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"""This file contains the function calling implementation for different actions.
This is similar to the functionality of `CodeActResponseParser`.
"""
import json
from litellm import (
ChatCompletionToolParam,
ModelResponse,
)
from openhands.agenthub.codeact_agent.function_calling import combine_thought
from openhands.agenthub.codeact_agent.tools import FinishTool
from openhands.agenthub.loc_agent.tools import (
SearchEntityTool,
SearchRepoTool,
create_explore_tree_structure_tool,
)
from openhands.core.exceptions import (
FunctionCallNotExistsError,
)
from openhands.core.logger import openhands_logger as logger
from openhands.events.action import (
Action,
AgentFinishAction,
IPythonRunCellAction,
MessageAction,
)
from openhands.events.tool import ToolCallMetadata
def response_to_actions(
response: ModelResponse,
mcp_tool_names: list[str] | None = None,
) -> list[Action]:
actions: list[Action] = []
assert len(response.choices) == 1, 'Only one choice is supported for now'
choice = response.choices[0]
assistant_msg = choice.message
if hasattr(assistant_msg, 'tool_calls') and assistant_msg.tool_calls:
# Check if there's assistant_msg.content. If so, add it to the thought
thought = ''
if isinstance(assistant_msg.content, str):
thought = assistant_msg.content
elif isinstance(assistant_msg.content, list):
for msg in assistant_msg.content:
if msg['type'] == 'text':
thought += msg['text']
# Process each tool call to OpenHands action
for i, tool_call in enumerate(assistant_msg.tool_calls):
action: Action
logger.debug(f'Tool call in function_calling.py: {tool_call}')
try:
arguments = json.loads(tool_call.function.arguments)
except json.decoder.JSONDecodeError as e:
raise RuntimeError(
f'Failed to parse tool call arguments: {tool_call.function.arguments}'
) from e
# ================================================
# LocAgent's Tools
# ================================================
ALL_FUNCTIONS = [
'explore_tree_structure',
'search_code_snippets',
'get_entity_contents',
]
if tool_call.function.name in ALL_FUNCTIONS:
# We implement this in agent_skills, which can be used via Jupyter
func_name = tool_call.function.name
code = f'print({func_name}(**{arguments}))'
logger.debug(f'TOOL CALL: {func_name} with code: {code}')
action = IPythonRunCellAction(code=code)
# ================================================
# AgentFinishAction
# ================================================
elif tool_call.function.name == FinishTool['function']['name']:
action = AgentFinishAction(
final_thought=arguments.get('message', ''),
task_completed=arguments.get('task_completed', None),
)
else:
raise FunctionCallNotExistsError(
f'Tool {tool_call.function.name} is not registered. (arguments: {arguments}). Please check the tool name and retry with an existing tool.'
)
# We only add thought to the first action
if i == 0:
action = combine_thought(action, thought)
# Add metadata for tool calling
action.tool_call_metadata = ToolCallMetadata(
tool_call_id=tool_call.id,
function_name=tool_call.function.name,
model_response=response,
total_calls_in_response=len(assistant_msg.tool_calls),
)
actions.append(action)
else:
actions.append(
MessageAction(
content=str(assistant_msg.content) if assistant_msg.content else '',
wait_for_response=True,
)
)
# Add response id to actions
# This will ensure we can match both actions without tool calls (e.g. MessageAction)
# and actions with tool calls (e.g. CmdRunAction, IPythonRunCellAction, etc.)
# with the token usage data
for action in actions:
action.response_id = response.id
assert len(actions) >= 1
return actions
def get_tools() -> list[ChatCompletionToolParam]:
tools = [FinishTool]
tools.append(SearchRepoTool)
tools.append(SearchEntityTool)
tools.append(create_explore_tree_structure_tool(use_simplified_description=True))
return tools
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