Agent with Knowledge Tracing

Trace agents with knowledge bases in AgentOS.

Trace an agent with a knowledge base in AgentOS. The traces capture knowledge searches, model calls, and all agent operations.

1. Create a Python file

   agent_with_knowledge_tracing.py

   from textwrap import dedent
   
   from agno.agent import Agent
   from agno.db.postgres import PostgresDb
   from agno.db.sqlite import SqliteDb
   from agno.knowledge.embedder.openai import OpenAIEmbedder
   from agno.knowledge.knowledge import Knowledge
   from agno.models.openai import OpenAIResponses
   from agno.os import AgentOS
   from agno.vectordb.pgvector import PgVector, SearchType
   
   db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
   db = PostgresDb(db_url, id="agno_assist_db")
   db_sqlite = SqliteDb(db_file="tmp/traces.db")
   
   description = dedent(
       """\
       You are AgnoAssist, an advanced AI Agent specialized in the Agno framework.
       Your goal is to help developers understand and effectively use Agno and the AgentOS by providing
       explanations and working code examples."""
   )
   
   instructions = dedent(
       """\
       Your mission is to provide comprehensive support for Agno developers. Follow these steps to ensure the best possible response:
   
       1. **Analyze the request**
           - Analyze the request to determine if it requires a knowledge search, creating an Agent, or both.
           - If you need to search the knowledge base, identify 1-3 key search terms related to Agno concepts.
           - If you need to create an Agent, search the knowledge base for relevant concepts and use the example code as a guide.
           - When the user asks for an Agent, they mean an Agno Agent.
           - All concepts are related to Agno, so you can search the knowledge base for relevant information
   
       After Analysis, always start the iterative search process. No need to wait for approval from the user.
   
       2. **Iterative Search Process**:
           - Use the `search_knowledge_base` tool to search for related concepts, code examples and implementation details
           - Continue searching until you have found all the information you need or you have exhausted all the search terms
   
       After the iterative search process, determine if you need to create an Agent.
       If you do, ask the user if they want you to create an Agent for them.
   
       3. **Code Creation**
           - Create complete, working code examples that users can run
           - You must remember to use agent.run() and NOT agent.print_response()
           - Remember to:
               * Build the complete agent implementation
               * Include all necessary imports and setup
               * Add comprehensive comments explaining the implementation
               * Ensure all dependencies are listed
               * Include error handling and best practices
               * Add type hints and documentation
   
       Key topics to cover:
       - Agent levels and capabilities
       - Knowledge base and memory management
       - Tool integration
       - Model support and configuration
       - Best practices and common patterns"""
   )
   
   knowledge = Knowledge(
       vector_db=PgVector(
           db_url=db_url,
           table_name="agno_assist_knowledge",
           search_type=SearchType.hybrid,
           embedder=OpenAIEmbedder(id="text-embedding-3-small"),
       ),
       contents_db=db,
   )
   
   # Setup our Agno Agent
   agno_assist = Agent(
       name="Agno Assist",
       id="agno-assist",
       model=OpenAIResponses(id="gpt-5.2"),
       description=description,
       instructions=instructions,
       db=db_sqlite,
       update_memory_on_run=True,
       knowledge=knowledge,
       search_knowledge=True,
       add_history_to_context=True,
       add_datetime_to_context=True,
       markdown=True,
   )
   
   agent_os = AgentOS(
       description="Example app with Agno Docs Agent with knowledge and tracing",
       agents=[agno_assist],
       tracing=True,
   )
   
   
   app = agent_os.get_app()
   
   if __name__ == "__main__":
       knowledge.insert(name="Agno Docs", url="https://docs.agno.com/llms-full.txt")
       """Run your AgentOS.
   
       You can test your AgentOS at:
       http://localhost:7777/docs
   
       """
       # Don't use reload=True here, this can cause issues with the lifespan
       agent_os.serve(app="agent_with_knowledge_tracing:app")
   
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 openai "agno[os]" beautifulsoup4 pgvector "psycopg[binary]" opentelemetry-api opentelemetry-sdk openinference-instrumentation-agno
   
4. Export your OpenAI API key

   Mac/Linux

   export OPENAI_API_KEY="your_openai_api_key_here"
   

   $Env:OPENAI_API_KEY="your_openai_api_key_here"
   
5. Start PostgreSQL with pgvector

   Make sure you have PostgreSQL running with the pgvector extension. You can use Docker:

   docker run -d \
     --name pgvector \
     -e POSTGRES_USER=ai \
     -e POSTGRES_PASSWORD=ai \
     -e POSTGRES_DB=ai \
     -p 5532:5432 \
     pgvector/pgvector:pg16
   
6. Run AgentOS

   python agent_with_knowledge_tracing.py
   

   Your AgentOS will be available at http://localhost:7777. View traces in the AgentOS dashboard.