LangDB

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

Integrating Agno with LangDB

LangDB traces Agno agent runs, tool calls, team interactions, and model metrics.

For detailed integration instructions, see the LangDB Agno documentation.

LangDB Finance Team Trace

Prerequisites

1. Install Dependencies

   Ensure you have the necessary packages installed:

   uv pip install agno 'pylangdb[agno]' openai yfinance
   

2. Setup LangDB Account

   • Sign up for an account at LangDB
   • 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:

   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.

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:

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

LangDB Finance Team Thread

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
• Building a Reasoning Finance Team Guide
• LangDB GitHub Samples
• LangDB Dashboard