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docs: add LangDB integration documentation (#3228)
docs: update LangDB links in observability documentation - Removed references to the AI Gateway features in both English and Portuguese documentation. - Updated the Model Catalog links to point to the new app.langdb.ai domain. - Ensured consistency across both language versions of the documentation.
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Mrunmay Shelar
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docs/en/observability/langdb.mdx
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docs/en/observability/langdb.mdx
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---
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title: LangDB Integration
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description: Govern, secure, and optimize your CrewAI workflows with LangDB AI Gateway—access 350+ models, automatic routing, cost optimization, and full observability.
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icon: database
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---
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# Introduction
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[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.
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<Frame caption="LangDB CrewAI Trace Example">
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<img src="/images/langdb-1.png" alt="LangDB CrewAI trace example" />
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</Frame>
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**Checkout:** [View the live trace example](https://app.langdb.ai/sharing/threads/3becbfed-a1be-ae84-ea3c-4942867a3e22)
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## Features
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### AI Gateway Capabilities
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- **Access to 350+ LLMs**: Connect to all major language models through a single integration
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- **Virtual Models**: Create custom model configurations with specific parameters and routing rules
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- **Virtual MCP**: Enable compatibility and integration with MCP (Model Context Protocol) systems for enhanced agent communication
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- **Guardrails**: Implement safety measures and compliance controls for agent behavior
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### Observability & Tracing
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- **Automatic Tracing**: Single `init()` call captures all CrewAI interactions
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- **End-to-End Visibility**: Monitor agent workflows from start to finish
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- **Tool Usage Tracking**: Track which tools agents use and their outcomes
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- **Model Call Monitoring**: Detailed insights into LLM interactions
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- **Performance Analytics**: Monitor latency, token usage, and costs
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- **Debugging Support**: Step-through execution for troubleshooting
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- **Real-time Monitoring**: Live traces and metrics dashboard
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## Setup Instructions
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<Steps>
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<Step title="Install LangDB">
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Install the LangDB client with CrewAI feature flag:
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```bash
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pip install 'pylangdb[crewai]'
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```
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</Step>
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<Step title="Set Environment Variables">
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Configure your LangDB credentials:
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```bash
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export LANGDB_API_KEY="<your_langdb_api_key>"
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export LANGDB_PROJECT_ID="<your_langdb_project_id>"
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export LANGDB_API_BASE_URL='https://api.us-east-1.langdb.ai'
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```
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</Step>
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<Step title="Initialize Tracing">
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Import and initialize LangDB before configuring your CrewAI code:
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```python
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from pylangdb.crewai import init
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# Initialize LangDB
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init()
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```
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</Step>
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<Step title="Configure CrewAI with LangDB">
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Set up your LLM with LangDB headers:
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```python
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from crewai import Agent, Task, Crew, LLM
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import os
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# Configure LLM with LangDB headers
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llm = LLM(
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model="openai/gpt-4o", # Replace with the model you want to use
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api_key=os.getenv("LANGDB_API_KEY"),
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base_url=os.getenv("LANGDB_API_BASE_URL"),
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extra_headers={"x-project-id": os.getenv("LANGDB_PROJECT_ID")}
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)
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```
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</Step>
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</Steps>
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## Quick Start Example
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Here's a simple example to get you started with LangDB and CrewAI:
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```python
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import os
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from pylangdb.crewai import init
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from crewai import Agent, Task, Crew, LLM
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# Initialize LangDB before any CrewAI imports
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init()
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def create_llm(model):
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return LLM(
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model=model,
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api_key=os.environ.get("LANGDB_API_KEY"),
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base_url=os.environ.get("LANGDB_API_BASE_URL"),
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extra_headers={"x-project-id": os.environ.get("LANGDB_PROJECT_ID")}
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)
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# Define your agent
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researcher = Agent(
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role="Research Specialist",
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goal="Research topics thoroughly",
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backstory="Expert researcher with skills in finding information",
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llm=create_llm("openai/gpt-4o"), # Replace with the model you want to use
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verbose=True
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)
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# Create a task
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task = Task(
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description="Research the given topic and provide a comprehensive summary",
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agent=researcher,
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expected_output="Detailed research summary with key findings"
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)
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# Create and run the crew
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crew = Crew(agents=[researcher], tasks=[task])
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result = crew.kickoff()
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print(result)
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```
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## Complete Example: Research and Planning Agent
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This comprehensive example demonstrates a multi-agent workflow with research and planning capabilities.
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### Prerequisites
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```bash
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pip install crewai 'pylangdb[crewai]' crewai_tools setuptools python-dotenv
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```
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### Environment Setup
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```bash
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# LangDB credentials
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export LANGDB_API_KEY="<your_langdb_api_key>"
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export LANGDB_PROJECT_ID="<your_langdb_project_id>"
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export LANGDB_API_BASE_URL='https://api.us-east-1.langdb.ai'
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# Additional API keys (optional)
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export SERPER_API_KEY="<your_serper_api_key>" # For web search capabilities
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```
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### Complete Implementation
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```python
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#!/usr/bin/env python3
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import os
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import sys
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from pylangdb.crewai import init
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init() # Initialize LangDB before any CrewAI imports
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from dotenv import load_dotenv
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from crewai import Agent, Task, Crew, Process, LLM
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from crewai_tools import SerperDevTool
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load_dotenv()
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def create_llm(model):
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return LLM(
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model=model,
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api_key=os.environ.get("LANGDB_API_KEY"),
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base_url=os.environ.get("LANGDB_API_BASE_URL"),
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extra_headers={"x-project-id": os.environ.get("LANGDB_PROJECT_ID")}
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)
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class ResearchPlanningCrew:
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def researcher(self) -> Agent:
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return Agent(
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role="Research Specialist",
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goal="Research topics thoroughly and compile comprehensive information",
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backstory="Expert researcher with skills in finding and analyzing information from various sources",
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tools=[SerperDevTool()],
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llm=create_llm("openai/gpt-4o"),
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verbose=True
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)
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def planner(self) -> Agent:
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return Agent(
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role="Strategic Planner",
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goal="Create actionable plans based on research findings",
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backstory="Strategic planner who breaks down complex challenges into executable plans",
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reasoning=True,
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max_reasoning_attempts=3,
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llm=create_llm("openai/anthropic/claude-3.7-sonnet"),
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verbose=True
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)
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def research_task(self) -> Task:
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return Task(
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description="Research the topic thoroughly and compile comprehensive information",
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agent=self.researcher(),
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expected_output="Comprehensive research report with key findings and insights"
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)
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def planning_task(self) -> Task:
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return Task(
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description="Create a strategic plan based on the research findings",
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agent=self.planner(),
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expected_output="Strategic execution plan with phases, goals, and actionable steps",
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context=[self.research_task()]
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)
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def crew(self) -> Crew:
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return Crew(
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agents=[self.researcher(), self.planner()],
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tasks=[self.research_task(), self.planning_task()],
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verbose=True,
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process=Process.sequential
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)
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def main():
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topic = sys.argv[1] if len(sys.argv) > 1 else "Artificial Intelligence in Healthcare"
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crew_instance = ResearchPlanningCrew()
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# Update task descriptions with the specific topic
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crew_instance.research_task().description = f"Research {topic} thoroughly and compile comprehensive information"
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crew_instance.planning_task().description = f"Create a strategic plan for {topic} based on the research findings"
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result = crew_instance.crew().kickoff()
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print(result)
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if __name__ == "__main__":
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main()
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```
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### Running the Example
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```bash
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python main.py "Sustainable Energy Solutions"
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```
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## Viewing Traces in LangDB
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After running your CrewAI application, you can view detailed traces in the LangDB dashboard:
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<Frame caption="LangDB Trace Dashboard">
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<img src="/images/langdb-2.png" alt="LangDB trace dashboard showing CrewAI workflow" />
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</Frame>
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### What You'll See
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- **Agent Interactions**: Complete flow of agent conversations and task handoffs
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- **Tool Usage**: Which tools were called, their inputs, and outputs
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- **Model Calls**: Detailed LLM interactions with prompts image.pngand responses
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- **Performance Metrics**: Latency, token usage, and cost tracking
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- **Execution Timeline**: Step-by-step view of the entire workflow
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## Troubleshooting
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### Common Issues
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- **No traces appearing**: Ensure `init()` is called before any CrewAI imports
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- **Authentication errors**: Verify your LangDB API key and project ID
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## Resources
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<CardGroup cols={3}>
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<Card title="LangDB Documentation" icon="book" href="https://docs.langdb.ai">
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Official LangDB documentation and guides
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</Card>
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<Card title="LangDB Guides" icon="graduation-cap" href="https://docs.langdb.ai/guides">
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Step-by-step tutorials for building AI agents
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</Card>
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<Card title="GitHub Examples" icon="github" href="https://github.com/langdb/langdb-samples/tree/main/examples/crewai" >
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Complete CrewAI integration examples
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</Card>
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<Card title="LangDB Dashboard" icon="chart-line" href="https://app.langdb.ai">
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Access your traces and analytics
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</Card>
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<Card title="Model Catalog" icon="list" href="https://app.langdb.ai/models">
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Browse 350+ available language models
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</Card>
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<Card title="Enterprise Features" icon="building" href="https://docs.langdb.ai/enterprise">
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Self-hosted options and enterprise capabilities
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</Card>
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</CardGroup>
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## Next Steps
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This guide covered the basics of integrating LangDB AI Gateway with CrewAI. To further enhance your AI workflows, explore:
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- **Virtual Models**: Create custom model configurations with routing strategies
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- **Guardrails & Safety**: Implement content filtering and compliance controls
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- **Production Deployment**: Configure fallbacks, retries, and load balancing
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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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@@ -25,6 +25,10 @@ Observability is crucial for understanding how your CrewAI agents perform, ident
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Session replays, metrics, and monitoring for agent development and production.
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</Card>
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<Card title="LangDB" icon="database" href="/en/observability/langdb">
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End-to-end tracing for CrewAI workflows with automatic agent interaction capture.
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</Card>
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<Card title="OpenLIT" icon="magnifying-glass-chart" href="/en/observability/openlit">
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OpenTelemetry-native monitoring with cost tracking and performance analytics.
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</Card>
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