# Agentica Sdk

Build Python agents with Agentica SDK - @agentic decorator, spawn(), persistence, MCP...

- Repository: https://github.com/parcadei/Continuous-Claude-v3 (3947 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-01-26
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add parcadei/Continuous-Claude-v3 --skill agentica-sdk -g -a claude-code -y`
Codex: `npx skills add parcadei/Continuous-Claude-v3 --skill agentica-sdk -g -a codex -y`
Cursor: `npx skills add parcadei/Continuous-Claude-v3 --skill agentica-sdk -g -a cursor -y`
Gemini CLI: `npx skills add parcadei/Continuous-Claude-v3 --skill agentica-sdk -g -a gemini-cli -y`

## Description

Build Python agents with Agentica SDK - @agentic decorator, spawn(), persistence, MCP integration

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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)

```python
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)

```python
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

```python
# 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

```python
# 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)

```python
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

The rest: https://github.com/parcadei/Continuous-Claude-v3/blob/HEAD/.claude/skills/agentica-sdk/SKILL.md