Metadata-Version: 2.5
Name: llama-index-llms-runcrate
Version: 0.1.0
Summary: LlamaIndex LLM integration for Runcrate: 200+ open models behind one OpenAI-compatible API
Project-URL: Homepage, https://www.runcrate.ai/docs?utm_source=pypi-llama-index-llms-runcrate&utm_medium=directory&utm_campaign=gpu_pricing
Project-URL: Documentation, https://www.runcrate.ai/docs/api-reference/chat/chat-llm-completion?utm_source=pypi-llama-index-llms-runcrate&utm_medium=directory&utm_campaign=gpu_pricing
Project-URL: Pricing, https://www.runcrate.ai/inference/pricing?utm_source=pypi-llama-index-llms-runcrate&utm_medium=directory&utm_campaign=gpu_pricing
Author-email: Aeonmind LLC <hello@runcrate.ai>
License: MIT
License-File: LICENSE
Keywords: inference,llama-index,llm,openai-compatible,runcrate
Requires-Python: <4.0,>=3.10
Requires-Dist: llama-index-core<0.15,>=0.13.0
Requires-Dist: llama-index-llms-openai-like>=0.5.0
Description-Content-Type: text/markdown

# llama-index-llms-runcrate

LlamaIndex LLM for [Runcrate](https://www.runcrate.ai/?utm_source=pypi-llama-index-llms-runcrate&utm_medium=directory&utm_campaign=gpu_pricing): 200+ open models (DeepSeek, Qwen, Llama, Kimi, gpt-oss and more) behind one OpenAI-compatible API, billed per token from one prepaid balance. The same account rents GPUs by the hour for fine-tuning or serving your own model.

## Install

```bash
pip install llama-index-llms-runcrate
```

Create an API key ([docs](https://www.runcrate.ai/docs/account/api-keys?utm_source=pypi-llama-index-llms-runcrate&utm_medium=directory&utm_campaign=gpu_pricing)) and `export RUNCRATE_API_KEY=...`.

## Use

```python
from llama_index.llms.runcrate import Runcrate
from llama_index.core.llms import ChatMessage

llm = Runcrate(model="deepseek-ai/DeepSeek-V3.2")

print(llm.complete("Explain LoRA fine-tuning in one sentence."))
print(llm.chat([ChatMessage(role="user", content="Name three uses of an H100.")]))

for chunk in llm.stream_complete("Write a haiku about GPUs."):
    print(chunk.delta, end="")
```

### As the default LLM for RAG and agents

```python
from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.agent.workflow import FunctionAgent

Settings.llm = Runcrate(model="Qwen/Qwen3-235B-A22B-Instruct-2507", context_window=131072)

def multiply(a: float, b: float) -> float:
    """Multiply two numbers."""
    return a * b

agent = FunctionAgent(tools=[multiply], llm=Settings.llm)
# response = await agent.run("What is 1234 * 4567?")
```

## Arguments

| Argument | Default |
|---|---|
| `model` | required, any id from `GET https://api.runcrate.ai/v1/models` |
| `api_key` | `RUNCRATE_API_KEY` |
| `api_base` | `RUNCRATE_API_BASE` or `https://api.runcrate.ai/v1` |
| `context_window` | 32768 (set it to the model's real window) |
| `is_function_calling_model` | `True` |

Other `OpenAILike` arguments (`temperature`, `max_tokens`, `timeout`, `additional_kwargs` ...) pass through. Models and prices: [runcrate.ai/inference/pricing](https://www.runcrate.ai/inference/pricing?utm_source=pypi-llama-index-llms-runcrate&utm_medium=directory&utm_campaign=gpu_pricing).

## Let your agent rent GPUs too

Remote MCP server `https://mcp.runcrate.ai/mcp` (OAuth sign-in, no key): [setup](https://www.runcrate.ai/docs/mcp/setup?utm_source=pypi-llama-index-llms-runcrate&utm_medium=directory&utm_campaign=mcp), client packages [aeonmindai/runcrate-mcp](https://github.com/aeonmindai/runcrate-mcp).

## License

MIT, Aeonmind LLC.
