1from kern.agent import Agent
2from kern.knowledge.embedder.openai import OpenAIEmbedder
3from kern.knowledge.knowledge import Knowledge
4from kern.models.openai import OpenAIResponses
5from kern.tools.knowledge import KnowledgeTools
6from kern.vectordb.lancedb import LanceDb, SearchType
7
8# Create a knowledge containing information from a URL
9agno_docs = Knowledge(
10 # Use LanceDB as the vector database and store embeddings in the `agno_docs` table
11 vector_db=LanceDb(
12 uri="tmp/lancedb",
13 table_name="agno_docs",
14 search_type=SearchType.hybrid,
15 embedder=OpenAIEmbedder(id="text-embedding-3-small"),
16 ),
17)
18# Add content to the knowledge
19agno_docs.insert(url="https://kern.ndx.rocks/llms-full.txt")
20
21knowledge_tools = KnowledgeTools(
22 knowledge=agno_docs,
23 think=True,
24 search=True,
25 analyze=True,
26 add_few_shot=True,
27)
28
29agent = Agent(
30 model=OpenAIResponses(id="gpt-5.2"),
31 tools=[knowledge_tools],
32 markdown=True,
33)
34
35if __name__ == "__main__":
36 agent.print_response(
37 "How do I build a team of agents in kern?",
38 markdown=True,
39 stream=True,
40 stream_tools=True,
41 )
