Store Types

from praisonaiagents import Agent

# Session 1: Start a conversation
agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant",
    memory={"session_id": "my-session-123"}
)
agent.start("My name is Alice and I love Python")

# Session 2 (later): History restored automatically
agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant",
    memory={"session_id": "my-session-123"}
)
agent.start("What's my name?")  # Remembers: "Alice"

from praisonaiagents import Agent

agent = Agent(
    name="Support Agent",
    instructions="Answer questions using the documentation",
    knowledge=["docs/manual.pdf", "docs/faq.txt"]
)

agent.start("How do I reset my password?")

from praisonaiagents import Agent

# Session 1: Agent learns
agent = Agent(
    name="Assistant",
    instructions="Remember user preferences",
    memory=True
)
agent.start("I prefer dark mode and formal responses")

# Session 2: Agent recalls
agent = Agent(
    name="Assistant",
    instructions="Remember user preferences",
    memory=True
)
agent.start("What are my preferences?")
# Remembers: "dark mode and formal responses"

from praisonaiagents import Agent, MemoryConfig

# State Store with Redis
agent = Agent(
    name="Assistant",
    instructions="Remember user preferences",
    memory=MemoryConfig(
        backend="redis",     # or "sqlite", "postgres", "mongodb"
        learn=True           # Enable learning from interactions
    )
)

# State Store with MongoDB
agent = Agent(
    name="Assistant",
    instructions="Remember user preferences",
    memory=MemoryConfig(
        backend="mongodb",
        auto_memory=True      # Auto-extract facts from conversations
    )
)

from praisonaiagents import Agent, AgentTeam, MemoryConfig

# Agent with all three store types
support = Agent(
    name="Support",
    instructions="Answer questions using docs. Remember user issues.",
    knowledge=["docs/manual.pdf"],    # Knowledge Store
    memory=MemoryConfig(              # Conversation + State Store
        session_id="support-123",
        auto_memory=True
    )
)

escalation = Agent(
    name="Escalation",
    instructions="Handle complex issues based on support findings"
)

team = AgentTeam(
    agents=[support, escalation],
    process="sequential"
)

team.start("My account is locked")

from praisonaiagents import Agent, MemoryConfig, KnowledgeConfig

agent = Agent(
    name="Support",
    instructions="Answer questions using docs. Remember user issues.",

    # Knowledge Store → Qdrant vector DB
    knowledge=KnowledgeConfig(
        sources=["docs/manual.pdf"],
        vector_store={
            "provider": "qdrant",
            "url": "http://localhost:6333"
        }
    ),

    # Conversation + State Store → PostgreSQL
    memory=MemoryConfig(
        backend="postgres",           # Conversations persisted to PostgreSQL
        session_id="support-123",
        learn=True                    # State/learning also stored in PostgreSQL
    )
)