Background jobs

The agent loop is implemented in src/smolagents/agents.py as a class hierarchy rooted at MultiStepAgent. Concrete subclasses are CodeAgent and ToolCallingAgent. The loop (_run_stream) cycles through ReAct steps: generate → parse action → execute → store observation → repeat until final_answer() is called or max_steps is reached.

Key design points for engineers:

• Steps are yielded as a generator (_run_stream), enabling both blocking (run()) and streaming (run(stream=True)) modes.

• A planning_interval parameter triggers an optional planning step that injects an explicit plan into the agent's context every N steps.

• final_answer_checks accepts a list of validator callables that gate whether a proposed final answer is accepted.

• MultiStepAgent is abstract; subclasses must implement initialize_system_prompt() and _step_stream().

• RunResult is the return type when return_full_result=True; it carries output, state, step list, token usage, and timing.

• src/smolagents/agents.py