[Feat] New Provider - add langgraph

docs/my-website/docs/providers/langgraph.md

@@ -0,0 +1,240 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# LangGraph
+
+Call LangGraph agents through LiteLLM using the OpenAI chat completions format.
+
+| Property | Details |
+|----------|---------|
+| Description | LangGraph is a framework for building stateful, multi-actor applications with LLMs. LiteLLM supports calling LangGraph agents via their streaming and non-streaming endpoints. |
+| Provider Route on LiteLLM | `langgraph/{agent_id}` |
+| Provider Doc | [LangGraph Platform ↗](https://langchain-ai.github.io/langgraph/cloud/quick_start/) |
+
+**Prerequisites:** You need a running LangGraph server. See [Setting Up a Local LangGraph Server](#setting-up-a-local-langgraph-server) below.
+
+## Quick Start
+
+### Model Format
+
+```shell showLineNumbers title="Model Format"
+langgraph/{agent_id}
+```
+
+**Example:**
+- `langgraph/agent` - calls the default agent
+
+### LiteLLM Python SDK
+
+```python showLineNumbers title="Basic LangGraph Completion"
+import litellm
+
+response = litellm.completion(
+    model="langgraph/agent",
+    messages=[
+        {"role": "user", "content": "What is 25 * 4?"}
+    ],
+    api_base="http://localhost:2024",
+)
+
+print(response.choices[0].message.content)
+```
+
+```python showLineNumbers title="Streaming LangGraph Response"
+import litellm
+
+response = litellm.completion(
+    model="langgraph/agent",
+    messages=[
+        {"role": "user", "content": "What is the weather in Tokyo?"}
+    ],
+    api_base="http://localhost:2024",
+    stream=True,
+)
+
+for chunk in response:
+    if chunk.choices[0].delta.content:
+        print(chunk.choices[0].delta.content, end="")
+```
+
+### LiteLLM Proxy
+
+#### 1. Configure your model in config.yaml
+
+<Tabs>
+<TabItem value="config-yaml" label="config.yaml">
+
+```yaml showLineNumbers title="LiteLLM Proxy Configuration"
+model_list:
+  - model_name: langgraph-agent
+    litellm_params:
+      model: langgraph/agent
+      api_base: http://localhost:2024
+```
+
+</TabItem>
+</Tabs>
+
+#### 2. Start the LiteLLM Proxy
+
+```bash showLineNumbers title="Start LiteLLM Proxy"
+litellm --config config.yaml
+```
+
+#### 3. Make requests to your LangGraph agent
+
+<Tabs>
+<TabItem value="curl" label="Curl">
+
+```bash showLineNumbers title="Basic Request"
+curl http://localhost:4000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $LITELLM_API_KEY" \
+  -d '{
+    "model": "langgraph-agent",
+    "messages": [
+      {"role": "user", "content": "What is 25 * 4?"}
+    ]
+  }'
+```
+
+```bash showLineNumbers title="Streaming Request"
+curl http://localhost:4000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $LITELLM_API_KEY" \
+  -d '{
+    "model": "langgraph-agent",
+    "messages": [
+      {"role": "user", "content": "What is the weather in Tokyo?"}
+    ],
+    "stream": true
+  }'
+```
+
+</TabItem>
+
+<TabItem value="openai-sdk" label="OpenAI Python SDK">
+
+```python showLineNumbers title="Using OpenAI SDK with LiteLLM Proxy"
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:4000",
+    api_key="your-litellm-api-key"
+)
+
+response = client.chat.completions.create(
+    model="langgraph-agent",
+    messages=[
+        {"role": "user", "content": "What is 25 * 4?"}
+    ]
+)
+
+print(response.choices[0].message.content)
+```
+
+```python showLineNumbers title="Streaming with OpenAI SDK"
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:4000",
+    api_key="your-litellm-api-key"
+)
+
+stream = client.chat.completions.create(
+    model="langgraph-agent",
+    messages=[
+        {"role": "user", "content": "What is the weather in Tokyo?"}
+    ],
+    stream=True
+)
+
+for chunk in stream:
+    if chunk.choices[0].delta.content is not None:
+        print(chunk.choices[0].delta.content, end="")
+```
+
+</TabItem>
+</Tabs>
+
+## Environment Variables
+
+| Variable | Description |
+|----------|-------------|
+| `LANGGRAPH_API_BASE` | Base URL of your LangGraph server (default: `http://localhost:2024`) |
+| `LANGGRAPH_API_KEY` | Optional API key for authentication |
+
+## Supported Parameters
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `model` | string | The agent ID in format `langgraph/{agent_id}` |
+| `messages` | array | Chat messages in OpenAI format |
+| `stream` | boolean | Enable streaming responses |
+| `api_base` | string | LangGraph server URL |
+| `api_key` | string | Optional API key |
+
+
+## Setting Up a Local LangGraph Server
+
+Before using LiteLLM with LangGraph, you need a running LangGraph server.
+
+### Prerequisites
+
+- Python 3.11+
+- An LLM API key (OpenAI or Google Gemini)
+
+### 1. Install the LangGraph CLI
+
+```bash
+pip install "langgraph-cli[inmem]"
+```
+
+### 2. Create a new LangGraph project
+
+```bash
+langgraph new my-agent --template new-langgraph-project-python
+cd my-agent
+```
+
+### 3. Install dependencies
+
+```bash
+pip install -e .
+```
+
+### 4. Set your API key
+
+```bash
+echo "OPENAI_API_KEY=your_key_here" > .env
+```
+
+### 5. Start the server
+
+```bash
+langgraph dev
+```
+
+The server will start at `http://localhost:2024`.
+
+### Verify the server is running
+
+```bash
+curl -s --request POST \
+  --url "http://localhost:2024/runs/wait" \
+  --header 'Content-Type: application/json' \
+  --data '{
+    "assistant_id": "agent",
+    "input": {
+      "messages": [{"role": "human", "content": "Hello!"}]
+    }
+  }'
+```
+
+
+
+## Further Reading
+
+- [LangGraph Platform Documentation](https://langchain-ai.github.io/langgraph/cloud/quick_start/)
+- [LangGraph GitHub](https://github.com/langchain-ai/langgraph)
+

docs/my-website/sidebars.js

@@ -706,6 +706,7 @@ const sidebars = {
         "providers/infinity",
         "providers/jina_ai",
         "providers/lambda_ai",
+        "providers/langgraph",
         "providers/lemonade",
         "providers/llamafile",
         "providers/lm_studio",

litellm/litellm_core_utils/get_llm_provider_logic.py

@@ -872,6 +872,14 @@ def _get_openai_compatible_provider_info(  # noqa: PLR0915
             full_model, api_base, api_key, "ragflow"
         )
         model = full_model
+    elif custom_llm_provider == "langgraph":
+        # LangGraph is a custom provider, just need to set api_base
+        api_base = (
+            api_base
+            or get_secret_str("LANGGRAPH_API_BASE")
+            or "http://localhost:2024"
+        )
+        dynamic_api_key = api_key or get_secret_str("LANGGRAPH_API_KEY")
 
     if api_base is not None and not isinstance(api_base, str):
         raise Exception("api base needs to be a string. api_base={}".format(api_base))

litellm/llms/langgraph/__init__.py

@@ -0,0 +1,4 @@
+from litellm.llms.langgraph.chat.transformation import LangGraphConfig
+
+__all__ = ["LangGraphConfig"]
+

litellm/llms/langgraph/chat/__init__.py

@@ -0,0 +1,4 @@
+from litellm.llms.langgraph.chat.transformation import LangGraphConfig
+
+__all__ = ["LangGraphConfig"]
+