> ## Documentation Index
> Fetch the complete documentation index at: https://docs.crewai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# LangDB Integration

> Govern, secure, and optimize your CrewAI workflows with LangDB AI Gateway—access 350+ models, automatic routing, cost optimization, and full observability.

# Introduction

[LangDB AI Gateway](https://langdb.ai) provides OpenAI-compatible APIs to connect with multiple Large Language Models and serves as an observability platform that makes it effortless to trace CrewAI workflows end-to-end while providing access to 350+ language models. With a single `init()` call, all agent interactions, task executions, and LLM calls are captured, providing comprehensive observability and production-ready AI infrastructure for your applications.

![LangDB CrewAI trace example](https://mintcdn.com/crewai/qVjgZHKAyEOgSSUS/images/langdb-1.png?fit=max\&auto=format\&n=qVjgZHKAyEOgSSUS\&q=85\&s=6303d15055c19a7a6ec9d0c664f15c9f)

LangDB CrewAI Trace Example

**Checkout:** [View the live trace example](https://app.langdb.ai/sharing/threads/3becbfed-a1be-ae84-ea3c-4942867a3e22)

## Features

### AI Gateway Capabilities

* **Access to 350+ LLMs**: Connect to all major language models through a single integration
* **Virtual Models**: Create custom model configurations with specific parameters and routing rules
* **Virtual MCP**: Enable compatibility and integration with MCP (Model Context Protocol) systems for enhanced agent communication
* **Guardrails**: Implement safety measures and compliance controls for agent behavior

### Observability & Tracing

* **Automatic Tracing**: Single `init()` call captures all CrewAI interactions
* **End-to-End Visibility**: Monitor agent workflows from start to finish
* **Tool Usage Tracking**: Track which tools agents use and their outcomes
* **Model Call Monitoring**: Detailed insights into LLM interactions
* **Performance Analytics**: Monitor latency, token usage, and costs
* **Debugging Support**: Step-through execution for troubleshooting
* **Real-time Monitoring**: Live traces and metrics dashboard

## Setup Instructions

**1. Install LangDB**

Install the LangDB client with CrewAI feature flag:

```bash theme={null}
pip install 'pylangdb[crewai]'
```

**2. Set Environment Variables**

Configure your LangDB credentials:

```bash theme={null}
export LANGDB_API_KEY="<your_langdb_api_key>"
export LANGDB_PROJECT_ID="<your_langdb_project_id>"
export LANGDB_API_BASE_URL='https://api.us-east-1.langdb.ai'
```

**3. Initialize Tracing**

Import and initialize LangDB before configuring your CrewAI code:

```python theme={null}
from pylangdb.crewai import init
# Initialize LangDB
init()
```

**4. Configure CrewAI with LangDB**

Set up your LLM with LangDB headers:

```python theme={null}
from crewai import Agent, Task, Crew, LLM
import os

# Configure LLM with LangDB headers
llm = LLM(
    model="openai/gpt-4o", # Replace with the model you want to use
    api_key=os.getenv("LANGDB_API_KEY"),
    base_url=os.getenv("LANGDB_API_BASE_URL"),
    extra_headers={"x-project-id": os.getenv("LANGDB_PROJECT_ID")}
)
```

## Quick Start Example

Here's a simple example to get you started with LangDB and CrewAI:

```python theme={null}
import os
from pylangdb.crewai import init
from crewai import Agent, Task, Crew, LLM

# Initialize LangDB before any CrewAI imports
init()

def create_llm(model):
    return LLM(
        model=model,
        api_key=os.environ.get("LANGDB_API_KEY"),
        base_url=os.environ.get("LANGDB_API_BASE_URL"),
        extra_headers={"x-project-id": os.environ.get("LANGDB_PROJECT_ID")}
    )

# Define your agent
researcher = Agent(
    role="Research Specialist",
    goal="Research topics thoroughly",
    backstory="Expert researcher with skills in finding information",
    llm=create_llm("openai/gpt-4o"), # Replace with the model you want to use
    verbose=True
)

# Create a task
task = Task(
    description="Research the given topic and provide a comprehensive summary",
    agent=researcher,
    expected_output="Detailed research summary with key findings"
)

# Create and run the crew
crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()
print(result)
```

## Complete Example: Research and Planning Agent

This comprehensive example demonstrates a multi-agent workflow with research and planning capabilities.

### Prerequisites

```bash theme={null}
pip install crewai 'pylangdb[crewai]' crewai_tools setuptools python-dotenv
```

### Environment Setup

```bash theme={null}
# LangDB credentials
export LANGDB_API_KEY="<your_langdb_api_key>"
export LANGDB_PROJECT_ID="<your_langdb_project_id>"
export LANGDB_API_BASE_URL='https://api.us-east-1.langdb.ai'

# Additional API keys (optional)
export SERPER_API_KEY="<your_serper_api_key>"  # For web search capabilities
```

### Complete Implementation

```python theme={null}
#!/usr/bin/env python3

import os
import sys
from pylangdb.crewai import init
init()  # Initialize LangDB before any CrewAI imports
from dotenv import load_dotenv
from crewai import Agent, Task, Crew, Process, LLM
from crewai_tools import SerperDevTool

load_dotenv()

def create_llm(model):
    return LLM(
        model=model,
        api_key=os.environ.get("LANGDB_API_KEY"),
        base_url=os.environ.get("LANGDB_API_BASE_URL"),
        extra_headers={"x-project-id": os.environ.get("LANGDB_PROJECT_ID")}
    )

class ResearchPlanningCrew:
    def researcher(self) -> Agent:
        return Agent(
            role="Research Specialist",
            goal="Research topics thoroughly and compile comprehensive information",
            backstory="Expert researcher with skills in finding and analyzing information from various sources",
            tools=[SerperDevTool()],
            llm=create_llm("openai/gpt-4o"),
            verbose=True
        )
    
    def planner(self) -> Agent:
        return Agent(
            role="Strategic Planner",
            goal="Create actionable plans based on research findings",
            backstory="Strategic planner who breaks down complex challenges into executable plans",
            reasoning=True,
            max_reasoning_attempts=3,
            llm=create_llm("openai/anthropic/claude-3.7-sonnet"),
            verbose=True
        )
    
    def research_task(self) -> Task:
        return Task(
            description="Research the topic thoroughly and compile comprehensive information",
            agent=self.researcher(),
            expected_output="Comprehensive research report with key findings and insights"
        )
    
    def planning_task(self) -> Task:
        return Task(
            description="Create a strategic plan based on the research findings",
            agent=self.planner(),
            expected_output="Strategic execution plan with phases, goals, and actionable steps",
            context=[self.research_task()]
        )
    
    def crew(self) -> Crew:
        return Crew(
            agents=[self.researcher(), self.planner()],
            tasks=[self.research_task(), self.planning_task()],
            verbose=True,
            process=Process.sequential
        )

def main():
        topic = sys.argv[1] if len(sys.argv) > 1 else "Artificial Intelligence in Healthcare"
        
        crew_instance = ResearchPlanningCrew()
        
        # Update task descriptions with the specific topic
        crew_instance.research_task().description = f"Research {topic} thoroughly and compile comprehensive information"
    crew_instance.planning_task().description = f"Create a strategic plan for {topic} based on the research findings"
    
    result = crew_instance.crew().kickoff()
    print(result)

if __name__ == "__main__":
    main()
```

### Running the Example

```bash theme={null}
python main.py "Sustainable Energy Solutions"
```

## Viewing Traces in LangDB

After running your CrewAI application, you can view detailed traces in the LangDB dashboard:

![LangDB trace dashboard showing CrewAI workflow](https://mintcdn.com/crewai/qVjgZHKAyEOgSSUS/images/langdb-2.png?fit=max\&auto=format\&n=qVjgZHKAyEOgSSUS\&q=85\&s=74a88afefce3f84fb6f9482a1790b413)

LangDB Trace Dashboard

### What You'll See

* **Agent Interactions**: Complete flow of agent conversations and task handoffs
* **Tool Usage**: Which tools were called, their inputs, and outputs
* **Model Calls**: Detailed LLM interactions with prompts image.pngand responses
* **Performance Metrics**: Latency, token usage, and cost tracking
* **Execution Timeline**: Step-by-step view of the entire workflow

## Troubleshooting

### Common Issues

* **No traces appearing**: Ensure `init()` is called before any CrewAI imports
* **Authentication errors**: Verify your LangDB API key and project ID

## Resources

> **[LangDB Documentation](https://docs.langdb.ai)**
>
> Official LangDB documentation and guides

> **[LangDB Guides](https://docs.langdb.ai/guides)**
>
> Step-by-step tutorials for building AI agents

> **[GitHub Examples](https://github.com/langdb/langdb-samples/tree/main/examples/crewai)**
>
> Complete CrewAI integration examples

> **[LangDB Dashboard](https://app.langdb.ai)**
>
> Access your traces and analytics

> **[Model Catalog](https://app.langdb.ai/models)**
>
> Browse 350+ available language models

> **[Enterprise Features](https://docs.langdb.ai/enterprise)**
>
> Self-hosted options and enterprise capabilities

## Next Steps

This guide covered the basics of integrating LangDB AI Gateway with CrewAI. To further enhance your AI workflows, explore:

* **Virtual Models**: Create custom model configurations with routing strategies
* **Guardrails & Safety**: Implement content filtering and compliance controls
* **Production Deployment**: Configure fallbacks, retries, and load balancing

For more advanced features and use cases, visit the [LangDB Documentation](https://docs.langdb.ai) or explore the [Model Catalog](https://app.langdb.ai/models) to discover all available models.


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