Celeste AI

The primitive layer for multi-modal AI

All modalities. All providers. One interface.

Primitives, not frameworks.

Quick Start • Request Provider

Celeste AI

Type-safe, modality/provider-agnostic primitives.

• Unified Interface: One API for OpenAI, Anthropic, Gemini, Mistral, and 14+ others.
• True Multi-Modal: Text, Image, Audio, Video, Embeddings, Search —all first-class citizens.
• Type-Safe by Design: Full Pydantic validation and IDE autocomplete.
• Zero Lock-In: Switch providers instantly by changing a single config string.
• Primitives, Not Frameworks: No agents, no chains, no magic. Just clean I/O.
• Lightweight Architecture: No vendor SDKs. Pure, fast HTTP.

🚀 Quick Start

import celeste

# One SDK. Every modality. Any provider.
text   = await celeste.text.generate("Explain quantum computing", model="claude-opus-4-5")
image  = await celeste.images.generate("A serene mountain lake at dawn", model="flux-2-pro")
speech = await celeste.audio.speak("Welcome to the future", model="eleven_v3")
video  = await celeste.videos.analyze(video_file, prompt="Summarize this clip", model="gemini-3.1-pro-preview")
embeddings = await celeste.text.embed(["lorep ipsum", "dolor sit amet"], model="gemini-embedding-2")

15+ providers. Zero lock-in.

and many more

Missing a provider? Request it – ⚡ we ship fast.

Operations by Domain

_{✓ Available · ○ Planned}

🔄 Switch providers in one line

from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

# Model IDs
anthropic_model_id = "claude-4-5-sonnet"
google_model_id = "gemini-2.5-flash"

# ❌ Anthropic Way
from anthropic import Anthropic
import json

client = Anthropic()
response = client.messages.create(
    model=anthropic_model_id,
    messages=[
        {"role": "user",
         "content": "Extract user info: John is 30"}
    ],
    output_format={
        "type": "json_schema",
        "schema": User.model_json_schema()
    }
)
user_data = json.loads(response.content[0].text)

# ❌ Google Gemini Way
from google import genai
from google.genai import types

client = genai.Client()
response = await client.aio.models.generate_content(
    model=gemini_model_id,
    contents="Extract user info: John is 30",
    config=types.GenerateContentConfig(
        response_mime_type="application/json",
        response_schema=User
    )
)
user = response.parsed

# ✅ Celeste Way
import celeste

response = await celeste.text.generate(
    "Extract user info: John is 30",
    model=google_model_id,  # <--- Choose any model from any provider
    output_schema=User,  # <--- Unified parameter working across all providers
)
user = response.content  # Already parsed as User instance

⚙️ Advanced: Create Client

For explicit configuration or client reuse, use create_client with modality + operation. This is modality-first: you choose the output type and operation explicitly.

from celeste import create_client, Modality, Operation, Provider

client = create_client(
    modality=Modality.TEXT,
    operation=Operation.GENERATE,
    provider=Provider.OLLAMA,
    model="llama3.2",
)
response = await client.generate("Extract user info: John is 30", output_schema=User)

capability is still supported but deprecated. Prefer modality + operation.

🪶 Install

uv add celeste-ai
# or
pip install celeste-ai

🔧 Type-Safe by Design

# Full IDE autocomplete
import celeste

response = await celeste.text.generate(
    "Explain AI",
    model="gpt-4o-mini",
    temperature=0.7,    # ✅ Validated against the model's declared Range
    max_tokens=100,     # ✅ Validated when the model declares a constraint
)

# Typed response
print(response.content)              # str (IDE knows the type)
print(response.usage.input_tokens)   # int
print(response.metadata["model"])     # str

Declared constraints raise locally before the API call; unconstrained parameters pass through to the provider.

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md.

Request a provider: GitHub Issues Report bugs: GitHub Issues

📄 License

MIT license – see LICENSE for details.