Tool-call loop#

This document describes how, within one model call, the LLM "chooses" on every round — pick a function to run, or emit text and finish.

Companion doc: calling-unification.md covers the design of the whole function-calling framework — the @function / @agentic_function decorators, the shared registry, 6-layer gating, deferred loading, etc. This page only covers the loop mechanics of the "pick the next step" part.

One-sentence summary#

Give the LLM a set of tools (@agentic_functions or tool dicts); each round it returns one assistant message. If the message content contains a ToolCall, it picked a function — the framework executes it, feeds the result back into the history, and lets it pick again. If the message is text only, with no ToolCall, it picked "finish" — the text is returned as the final reply. The loop runs in openprogram/agent/agent_loop.py::_run_loop.

Entry point: runtime.exec#

Inside an @agentic_function, call runtime.exec(content, tools=..., tool_choice=..., max_iterations=...):

• tools is the menu of functions the LLM may pick from. Each entry can be an @agentic_function, a {"spec":..., "execute":...} dict, or an object with .spec / .execute.
• Tools are on by default. With neither tools= nor toolset= passed, exec resolves the FULL registry toolset, so any function can search, run code, and edit files without opting in. A call that genuinely wants no tools opts out explicitly with toolset="none" (or tools=[]) — only then is it a pure reasoning call where the model can only emit text. A nested exec inside a tool body inherits the outer call's tools= list (via the _current_tools contextvar).
• To trim the tool menu, exec also takes the policy parameters tools_source, tools_allow, and tools_deny.
• With tools set, exec enters the tool loop until the model returns pure text (or the loop's hard cap is hit — see Termination).

tool_choice controls whether picking is allowed / required per round — "auto" (default: the model decides), "required" (must pick a function), "none" (text only), or {"type": "function", "name": "X"} to force one function. It is forwarded to the provider, which maps it onto its own protocol shape (OpenAI, Anthropic, Gemini, and Bedrock are covered). parallel_tool_calls=False forbids several picks in one round where the provider supports the knob. max_iterations caps the loop's rounds — the effective cap is min(50, max_iterations), floored at 1 (see Termination). For a forced, structured decision ending (rather than per-round control), exec(choices=...) remains the richer tool — see next-step decision.

Loop body: _run_loop#

_run_loop has an inner while has_more_tool_calls or pending_messages; each round:

1. Get the model's output for this round — _stream_assistant_response streams from the provider and returns one AssistantMessage.
2. Check for terminal errors — message.stop_reason in ("error", "aborted") → end the stream immediately, no more looping.
3. See what the model picked —

   tool_calls = [c for c in message.content if isinstance(c, ToolCall)]
   has_more_tool_calls = len(tool_calls) > 0
   

   • tool_calls non-empty → the model picked functions → go to step 4, then back to the top of the loop to pick again.
   • tool_calls empty → the model emitted only TextContent this round → has_more_tool_calls=False → the inner while exits → that text is the result.
4. Execute the picked functions — _execute_tool_calls runs them one by one, producing ToolResultMessages appended to current_context.messages and new_messages. The history the LLM sees next round now carries the tool results, and it decides what to pick next based on them.

In other words: "picking the next step" is not a separate decision module — it is the ToolCall-vs-TextContent dichotomy inside the assistant message the provider returns. The framework never decides for the model; it only parses the output and branches on it.

Function execution: _execute_tool_calls#

For each ToolCall the model picked, look up the tool in tools by tool_call.name:

tool not found                        → ValueError, produces an is_error result
validate_tool_arguments fails         → exception, produces an is_error result
tool.execute(...) raises              → caught; the exception text becomes an is_error result
success                               → result content wrapped in a ToolResultMessage

Neither validation nor execution exceptions break the loop — they become an is_error=True tool result fed back to the model, so it can see "wrong function / wrong arguments" and correct itself.

Parallel picks execute sequentially in order. If get_steering_messages returns user-queued messages mid-way, the remaining unexecuted ToolCalls are marked by _skip_tool_call as "Skipped due to queued user message" and the user messages take priority.

Termination#

The inner picking loop stops on any of:

model picked no function (pure text)   normal finish; the text is the result
stop_reason = error / aborted          error / cancel finish
inner_iterations > 50                  hard cap MAX_INNER_ITERATIONS against idle spinning;
                                       treated as a normal finish, returns what exists

One continuation condition is easy to miss: the inner while also runs on pending_messages, so queued user (steering) messages keep the loop alive even after a pure-text reply.

After the inner loop exits, get_follow_up_messages may supply follow-up messages which become pending_messages for another round; otherwise the run ends for good and pushes AgentEventAgentEnd.

Relation to @agentic_function#

An @agentic_function passed as a tool to exec(tools=[...]) is, in the model's eyes, just one pickable function. The model picks it → _execute_tool_calls invokes its .execute → if that function body calls runtime.exec again, another layer of the same picking loop opens. "Picking the next function to run" under nested agentic functions is the same mechanism unfolding recursively.