1 of 18

Integrations

Examples

All the provider and framework integrations supported on LangDB

Providers

Frameworks

OpenAI

Connect LangDB seamlessly using the OpenAI Client SDK for Python, Node.js, or cURL with minimal code changes.

LangDB seamlessly integrate with OpenAI Client.

Install OpenAI Client SDK

Add LangDB to your code

curl "https://api.us-east-1.langdb.ai/v1/chat/completions" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $LANGDB_API_KEY" \
    -X "X-Project-Id: $Project_ID" \
    -d '{
        "model": "gpt-4o",
        "messages": [
            {
                "role": "user",
                "content": "Write a haiku about recursion in programming."
            }
        ],
        "temperature": 0.8
    }'

Vercel AI SDK

Connect LangDB to your Vercel AI projects using simple setup and start generating text with powerful LLM models instantl

LangDB integrates with applications built Vercel AI SDK.

Install Vercel AI SDK

Add LangDB to your Code

DeepSeek

Access DeepSeek models through LangDB’s API using OpenAI SDK, enabling fast, reliable AI performance across platforms.

LangDB provides first class support for DeepSeek AI’s models.

You can use OpenAI SDK to run the deepseek models.

# Please install OpenAI SDK first: `pip3 install openai`
from openai import OpenAI
api_base = "https://api.us-east-1.langdb.ai"  # LangDB API base URL
api_key = "xxxxx"  # Replace with your LangDB token
default_headers = {"x-project-id": "xxxxx"} # LangDB Project ID
client = OpenAI(
    base_url=api_base,
    api_key=api_key,
)
response = client.chat.completions.create(
    model="deepseek-reasoner",
    messages=[
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "Hello"}
    ],
    stream=False
)
print(response.choices[0].message.content)

// Please install OpenAI SDK first: `npm install openai`
import OpenAI from "openai";
const apiBase = "https://api.us-east-1.langdb.ai";  // LangDB API base URL
const apiKey = "LANGDB_API_KEY";  // Replace with your LangDB token
const defaultHeaders = { "x-project-id": "xxxx" };  // LangDB Project ID
const client = new OpenAI({
  baseURL: apiBase,
  apiKey: apiKey,
});
async function main() {
  const completion = await client.chat.completions.create({
    messages: [{ role: "system", content: "You are a helpful assistant." }, {"role": "user", "content": "Hello"}],
    model: "deepseek-reasoner",
  }, { headers: defaultHeaders });
  console.log(completion.choices[0].message.content);
}
main();

curl "https://api.us-east-1.langdb.ai/v1/chat/completions" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $LANGDB_API_KEY" \
    -X "X-Project-Id: $Project_ID" \
    -d '{
        "model": "deepseek-reasoner",
        "messages": [
            {
                "role": "user",
                "content": "Write a haiku about recursion in programming."
            }
        ]
    }'

Anthropic

Access Anthropic models through LangDB’s API using OpenAI SDK

LangDB provides first class support for Anthropic models.

You can use OpenAI SDK to run the Anthropic models.

curl "https://api.us-east-1.langdb.ai/v1/chat/completions" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $LANGDB_API_KEY" \
    -X "X-Project-Id: $Project_ID" \
    -d '{
        "model": "claude-3-5-sonnet-20240620",
        "messages": [
            {
                "role": "system",
                "content": "You are helpful assistant",
            },
            {
                "role": "user",
                "content": "Write a haiku about recursion in programming."
            }
        ]
    }'

LangChain

Connect LangDB to LangChain using OpenAI-compatible setup to enhance LLM workflows with full tracing and streamlined monitoring.

LangDB integrates seamlessly with popular libraries like LangChain, providing tracing support to capture detailed logs for workflows. Below is a complete example of using LangChain with LangDB.

Install LangChain

pip install langchain langchain-openai

Add LangDB to your Code

from langchain_openai import ChatOpenAI

api_base = "https://api.us-east-1.langdb.ai"
api_key = "xxxx" # LangDB API Key
default_headers = {
    "x-project-id": "xxxxx",  ### LangDB Project ID
}
llm = ChatOpenAI(
    model_name='gpt-4o' , 
    temperature=0.3, 
    openai_api_base=api_base, 
    default_headers=default_headers,
    disable_streaming=True )

# Your LangChain Code here

Check out full examples in the samples.

CrewAI

Connect LangDB to CrewAI using OpenAI-compatible setup to enhance LLM workflows with full tracing and streamlined monitoring.

LangDB integrates seamlessly with popular libraries like CrewAI, providing tracing support to capture detailed logs for workflows. Below is a complete example of using CrewAI with LangDB.

Install CrewAI

Add LangDB to your Code

Check out full examples .

Gemini

Access Gemini models through LangDB’s API using OpenAI SDK

LangDB provides first class support for Gemini models.

You can use to run the Gemini models.

Bedrock

Access Amazon Bedrock models through LangDB’s API using OpenAI SDK

LangDB provides first class support for Bedrock models.

You can use to run the Bedrock models.

xAI

Access xAI models through LangDB’s API using OpenAI SDK

LangDB provides first class support for xAI models.

You can use to run the xAI models.

TogetherAI

Access TogetherAI models through LangDB’s API using OpenAI SDK

LangDB provides first class support for TogetherAI opensource hosted models.

You can use to run the xAI models.

FireworksAI

Access FireworksAI models through LangDB’s API using OpenAI SDK

LangDB provides first class support for FireworksAI opensource hosted models.

You can use to run the xAI models.

DeepInfra

Access DeepInfra models through LangDB’s API using OpenAI SDK

LangDB provides first class support for DeepInfra opensource hosted models.

You can use OpenAI SDK to run the xAI models.

curl "https://api.us-east-1.langdb.ai/v1/chat/completions" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $LANGDB_API_KEY" \
    -X "X-Project-Id: $Project_ID" \
    -d '{
        "model": "deepinfra/phi-4",
        "messages": [
            {
                "role": "user",
                "content": "Write a haiku about recursion in programming."
            }
        ],
    }'

OpenRouter

Access OpenRouter models through LangDB’s API using OpenAI SDK

LangDB provides first class support for OpenRouter models.

You can use to run the xAI models.

Smithery

Integrate Smithery's EXA MCP server into LangDB to enhance AI workflows with real-time, tool-driven interactions via WebSocket.

LangDB supports MCP servers provided by .

This particular example is for .

LlamaIndex

Connect LangDB to LlamaIndex using OpenAI-compatible setup to enhance LLM workflows with full tracing and streamlined monitoring.

Install LlamaIndex

Add LangDB into your Code

Check out full examples in the .

Supabase

Connect LangDB to LangChain using OpenAI-compatible setup to generate embeddings and store them in Supabase for efficient data retrieval.

LangDB integrates AI models like OpenAI to generate embeddings and store them in Supabase for efficient data retrieval.

Preparing Database

Create a Supabase Project

Go to Supabase and create a new project.

Enable PgVector

create extension vector;

Create a table to store Embeddings

create table embeddings (
  id bigserial primary key,
  content text,
  embedding vector(1536)
);

Generating and Storing Embeddings

Install Libraries

pip install openai python-dotenv supabase

Generate Embeddings

from openai import OpenAI
from dotenv import load_dotenv
from supabase import create_client, Client
import os

load_dotenv()
api_key = os.getenv("LANGDB_API_KEY")
project_id = os.getenv("LANGDB_PROJECT_ID")
base_url = f"https://api.us-east-1.langdb.ai/{project_id}/v1"


client = OpenAI(
    base_url=base_url,
    api_key=api_key,
)

text = "Hello LangDB"
response = client.embeddings.create(
    model="text-embedding-ada-002",
    input=text,
)

embedding = response.data[0].embedding

Store in Supabase

# # Store in Supabase
result = supabase.table('embeddings').insert({
    "content": text,
    "embedding": embedding
}).execute()

Mem0

Use LangDB with Mem0 to store memories, embed text, and streamline LLM interactions across scalable applications.

LangDB seemlessly integrate with Mem0.

Check out the Mem0 documentation for more information.

Install mem0 Client

pip install mem0ai python-dotenv

Add LangDB into your code

from mem0 import Memory
from dotenv import load_dotenv
load_dotenv()
import os
langdb_api_key = os.getenv("LANGDB_API_KEY")
langdb_project_id = os.getenv("LANGDB_PROJECT_ID")
base_url =  f"https://api.us-east-1.langdb.ai/{langdb_project_id}/v1"  

config = {
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-4o",
            "temperature": 0.0,
            "api_key": langdb_api_key,
            "openai_base_url": base_url,
        },
    },
    "embedder": {
        "provider": "openai",
        "config": {
            "model": "text-embedding-ada-002",
            "api_key": langdb_api_key,
            "openai_base_url": base_url,
        },
    }
}
m = Memory.from_config(config_dict=config)

result = m.add(
    "I like to take long walks on weekends.",
    user_id="alice",
    metadata={"category": "hobbies"},
)

print(result)