Agentica SDK Reference (v0.3.1)

Build AI agents in Python using the Agentica framework. Agents can implement functions, maintain state, use tools, and coordinate with each other.

When to Use

Use this skill when:

• Building new Python agents
• Adding agentic capabilities to existing code
• Integrating MCP tools with agents
• Implementing multi-agent orchestration
• Debugging agent behavior

Quick Start

Agentic Function (simplest)

from agentica import agentic

@agentic()
async def add(a: int, b: int) -> int:
    """Returns the sum of a and b"""
    ...

result = await add(1, 2)  # Agent computes: 3

Spawned Agent (more control)

from agentica import spawn

agent = await spawn(premise="You are a truth-teller.")
result: bool = await agent.call(bool, "The Earth is flat")
# Returns: False

Core Patterns

Return Types

# String (default)
result = await agent.call("What is 2+2?")

# Typed output
result: int = await agent.call(int, "What is 2+2?")
result: dict[str, int] = await agent.call(dict[str, int], "Count items")

# Side-effects only
await agent.call(None, "Send message to John")

Premise vs System Prompt

# Premise: adds to default system prompt
agent = await spawn(premise="You are a math expert.")

# System: full control (replaces default)
agent = await spawn(system="You are a JSON-only responder.")

Passing Tools (Scope)

from agentica import agentic, spawn

# In decorator
@agentic(scope={'web_search': web_search_fn})
async def researcher(query: str) -> str:
    """Research a topic."""
    ...

# In spawn
agent = await spawn(
    premise="Data analyzer",
    scope={"analyze": custom_analyzer}
)

# Per-call scope
result = await agent.call(
    dict[str, int],
    "Analyze the dataset",
    dataset=data,           # Available as 'dataset'
    analyzer=custom_fn      # Available as 'analyzer'
)

SDK Integration Pattern