Knowledge vs Memory vs Context vs RAG

Understand the four key information systems in PraisonAI and when to use each

Understanding how agents access and store information is key to building effective AI applications.

Quick Comparison

Aspect	Context	Memory	Knowledge	RAG
What	Runtime data flow	Persistent storage	Pre-loaded docs	Search technique
When	During execution	Across sessions	Before execution	Query time
Lifetime	Session only	Permanent	Permanent	N/A
Direction	Read-only	Read + Write	Read-only	Read-only
Agent Param	context=	memory=	knowledge=	Part of knowledge
Dependencies	None	None (file)	chromadb	chromadb

---

The Four Concepts Explained

1. Context = Runtime Data Flow

What it is: Data passed between agents during a single workflow execution.

from praisonaiagents import Agent, AgentTeam

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

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

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

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

Key Point: Context is ephemeral - lost when the session ends. Use for workflow data flow.

---

2. Memory = Persistent Learning

What it is: Information stored and recalled across sessions. The agent “remembers”.

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"

Key Point: Memory persists across sessions. Use for user preferences, learning, conversation history.

---

3. Knowledge = Pre-loaded Documents

What it is: Reference documents loaded before the agent runs. Static information.

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?")

Key Point: Knowledge is read-only reference data. Use for manuals, FAQs, documentation.

---

4. RAG = Retrieval Augmented Generation

What it is: A technique (not a separate system) that powers Knowledge search.

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"
    )
)

Key Point: RAG is HOW knowledge search works, not a separate system. It’s the retrieval technique.

---

Decision Tree: Which to Use?

---

When to Use What

Context

Use for: Agent-to-agent data flow, tool results, single-session workflows

Don’t use for: Anything that needs to persist

Memory

Use for: User preferences, conversation history, learning over time

Don’t use for: Large document collections

Knowledge

Use for: Reference docs, manuals, FAQs, static information

Don’t use for: Dynamic data that changes frequently

RAG

Use for: Semantic search over large documents

Note: This is a technique, not a separate param

---

Agent Parameters Summary

from praisonaiagents import Agent, MemoryConfig, KnowledgeConfig

agent = Agent(
    name="Complete Agent",
    instructions="You are a helpful assistant",
    
    # CONTEXT: Auto-enabled when using AgentTeam
    # 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,
    ),
)

Parameter Quick Reference

Parameter	Type	Description
context=True	bool	Enable context management
context=ManagerConfig(...)	ManagerConfig	Custom context settings
memory=True	bool	Enable file-based memory
memory={"session_id": "..."}	dict	Memory with session
memory=MemoryConfig(...)	MemoryConfig	Full memory config
knowledge=["file.pdf"]	list	Simple knowledge sources
knowledge=KnowledgeConfig(...)	KnowledgeConfig	Full knowledge config

---

Using All Together

The most powerful pattern combines all three:

from praisonaiagents import Agent, AgentTeam, 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
team = AgentTeam(
    agents=[support_agent, escalation_agent],
    process="sequential"
)

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

---

Performance Comparison

System	Setup Time	Query Time	Dependencies	Storage
Context	0ms	0ms	None	Memory only
Memory (file)	0ms	1-5ms	None	JSON files
Memory (sqlite)	0ms	5-10ms	Built-in	SQLite DB
Knowledge	1-5s/doc	50-200ms	chromadb	Vector DB

---

Summary

Concept	One-liner
Context	Runtime data flow between agents (lost after session)
Memory	Persistent storage for learning and recall
Knowledge	Pre-loaded reference documents
RAG	Semantic search technique powering Knowledge

Related Documentation

Memory

Detailed memory configuration

Knowledge

Knowledge base setup

Context Management

Context optimization

RAG Features

Advanced RAG configuration