agentpack

agentpack

Zero-Config Python SDK for Agentpack Tools

Drop a .py file in your repo and get a production-ready, containerized, gRPC-enabled tool—instantly. No servers. No Docker. No infrastructure code.

Installation

pip install agentpack
# or
poetry add agentpack
# or
uv pip install agentpack

Quick Start

1. Write ONLY Business Logic

# tools/greet.py
from pydantic import BaseModel
from agentpack import tool, ExecutionContext

class GreetInput(BaseModel):
    name: str

class GreetOutput(BaseModel):
    greeting: str

@tool(
    name="greet",
    description="Greet a person by name",
    input_schema=GreetInput,
    output_schema=GreetOutput,
)
async def greet(input: GreetInput, ctx: ExecutionContext) -> GreetOutput:
    ctx.logger.info("Greeting user", name=input.name)
    return GreetOutput(greeting=f"Hello, {input.name}!")

2. That's It!

No step 2. Agentpack Builder automatically:

• Discovers tools in tools/ directory
• Generates .agentpack-manifest.json
• Creates optimized Dockerfile
• Builds OCI image with gRPC runtime
• Deploys to container backend

Agentpack Runtime automatically:

• Loads tools from manifest
• Starts gRPC server (port 50051)
• Handles execution via Rust core
• Provides full observability

Zero-Config Architecture

┌─────────────────────────────────────────────────┐
│  YOU: Write tools/calculator.py                 │
│  ↓                                              │
│  BUILDER: Scan → Manifest → Dockerfile → Image │
│  ↓                                              │
│  RUNTIME: Load → gRPC Server → Execute         │
│  ↓                                              │
│  RUST CORE: Orchestrate with Tower middleware  │
└─────────────────────────────────────────────────┘

You own: Business logic (100% focus) Agentpack owns: Servers, containers, orchestration, observability

Features

Built-In, Zero Setup:

• ✅ Type-Safe: Full type inference with Pydantic
• ✅ Validated: Automatic input/output validation
• ✅ Observable: OpenTelemetry tracing (no config)
• ✅ Secure: Secret management via dashboard
• ✅ Resilient: HTTP client with automatic retries
• ✅ Structured Logging: Request-scoped context
• ✅ gRPC: High-performance protocol (auto-configured)
• ✅ Containerized: OCI images with base runtime
• ✅ Sandboxed: gVisor/Firecracker isolation

Execution Context

Every tool handler receives ctx with production-ready utilities:

Secrets

# Get optional secret
api_key = await ctx.secrets.get("API_KEY")

# Get required secret (raises if missing)
api_key = await ctx.secrets.require("API_KEY")

# Secrets configured via Agentpack Dashboard:
# - vault://kv/my-secret
# - aws-sm://secret-name
# - gcp-sm://projects/*/secrets/*

HTTP Client

# GET with automatic retries
response = await ctx.http.get(
    "https://api.example.com/data",
    params={"limit": 10},
    headers={"Authorization": f"Bearer {token}"},
)

# POST, PUT, DELETE also available
response = await ctx.http.post(
    "https://api.example.com/data",
    json={"name": "value"},
)

Logger

ctx.logger.info("Processing request", user_id="123")
ctx.logger.error("Something went wrong", error=str(err))

# Logs automatically include:
# - Request ID
# - Trace ID
# - Tool name
# - Timestamp

Tracer (OpenTelemetry)

span = ctx.trace.start_span("external-api-call")
try:
    # ... do work ...
    span.set_attribute("items.count", len(items))
finally:
    span.end()

# Traces sent to OpenTelemetry Collector automatically

Examples

See examples/ directory:

Simple Tool (examples/calculator.py)

from enum import Enum
from pydantic import BaseModel
from agentpack import tool, ExecutionContext

class Operation(str, Enum):
    ADD = "add"
    SUBTRACT = "subtract"
    MULTIPLY = "multiply"
    DIVIDE = "divide"

class CalculatorInput(BaseModel):
    operation: Operation
    a: float
    b: float

class CalculatorOutput(BaseModel):
    result: float
    operation: str

@tool(
    name="calculator",
    description="Perform basic arithmetic operations",
    input_schema=CalculatorInput,
    output_schema=CalculatorOutput,
)
async def calculator(input: CalculatorInput, ctx: ExecutionContext) -> CalculatorOutput:
    # Pure business logic
    if input.operation == Operation.ADD:
        result = input.a + input.b
    elif input.operation == Operation.SUBTRACT:
        result = input.a - input.b
    elif input.operation == Operation.MULTIPLY:
        result = input.a * input.b
    elif input.operation == Operation.DIVIDE:
        if input.b == 0:
            raise ValueError("Division by zero")
        result = input.a / input.b

    return CalculatorOutput(
        result=result,
        operation=f"{input.a} {input.operation.value} {input.b}"
    )

External API Tool (examples/weather.py)

Demonstrates:

• Secret management (ctx.secrets.require())
• HTTP client with retries (ctx.http.get())
• Structured logging
• Input validation with Pydantic

API Reference

@tool Decorator

Create a tool definition.

from pydantic import BaseModel
from agentpack import tool, ExecutionContext

class MyInput(BaseModel):
    value: int

class MyOutput(BaseModel):
    result: int

@tool(
    name="my_tool",                    # Unique tool identifier
    description="My tool description", # Human-readable description
    input_schema=MyInput,              # Pydantic input model
    output_schema=MyOutput,            # Pydantic output model
)
async def my_tool(input: MyInput, ctx: ExecutionContext) -> MyOutput:
    return MyOutput(result=input.value * 2)

Parameters:

• name (str): Unique tool name
• description (str): Human-readable description
• input_schema (BaseModel): Pydantic input validation model
• output_schema (BaseModel): Pydantic output validation model

Returns: Tool instance (internal use by runtime)

Error Handling

from agentpack import (
    ToolError,
    ValidationError,
    SecretNotFoundError,
    HttpError,
)

async def my_handler(input: MyInput, ctx: ExecutionContext) -> MyOutput:
    # Validation errors raised automatically by Pydantic

    # Secret not found
    key = await ctx.secrets.require("API_KEY")  # raises SecretNotFoundError

    # HTTP errors from external APIs
    response = await ctx.http.get("...")  # raises HttpError on failure

    # Custom errors
    if not input.valid:
        raise ToolError("Invalid input", "INVALID_INPUT")

Multi-File Tools

Tools can have dependencies:

tools/
├── calculator/
│   ├── __init__.py      # Entry point (exports tool)
│   ├── operations.py    # Helper functions
│   └── types.py         # Shared types
└── weather.py           # Single-file tool

Builder discovers both patterns automatically:

• Single-file: tools/weather.py
• Multi-file: tools/calculator/__init__.py

Testing

import pytest
from pydantic import BaseModel
from agentpack import tool, ExecutionContext

class TestInput(BaseModel):
    value: int

class TestOutput(BaseModel):
    result: int

@tool(
    name="test_tool",
    description="Test tool",
    input_schema=TestInput,
    output_schema=TestOutput,
)
async def test_tool(input: TestInput, ctx: ExecutionContext) -> TestOutput:
    return TestOutput(result=input.value * 2)

@pytest.mark.asyncio
async def test_my_tool():
    # Create mock context
    from agentpack.context import create_execution_context
    from agentpack.types import RequestMetadata

    metadata = RequestMetadata(request_id="123")
    ctx = create_execution_context(metadata)

    # Execute tool
    result = await test_tool.execute({"value": 5}, ctx)
    assert result.result == 10

Deployment

Nothing to do! Builder + Runtime handle everything:

1. Build: agentpack build (or automatic via GitHub App)
2. Deploy: Automatic to Agentpack infrastructure
3. Scale: Automatic based on load
4. Monitor: Traces/logs in dashboard

Configuration

Optional agentpack.yaml for advanced settings:

version: 0.1
project: my-tools

agents:
  - name: default
    model: { provider: anthropic, name: claude-3-5-sonnet }
    tools: ["*"]  # All tools in tools/

resources:
  - name: stripe_secret
    type: secret
    source: vault://kv/stripe

policies:
  outbound_network:
    allow: ["*.stripe.com", "*.openai.com"]

Thin SDK Philosophy

This SDK is intentionally minimal:

• ✅ @tool decorator for definitions
• ✅ ExecutionContext types
• ✅ Error classes
• ❌ NO server creation
• ❌ NO Docker setup
• ❌ NO deployment code

All orchestration lives in Rust core for:

• Consistent cross-language behavior
• Performance (Tower middleware)
• Security (policy enforcement)
• Observability (OpenTelemetry)

Type Checking

The SDK is fully typed and works with mypy:

mypy tools/ --strict

Troubleshooting

"Tool not found" error

• Ensure tool is in tools/ directory
• Check file exports tool with @tool decorator
• Verify .agentpack-manifest.json was generated

Validation errors

• Check Pydantic model matches input
• Use Optional[T] or = Field(default=...) for optional fields
• Test with mock data

Secret not found

• Configure secrets in Agentpack Dashboard
• Use correct secret key name
• Check environment variables in development

Related Packages

• agentpack-runtime-python - Internal gRPC runtime (private)
• @agentpack/sdk - TypeScript SDK (same zero-config approach)

License

MIT OR Apache-2.0