Overview The FnCallAgent is a widely applicable function calling agent that integrates LLM capabilities with tool usage. It’s the foundation for other agents like Assistant and ReActChat, providing core function calling and tool execution functionality.
Key Features
Native Function Calling Built-in support for OpenAI-compatible function calling
Parallel Execution Execute multiple tools simultaneously for efficiency
Memory Management Automatic file and conversation context management
Iterative Tool Use Multi-step reasoning with repeated tool calls
Constructor from qwen_agent.agents import FnCallAgent agent = FnCallAgent( function_list = [ 'code_interpreter' , 'web_search' ], llm = { 'model' : 'qwen-max-latest' , 'model_type' : 'qwen_dashscope' }, system_message = 'You are a helpful assistant.' , name = 'MyAgent' , description = 'A function calling agent' , files = [ './context.txt' ] )
Parameters List of tools to enable. Can be:
Tool names: 'code_interpreter'
Tool configs: {'name': 'code_interpreter', 'timeout': 30}
Tool objects: CodeInterpreter()
LLM configuration with model, model_type, and optionally api_key, model_server, generate_cfg.
system_message
str
default: "You are a helpful assistant"
System message defining the agent’s role and behavior.
Agent identifier for multi-agent scenarios.
Agent description used for routing in multi-agent systems.
Initial files to load into the agent’s memory for context.
Basic Usage
Initialize the Agent
from qwen_agent.agents import FnCallAgent agent = FnCallAgent( function_list = [ 'code_interpreter' ], llm = { 'model' : 'qwen-max-latest' , 'model_type' : 'qwen_dashscope' , 'api_key' : 'YOUR_API_KEY' } )
Run the Agent
messages = [{ 'role' : 'user' , 'content' : 'Calculate the factorial of 10' }] for response in agent.run( messages = messages): print (response)
Function Calling Example from qwen_agent.agents import FnCallAgent from qwen_agent.tools.base import BaseTool, register_tool import json5 # Define a custom tool @register_tool ( 'get_weather' ) class WeatherTool ( BaseTool ): description = 'Get current weather for a location' parameters = [{ 'name' : 'location' , 'type' : 'string' , 'description' : 'City name' , 'required' : True }] def call ( self , params : str , ** kwargs ) -> str : location = json5.loads(params)[ 'location' ] # Call weather API return json5.dumps({ 'temperature' : 72 , 'condition' : 'sunny' }) # Create agent with the tool agent = FnCallAgent( function_list = [ 'get_weather' ], llm = { 'model' : 'qwen-max-latest' , 'model_type' : 'qwen_dashscope' } ) messages = [{ 'role' : 'user' , 'content' : 'What \' s the weather in San Francisco?' }] for response in agent.run( messages = messages): print (response)
Parallel Function Calling The agent automatically handles parallel tool execution when appropriate:
from qwen_agent.agents import FnCallAgent agent = FnCallAgent( function_list = [ 'web_search' , 'code_interpreter' ], llm = { 'model' : 'qwen-max-latest' , 'model_type' : 'qwen_dashscope' } ) # This query may trigger multiple tools in parallel messages = [{ 'role' : 'user' , 'content' : 'Search for Python statistics libraries and calculate their GitHub stars' }] for response in agent.run( messages = messages): print (response)
Multi-turn Conversations messages = [] # First turn messages.append({ 'role' : 'user' , 'content' : 'Calculate 5 factorial' }) for response in agent.run( messages = messages): pass messages.extend(response) # Second turn - agent remembers context messages.append({ 'role' : 'user' , 'content' : 'Now multiply that by 3' }) for response in agent.run( messages = messages): pass messages.extend(response) print (messages[ - 1 ][ 'content' ]) # Final answer
With File Context from qwen_agent.agents import FnCallAgent # Agent can access file content for context agent = FnCallAgent( function_list = [ 'code_interpreter' ], llm = { 'model' : 'qwen-max-latest' , 'model_type' : 'qwen_dashscope' }, files = [ './data.csv' , './config.json' ] ) messages = [{ 'role' : 'user' , 'content' : 'Analyze the data.csv file and create a visualization' }] for response in agent.run( messages = messages): print (response)
from qwen_agent.tools.base import BaseTool import json5 class StatefulTool ( BaseTool ): name = 'counter' description = 'A counter that tracks state across calls' parameters = [{ 'name' : 'operation' , 'type' : 'string' , 'description' : 'increment or get' , 'required' : True }] def __init__ ( self , cfg = None ): super (). __init__ (cfg) self .count = 0 def call ( self , params : str , ** kwargs ) -> str : operation = json5.loads(params)[ 'operation' ] if operation == 'increment' : self .count += 1 return json5.dumps({ 'count' : self .count}) # Use the stateful tool tool = StatefulTool() agent = FnCallAgent( function_list = [tool], llm = { 'model' : 'qwen-max-latest' , 'model_type' : 'qwen_dashscope' } )
Best Practices
Tools should return error messages in JSON format
Handle timeouts gracefully with appropriate timeout values
Log tool execution for debugging
Validate tool parameters before execution
Comparison with Other Agents Feature FnCallAgent Assistant ReActChat Function Calling ✅ ✅ ✅ RAG Support ❌ ✅ ❌ Format OpenAI OpenAI ReAct Text Parallel Tools ✅ ✅ ❌ Best For Tool usage RAG + Tools Reasoning traces
Function Calling Learn about function calling concepts
Custom Tools Create custom tools for your agent
Assistant Agent Use Assistant for RAG + function calling
API Reference Complete API documentation
