---
title: LangDB Integration
description: How to use LangDB AI Gateway with CrewAI
icon: database
---
## Introduction
LangDB is the fastest enterprise AI gateway that enhances CrewAI with production-ready observability and optimization features. It provides:
- **Complete end-to-end tracing** of every agent interaction and LLM call
- **Real-time cost monitoring** and optimization across 250+ LLMs
- **Performance analytics** with detailed metrics and insights
- **Secure governance** for enterprise AI deployments
- **OpenAI-compatible APIs** for seamless integration
- **Fine-grained control** over agent workflows and resource usage
### Installation & Setup
```bash
pip install -U crewai langdb
```
Configure your LangDB credentials from the [LangDB dashboard](https://app.langdb.ai/):
```bash
export LANGDB_API_KEY="your_langdb_api_key"
export LANGDB_PROJECT_ID="your_project_id"
```
The integration requires a single initialization call before creating your agents:
```python
from langdb import LangDB
from crewai import Agent, Task, Crew, LLM
# Initialize LangDB tracing
LangDB.init()
# Create LLM instance - LangDB automatically traces all calls
llm = LLM(
model="gpt-4o",
temperature=0.7
)
# Create your agents as usual
@agent
def research_agent(self) -> Agent:
return Agent(
role="Senior Research Analyst",
goal="Conduct comprehensive research on assigned topics",
backstory="You are an expert researcher with deep analytical skills.",
llm=llm,
verbose=True
)
```
## Key Features
### 1. Comprehensive Observability
LangDB provides complete visibility into your CrewAI agent workflows with minimal setup overhead.
LangDB automatically captures every LLM interaction in your crew execution:
```python
from langdb import LangDB
from crewai import Agent, Task, Crew, LLM
# Initialize with custom trace metadata
LangDB.init(
metadata={
"environment": "production",
"crew_type": "research_workflow",
"user_id": "user_123"
}
)
# All agent interactions are automatically traced
crew = Crew(
agents=[research_agent, writer_agent],
tasks=[research_task, writing_task],
verbose=True
)
# Execute with full tracing
result = crew.kickoff(inputs={"topic": "AI trends 2025"})
```
View detailed traces in the LangDB dashboard showing:
- Complete agent conversation flows
- Tool usage and function calls
- Task execution timelines
- LLM request/response pairs
LangDB tracks comprehensive performance metrics for your crews:
- **Execution Time**: Total and per-task execution duration
- **Token Usage**: Input/output tokens for cost optimization
- **Success Rates**: Task completion and failure analytics
- **Latency Analysis**: Response times and bottleneck identification
```python
# Access metrics programmatically
from langdb import LangDB
# Get crew execution metrics
metrics = LangDB.get_metrics(
project_id="your_project_id",
filters={
"crew_type": "research_workflow",
"time_range": "last_24h"
}
)
print(f"Average execution time: {metrics.avg_execution_time}")
print(f"Total cost: ${metrics.total_cost}")
print(f"Success rate: {metrics.success_rate}%")
```
Track and optimize AI spending across your CrewAI deployments:
```python
from langdb import LangDB
# Initialize with cost tracking
LangDB.init(
cost_tracking=True,
budget_alerts={
"daily_limit": 100.0, # $100 daily limit
"alert_threshold": 0.8 # Alert at 80% of limit
}
)
# LangDB automatically tracks costs for all LLM calls
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
# View cost breakdown
cost_report = LangDB.get_cost_report(
breakdown_by=["model", "agent", "task"]
)
```
Features include:
- Real-time cost tracking across all models
- Budget alerts and spending limits
- Cost optimization recommendations
- Detailed cost attribution by agent and task
### 2. Advanced Analytics & Insights
LangDB provides powerful analytics to optimize your CrewAI workflows.
Analyze individual agent performance and identify optimization opportunities:
```python
from langdb import LangDB
# Get agent-specific analytics
analytics = LangDB.get_agent_analytics(
agent_role="Senior Research Analyst",
time_range="last_week"
)
print(f"Average task completion time: {analytics.avg_completion_time}")
print(f"Most used tools: {analytics.top_tools}")
print(f"Success rate: {analytics.success_rate}%")
print(f"Cost per task: ${analytics.cost_per_task}")
```
Identify bottlenecks and optimization opportunities in your crew workflows:
```python
# Analyze crew workflow patterns
workflow_analysis = LangDB.analyze_workflow(
crew_id="research_crew_v1",
optimization_focus=["speed", "cost", "quality"]
)
# Get optimization recommendations
recommendations = workflow_analysis.recommendations
for rec in recommendations:
print(f"Optimization: {rec.type}")
print(f"Potential savings: {rec.estimated_savings}")
print(f"Implementation: {rec.implementation_guide}")
```
### 3. Production-Ready Features
Automatic detection and alerting for agent failures, LLM errors, and workflow issues.
Intelligent rate limiting to prevent API quota exhaustion and optimize throughput.
Smart caching of LLM responses to reduce costs and improve response times.
Distribute requests across multiple LLM providers for reliability and performance.
### 4. Enterprise Security & Governance
LangDB provides enterprise-grade security features for production CrewAI deployments:
```python
from langdb import LangDB
# Initialize with security configurations
LangDB.init(
security_config={
"pii_detection": True,
"content_filtering": True,
"audit_logging": True,
"data_retention_days": 90
}
)
# All crew interactions are automatically secured
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
```
Security features include:
- **PII Detection**: Automatic detection and redaction of sensitive information
- **Content Filtering**: Block inappropriate or harmful content
- **Audit Logging**: Complete audit trails for compliance
- **Data Governance**: Configurable data retention and privacy controls
## Advanced Configuration
### Custom Metadata and Filtering
Add custom metadata to enable powerful filtering and analytics:
```python
from langdb import LangDB
from crewai import Agent, Crew, Task
# Initialize with rich metadata
LangDB.init(
metadata={
"environment": "production",
"team": "research_team",
"version": "v2.1.0",
"customer_tier": "enterprise"
}
)
# Add task-specific metadata
@task
def research_task(self) -> Task:
return Task(
description="Research the latest AI trends",
expected_output="Comprehensive research report",
agent=research_agent,
metadata={
"task_type": "research",
"priority": "high",
"estimated_duration": "30min"
}
)
```
### Multi-Environment Setup
Configure different LangDB projects for different environments:
```python
import os
from langdb import LangDB
# Environment-specific configuration
environment = os.getenv("ENVIRONMENT", "development")
if environment == "production":
LangDB.init(
project_id="prod_project_id",
sampling_rate=1.0, # Trace all requests
cost_tracking=True
)
elif environment == "staging":
LangDB.init(
project_id="staging_project_id",
sampling_rate=0.5, # Sample 50% of requests
cost_tracking=False
)
else:
LangDB.init(
project_id="dev_project_id",
sampling_rate=0.1, # Sample 10% of requests
cost_tracking=False
)
```
## Best Practices
### Development Phase
- Use detailed tracing to understand agent behavior patterns
- Monitor resource usage during testing and development
- Set up cost alerts to prevent unexpected spending
- Implement comprehensive error handling and monitoring
### Production Phase
- Enable full request tracing for complete observability
- Set up automated alerts for performance degradation
- Implement cost optimization strategies based on analytics
- Use metadata for detailed filtering and analysis
### Continuous Improvement
- Regular performance reviews using LangDB analytics
- A/B testing of different agent configurations
- Cost optimization based on usage patterns
- Workflow optimization using bottleneck analysis
## Getting Started
1. **Sign up** for a LangDB account at [app.langdb.ai](https://app.langdb.ai)
2. **Install** the LangDB package: `pip install langdb`
3. **Initialize** LangDB in your CrewAI application
4. **Deploy** your crews with automatic observability
5. **Monitor** and optimize using the LangDB dashboard
Explore comprehensive LangDB documentation and advanced features
LangDB transforms your CrewAI agents into production-ready, observable, and optimized AI workflows with minimal code changes and maximum insights.