SKILL.md
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
from slack_sdk import WebClient
slack = WebClient(token=SLACK_TOKEN)
Extract specific methods
@agentic(scope={
'list_users': slack.users_list,
'send_message': slack.chat_postMessage
})
async def team_notifier(message: str) -> None:
"""Send team notifications."""
...
## Agent Instantiation
### spawn() - Async (most cases)
agent = await spawn(premise="Helpful assistant")
### Agent() - Sync (for __init__ )
from agentica.agent import Agent
class CustomAgent:
def __init__(self):
# Synchronous - use Agent() not spawn()
self._brain = Agent(
premise="Specialized assistant",
scope={"tool": some_tool}
)
async def run(self, task: str) -> str:
return await self._brain(str, task)
## Model Selection
In spawn
agent = await spawn(
premise="Fast responses",
model="openai:gpt-5" # Default: openai:gpt-4.1
)
In decorator
@agentic(model="anthropic:claude-sonnet-4.5")
async def analyze(text: str) -> dict:
"""Analyze text."""
...
**Available models:**
- `openai:gpt-3.5-turbo`, `openai:gpt-4o`, `openai:gpt-4.1`, `openai:gpt-5`
- `anthropic:claude-sonnet-4`, `anthropic:claude-opus-4.1`
- `anthropic:claude-sonnet-4.5`, `anthropic:claude-opus-4.5`
- Any OpenRouter slug (e.g., `google/gemini-2.5-flash`)
## Persistence (Stateful Agents)
@agentic(persist=True)
async def chatbot(message: str) -> str:
"""Remembers conversation history."""
...
await chatbot("My name is Alice")
await chatbot("What's my name?") # Knows: Alice
For `spawn()` agents, state is automatic across calls to the same instance.
## Token Limits
from agentica import spawn, MaxTokens
Simple limit
agent = await spawn(
premise="Brief responses",
max_tokens=500
)
Fine-grained control
agent = await spawn(
premise="Controlled output",
max_tokens=MaxTokens(
per_invocation=5000, # Total across all rounds
per_round=1000, # Per inference round
rounds=5 # Max inference rounds
)
)
## Token Usage Tracking
from agentica import spawn, last_usage, total_usage
agent = await spawn(premise="You are helpful.")
await agent.call(str, "Hello!")
Agent method
usage = agent.last_usage()
print(f"Last: {usage.input_tokens} in, {usage.output_tokens} out")
usage = agent.total_usage()
print(f"Total: {usage.total_tokens} processed")
For @agentic functions
@agentic()
async def my_fn(x: str) -> str: ...
await my_fn("test")
print(last_usage(my_fn))
print(total_usage(my_fn))
## Streaming
from agentica import spawn
from agentica.logging.loggers import StreamLogger
import asyncio
agent = await spawn(premise="You are helpful.")
stream = StreamLogger()
with stream:
result = asyncio.create_task(
agent.call(bool, "Is Paris the capital of France?")
)
Consume stream FIRST for live output
async for chunk in stream:
print(chunk.content, end="", flush=True)
chunk.role is 'user', 'agent', or 'system'
Then await result
final = await result
## MCP Integration
from agentica import spawn, agentic
Via config file
agent = await spawn(
premise="Tool-using agent",
mcp="path/to/mcp_config.json"
)
@agentic(mcp="path/to/mcp_config.json")
async def tool_user(query: str) -> str:
"""Uses MCP tools."""
...
**mcp_config.json format:**
{
"mcpServers": {
"tavily-remote-mcp": {
"command": "npx -y mcp-remote https://mcp.tavily.com/mcp/?tavilyApiKey=<key>",
"env": {}
}
}
}
## Logging
### Default Behavior
- Prints to stdout with colors
- Writes to `./logs/agent-<id>.log`
### Contextual Logging
from agentica.logging.loggers import FileLogger, PrintLogger
from agentica.logging.agent_logger import NoLogging
File only
with FileLogger():
agent = await spawn(premise="Debug agent")
await agent.call(int, "Calculate")
Silent
with NoLogging():
agent = await spawn(premise="Silent agent")
### Per-Agent Logging
Listeners are in agent_listener submodule (NOT exported from agentica.logging)
from agentica.logging.agent_listener import (
PrintOnlyListener, # Console output only
FileOnlyListener, # File logging only
StandardListener, # Both console + file (default)
NoopListener, # Silent - no logging
)
agent = await spawn(
premise="Custom logging",
listener=PrintOnlyListener
)
Silent agent
agent = await spawn(
premise="Silent agent",
listener=NoopListener
)
### Global Config
from agentica.logging.agent_listener import (
set_default_agent_listener,
get_default_agent_listener,
PrintOnlyListener,
)
set_default_agent_listener(PrintOnlyListener)
set_default_agent_listener(None) # Disable all
## Error Handling
from agentica.errors import (
AgenticaError, # Base for all SDK errors
RateLimitError, # Rate limiting
InferenceError, # HTTP errors from inference
MaxTokensError, # Token limit exceeded
MaxRoundsError, # Max inference rounds exceeded
ContentFilteringError, # Content filtered
APIConnectionError, # Network issues
APITimeoutError, # Request timeout
InsufficientCreditsError,# Out of credits
OverloadedError, # Server overloaded
ServerError, # Generic server error
)
try:
result = await agent.call(str, "Do something")
except RateLimitError:
await asyncio.sleep(60)
result = await agent.call(str, "Do something")
except MaxTokensError:
# Reduce scope or increase limits
pass
except ContentFilteringError:
# Content was filtered
pass
except InferenceError as e:
logger.error(f"Inference failed: {e}")
except AgenticaError as e:
logger.error(f"SDK error: {e}")
### Custom Exceptions
class DataValidationError(Exception):
"""Invalid input data."""
pass
@agentic(DataValidationError) # Pass exception type
async def analyze(data: str) -> dict:
"""
Analyze data.
Raises:
DataValidationError: If data is malformed
"""
...
try:
result = await analyze(raw_data)
except DataValidationError as e:
logger.warning(f"Invalid: {e}")
## Multi-Agent Patterns
### Custom Agent Class
from agentica.agent import Agent
class ResearchAgent:
def __init__(self, web_search_fn):
self._brain = Agent(
premise="Research assistant.",
scope={"web_search": web_search_fn}
)
async def research(self, topic: str) -> str:
return await self._brain(str, f"Research: {topic}")
async def summarize(self, text: str) -> str:
return await self._brain(str, f"Summarize: {text}")
### Agent Orchestration
class LeadResearcher:
def __init__(self):
self._brain = Agent(
premise="Coordinate research across subagents.",
scope={"SubAgent": ResearchAgent}
)
async def __call__(self, query: str) -> str:
return await self._brain(str, query)
lead = LeadResearcher()
report = await lead("Research AI agent frameworks 2025")
## Tracing & Debugging
### OpenTelemetry Tracing
from agentica import initialize_tracing
Initialize tracing (returns TracerProvider)
tracer = initialize_tracing(
service_name="my-agent-app",
environment="development", # Optional
tempo_endpoint="http://localhost:4317", # Optional: Grafana Tempo
organization_id="my-org", # Optional
log_level="INFO", # DEBUG, INFO, WARNING, ERROR
instrument_httpx=False, # Optional: trace HTTP calls
)
### SDK Debug Logging
from agentica import enable_sdk_logging
Enable internal SDK logs (for debugging the SDK itself)
disable_fn = enable_sdk_logging(log_tags="1")
... run agents ...
disable_fn() # Disable when done
## Top-Level Exports
Main imports from agentica
from agentica import (
# Core
Agent, # Synchronous agent class
agentic, # @agentic decorator
spawn, # Async agent creation
# Configuration
ModelStrings, # Model string type hints
AgenticFunction, # Agentic function type
# Token tracking
last_usage, # Get last call's token usage
total_usage, # Get cumulative token usage
# Tracing/Logging
initialize_tracing, # OpenTelemetry setup
enable_sdk_logging, # SDK debug logs
# Version
__version__, # "0.3.1"
)