Quickstart

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=SqliteDb(db_file="tmp/agents.db"),
    learning=True,
    markdown=True,
)

if __name__ == "__main__":
    user_id = "[email protected]"

    # Session 1: Share information naturally
    print("\n--- Session 1: Extraction happens automatically ---\n")
    agent.print_response(
        "Hi! I'm Alice. I work at Anthropic as a research scientist. "
        "I prefer concise responses without too much explanation.",
        user_id=user_id,
        session_id="session_1",
        stream=True,
    )
    lm = agent.get_learning_machine()
    lm.user_profile_store.print(user_id=user_id)
    lm.user_memory_store.print(user_id=user_id)

    # Session 2: New session - agent remembers
    print("\n--- Session 2: Agent remembers across sessions ---\n")
    agent.print_response(
        "What do you know about me?",
        user_id=user_id,
        session_id="session_2",
        stream=True,
    )

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.learn import (
    LearningMachine,
    LearningMode,
    UserMemoryConfig,
    UserProfileConfig,
)
from agno.models.openai import OpenAIResponses

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=SqliteDb(db_file="tmp/agents.db"),
    learning=LearningMachine(
        user_profile=UserProfileConfig(mode=LearningMode.AGENTIC),
        user_memory=UserMemoryConfig(mode=LearningMode.AGENTIC),
    ),
    markdown=True,
)

if __name__ == "__main__":
    user_id = "[email protected]"

    # Session 1: Agent decides what to save via tool calls
    print("\n--- Session 1: Agent uses tools to save profile and memories ---\n")
    agent.print_response(
        "Hi! I'm Alice. I work at Anthropic as a research scientist. "
        "I prefer concise responses without too much explanation.",
        user_id=user_id,
        session_id="session_1",
        stream=True,
    )
    lm = agent.get_learning_machine()
    lm.user_profile_store.print(user_id=user_id)
    lm.user_memory_store.print(user_id=user_id)

    # Session 2: New session - agent remembers
    print("\n--- Session 2: Agent remembers across sessions ---\n")
    agent.print_response(
        "What do you know about me?",
        user_id=user_id,
        session_id="session_2",
        stream=True,
    )

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.knowledge import Knowledge
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.learn import LearnedKnowledgeConfig, LearningMachine, LearningMode
from agno.models.openai import OpenAIResponses
from agno.vectordb.chroma import ChromaDb, SearchType

knowledge = Knowledge(
    name="Agent Learnings",
    vector_db=ChromaDb(
        name="learnings",
        path="tmp/chromadb",
        persistent_client=True,
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=SqliteDb(db_file="tmp/agents.db"),
    learning=LearningMachine(
        knowledge=knowledge,
        learned_knowledge=LearnedKnowledgeConfig(mode=LearningMode.AGENTIC),
    ),
    markdown=True,
)

if __name__ == "__main__":
    # Session 1: User 1 teaches the agent
    print("\n--- Session 1: User 1 saves a learning ---\n")
    agent.print_response(
        "We're trying to reduce our cloud egress costs. Remember this.",
        user_id="[email protected]",
        session_id="session_1",
        stream=True,
    )
    lm = agent.get_learning_machine()
    lm.learned_knowledge_store.print(query="cloud")

    # Session 2: User 2 benefits from the learning
    print("\n--- Session 2: User 2 asks a related question ---\n")
    agent.print_response(
        "I'm picking a cloud provider for a data pipeline. Key considerations?",
        user_id="[email protected]",
        session_id="session_2",
        stream=True,
    )