Agentica Sdk
Build Python agents with decorators, state, and MCP tool integration
What You Can Do
You can build AI-powered Python agents quickly using the @agentic decorator for simple functions or spawn() for full multi-agent systems with behavioral control. Agents maintain persistent state, integrate with MCP tools, handle typed return values, and coordinate with each other through a unified framework—enabling everything from basic AI-enhanced functions to complex agent orchestration pipelines.
Features
Turn any async function into an AI agent with minimal code
Create and control multiple agents with spawn() for orchestration
Specify expected output types (int, dict, custom objects) and get validated results
Connect agents to external tools and services through Model Context Protocol
Agents maintain memory and context across multiple invocations
Use premise for character/behavior or system for full control
Pass tools and context dynamically at invocation time
Full control over agent reasoning and decision-making
Example Output
Example 1: Simple agentic function
@agentic()
async def summarize(text: str) -> str:
"""Summarize the given text"""
...
result = await summarize("Long document here...")
# Returns: "Concise summary of the document"
Example 2: Multi-agent system with tools
agent = await spawn(
premise="You are a data analyst.",
scope={"analyze": my_analyzer, "query_db": db_query}
)
result: dict[str, int] = await agent.call(
dict[str, int],
"Analyze Q3 sales trends"
)
# Returns: {"growth": 23, "peak_month": 7}
Example 3: Agent coordination
researcher = await spawn(premise="Research expert")
writer = await spawn(premise="Technical writer")
findings = await researcher.call(str, "Research AI safety")
article = await writer.call(str, f"Write about: {findings}")
What's Included
- SKILL.md: Complete Agentica SDK reference with patterns and API documentation
- Quick Start Examples: @agentic decorator, spawn(), and multi-agent patterns
- Tool Integration Guide: MCP tool binding and scope management
- Type Safety Patterns: Return type specification and validation examples
- Prompting Framework: Premise vs system prompt strategies and customization
Who It's For
- Python developers building AI-powered applications
- AI/ML engineers implementing multi-agent systems
- Data scientists automating analysis workflows with agents
- Backend engineers integrating agentic capabilities into services
- DevOps/automation specialists orchestrating agent pipelines
Best For
- Creating AI-enhanced Python functions with minimal boilerplate
- Building multi-agent orchestration systems for complex workflows
- Integrating external tools and APIs with intelligent agents
- Implementing chatbots, researchers, and autonomous decision-makers
- Debugging and controlling agent behavior in production systems
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