cleveractors-core/robot/llm_tool_calling.robot

*** Settings ***
Documentation    LLM tool-calling integration tests.
...              Exercises the full pipeline (Executor → LLMAgent → ToolAgent)
...              with a mock chat model that returns tool_calls, so no real
...              LLM API key is required.
Library          ToolCallingTestLib.py

*** Test Cases ***
LLM Agent Receives Tools And Executes Tool Call
    [Documentation]    Full lifecycle: config with echo tool → mock returns
    ...    tool_calls → ToolAgent executes echo → final response produced.
    Create Executor With Tool Calling Agent    tool_name=echo    message=hello_from_tool
    Execute With Message    Tell me something using the echo tool
    Result Is Valid Actor Result
    Result Has Nodes
    Chat Model Was Invoked With Tools
    Multiple Tool Rounds Occurred
    Result Response Contains    Final answer after tool use

LLM Agent Tool Calling Works With MATH Tool
    [Documentation]    MATH tool recognises the expression and computes result.
    Create Executor With Tool Calling Agent    tool_name=math    message=2+2
    Execute With Message    What is 2+2? Use the math tool.
    Result Is Valid Actor Result
    Chat Model Was Invoked With Tools
    Multiple Tool Rounds Occurred
    Result Response Contains    Final answer after tool use

LLM Agent With FILE_READ Tool Receives Tool Calls
    [Documentation]    file_read tool is normalised and passed to the model.
    Create Executor With Tool Calling Agent    tool_name=file_read    message=some_file.txt
    Execute With Message    Read the file some_file.txt for me
    Result Is Valid Actor Result
    Chat Model Was Invoked With Tools

Tool Calling Produces Non-Empty Final Response
    [Documentation]    After tool execution, the final response is populated.
    Create Executor With Tool Calling Agent    tool_name=echo    message=tool_data
    Execute With Message    Use the echo tool and report back
    Result Is Valid Actor Result
    Tool Call Result Is In Response
    Result Prompt Tokens Greater Than Zero

Streaming Path Exercises Tool Loop Via Execute Stream
    [Documentation]    execute_stream on a tool-declaring single-LLM actor exercises
    ...    the _execute_tool_loop() path (issue #67): the tool-call loop runs via
    ...    ainvoke(), the final response is yielded as a single content chunk, and
    ...    executor.last_result reports accumulated token counts covering all rounds.
    Create Executor With Tool Calling Agent    tool_name=echo    message=from_stream
    Execute Stream With Message    Tell me about yourself
    Result Is Valid Actor Result
    Result Has Nodes
    Stream Result Response Is Non Empty
    Stream Result Prompt Tokens Greater Than Zero

Streaming Path Accumulates Tokens Across Two Tool Rounds
    [Documentation]    When the mock LLM returns tool_calls for two rounds before the
    ...    final answer, execute_stream yields the final answer and the accumulated
    ...    prompt_tokens equals the sum across all three invocations.
    Create Executor With Multi Round Tool Calling Agent    tool_name=echo    rounds=2
    Execute Stream With Message    Multi round test
    Result Is Valid Actor Result
    Stream Result Response Contains    Multi round final answer
    Stream Result Prompt Tokens Equals Multi Round Sum