runware provider docs

docs/providers/runware.md

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+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Runware
+
+https://runware.ai
+
+**We support all Runware text / LLM models — just set `model=runware/<model-id>` when sending LiteLLM requests.**
+
+| Property | Details |
+|-------|-------|
+| Description | Runware is an OpenAI-compatible inference platform serving open-weight and frontier LLMs for chat, coding, and agentic workloads. |
+| Provider Route on LiteLLM | `runware/` |
+| Supported Endpoints | `/chat/completions` |
+| API Reference | [Runware OpenAI compatibility](https://runware.ai/docs/platform/openai) |
+| Base URL | `https://api.runware.ai/v1` |
+
+## Required Variables
+
+```python showLineNumbers title="Environment Variables"
+os.environ["RUNWARE_API_KEY"] = ""  # your Runware API key
+```
+
+Get your Runware API key from the [Runware dashboard](https://runware.ai/dashboard).
+
+## Model IDs
+
+Runware addresses each model by its model id — e.g. `minimax-m2-7`, `deepseek-v4-pro`, `alibaba-qwen3-5-397b`.
+
+Prefix the id with `runware/` (e.g. `runware/minimax-m2-7`). LiteLLM forwards the id to Runware verbatim.
+
+## Usage - LiteLLM Python SDK
+
+### Non-streaming
+
+```python showLineNumbers title="Runware Non-streaming Completion"
+from litellm import completion
+import os
+
+os.environ["RUNWARE_API_KEY"] = ""  # your Runware API key
+response = completion(
+    model="runware/minimax-m2-7",
+    messages=[{"role": "user", "content": "What is LiteLLM?"}]
+)
+print(response)
+```
+
+### Streaming
+
+```python showLineNumbers title="Runware Streaming Completion"
+from litellm import completion
+import os
+
+os.environ["RUNWARE_API_KEY"] = ""  # your Runware API key
+stream = completion(
+    model="runware/minimax-m2-7",
+    messages=[{"role": "user", "content": "What is LiteLLM?"}],
+    stream=True
+)
+
+for chunk in stream:
+    if chunk.choices[0].delta.content is not None:
+        print(chunk.choices[0].delta.content, end="")
+```
+
+:::info
+Reasoning models (such as MiniMax M2.7) emit their internal reasoning on a separate channel. LiteLLM surfaces it as `chunk.choices[0].delta.reasoning_content`, which arrives before the regular `delta.content`.
+:::
+
+### Tool Calling
+
+:::warning
+LiteLLM only forwards `tools` / `tool_choice` for models it knows support function calling. Runware models are not yet present in LiteLLM's model cost map, so **by default the `tools` parameter is dropped before the request is sent**. Register the model first (see the snippet below) so LiteLLM keeps the tool params. Runware's endpoint itself does support function calling for tool-capable models such as `minimax-m2-7`.
+:::
+
+```python showLineNumbers title="Runware Tool Calling"
+from litellm import completion
+import litellm
+import os
+
+os.environ["RUNWARE_API_KEY"] = ""  # your Runware API key
+
+# Register the model so LiteLLM keeps tool params (and can track cost).
+litellm.register_model({
+    "runware/minimax-m2-7": {
+        "max_tokens": 196608,
+        "input_cost_per_token": 0.0000003,
+        "output_cost_per_token": 0.0000012,
+        "litellm_provider": "runware",
+        "mode": "chat",
+        "supports_function_calling": True,
+    }
+})
+
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_weather",
+            "description": "Get current weather",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {"type": "string"}
+                }
+            }
+        }
+    }
+]
+
+response = completion(
+    model="runware/minimax-m2-7",
+    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
+    tools=tools
+)
+print(response)
+```
+
+## Usage - LiteLLM Proxy
+
+Add the following to your LiteLLM Proxy configuration file. Use `model_info` to flag function-calling support for tool-capable models:
+
+```yaml showLineNumbers title="config.yaml"
+model_list:
+  - model_name: runware/minimax-m2-7
+    litellm_params:
+      model: runware/minimax-m2-7
+      api_key: os.environ/RUNWARE_API_KEY
+    model_info:
+      supports_function_calling: true
+  - model_name: runware/deepseek-v4-pro
+    litellm_params:
+      model: runware/deepseek-v4-pro
+      api_key: os.environ/RUNWARE_API_KEY
+```
+
+Start your LiteLLM Proxy server:
+
+```bash showLineNumbers title="Start LiteLLM Proxy"
+litellm --config config.yaml
+
+# RUNNING on http://0.0.0.0:4000
+```
+
+<Tabs>
+<TabItem value="openai-sdk" label="OpenAI SDK">
+
+```python showLineNumbers title="Runware via Proxy - Non-streaming"
+from openai import OpenAI
+
+# Initialize client with your proxy URL
+client = OpenAI(
+    base_url="http://localhost:4000",  # Your proxy URL
+    api_key="your-proxy-api-key"       # Your proxy API key
+)
+
+response = client.chat.completions.create(
+    model="runware/minimax-m2-7",
+    messages=[{"role": "user", "content": "What is LiteLLM?"}]
+)
+
+print(response.choices[0].message.content)
+```
+
+</TabItem>
+
+<TabItem value="litellm-sdk" label="LiteLLM SDK">
+
+```python showLineNumbers title="Runware via Proxy - LiteLLM SDK"
+import litellm
+
+response = litellm.completion(
+    model="litellm_proxy/runware/minimax-m2-7",
+    messages=[{"role": "user", "content": "What is LiteLLM?"}],
+    api_base="http://localhost:4000",
+    api_key="your-proxy-api-key"
+)
+
+print(response.choices[0].message.content)
+```
+
+</TabItem>
+
+<TabItem value="curl" label="cURL">
+
+```bash showLineNumbers title="Runware via Proxy - cURL"
+curl http://localhost:4000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer your-proxy-api-key" \
+  -d '{
+    "model": "runware/minimax-m2-7",
+    "messages": [{"role": "user", "content": "What is LiteLLM?"}]
+  }'
+```
+
+</TabItem>
+</Tabs>
+
+## Additional Resources
+
+- [Runware Website](https://runware.ai)
+- [Runware OpenAI Compatibility Docs](https://runware.ai/docs/platform/openai)
+- [Runware Models Catalogue](https://runware.ai/docs/models)

sidebars.js

@@ -1048,6 +1048,7 @@ const sidebars = {
         "providers/ragflow",
         "providers/recraft",
         "providers/replicate",
+        "providers/runware",
         {
           type: "category",
           label: "RunwayML",