> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-service-account.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# LangDB

> Send Agno agent runs, team runs, and tool-call traces to LangDB.

## Integrating Agno with LangDB

[LangDB](https://langdb.ai/) traces Agno agent runs, tool calls, team interactions, and model metrics.

For detailed integration instructions, see the [LangDB Agno documentation](https://docs.langdb.ai/getting-started/working-with-agent-frameworks/working-with-agno).

<Frame caption="LangDB Finance Team Trace">
  <img src="https://mintcdn.com/agno-v2-service-account/aOBqxL3H5mlin2p4/images/langdb-finance-trace.png?fit=max&auto=format&n=aOBqxL3H5mlin2p4&q=85&s=50b5ecddcedb44aebb0570388470a45c" style={{ borderRadius: '10px', width: '100%', maxWidth: '800px' }} alt="langdb-agno finance team observability" width="1623" height="900" data-path="images/langdb-finance-trace.png" />
</Frame>

## Prerequisites

1. **Install Dependencies**

   Ensure you have the necessary packages installed:

   ```bash theme={null}
   uv pip install agno 'pylangdb[agno]' openai yfinance
   ```

2. **Setup LangDB Account**

   * Sign up for an account at [LangDB](https://app.langdb.ai/signup)
   * Create a new project in the LangDB dashboard
   * Obtain your API key and Project ID from the project settings

3. **Set Environment Variables**

   Configure your environment with the LangDB credentials:

   ```bash theme={null}
   export LANGDB_API_KEY="<your_langdb_api_key>"
   export LANGDB_PROJECT_ID="<your_langdb_project_id>"
   ```

## Sending Traces to LangDB

### Example: Basic Agent Setup

Instrument your Agno agent with LangDB tracing.

```python theme={null}
from pylangdb.agno import init

# Initialize LangDB tracing - must be called before creating agents
init()

from agno.agent import Agent
from agno.models.langdb import LangDB
from agno.tools.hackernews import HackerNewsTools

# Create agent with LangDB model (uses environment variables automatically)
agent = Agent(
    name="Hacker News Research Agent",
    model=LangDB(id="openai/gpt-4.1"),
    tools=[HackerNewsTools()],
    instructions="Answer questions using current Hacker News data."
)

# Use the agent
response = agent.run("What are the latest developments in AI agents?")
print(response)
```

### Example: Multi-Agent Team Coordination

For more complex workflows, you can use Agno's `Team` class with LangDB tracing:

```python theme={null}
from pylangdb.agno import init
init()

from agno.agent import Agent
from agno.team import Team
from agno.models.langdb import LangDB
from agno.tools.hackernews import HackerNewsTools
from agno.tools.yfinance import YFinanceTools

# Research Agent
web_agent = Agent(
    name="Market Research Agent",
    model=LangDB(id="openai/gpt-4.1"),
    tools=[HackerNewsTools()],
    instructions="Research current market conditions and news"
)

# Financial Analysis Agent
finance_agent = Agent(
    name="Financial Analyst",
    model=LangDB(id="xai/grok-4"),
    tools=[YFinanceTools(enable_stock_price=True, enable_company_info=True)],
    instructions="Perform quantitative financial analysis"
)

# Coordinated Team
reasoning_team = Team(
    name="Finance Reasoning Team",
    model=LangDB(id="xai/grok-4"),
    members=[web_agent, finance_agent],
    instructions=[
        "Collaborate to provide comprehensive financial insights",
        "Consider both fundamental analysis and market sentiment"
    ]
)

# Execute team workflow
reasoning_team.print_response("Analyze Apple (AAPL) investment potential")
```

## Sample Trace

View a complete example trace in the LangDB dashboard: [Finance Reasoning Team Trace](https://app.langdb.ai/sharing/threads/73c91c58-eab7-4c6b-afe1-5ab6324f1ada)

<Frame caption="LangDB Finance Team Thread">
  <img src="https://mintcdn.com/agno-v2-service-account/aOBqxL3H5mlin2p4/images/langdb-finance-thread.png?fit=max&auto=format&n=aOBqxL3H5mlin2p4&q=85&s=4be277b040ed1909b3ba00663b1afc95" style={{ borderRadius: '10px', width: '100%', maxWidth: '800px' }} alt="langdb-agno finance team observability" width="1241" height="916" data-path="images/langdb-finance-thread.png" />
</Frame>

## Advanced Features

### LangDB Capabilities

* **Virtual Models**: Save and reuse model configurations with prompts, parameters, tools, and routing logic.
* **MCP Support**: Attach Model Context Protocol servers to models.
* **Multi-Provider**: Route requests to OpenAI, Anthropic, Google, xAI, and other providers.

## Notes

* **Initialization Order**: Always call `init()` before creating any Agno agents or teams
* **Environment Variables**: With `LANGDB_API_KEY` and `LANGDB_PROJECT_ID` set, you can create models with just `LangDB(id="model_name")`

## Resources

* [LangDB Documentation](https://docs.langdb.ai/)
* [Building a Reasoning Finance Team Guide](https://docs.langdb.ai/guides/building-agents/building-a-reasoning-finance-team-with-agno)
* [LangDB GitHub Samples](https://github.com/langdb/langdb-samples/tree/main/examples/agno)
* [LangDB Dashboard](https://app.langdb.ai/)