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Callables as First-Class Citizens

Regular Python functions (callables) are treated as first-class citizens and can be used interchangeably with agents in most contexts. This enables seamless integration of custom processing functions into agent workflows.

Direct Agent Creation

You can create agents directly from callables, preserving their type information:

# Simple untyped function becomes Agent[None, str]
def process(message: str) -> str:
    return message.upper()

agent = Agent.from_callback(process)

# Typed function becomes Agent[None, ResultType]

class AnalysisResult(BaseModel):
    sentiment: float
    topics: list[str]

def analyze(message: str) -> AnalysisResult:
    return AnalysisResult(sentiment=0.8, topics=["AI"])

agent = Agent.from_callback(analyze)

Automatic Conversion in Workflows

Callables are automatically converted to agents when used in:

Teams (using &)

def analyze(text: str) -> AnalysisResult:
    return AnalysisResult(...)

def summarize(text: str) -> str:
    return "Summary: " + text

# Both functions become agents in the team
team = analyzer & analyze & summarize

Pipelines (using |)

# Functions become agents in the pipeline
pipeline = agent | analyze | summarize

Connections (using >>)

# Function becomes agent when used as target
agent >> analyze

Context injection

Functions can optionally accept agentContext:

def process(ctx: AgentContext, message: str) -> str:
    # Access agent capabilities, configuration, etc.
    return f"Processed by {ctx.node_name}: {message}"

# Context is automatically injected
agent = Agent(provider=process)

This seamless integration of callables allows you to:

  • Mix and match agents with regular functions
  • Create lightweight processing steps without full agent overhead
  • Preserve type safety throughout the workflow
  • Gradually convert functions to full agents as needed

Callables for prompts

LLMling-Agent also allows to pass Callables for system and user prompts which can get re-evaluted for each run.

def my_system_prompt(ctx: AgentContext) -> str:  # context optional
    return "You are an AI assistant."

agent = Agent(system_prompts=[my_system_prompt])
agent.run("Hello, how are you?")
# or:
agent.run(my_user_prompt)