure this in the system message: ```python system_message = ( "You solve math problems. When you have the final answer, " "reply with TERMINATE." ) ```