What is AgentExecutor?

AgentExecutor is the runtime loop that drives an agent to completion. It takes an agent (which decides actions) and a list of tools (which execute those actions), and repeatedly calls the agent, executes the selected tool, feeds the observation back, and repeats until the agent returns an AgentFinish or a stopping condition is reached.

from langchain.agents import AgentExecutor

executor = AgentExecutor(
    agent=agent,
    tools=tools,
    verbose=True,                  # print each step
    max_iterations=10,             # prevent infinite loops
    return_intermediate_steps=True, # include tool call history in output
    handle_parsing_errors=True,    # auto-retry if output parse fails
)

result = executor.invoke({"input": "Find the CEO of Anthropic"})
print(result["output"])            # final answer
print(result["intermediate_steps"]) # list of (AgentAction, observation)

Key configuration options: max_iterations prevents runaway loops, max_execution_time adds a wall-clock timeout, early_stopping_method controls whether the agent generates a final answer when max_iterations is hit or just stops, and handle_parsing_errors retries if the LLM produces malformed output instead of crashing the loop.

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