Knowledge vs Memory vs Context vs RAG

from praisonaiagents import Agent, PraisonAIAgents

researcher = Agent(
    name="Researcher",
    instructions="Research the topic"
)

writer = Agent(
    name="Writer",
    instructions="Write based on research"
)

# Context flows automatically: Researcher → Writer
agents = PraisonAIAgents(
    agents=[researcher, writer],
    process="sequential"
)

result = agents.start("Write about AI")
# Context is LOST after this completes

from praisonaiagents import Agent

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

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

from praisonaiagents import Agent

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

# Agent searches knowledge to answer
agent.start("How do I reset my password?")

from praisonaiagents import Agent, KnowledgeConfig

agent = Agent(
    name="RAG Agent",
    instructions="Answer using the knowledge base",
    knowledge=KnowledgeConfig(
        sources=["docs/"],
        retrieval_k=5,        # Return top 5 chunks
        rerank=True,          # Rerank for relevance
        chunking_strategy="semantic"
    )
)

from praisonaiagents import Agent, MemoryConfig, KnowledgeConfig

agent = Agent(
    name="Complete Agent",
    instructions="You are a helpful assistant",
    
    # CONTEXT: Auto-enabled when using PraisonAIAgents
    # Manages token limits, deduplication, summarization
    context=True,  # or ManagerConfig for advanced settings
    
    # MEMORY: Persistent storage across sessions
    memory=MemoryConfig(
        session_id="user-123",
        user_id="user-123",
        backend="file",      # file, sqlite, redis, postgres
        auto_memory=True,    # Auto-extract important info
    ),
    
    # KNOWLEDGE: Pre-loaded documents with RAG
    knowledge=KnowledgeConfig(
        sources=["docs/"],
        retrieval_k=5,
        rerank=True,
    ),
)

from praisonaiagents import Agent, PraisonAIAgents, MemoryConfig

# Agent with Knowledge + Memory
support_agent = Agent(
    name="Support",
    instructions="Answer questions using docs. Remember user issues.",
    knowledge=["docs/manual.pdf"],  # Pre-loaded reference
    memory=MemoryConfig(            # Persistent learning
        session_id="support-session",
        auto_memory=True
    )
)

# Agent receives Context from support_agent
escalation_agent = Agent(
    name="Escalation",
    instructions="Handle complex issues based on support findings"
)

# Context flows between agents in workflow
agents = PraisonAIAgents(
    agents=[support_agent, escalation_agent],
    process="sequential"
)

result = agents.start("My account is locked")