crewAI/docs/edge/en/observability/langdb.mdx

---
title: LangDB Integration
description: Govern, secure, and optimize your CrewAI workflows with LangDB AI Gateway—access 350+ models, automatic routing, cost optimization, and full observability.
icon: database
mode: "wide"
---

# 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.

<Frame caption="LangDB CrewAI Trace Example">
  <img src="/images/langdb-1.png" alt="LangDB CrewAI trace example" />
</Frame>

**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

<Steps>
  <Step title="Install LangDB">
    Install the LangDB client with CrewAI feature flag:
    ```bash
    pip install 'pylangdb[crewai]'
    ```
  </Step>
  <Step title="Set Environment Variables">
    Configure your LangDB credentials:
    ```bash
    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'
    ```
  </Step>
  <Step title="Initialize Tracing">
    Import and initialize LangDB before configuring your CrewAI code:
    ```python
    from pylangdb.crewai import init
    # Initialize LangDB
    init()
    ```
  </Step>
  <Step title="Configure CrewAI with LangDB">
    Set up your LLM with LangDB headers:
    ```python
    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")}
    )
    ```
  </Step>
</Steps>

## Quick Start Example

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

```python
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
pip install crewai 'pylangdb[crewai]' crewai_tools setuptools python-dotenv
```

### Environment Setup

```bash
# 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
#!/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
python main.py "Sustainable Energy Solutions"
```

## Viewing Traces in LangDB

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

<Frame caption="LangDB Trace Dashboard">
  <img src="/images/langdb-2.png" alt="LangDB trace dashboard showing CrewAI workflow" />
</Frame>

### 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

<CardGroup cols={3}>
  <Card title="LangDB Documentation" icon="book" href="https://docs.langdb.ai">
    Official LangDB documentation and guides
  </Card>
  <Card title="LangDB Guides" icon="graduation-cap" href="https://docs.langdb.ai/guides">
    Step-by-step tutorials for building AI agents
  </Card>
  <Card title="GitHub Examples" icon="github" href="https://github.com/langdb/langdb-samples/tree/main/examples/crewai" >
    Complete CrewAI integration examples
  </Card>
  <Card title="LangDB Dashboard" icon="chart-line" href="https://app.langdb.ai">
    Access your traces and analytics
  </Card>
  <Card title="Model Catalog" icon="list" href="https://app.langdb.ai/models">
    Browse 350+ available language models
  </Card>
  <Card title="Enterprise Features" icon="building" href="https://docs.langdb.ai/enterprise">
    Self-hosted options and enterprise capabilities
  </Card>
</CardGroup>

## 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.