Prefactor Core

High-level Prefactor SDK with async queue-based processing.

Features

• Queue-Based Processing: Operations are queued and processed asynchronously by a worker pool
• Non-Blocking API: Agent execution is never blocked by observability calls
• Automatic Parent Detection: Nested spans automatically detect their parent from the context stack
• Schema Registry: Compose and register span schemas before instance creation
• Configurable Workers: Tune concurrency and retry behavior for the background queue

Installation

pip install prefactor-core

Quick Start

import asyncio
from prefactor_core import PrefactorCoreClient, PrefactorCoreConfig, SchemaRegistry
from prefactor_http import HttpClientConfig

registry = SchemaRegistry()
registry.register_type(
    name="agent:llm",
    params_schema={
        "type": "object",
        "properties": {
            "model": {"type": "string"},
            "prompt": {"type": "string"},
        },
        "required": ["model", "prompt"],
    },
    result_schema={
        "type": "object",
        "properties": {"response": {"type": "string"}},
    },
    title="LLM Call",
    description="A call to a language model",
    template="{{model}}: {{prompt}} → {{response}}",
)

async def main():
    config = PrefactorCoreConfig(
        http_config=HttpClientConfig(
            api_url="https://api.prefactor.ai",
            api_token="your-token",
        ),
        schema_registry=registry,
    )

    async with PrefactorCoreClient(config) as client:
        instance = await client.create_agent_instance(
            agent_id="my-agent",
            agent_version={"name": "My Agent", "external_identifier": "v1.0.0"},
        )

        await instance.start()

        async with instance.span("agent:llm") as span:
            await span.start({"model": "gpt-4", "prompt": "Hello"})
            result = await call_llm()
            await span.complete({"response": result})

        await instance.finish()

asyncio.run(main())

create_agent_instance() supports two auth modes:

• Account-scoped token: pass agent_id and usually environment_id.
• Deployment-scoped token: omit agent_id and environment_id; the API derives both from the token.

API Reference

PrefactorCoreClient

The main entry point. Use as an async context manager or call initialize() / close() manually.

client = PrefactorCoreClient(config)
await client.initialize()
# ... use client ...
await client.close()

create_agent_instance

handle = await client.create_agent_instance(
    agent_version={"name": "My Agent", "external_identifier": "v1.0.0"},
    agent_schema_version=None,        # Optional: auto-generated if schema_registry is configured
    agent_id="my-agent",              # Optional for deployment-scoped tokens
    external_schema_version_id=None,  # Optional: reference an existing schema version
) -> AgentInstanceHandle

span (context manager)

async with client.span(
    instance_id="instance_123",
    schema_name="agent:llm",
    parent_span_id=None,  # Optional: auto-detected from context stack if omitted
    payload=None,         # Optional: used as params if span.start() is never called explicitly
) as span:
    await span.start({"model": "gpt-4", "prompt": "Hello"})
    result = await call_llm()
    await span.complete({"response": result})

AgentInstanceHandle

Returned by create_agent_instance. Manages the lifecycle of a single agent instance.

handle.id  # -> str

await handle.start()
await handle.finish()

async with handle.span("agent:llm") as span:
    ...

SpanContext

The object yielded by span context managers. Spans follow a three-phase lifecycle:

1. Enter context — span is prepared locally, no HTTP call yet.
2. await span.start(payload) — POSTs the span to the API as active with the given params payload.
3. await span.complete(result) / span.fail(result) / span.cancel() — finishes the span with a terminal status.

If start() or a finish method is not called explicitly, the context manager handles them automatically on exit.

span.id                            # -> str (API-generated after start())

await span.start(payload: dict)    # POST span as active with params payload
await span.complete(result: dict)  # finish with status "complete"
await span.fail(result: dict)      # finish with status "failed"
await span.cancel()                # finish with status "cancelled"

span.set_result(data: dict)        # accumulate result data for auto-finish
await span.finish()                # finish with current status (default: "complete")

Status note: cancel() can be called before or after start(). If called before start(), the span is posted as pending and immediately cancelled — the only valid pre-active cancellation path the API supports.

Full lifecycle example

async with instance.span("agent:llm") as span:
    await span.start({"model": "gpt-4", "prompt": "Hello"})
    try:
        result = await call_llm()
        await span.complete({"response": result})
    except Exception as exc:
        await span.fail({"error": str(exc)})

# Cancel before starting (e.g. a conditional step that is skipped):
async with instance.span("agent:retrieval") as span:
    if not needed:
        await span.cancel()
    else:
        await span.start({"query": "..."})
        docs = await retrieve()
        await span.complete({"documents": docs, "count": len(docs)})

Configuration

from prefactor_core import PrefactorCoreConfig, QueueConfig
from prefactor_http import HttpClientConfig

config = PrefactorCoreConfig(
    http_config=HttpClientConfig(
        api_url="https://api.prefactor.ai",
        api_token="your-token",
    ),
    queue_config=QueueConfig(
        num_workers=3,        # Number of background workers
        max_retries=3,        # Retries per operation
        retry_delay_base=1.0, # Base delay (seconds) for exponential backoff
    ),
    schema_registry=None,  # Optional: SchemaRegistry instance
)

Schema Registry

Use SchemaRegistry to compose span schemas from multiple sources and auto-generate the agent_schema_version passed to create_agent_instance.

from prefactor_core import SchemaRegistry

registry = SchemaRegistry()

registry.register_type(
    name="agent:llm",
    params_schema={
        "type": "object",
        "properties": {
            "model": {"type": "string"},
            "prompt": {"type": "string"},
        },
        "required": ["model", "prompt"],
    },
    result_schema={
        "type": "object",
        "properties": {"response": {"type": "string"}},
    },
    title="LLM Call",
    description="A call to a language model",
    template="{{model}}: {{prompt}} → {{response}}",
)
registry.register_type(
    name="agent:tool",
    params_schema={"type": "object", "properties": {...}},
    result_schema={"type": "object", "properties": {...}},
    title="Tool Call",
)

config = PrefactorCoreConfig(
    http_config=...,
    schema_registry=registry,
)

async with PrefactorCoreClient(config) as client:
    # agent_schema_version is generated automatically from the registry
    instance = await client.create_agent_instance(
        agent_version={"name": "My Agent", "external_identifier": "v1.0.0"},
        agent_id="my-agent",  # Optional for deployment-scoped tokens
    )

Error Handling

from prefactor_core import (
    PrefactorCoreError,
    ClientNotInitializedError,
    ClientAlreadyInitializedError,
    OperationError,
    InstanceNotFoundError,
    SpanNotFoundError,
)

Architecture

The client uses a three-layer design:

1. Queue infrastructure: InMemoryQueue + TaskExecutor worker pool process operations in the background
2. Managers: AgentInstanceManager and SpanManager translate high-level calls into Operation objects and route them to the HTTP client
3. Client API: PrefactorCoreClient exposes the user-facing interface and wires the layers together

All observability operations are enqueued and executed asynchronously — the calling code is never blocked waiting for API responses.

License

MIT