Access Dependencies in Team Tool

This example demonstrates how team tools can access dependencies passed to the team, allowing tools to utilize dynamic context like team metrics and current time information while team members collaborate with shared data sources.

1. Create a Python file

   access_dependencies_in_tool.py

   from typing import Dict, Any, Optional
   from datetime import datetime
   
   from agno.agent import Agent
   from agno.team import Team
   from agno.models.openai import OpenAIResponses
   
   
   def get_current_context() -> dict:
       """Get current contextual information like time, weather, etc."""
       return {
           "current_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
           "timezone": "PST",
           "day_of_week": datetime.now().strftime("%A"),
       }
   
   def analyze_team_performance(team_id: str, dependencies: Optional[Dict[str, Any]] = None) -> str:
       """
       Analyze team performance using available data sources.
   
       This tool analyzes team metrics and provides insights.
       Call this tool with the team_id you want to analyze.
   
       Args:
           team_id: The team ID to analyze (e.g., 'engineering_team', 'sales_team')
           dependencies: Available data sources (automatically provided)
   
       Returns:
           Detailed team performance analysis and insights
       """
       if not dependencies:
           return "No data sources available for analysis."
   
       print(f"--> Team tool received data sources: {list(dependencies.keys())}")
   
       results = [f"=== TEAM PERFORMANCE ANALYSIS FOR {team_id.upper()} ==="]
   
       if "team_metrics" in dependencies:
           metrics_data = dependencies["team_metrics"]
           results.append(f"Team Metrics: {metrics_data}")
   
           if metrics_data.get("productivity_score"):
               score = metrics_data["productivity_score"]
               if score >= 8:
                   results.append(f"Performance Analysis: Excellent performance with {score}/10 productivity score")
               elif score >= 6:
                   results.append(f"Performance Analysis: Good performance with {score}/10 productivity score")
               else:
                   results.append(f"Performance Analysis: Needs improvement with {score}/10 productivity score")
   
       if "current_context" in dependencies:
           context_data = dependencies["current_context"]
           results.append(f"Current Context: {context_data}")
           results.append(f"Time-based Analysis: Team analysis performed on {context_data['day_of_week']} at {context_data['current_time']}")
   
       print(f"--> Team tool returned results: {results}")
   
       return "\n\n".join(results)
   
   data_analyst = Agent(
       model=OpenAIResponses(id="gpt-5.2"),
       name="Data Analyst",
       description="Specialist in analyzing team metrics and performance data",
       instructions=[
           "You are a data analysis expert focusing on team performance metrics.",
           "Interpret quantitative data and identify trends.",
           "Provide data-driven insights and recommendations.",
       ],
   )
   
   team_lead = Agent(
       model=OpenAIResponses(id="gpt-5.2"),
       name="Team Lead",
       description="Experienced team leader who provides strategic insights",
       instructions=[
           "You are an experienced team leader and management expert.",
           "Focus on leadership insights and team dynamics.",
           "Provide strategic recommendations for team improvement.",
           "Collaborate with the data analyst to get comprehensive insights.",
       ],
   )
   
   performance_team = Team(
       model=OpenAIResponses(id="gpt-5.2"),
       members=[data_analyst, team_lead],
       tools=[analyze_team_performance],
       name="Team Performance Analysis Team",
       description="A team specialized in analyzing team performance using integrated data sources.",
       instructions=[
           "You are a team performance analysis unit with access to team metrics and analysis tools.",
           "When asked to analyze any team, use the analyze_team_performance tool first.",
           "This tool has access to team metrics and current context through integrated data sources.",
           "Data Analyst: Focus on the quantitative metrics and trends.",
           "Team Lead: Provide strategic insights and management recommendations.",
           "Work together to provide comprehensive team performance insights.",
       ],
   )
   
   print("=== Team Tool Dependencies Access Example ===\n")
   
   response = performance_team.run(
       input="Please analyze the 'engineering_team' performance and provide comprehensive insights about their productivity and recommendations for improvement.",
       dependencies={
           "team_metrics": {
               "team_name": "Engineering Team Alpha",
               "team_size": 8,
               "productivity_score": 7.5,
               "sprint_velocity": 85,
               "bug_resolution_rate": 92,
               "code_review_turnaround": "2.3 days",
               "areas": ["Backend Development", "Frontend Development", "DevOps"],
           },
           "current_context": get_current_context,
       },
       session_id="test_team_tool_dependencies",
   )
   
   print(f"\nTeam Response: {response.content}")
   
2. Set up your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   

   uv venv --python 3.12
   .venv\Scripts\activate
   
3. Install dependencies

   uv pip install -U agno openai
   
4. Export your OpenAI API key

   export OPENAI_API_KEY=your_openai_api_key_here
   
5. Run Team

   python access_dependencies_in_tool.py