agentica-sdk

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

Repositório: parcadei/Continuous-Claude-v3
Última atividade na origem: 10 de janeiro de 2026 às 01:54
Idioma detectado do SKILL.md: inglês
Estrelas: 3.885
Forks: 296

Opções de instalação

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npx skills add https://github.com/parcadei/Continuous-Claude-v3 --skill agentica-sdk

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SKILL.md

Instruções da origem · Visualização somente leitura

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commit

Create git commits with user approval and no Claude attribution

2026-01-22

continuity-ledger

Create or update continuity ledger for state preservation across clears

2026-01-22

create-handoff

Create handoff document for transferring work to another session

2026-01-22

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# 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: 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.")

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'
)

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 = await spawn(premise="Helpful assistant")

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)

# 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."""
    ...

@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

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
    )
)

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))

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

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."""
    ...

{
  "mcpServers": {
    "tavily-remote-mcp": {
      "command": "npx -y mcp-remote https://mcp.tavily.com/mcp/?tavilyApiKey=<key>",
      "env": {}
    }
  }
}

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")

# 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
)

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

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}")

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}")

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}")

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")

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
)

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

# 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"
)

name	agentica-sdk
description	Build Python agents with Agentica SDK - @agentic decorator, spawn(), persistence, MCP integration
allowed-tools	["Bash","Read","Write","Edit"]

agentica-sdk

Informações da origem

Opções de instalação

Revise os arquivos de origem

Mais deste repositório

Mais deste repositório

Agentica SDK Reference (v0.3.1)

When to Use

Quick Start

Agentic Function (simplest)

Spawned Agent (more control)

Agentica SDK Reference (v0.3.1)

When to Use

Quick Start

Agentic Function (simplest)

Spawned Agent (more control)

Core Patterns

Return Types

Premise vs System Prompt

Passing Tools (Scope)

SDK Integration Pattern

Agent Instantiation

spawn() - Async (most cases)

Agent() - Sync (for __init__)

Model Selection

Persistence (Stateful Agents)

Token Limits

Token Usage Tracking

Streaming

MCP Integration

Logging

Default Behavior

Contextual Logging

Per-Agent Logging

Global Config

Error Handling

Custom Exceptions

Multi-Agent Patterns

Custom Agent Class

Agent Orchestration

Tracing & Debugging

OpenTelemetry Tracing

SDK Debug Logging

Top-Level Exports

Checklist

Agent() - Sync (for `init`)