starseer 0.1.0

Route LLM calls through Starport gateway

Starseer Python SDK

Route LLM API calls through Starport gateway.

Install

pip install starseer

Quick Setup (CLI) — Agent Frameworks

The SDK ships a starseer install CLI that configures AI agent frameworks (e.g. Claude Code) to route their traffic through Starport. If you want to run that tool directly from the repo rather than installing via pip:

git clone https://github.com/Starseer-ai/starseer-python-sdk
cd starseer-python-sdk
uv sync
uv run starseer install
#uv run python -m starseer install #(on Windows)

Configure AI agent frameworks (Claude Code, etc.) to use Starport with a single command:

# Interactive — walks you through setup
starseer install

# Silent — provide all options via flags
starseer install --api-key ssk_... --frameworks claude-code --scope global

Run starseer install --help for all options.

Usage — Python SDK

import starseer
import openai

starseer.protect(openai)

client = openai.OpenAI()
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)

Async clients are also supported:

client = openai.AsyncOpenAI()
response = await client.chat.completions.create(...)

To restore original SDK behavior:

starseer.unprotect(openai)

Tracing

Group multiple LLM calls under a single trace for correlated observability:

with starseer.trace() as t:
    response1 = openai_client.chat.completions.create(...)
    response2 = anthropic_client.messages.create(...)
    print(f"Trace ID: {t.trace_id}")

All calls within the context share the same W3C trace ID.

Configuration

Set your virtual key via environment variable:

export STARSEER_API_KEY=ssk_...

Or configure in code:

starseer.configure(
    api_key="ssk_...",
    gateway_url="https://gateway.starseer.ai",  # optional
    openai_route="/openai/v1",              # optional, customize routing
    anthropic_route="/anthropic",           # optional
    auto_trace=True,                        # optional, auto-generate trace headers
)

Environment Variables

Variable	Required	Default
STARSEER_API_KEY	Yes	-
STARSEER_GATEWAY_URL	No	https://gateway.starseer.ai

vLLM Routing

Route OpenAI SDK calls to a vLLM backend through the gateway:

starseer.configure(
    gateway_url="http://localhost:8080",
    api_key="ssk_...",
    openai_route="/vllm/v1"
)

starseer.protect(openai)

client = openai.OpenAI()
response = client.chat.completions.create(
    model="meta-llama/Llama-3-8b",
    messages=[{"role": "user", "content": "Hello!"}]
)

vLLM provides an OpenAI-compatible API, you can use the standard OpenAI SDK with the openai_route pointed to your vLLM endpoint.

Cross-Provider Routing (Starport)

Use the OpenAI SDK to call Anthropic or other providers through Starport's OpenAI-compatible translation layer:

import starseer
import openai

starseer.configure(
    gateway_url="https://your-starport-proxy.com",
    openai_route="/anthropic/openai/v1"
)

starseer.protect(openai)

client = openai.OpenAI()
response = client.chat.completions.create(
    model="claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Hello!"}]
)

This allows you to use OpenAI SDK patterns while routing requests to Anthropic models on the backend.

Supported SDKs

• openai (sync and async)
• anthropic (sync and async)
• google-genai (also detects the deprecated google-generativeai)

Development

# Install with dev dependencies
uv sync --dev

# Run tests
uv run pytest

# Lint and format
uv run ruff check src/
uv run ruff format src/