runlet 0.2.0a2

A tiny observable runtime for Python agents.

Runlet

Runlet is a tiny observable runtime for Python agents.

The project is a library, not an application framework. Its core direction is a provider-neutral, async-first agent runtime with strict context budgeting, structured observability, and flexible hooks around model and tool execution.

Current Status

This repository now contains an MVP runtime skeleton with core contracts, events, tools, hooks, context budgeting, streaming, provider adapters, and in-memory state. The API is not stable yet.

Design Goals

• Keep the core runtime small and embeddable.
• Treat context budgeting and compression as mandatory runtime safety checks.
• Expose hooks before and after model calls, tool calls, state operations, and context compression.
• Emit structured events for runs, steps, model calls, tool calls, context changes, state changes, and failures.
• Stay provider-neutral: model SDKs integrate through adapters, not core dependencies.

Non-Goals

Runlet core does not aim to provide:

• A web application framework.
• A hosted agent platform.
• A task queue or worker system.
• A UI or trace viewer.
• A multi-tenant control plane.
• A graph workflow engine in the first release.

Project Documents

• Architecture
• Design Principles
• Comparison
• Roadmap
• Contributing
• Security

Minimal Shape

from runlet import Agent, Runtime, tool


@tool
async def lookup(order_id: str) -> str:
    return f"order {order_id}"


agent = Agent(
    name="support",
    instructions="Help users with orders.",
    model=my_model_provider,
    tools=(lookup,),
)

result = await Runtime().run(agent, "Where is order 123?")

OpenAI Provider

Install the optional OpenAI dependency:

pip install "runlet[openai]"

Install the latest pre-release explicitly:

pip install --pre "runlet[openai]"

Minimal example:

from runlet import Agent, Runtime
from runlet.providers import OpenAIResponsesProvider


provider = OpenAIResponsesProvider(model="gpt-5.5")

agent = Agent(
    name="assistant",
    instructions="Be helpful.",
    model=provider,
)

result = await Runtime().run(agent, "Say hello in one sentence.")

Custom base URL:

from runlet.providers import OpenAIResponsesProvider


provider = OpenAIResponsesProvider(
    model="gpt-5.5",
    base_url="https://your-endpoint.example/v1",
)

Provider-specific request options:

from runlet.core import Message
from runlet.core.models import ModelRequest
from runlet.providers import OpenAIResponsesProvider


provider = OpenAIResponsesProvider(model="gpt-5.5")


request = ModelRequest(
    messages=[Message.user("Summarize this briefly.")],
    options={
        "openai": {
            "extra_body": {
                "reasoning": {"effort": "medium"},
            },
        },
    },
)

response = await provider.complete(request)

Streaming text deltas:

from runlet import Agent, Runtime
from runlet.providers import OpenAIResponsesProvider


provider = OpenAIResponsesProvider(model="gpt-5.5")
agent = Agent(
    name="assistant",
    instructions="Be helpful.",
    model=provider,
)

async for event in Runtime().stream(agent, "Explain recursion in one sentence."):
    if event.type == "model.stream.delta":
        print(event.payload["delta"], end="")

Streaming with tool execution:

from runlet import Agent, Runtime, tool
from runlet.providers import OpenAIResponsesProvider


@tool
async def lookup_order(order_id: str) -> str:
    return f"order {order_id} shipped"


provider = OpenAIResponsesProvider(model="gpt-5.5")
agent = Agent(
    name="assistant",
    instructions="Use tools when needed.",
    model=provider,
    tools=(lookup_order,),
)

async for event in Runtime().stream(agent, "Check order 123 and tell me the result."):
    if event.type == "model.stream.delta":
        print(event.payload["delta"], end="")

When the provider emits a tool call during streaming, Runtime.stream() now executes the tool, appends the tool result to the conversation, and continues the next model round until the run completes.

Current scope of the provider:

• complete() supported
• capabilities() supported
• stream() supported
• text deltas supported
• streaming tool execution through Runtime.stream() supported
• base_url supported
• options["openai"]["extra_body"] supported
• provider-specific request options stay under ModelRequest.options["openai"]

Current streaming contract:

• providers can emit provider-neutral streaming step events internally
• Runtime.stream() handles multi-round tool execution loops
• OpenAI is the first provider implementation of this contract

Development

Run the current test suite:

PYTHONPATH=src python3 -m unittest discover tests

Releasing

Runlet publishes to PyPI from Git tags through GitHub Actions.

Release flow:

1. Update [project].version in pyproject.toml
2. Merge the release commit to main
3. Create a version tag such as v0.2.0a1
4. Push the tag to GitHub

The publish workflow will:

• verify the Git tag matches pyproject.toml
• run the test suite
• build sdist and wheel
• validate package metadata
• publish to PyPI through Trusted Publishing

Example:

git tag v0.2.0a1
git push origin v0.2.0a1

Repository setup requirement:

• configure PyPI Trusted Publishing for this GitHub repository and the .github/workflows/publish.yml workflow