eap-sdk 0.1.0

Domain-neutral orchestration SDK (flows/steps, local/remote, DI).

EAP SDK

A lightweight, domain-neutral orchestration SDK for authoring async flows and steps with a small runtime and CLI. The EAP SDK provides decorator-based flow and step definitions with dependency injection support.

Features

• Flow Decorators: @flow decorator for async business logic orchestration
• Step/Task Decorators: @step and @task decorators with retry, timeout, and backoff support
• RunContext: Dependency injection container for services and configuration
• Local & Remote Execution: Run flows locally or via HTTP transport
• Remote Execution: .serve() method for fire-and-forget remote execution
• Entrypoints: Module:function format for robot execution
• Flow Specs: Structured flow specifications for orchestration
• Plugin System: Register services via register_service() for dependency injection
• CLI Tool: Command-line interface for running flows
• Telemetry: Optional Prometheus metrics integration

Installation

The EAP SDK is part of the Enterprise Assistant Platform workspace. Install dependencies:

uv sync --all-packages

Or install as a standalone package:

pip install eap-sdk

Quick Start

Defining a Flow

from eap_sdk import RunContext, flow, step, task

@step(retries=3, timeout_s=30.0)
async def fetch_data(ctx: RunContext, source: str) -> dict:
    """Fetch data from a source."""
    api = ctx.get("api")  # Access injected service
    result = await api.fetch(source)
    return {"data": result}

# @task is an alias for @step
@task(retries=2, timeout_s=20.0)
async def process_data(ctx: RunContext, data: dict) -> dict:
    """Process data."""
    return {"processed": data}

@flow("my_flow")
async def my_flow(ctx: RunContext, name: str, count: int = 1) -> dict:
    """Example flow that orchestrates steps."""
    data = await fetch_data(ctx, f"source/{name}")
    processed = await process_data(ctx, data)
    return {
        "success": True,
        "processed": count,
        "result": processed
    }

Running a Flow

Using Python:

from eap_sdk import RunContext
from eap_sdk.decorators import get_flow

# Get registered flow
flow_fn = get_flow("my_flow")

# Create context and inject services
ctx = RunContext(run_id="run_123", tenant="acme")
ctx.set("api", my_api_client)

# Execute flow locally
result = await flow_fn(ctx, name="test", count=5)

# Or use remote execution via .serve() method
import os
os.environ["MAESTRO_HTTP"] = "http://maestro:8000"

# Submit for remote execution (returns run_id immediately)
run_id = my_flow.serve(pool="prod", params={"name": "test", "count": 5})
print(f"Started run: {run_id}")

# With scheduling
run_id = my_flow.serve(
    pool="prod",
    schedule="0 9 * * *",  # Daily at 9 AM
    params={"name": "test", "count": 5}
)

Using CLI:

# Run flow locally
uv run eap-sdk run my_flow --runner local \
  --param name=test --param count=5

# Run flow remotely (requires MAESTRO_ADDR or ROBOT_ADDR)
uv run eap-sdk run my_flow --runner remote \
  --param-json '{"name": "test", "count": 5}'

Core Concepts

RunContext

RunContext provides dependency injection and metadata:

from eap_sdk import RunContext

ctx = RunContext(
    run_id="unique_run_id",
    tenant="acme",
    labels={"env": "production"}
)

# Set services
ctx.set("api", api_client)
ctx.set("db", database_connection)

# Get services
api = ctx.get("api")
db = ctx.get("db")

Flow Decorator

Flows are async functions decorated with @flow(name):

@flow("process_data")
async def process_data(ctx: RunContext, input_data: dict) -> dict:
    # Flow logic here
    return {"success": True, "output": processed}

Requirements:

• Must be async
• First parameter must be RunContext
• Return type should be dict
• Registered automatically on import

Step/Task Decorator

Steps are functions (sync or async) decorated with @step(...) or @task(...):

@step(retries=3, timeout_s=30.0, backoff=1.5, base_delay=0.5)
async def api_call(ctx: RunContext, endpoint: str) -> dict:
    # Step logic with automatic retry
    return await http_client.get(endpoint)

# @task is an alias for @step
@task(retries=2, timeout_s=20.0)
async def process_data(ctx: RunContext, data: dict) -> dict:
    return {"processed": data}

Parameters:

• retries: Number of retry attempts (default: 0)
• timeout_s: Timeout in seconds (default: None)
• backoff: Exponential backoff multiplier (default: 1.5)
• base_delay: Base delay in seconds (default: 0.5)
• jitter: Random jitter factor (default: 0.2)

Features:

• Automatic retry on failure
• Timeout enforcement (sync steps run in thread pool)
• Exponential backoff with jitter

Note: @task and @step are interchangeable aliases. Use whichever naming convention fits your project.

Service Registration

Register services for dependency injection:

from eap_sdk.plugins import register_service

def create_api_client(ctx: RunContext):
    return ApiClient(host=ctx.tenant)

register_service("api", create_api_client)

Services are automatically built when creating a RunContext via build_services().

Runtime

Local Execution

from eap_sdk.runtime import arun

result = await arun("my_flow", runner="local", name="test", count=5)

Remote Execution

import os
os.environ["MAESTRO_ADDR"] = "http://maestro:8000"

result = await arun("my_flow", runner="remote", name="test", count=5)

Transport Protocols

• LocalTransport: Executes flows in-process
• HTTPTransport: Sends flow execution requests via HTTP

CLI Usage

# Basic usage
eap-sdk run <flow_name> [options]

# Options
--runner {local,remote}    Execution mode (default: local)
--param k=v                Parameter key-value pairs (repeatable)
--param-json JSON          JSON object with parameters

# Examples
eap-sdk run my_flow --param name=test --param count=5
eap-sdk run my_flow --param-json '{"name": "test", "count": 5}'
eap-sdk run my_flow --runner remote --param name=test

Parameter Coercion:

• --param values are auto-coerced (JSON, bool, number if possible)
• --param-json accepts a JSON object that's merged into parameters

Error Handling

Flows should return {"success": bool, ...} dictionaries:

@flow("safe_flow")
async def safe_flow(ctx: RunContext, data: dict) -> dict:
    try:
        result = await process(data)
        return {"success": True, "data": result}
    except Exception as e:
        return {"success": False, "error": str(e)}

The runtime wraps exceptions in RunResponse if they escape the flow.

Telemetry

Optional Prometheus metrics (requires prometheus-client):

• eap_jobs_total: Total jobs executed
• eap_jobs_failed_total: Failed jobs
• eap_step_duration_seconds: Step execution duration
• eap_step_retries_total: Step retry count

Install with observability extras:

pip install eap-sdk[observability]

Development

Running Tests

pytest tests/eap_sdk/

Linting & Formatting

ruff format src/eap_sdk/
ruff check src/eap_sdk/

Type Checking

pyright src/eap_sdk/

Architecture

eap_sdk/
├── __init__.py          # Public API exports
├── context.py           # RunContext class
├── decorators.py        # @flow, @step, and @task decorators
├── runtime.py           # Execution runtime (local/remote), flow specs, entrypoints
├── plugins.py           # Service registration system
├── contracts.py         # RunRequest/RunResponse models
├── telemetry.py         # Prometheus metrics
├── blocks.py            # Reusable blocks (HttpAuthBlock)
└── cli.py               # Command-line interface

New Features

Remote Execution with .serve()

The .serve() method provides a convenient way to submit flows for remote execution:

@flow("my_flow")
async def my_flow(ctx: RunContext, name: str) -> dict:
    return {"result": name}

# Set Maestro endpoint
import os
os.environ["MAESTRO_HTTP"] = "http://maestro:8000"

# Submit for execution
run_id = my_flow.serve(pool="prod", params={"name": "test"})

Benefits:

• Fire-and-forget execution (returns run_id immediately)
• Supports scheduling via cron expressions
• Automatic flow spec generation
• Better integration with orchestration systems

Entrypoints

Flows automatically generate entrypoints for robot execution:

@flow("my_flow")
async def my_flow(ctx: RunContext, name: str) -> dict:
    return {"result": name}

# Entrypoint is automatically stored
print(my_flow._eap_flow_entry)  # "my_module:my_flow"

# Robots can execute flows using entrypoints
from eap_sdk.runtime import run_entrypoint
result = run_entrypoint("my_module:my_flow", {"name": "test"})

Task Decorator

The @task decorator is an alias for @step:

from eap_sdk import task

@task(retries=3, timeout_s=30.0)
async def my_task(ctx: RunContext, data: dict) -> dict:
    return {"processed": data}

Use @task if you prefer that naming convention, or stick with @step - both work identically.

Requirements

• Python >= 3.10
• httpx >= 0.27, < 1.0
• pydantic >= 2.8, < 3.0

License

MIT License

This README includes:
- Overview and features
- Installation instructions
- Quick start examples
- Core concepts (RunContext, flows, steps)
- Service registration
- Runtime execution modes
- CLI usage
- Error handling
- Telemetry
- Development setup
- Architecture overview