ssimplifi 1.8.0

Python SDK for Prism by Ssimplifi — the AI gateway that picks the model for you.

ssimplifi — Python SDK for Prism

pip install ssimplifi

Quick start

from ssimplifi import Prism

client = Prism(api_key="prism_sk_...")

response = client.chat.completions.create(
    messages=[{"role": "user", "content": "What's the capital of Australia?"}],
    mode="balanced",
)
print(response.choices[0].message.content)

That's it. The SDK extends the OpenAI Python client, pointed at Prism's base URL. Everything you already know about openai — streaming, retries, response shape — works identically.

Prism-specific kwargs (what you get over plain openai.OpenAI)

Kwarg	Header	What it does
mode="eco" / "balanced" / "sport"	X-Prism-Mode	Quality/cost mode for auto-routing
model_prefer="claude-sonnet"	X-Prism-Model-Prefer	Force a specific model (overrides auto-routing)
session_id="user-abc"	X-Prism-Session	Multi-turn memory; persists across model switches
cache="off" / "exact" / "semantic"	X-Prism-Cache	Cache behavior for this request
request_tags={"feature": "search"}	X-Prism-Request-Tags	Attribution tags for usage breakdown

# Multi-turn chat with session memory
response = client.chat.completions.create(
    messages=[{"role": "user", "content": "Remember: my name is Ravi"}],
    session_id="user-abc",
)
# Next call: no need to resend history
response = client.chat.completions.create(
    messages=[{"role": "user", "content": "What's my name?"}],
    session_id="user-abc",
)
print(response.choices[0].message.content)  # "Your name is Ravi."

Admin methods (Pro/Team tier)

# Live model catalog
catalog = client.models.list(provider="groq")  # filter optional
print(catalog["summary"])  # {"provider_count_active": 7, "model_count_total": 23, ...}

# Your usage
print(client.usage.summary(days=7))

# Cache stats
print(client.cache.stats(days=7))

# API keys
print(client.keys.list())
new = client.keys.create(name="staging-app")
print(new["key"])  # full secret shown ONCE
client.keys.revoke(key_id="key-id-from-list")

# Identity + balance
print(client.whoami())
print(client.balance())

Submitting feedback

The chat response includes an X-Prism-Feedback-Id header you can use to submit thumbs / ratings later:

response = client.chat.completions.create(
    messages=[{"role": "user", "content": "..."}],
    mode="balanced",
)

# Pull the feedback id from the response object
feedback_id = response.response.headers.get("X-Prism-Feedback-Id")

# Later (e.g. when the user clicks thumbs up in your UI)
client.submit_feedback(feedback_id=feedback_id, thumb="up")
client.submit_feedback(feedback_id=feedback_id, rating=5, comment="great")

Drop-in replacement for plain OpenAI

If you've already got code using openai.OpenAI, change two lines:

# Before
from openai import OpenAI
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# After
from ssimplifi import Prism
client = Prism(api_key=os.environ["PRISM_API_KEY"])

Every client.chat.completions.create(...) call you wrote continues to work. Add mode= and you've got auto-routing + caching + multi-provider failover, no code rewrite.

Compatibility

• Python 3.10+
• Builds on top of openai>=1.40,<2.0 and httpx>=0.27
• Tied to the Prism backend /v1/ major version — SDK 1.x works with any backend 1.x.

License

Apache-2.0