Aiyallm

Aiyallm is a lightweight Python gateway for OpenAI/Anthropic-style language and vision models.

Aiyallm does not ship a built-in model catalog and does not read API keys implicitly. Every provider, base_url, API key, and model is declared by the application.

Table of Contents

• Features
• Install
• Quick Start
• Provider Configuration
• Routing
• Vision and Capability Filtering
• Usage Ledger
• Cost Calculation
• Streaming and Async
• Extending Providers

Features

• OpenAI and OpenAI-compatible providers
• Anthropic and Anthropic-compatible providers
• Chinese OpenAI-compatible providers such as DeepSeek, Qwen/DashScope, Moonshot/Kimi, Zhipu GLM, and SiliconFlow
• Explicit model selection, provider/model references, and provider route lists
• Routing policies: fallback, first, round_robin, random, and least_used
• Vision and multimodal message passthrough with capability filtering
• Sync and async chat and streaming
• Detailed token accounting with cache hit/miss fields
• Optional SQLite or JSONL usage persistence
• Optional cost calculation from an application-owned price book

Install

pip install -e .

Runtime dependencies are:

• openai
• anthropic
• httpx

Quick Start

There are no default providers. Pass them explicitly.

from aiyallm import Aiyallm

client = Aiyallm(
    providers=[
        {
            "name": "deepseek",
            "account": "billing-deepseek",
            "type": "openai_compatible",
            "base_url": "https://api.deepseek.com",
            "api_key": "your-deepseek-key",
            "models": ["deepseek-chat", "deepseek-reasoner"],
        }
    ]
)

response = client.chat(
    "用一句话解释什么是路由。",
    model="deepseek/deepseek-chat",
)

print(response.text)
print(response.usage)

models is optional when the provider allows unknown models. Configured providers default to allow_unknown_models=True, but the provider itself must still be declared.

Provider Configuration

Each provider accepts these common fields.

Field	Meaning
name	Unique provider name used by provider/model routes
account	Optional billing or account name recorded in usage
type	openai, openai_compatible, anthropic, or anthropic_compatible
base_url	Endpoint for compatible services
api_key	API key, or use api_key_env to read an environment variable
models	Optional model names or {"id", "capabilities", ...} mappings
allow_unknown_models	Allow model IDs not listed in models; defaults to True
timeout	SDK timeout

OpenAI-Compatible Examples

providers = [
    {
        "name": "deepseek",
        "type": "openai_compatible",
        "base_url": "https://api.deepseek.com",
        "api_key_env": "DEEPSEEK_API_KEY",
    },
    {
        "name": "qwen",
        "type": "openai_compatible",
        "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
        "api_key_env": "DASHSCOPE_API_KEY",
    },
    {
        "name": "moonshot",
        "type": "openai_compatible",
        "base_url": "https://api.moonshot.cn/v1",
        "api_key_env": "MOONSHOT_API_KEY",
    },
    {
        "name": "glm",
        "type": "openai_compatible",
        "base_url": "https://open.bigmodel.cn/api/paas/v4",
        "api_key_env": "ZHIPU_API_KEY",
    },
]

Use the base_url from each provider's current documentation. Aiyallm does not hard-code any endpoint or model name.

Routing

Use provider/model to target a provider.

response = client.chat("Hello", model="qwen/qwen-plus")

Use a route list for fallback.

response = client.chat(
    "Hello",
    model=["deepseek/deepseek-chat", "qwen/qwen-plus"],
    route_policy="fallback",
)

Available policies:

• fallback - try candidates in order
• first - use only the first candidate
• round_robin - rotate through candidates
• random - choose a random candidate
• least_used - choose the candidate with the lowest recorded token usage

The default policy is fallback.

Vision and Capability Filtering

Declare capabilities in models and pass require_vision=True when a request needs image input.

from aiyallm import Aiyallm

client = Aiyallm(
    providers=[
        {
            "name": "qwen",
            "type": "openai_compatible",
            "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
            "api_key": "your-key",
            "models": [
                {"id": "qwen-vl-plus", "capabilities": ["text", "vision"]}
            ],
        }
    ]
)

response = client.chat(
    [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is in this image?"},
                {
                    "type": "image_url",
                    "image_url": {"url": "https://example.com/image.jpg"},
                },
            ],
        }
    ],
    model="qwen/qwen-vl-plus",
    require_vision=True,
)

Usage Ledger

Every successful completion is recorded.

response = client.chat("Hello", model="deepseek/deepseek-chat")

print(response.usage.input_tokens)
print(response.usage.output_tokens)
print(response.usage.input_cache_hit_tokens)
print(response.usage.input_cache_miss_tokens)
print(response.usage.output_cache_hit_tokens)
print(response.usage.output_cache_miss_tokens)
print(response.usage.account)
print(response.usage.reasoning_tokens)

print(client.ledger.total())
print(client.ledger.by_model())
print(client.ledger.by_provider())
print(client.ledger.by_account())

client.ledger.export_jsonl("usage.jsonl")

For persistent accounting, pass a SQLite path.

from aiyallm import UsageLedger

ledger = UsageLedger(sqlite_path="data/usage.sqlite3")
client = Aiyallm(providers=providers, ledger=ledger)

Cost Calculation

Token counts are always recorded. Cost is calculated only when the application supplies a price.

from aiyallm import Aiyallm, Price, PriceBook

price_book = PriceBook(
    {
        "deepseek-chat": Price(
            input_per_mtok=0.27,
            output_per_mtok=1.10,
        )
    }
)

client = Aiyallm(providers=providers, price_book=price_book)
response = client.chat("Hello", model="deepseek/deepseek-chat")

print(response.usage.cost_usd)

Prices are US dollars per million tokens.

Streaming and Async

for chunk in client.stream("Hello", model="deepseek/deepseek-chat"):
    print(chunk.content, end="", flush=True)

import asyncio


async def main():
    response = await client.achat("Hello", model="deepseek/deepseek-chat")
    print(response.text)

    async for chunk in client.astream("Hello", model="deepseek/deepseek-chat"):
        print(chunk.content, end="", flush=True)


asyncio.run(main())

Extending Providers

For a non-standard provider, subclass aiyallm.BaseProvider and implement complete, acomplete, stream, and astream.

from aiyallm import Aiyallm, BaseProvider


class MyProvider(BaseProvider):
    def complete(self, request):
        ...

Then register the instance directly.

client = Aiyallm(providers=[MyProvider(name="my-provider", models=["my-model"])])