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import { generateText } from 'ai';
import { createLangDB } from '@langdb/vercel-provider';
const langdb = createLangDB({
apiKey: process.env.LANGDB_API_KEY,
projectId: 'your-project-id',
});
export async function generateTextExample() {
const { text } = await generateText({
model: langdb('openai/gpt-4o-mini'),
prompt: 'Write a Python function that sorts a list:',
});
console.log(text);
}OpenAI
Integration with OpenAI SDK
DeepSeek
Support for DeepSeek Models
Anthropic
Support for Anthropic Models
Gemini
Support for Gemini Models
Bedrock
Support for Bedrock Models
xAI
Support for xAI Models
TogetherAI
Support for TogetherAI Models
FireworksAI
Support for FireworksAI Models
DeepInfra
Support for DeepInfra Models
OpenRouter
Support for OpenRouter Models
Smithery
Support for MCP servers on smithery.ai
from openai import OpenAI
client = OpenAI(
base_url="https://api.us-east-1.langdb.ai" # LangDB API base URL,
api_key=api_key, # Replace with your LangDB token
)
# Make the API call to LangDB's Completions API
response = client.chat.completions.create(
model="gpt-4o", # Use the model
messages=[{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"}],
extra_headers={"x-project-id": "xxxxx"} # LangDB Project ID
)import { OpenAI } from 'openai'; // Assuming you're using the OpenAI Node.js SDK
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,
});
const response = await client.chat.completions.create({
model: "gpt-4o-mini", // Use the model
messages: [{"role": "user","content": "Hello?"}],
}, { headers: defaultHeaders });
// Rest of Your Codecurl "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
}'Check out full examples in the samples.
pip install llama-index openaiimport os
from llama_index.llms import openai
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core import Settings
from llama_index.llms.openai import OpenAI
Settings.llm = OpenAI(
base_url=os.getenv("OPENAI_API_BASE"),
api_key=os.getenv("OPENAI_API_KEY"),
model="gpt-4o-mini"
)
documents = SimpleDirectoryReader("data").load_data()
## Rest of your LlamaIndexAccess TogetherAI models through LangDB’s API using OpenAI SDK
LangDB provides first class support for TogetherAI opensource hosted models.
You can use OpenAI SDK to run the xAI models.
from openai import OpenAI
client = OpenAI(
base_url="https://api.us-east-1.langdb.ai" # LangDB API base URL,
api_key=api_key, # Replace with your LangDB token
)
# Make the API call to LangDB's Completions API
response = client.chat.completions
import { OpenAI } from 'openai'; // Assuming you're using the OpenAI Node.js SDK
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"
Access Gemini models through LangDB’s API using OpenAI SDK
LangDB provides first class support for Gemini models.
You can use OpenAI SDK to run the Gemini 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
// 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 =
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.
Connect LangDB to CrewAI using OpenAI-compatible setup to enhance LLM workflows with full tracing and streamlined monitoring.
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.
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.
Access Anthropic models through LangDB’s API using OpenAI SDK
LangDB provides first class support for Anthropic models.
You can use to run the Anthropic models.
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.
Access DeepInfra models through LangDB’s API using OpenAI SDK
LangDB provides first class support for DeepInfra opensource hosted models.
You can use 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": "togetherai/DeepSeek-V3",
"messages": [
{
"role": "user",
"content": "Write a haiku about recursion in programming."
}
],
}'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": "gemini/gemini-1.5-flash-8b",
"messages": [
{
"role": "system",
"content": "You are helpful assistant",
},
{
"role": "user",
"content": "Write a haiku about recursion in programming."
}
]
}'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)from crewai import LLM
project_id = "xxxx" ## LangDB Project ID
default_headers = {"x-project-id": project_id}
os.environ["OPENAI_API_KEY"] = (
"xxxx" ## LangDB API Key
)
llm_writer = LLM(model="gpt-4o-mini",
base_url=api_base,
extra_headers=default_headers)
# Your CrewAi code herefrom openai import OpenAI
client = OpenAI(
base_url="https://api.us-east-1.langdb.ai" # LangDB API base URL,
api_key=api_key, # Replace with your LangDB token
)
# Make the API call to LangDB's Completions API
response = client.chat.completions.create(
model="openrouter/aion-1.0-mini", # Use the model
messages=[{"role": "user", "content": "Hello!"}],
extra_headers={"x-project-id": "xxxxx"} # LangDB Project ID
)import { OpenAI } from 'openai'; // Assuming you're using the OpenAI Node.js SDK
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,
});
const response = await client.chat.completions.create({
model: "openrouter/aion-1.0-mini", // Use the model
messages: [{"role": "user","content": "What are the earnings of Apple in 2022?"}],
}, { headers: defaultHeaders });
// Rest of Your Code\from openai import OpenAI
client = OpenAI(
base_url="https://api.us-east-1.langdb.ai" # LangDB API base URL,
api_key=api_key, # Replace with your LangDB token
)
# Make the API call to LangDB's Completions API
response = client.chat.completions.create(
model="bedrock/command-r-v1:0", # Use the model
messages=[{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"}],
extra_headers={"x-project-id": "xxxxx"} # LangDB Project ID
)import { OpenAI } from 'openai'; // Assuming you're using the OpenAI Node.js SDK
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,
});
const response = await client.chat.completions.create({
model: "bedrock/command-r-v1:0", // Use the model
messages: [{"role": "user","content": "What are the earnings of Apple in 2022?"}],
}, { headers: defaultHeaders });
// Rest of Your Code# 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="claude-3-5-sonnet-20240620",
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: "claude-3-5-sonnet-20240620",
}, { headers: defaultHeaders });
console.log(completion.choices[0].message.content);
}
main();from openai import OpenAI
client = OpenAI(
base_url="https://api.us-east-1.langdb.ai" # LangDB API base URL,
api_key=api_key, # Replace with your LangDB token
)
# Make the API call to LangDB's Completions API
response = client.chat.completions.create(
model="fireworksai/deepseek-r1", # Use the model
messages=[{"role": "user", "content": "Hello!"}],
extra_headers={"x-project-id": "xxxxx"} # LangDB Project ID
)import { OpenAI } from 'openai'; // Assuming you're using the OpenAI Node.js SDK
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,
});
const response = await client.chat.completions.create({
model: "fireworksai/deepseek-r1", // Use the model
messages: [{"role": "user","content": "What are the earnings of Apple in 2022?"}],
}, { headers: defaultHeaders });
// Rest of Your Code\from openai import OpenAI
client = OpenAI(
base_url="https://api.us-east-1.langdb.ai" # LangDB API base URL,
api_key=api_key, # Replace with your LangDB token
)
# Make the API call to LangDB's Completions API
response = client.chat.completions.create(
model="deepinfra/phi-4", # Use the model
messages=[{"role": "user", "content": "Hello!"}],
extra_headers={"x-project-id": "xxxxx"} # LangDB Project ID
)import { OpenAI } from 'openai'; // Assuming you're using the OpenAI Node.js SDK
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,
});
const response = await client.chat.completions.create({
model: "deepinfra/phi-4", // Use the model
messages: [{"role": "user","content": "What are the earnings of Apple in 2022?"}],
}, { headers: defaultHeaders });
// Rest of Your Code\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 Smithery.
This particular example is for EXA MCP server.
from openai import OpenAI
from dotenv import load_dotenv
import os
import base64
import json
from urllib.parse import quote
load_dotenv()
def urlEncode(data_dict):
"""Convert dictionary to base64 and then URL encode it"""
return quote(base64.b64encode(json.dumps(data_dict).encode()).decode())
config = {
"exaApiKey": os.getenv("EXA_API_KEY")
}
config_str = urlEncode(config)
web_socket_url = "wss://your-mcp-server.com/ws"
extra_body = {
"mcp_servers": [
{
"server_url": f"{web_socket_url}?config={config_str}",
"type": "ws"
}
]
}
client = OpenAI(
api_key=os.getenv("LANGDB_API_KEY"),
base_url=os.getenv("LANGDB_API_URL")
)
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": "what is langdb?"}],
extra_body = extra_body
)
print(response)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": "openrouter/aion-1.0-mini",
"messages": [
{
"role": "user",
"content": "Write a haiku about recursion in programming."
}
],
}'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": "bedrock/command-r-v1:0",
"messages": [
{
"role": "user",
"content": "Write a haiku about recursion in programming."
}
],
}'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."
}
]
}'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": "fireworksai/deepseek-r1",
"messages": [
{
"role": "user",
"content": "Write a haiku about recursion in programming."
}
],
}'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."
}
],
}'Go to Supabase and create a new project.
Access xAI models through LangDB’s API using OpenAI SDK
LangDB provides first class support for xAI models.
You can use OpenAI SDK to run the xAI models.
from openai import OpenAI
client = OpenAI(
base_url="https://api.us-east-1.langdb.ai" # LangDB API base URL,
api_key=api_key, # Replace with your LangDB token
)
# Make the API call to LangDB's Completions API
response = client.chat.completions
import { OpenAI } from 'openai'; // Assuming you're using the OpenAI Node.js SDK
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"
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 to run the deepseek models.
create extension vector;create table embeddings (
id bigserial primary key,
content text,
embedding vector(1536)
);pip install openai python-dotenv supabasecurl "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": "xai/grok-2",
"messages": [
{
"role": "user",
"content": "Write a haiku about recursion in programming."
}
],
}'# 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();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
result = supabase.table('embeddings').insert({
"content": text,
"embedding": embedding
}).execute()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."
}
]
}'
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.
Check out full examples in the .
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