LangDB Samples

This repository provides comprehensive examples and integrations for LangDB, demonstrating its capabilities across various frameworks and use cases.

Getting Started

Follow these steps to set up and run the examples in this repository.

1. Set Up Your LangDB Credentials

To use these examples, you need credentials from LangDB. This involves signing up, creating a project, and generating an API key.

• Sign Up: If you don't have an account, sign up on the LangDB platform.
• Create a Project: Once logged in, create a new project. Each project has a unique Project ID.
• Generate an API Key: Navigate to the settings and generate a new API key. This will be your API Key.

2. Configure Your Environment

Most examples in this repository use a .env file to manage environment variables. You'll need to set the following:

# Create a .env file in the root of the example you want to run
# examples/crewai/report-writing-agent/.env

LANGDB_API_BASE_URL="https://api.us-east-1.langdb.ai"
LANGDB_API_KEY="xxxxxxxxxxxxxxxxxxxxxxxx"
LANGDB_PROJECT_ID="xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"

• LANGDB_API_BASE_URL: The base endpoint for the LangDB AI Gateway.
• LANGDB_API_KEY: The API key you generated in the previous step.
• LANGDB_PROJECT_ID: The Project ID from your LangDB project.

3. Review Example-Specific Requirements

Many examples, especially those in the "Featured Samples" section, require additional setup in the LangDB UI. Always check the README.md file inside the specific example's directory for detailed instructions.

Common requirements include:

• Configuring Models: Some examples use Virtual Models to attach tools or apply specific routing logic. To configure them, navigate to your project on the LangDB platform and select the Models tab. There, you can either select from a list of pre-configured models or create a new Virtual Model by clicking the "New Model" button.
• Configuring MCPs: Advanced examples may use MCPs to connect to external services. To set one up, navigate to your project and select the MCP Servers tab. There, you can select from a list of managed MCPs to compose a new Virtual MCP Server for your application.

4. Install Dependencies and Run the Example

Once you've completed the environment and any example-specific setup, you can install the dependencies and run the application.

# Navigate to an example directory
cd examples/openai/travel-agent

# Install required packages
pip install -r requirements.txt

# Run the application
python app.py

Featured Samples

Here are some of our most recent and powerful examples:

CrewAI: Report Writing Agent

!CrewAI Report Writing Agent Trace

Public Thread: https://app.langdb.ai/sharing/threads/3becbfed-a1be-ae84-ea3c-4942867a3e22

• Path: examples/crewai/report-writing-agent
• Architecture: A multi-agent system using CrewAI that researches a topic and writes a comprehensive report. It consists of a researcher, analyst, and writer agent working in sequence.

To integrate with LangDB, you first call pylangdb.crewai.init() and then manually configure your LLM instances to send requests through the LangDB gateway.

# main.py
import os
from pylangdb.crewai import init
from crewai import LLM
from dotenv import load_dotenv

load_dotenv()
init()

# Configure the LLM instance to use LangDB credentials
# and pass any additional tracing headers.
api_key = os.environ.get("LANGDB_API_KEY")
api_base = os.environ.get("LANGDB_API_BASE_URL")
project_id = os.environ.get("LANGDB_PROJECT_ID")

# Base LLM configuration
llm = LLM(
    model="openai/gpt-4o-mini",
    api_key=api_key,
    base_url=api_base,
    extra_headers={
        "x-project-id": project_id
    }
)

---

OpenAI SDK: Travel Agent

!OpenAI Travel Agent Trace

Public Thread: https://app.langdb.ai/sharing/threads/43cfa16f-042e-44ca-ad21-06f52afeca39

• Path: examples/openai/travel-agent
• Architecture: A multi-agent workflow using the OpenAI Agents SDK. It features a 4-agent pipeline (Query Router, Booking Specialist, Travel Recommendation Specialist, and Reply Agent) to handle complex travel queries. LangDB provides end-to-end tracing, dynamic tool integration via Virtual Models, and centralized model management.

1. Initialize Tracing

In your application entry point (app.py), call pylangdb.openai.init() before any other imports. This single line patches the OpenAI library to automatically trace all subsequent operations.

# app.py
from dotenv import load_dotenv
from pylangdb.openai import init

# Load environment variables and initialize tracing
load_dotenv()
init()

2. Configure the Client and Agents

pylangdb automatically configures the AsyncOpenAI client using your environment variables. You can then define agents using LangDB Virtual Models, which allows you to attach tools (like web search) and guardrails in the LangDB UI without changing your code.

# app.py
from openai import AsyncOpenAI
from agents import Agent, set_default_openai_client, OpenAIChatCompletionsModel

# Client is automatically configured by pylangdb.init()
client = AsyncOpenAI()
set_default_openai_client(client, use_for_tracing=True)

def get_model(model_name):
    return OpenAIChatCompletionsModel(model=model_name, openai_client=client)

# Define agents using virtual models from LangDB
travel_recommendation_agent = Agent(
    name="Travel Recommendation Specialist",
    model=get_model("langdb/travel-recommender") # A virtual model with search tools
)

query_router_agent = Agent(
    name="Query Router",
    model=get_model("langdb/query-router"), # A virtual model for routing
    tools=[travel_recommendation_agent.as_tool()]
)

3. Run the Workflow with Tracing

To link all steps of a session into a single trace, generate a unique group_id and pass it to the Runner.

# app.py
import uuid
from agents import Runner, RunConfig

# A unique group_id links all steps in this session's trace
group_id = str(uuid.uuid4())

response = await Runner.run(
    query_router_agent,
    input="I want to go to a sunny beach destination in December.",
    run_config=RunConfig(group_id=group_id)
)

---

Google ADK: Web Search Agent

!Google ADK Web Search Agent Trace

Public Thread: https://app.langdb.ai/sharing/threads/b6ddc154-33a0-403f-948c-9a559d93445a

• Path: examples/google-adk/web-search-agent
• Architecture: A two-step sequential agent system (Critic Agent, Reviser Agent) built with Google ADK. The Critic Agent conducts web searches and analyzes information, and the Reviser Agent synthesizes the findings into a structured answer.

Initialize LangDB tracing before importing any google.adk modules.

# web-search/agent.py
from pylangdb.adk import init

# Initialize LangDB tracing before importing any ADK modules
init()

from google.adk.agents import SequentialAgent
# ... rest of the agent setup

---

Google ADK: Travel Concierge

!Google ADK Travel Concierge Trace

Public Thread: https://app.langdb.ai/sharing/threads/8425e068-77de-4f41-8aa9-d1111fc7d2b7

• Path: examples/google-adk/travel-concierge
• Architecture: A hierarchical agent system where a main root_agent orchestrates a team of specialized sub-agents (Inspiration, Planning, Booking, etc.) to handle a complete travel journey.

Initialize LangDB tracing before importing any google.adk modules.

# travel_concierge/agent.py
from pylangdb.adk import init

# Initialize LangDB tracing before importing any ADK modules
init()

from google.adk.agents import Agent
# ... rest of the agent setup

All Examples

Basic Integration

Framework	Example	Path
OpenAI API	Simple Integration	examples/basic.py

Framework Integrations

Framework	Example	Path
LangChain	Basic Integration	examples/langchain/langchain-basic
LangChain	Multi-agent Setup	examples/langchain/langchain-multi-agent
LangChain	RAG-agent Setup	examples/langchain/langchain-rag-bot
LangChain	LangGraph Tracing	examples/langchain/langgraph-tracing
CrewAI	Basic Implementation	examples/crewai/crewai-basic
CrewAI	Multi-agent Orchestration	examples/crewai/crewai-multi-agent
CrewAI	Report Writing Agent	examples/crewai/report-writing-agent
CrewAI	Basic Tracing	examples/crewai/crewai-tracing
Agno	Basic Tracing	examples/agno/agno-basic
LlamaIndex	Basic Integration	examples/llamaindex/llamaindex-basic
Google ADK	Web Search Agent	examples/google-adk/web-search-agent
Google ADK	Travel Concierge	examples/google-adk/travel-concierge
Google ADK	Multi-tool Agent	examples/google-adk/multi-tool-agent
OpenAI Agents SDK	Customer Support Agent	examples/openai/customer-support
OpenAI Agents SDK	Travel Agent	examples/openai/travel-agent
OpenAI Agents SDK	Basic Tracing	examples/openai/openai-agents-tracing
Mem0	Memory System Integration	examples/mem0
Vercel AI SDK	JavaScript/Node.js Implementation	examples/vercel
Supabase	Database Integration	examples/supabase
Rasa	Conversational AI Integration	examples/rasa

Feature Examples

Feature	Example	Path
Routing	Basic Setup	examples/routing/routing-basic
Routing	Multi-agent Setup	examples/routing/routing-multi-agent
Evaluation	Model Evaluation & Cost Analysis	examples/evaluation

MCP Examples

Example	Description	Path
MCP Support	Model Provider Integration	examples/mcp/mcp-support.ipynb
Cafe Dashboard	Next.js with MCP Integration	examples/mcp/cafe-dashboard
Server Actions Demo	Next.js Server Actions with MCP	examples/mcp/nextjs-server-actions-demo
SvelteKit Integration	SvelteKit MCP Sample	examples/mcp/sveltekit-mcp-sample
Smithery	MCP Smithery Sample	examples/mcp/smithery

Key Features

🚀 High Performance

• Built in Rust for maximum speed and reliability
• Seamless integration with any framework (Langchain, Vercel AI SDK, CrewAI, etc.)
• Integrate with any MCP servers (https://docs.langdb.ai/ai-gateway/features/mcp-support)

📊 Enterprise Ready

• Comprehensive usage analytics and cost tracking
• Rate limiting and cost control
• Advanced routing, load balancing and failover
• Evaluations

🔒 Data Control

• Full ownership of your LLM usage data
• Detailed logging and tracing

Looking for More? Try Our Hosted & Enterprise Solutions

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• Zero infrastructure management
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• Enhanced features for large-scale deployments
• Advanced team management and access controls
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• Intuitive monitoring dashboard
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Built for Developers

LangDB's AI Gateway is designed with developers in mind, focusing on providing a practical and streamlined experience for integrating LLMs into your workflows. Whether you're building a new AI-powered application or enhancing existing systems, LangDB makes it easier to manage and scale your LLM implementations.

Support

For more information and support, visit our documentation.