Learned Knowledge Store

from agno.knowledge import Knowledge
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.vectordb.pgvector import PgVector, SearchType

knowledge = Knowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="learned_knowledge",
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.knowledge import Knowledge
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.learn import LearningMachine, LearnedKnowledgeConfig
from agno.models.openai import OpenAIResponses
from agno.vectordb.pgvector import PgVector, SearchType

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

knowledge = Knowledge(
    vector_db=PgVector(
        db_url=db_url,
        table_name="learned_knowledge",
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=PostgresDb(db_url=db_url),
    learning=LearningMachine(
        knowledge=knowledge,
        learned_knowledge=True,
    ),
)

# Agent can save insights
agent.print_response(
    "Save this: When comparing cloud providers, always check egress costs first - "
    "they can be 10x different between providers.",
    user_id="[email protected]",
)

# Agent automatically searches and applies relevant knowledge
agent.print_response(
    "I'm choosing between AWS and GCP for our data platform. What should I consider?",
    user_id="[email protected]",
)

from agno.learn import LearningMachine, LearningMode, LearnedKnowledgeConfig

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=db,
    learning=LearningMachine(
        knowledge=knowledge,
        learned_knowledge=LearnedKnowledgeConfig(
            mode=LearningMode.AGENTIC,
        ),
    ),
)

from agno.learn import LearningMachine, LearningMode, LearnedKnowledgeConfig

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=db,
    learning=LearningMachine(
        knowledge=knowledge,
        learned_knowledge=LearnedKnowledgeConfig(
            mode=LearningMode.PROPOSE,
        ),
    ),
)

# Agent proposes, user confirms
agent.print_response(
    "That's a great insight about Docker networking. We should remember that.",
    user_id="[email protected]",
)
# Agent: "💡 Proposed Learning: Docker localhost access on Mac..."
# User: "Yes, save that"

from agno.learn import LearningMachine, LearningMode, LearnedKnowledgeConfig

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=db,
    learning=LearningMachine(
        knowledge=knowledge,
        learned_knowledge=LearnedKnowledgeConfig(
            mode=LearningMode.ALWAYS,
        ),
    ),
)

# Get the learning machine
learning = agent.get_learning_machine()

# Search for relevant learnings
results = learning.learned_knowledge_store.search(
    query="cloud costs",
    limit=5
)

for result in results:
    print(f"Title: {result.title}")
    print(f"Learning: {result.learning}")
    print(f"Context: {result.context}")

# Debug output
learning.learned_knowledge_store.print(query="cloud costs")

<relevant_learnings>
Prior insights that may help with this task:

**Cloud egress cost variations**
Context: When selecting cloud providers for data-intensive workloads
Insight: Always check egress costs first - they can be 10x different between
providers. AWS charges $0.09/GB, GCP $0.12/GB, while Cloudflare R2 has free egress.

**API rate limiting strategies**
Context: When designing APIs with high traffic
Insight: Use token bucket algorithm for rate limiting - it handles bursts better
than fixed windows and is easier to reason about than leaky bucket.

Apply these naturally if relevant. Current context takes precedence.
</relevant_learnings>

from agno.learn import LearningMachine, LearnedKnowledgeConfig

# Global: shared with all users (default)
learned_knowledge=LearnedKnowledgeConfig(namespace="global")

# User: private per user
learned_knowledge=LearnedKnowledgeConfig(namespace="user")

# Custom: team or domain-specific
learned_knowledge=LearnedKnowledgeConfig(namespace="engineering")

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.knowledge import Knowledge
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.learn import LearningMachine, LearningMode, LearnedKnowledgeConfig
from agno.models.openai import OpenAIResponses
from agno.vectordb.pgvector import PgVector, SearchType

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

knowledge = Knowledge(
    vector_db=PgVector(
        db_url=db_url,
        table_name="tech_insights",
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=PostgresDb(db_url=db_url),
    learning=LearningMachine(
        knowledge=knowledge,
        learned_knowledge=LearnedKnowledgeConfig(
            mode=LearningMode.AGENTIC,
            namespace="global",
        ),
    ),
    instructions="""You are a technical advisor. Search for relevant learnings
    before answering questions. Save genuinely valuable insights that would help
    others facing similar problems.""",
)

# As users interact, the agent accumulates collective intelligence
# that benefits all future users

from agno.learn import (
    LearningMachine,
    UserProfileConfig,
    UserMemoryConfig,
    LearnedKnowledgeConfig,
)

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    db=db,
    learning=LearningMachine(
        knowledge=knowledge,
        user_profile=UserProfileConfig(),       # Who the user is
        user_memory=UserMemoryConfig(),         # User's preferences
        learned_knowledge=LearnedKnowledgeConfig(),  # Collective insights
    ),
)