ai5labs-relay 2.0.0

Official Python SDK for the Relay AI Gateway. One key, every model.

Relay AI SDK

Official Python SDK for the Relay AI Gateway. One key, every model.

pip install ai5labs-relay

With OpenTelemetry:

pip install ai5labs-relay[otel]

Quick start

from relay_ai import Relay

client = Relay(api_key="sk-relay-...")

response = client.chat("claude-sonnet-4.6", messages=[
    {"role": "user", "content": "Explain quantum computing in one sentence."}
])
print(response.text)
print(f"Tokens: {response.usage.total_tokens}")

Streaming

with client.chat("gemini-3.5-flash", messages=[
    {"role": "user", "content": "Write a haiku about code."}
], stream=True) as stream:
    for chunk in stream:
        print(chunk.text, end="", flush=True)

    final = stream.get_final_response()
    print(f"\nTokens: {final.usage.total_tokens}")

Async

from relay_ai import AsyncRelay

async with AsyncRelay() as client:
    response = await client.chat("claude-opus-4.8", messages=[
        {"role": "user", "content": "Hello!"}
    ])
    print(response.text)

Tool calling

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get current weather",
        "parameters": {
            "type": "object",
            "properties": {"city": {"type": "string"}},
            "required": ["city"],
        },
    },
}]

response = client.chat("claude-sonnet-4.6", messages=[
    {"role": "user", "content": "What's the weather in Tokyo?"}
], tools=tools)

for tc in response.tool_calls:
    print(f"{tc.function_name}({tc.function_arguments})")

Image generation

result = client.images("flux-schnell", prompt="A cat astronaut on Mars")
print(result.images[0])

Audio

# Transcription
transcript = client.transcribe("whisper-1", "meeting.mp3")
print(transcript.text)

# Text-to-speech
audio = client.speech("tts-1", "Hello from Relay!")
with open("output.mp3", "wb") as f:
    f.write(audio.audio)

Semantic routing

decision = client.route(
    messages=[{"role": "user", "content": "Prove the Riemann hypothesis"}],
    candidates=["claude-opus-4.8", "claude-sonnet-4.6", "gemini-3.5-flash"],
)
print(f"Best model: {decision.alias} ({decision.confidence:.0%})")
print(f"Reasoning: {decision.reasoning}")

Batch processing

results = client.batch("claude-sonnet-4.6", [
    {"messages": [{"role": "user", "content": "What is 2+2?"}]},
    {"messages": [{"role": "user", "content": "What is 3+3?"}]},
    {"messages": [{"role": "user", "content": "What is 4+4?"}]},
], max_concurrent=5)

for r in results:
    if r.response:
        print(f"[{r.index}] {r.response.text}")
    else:
        print(f"[{r.index}] Error: {r.error}")

Credits

state = client.credits()
print(f"Balance: ${state.balance_cents / 100:.2f}")

Error handling

from relay_ai import (
    RelayError,
    AuthenticationError,
    RateLimitError,
    InsufficientCreditsError,
    ModelNotFoundError,
)

try:
    response = client.chat("gpt-5", messages=[...])
except AuthenticationError:
    print("Invalid API key")
except RateLimitError as e:
    print(f"Rate limited. Retry after {e.retry_after}s")
except InsufficientCreditsError:
    print("Top up your credits at relay.ai5labs.com")
except ModelNotFoundError:
    print("Model not found")
except RelayError as e:
    print(f"Error: {e.message}")

CLI

export RELAY_API_KEY=sk-relay-...

relay models                                # List models
relay chat claude-sonnet-4.6 "Hello!"       # Quick chat
relay chat gemini-3.5-flash "Hi" --stream   # Stream tokens
relay credits                               # Check balance
relay version                               # SDK version

Configuration

client = Relay(
    api_key="sk-relay-...",       # or set RELAY_API_KEY env var
    base_url="https://...",       # custom gateway URL
    timeout=120.0,                # request timeout (seconds)
    max_retries=2,                # automatic retries on 429/5xx
    send_telemetry=True,          # usage analytics (metadata only)
    http_client=httpx.Client(),   # custom httpx client
)

Telemetry

The SDK sends anonymous usage metadata (model, token counts, latency) to improve the service. No message content, prompts, responses, or tool arguments are ever transmitted. This is enforced by a client-side allowlist and verified by server-side stripping.

Disable with:

client = Relay(send_telemetry=False)

OpenTelemetry

from relay_ai import Relay
from relay_ai._otel import instrument, RelaySpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

provider = TracerProvider()
provider.add_span_processor(
    BatchSpanProcessor(
        RelaySpanExporter(api_key="sk-relay-...", base_url="https://api.relay.ai5labs.com/v1")
    )
)

client = instrument(Relay())
response = client.chat(...)  # Automatically creates OTel spans

License

MIT