Context vs Knowledge

Understanding the key differences between context management and knowledge systems in PraisonAI agents.

Context vs Knowledge

PraisonAI provides two systems for providing information to agents: Context (runtime data flow) and Knowledge (pre-loaded reference data). Understanding when to use each is crucial for building efficient agents.

Diagram

Quick Comparison

Aspect	Context	Knowledge
When Loaded	Runtime (during execution)	Pre-loaded (before execution)
Source	Agent outputs, tool results	Files, URLs, documents
Storage	In-memory (ephemeral)	Vector DB (persistent)
Search	Sequential flow	Semantic search (RAG)
Use Case	Passing data between agents	Reference information
Dependencies	None	chromadb, mem0

Context: Runtime Data Flow

Context is how agents share information during a single workflow execution. Data flows from one agent to the next and is lost when the session ends.

Diagram

Context Code Example

python

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: User → Researcher → Writer
team = AgentTeam(
    agents=[researcher, writer],
    process="sequential"
)

result = team.start("Write about AI")

Knowledge: Pre-loaded Reference Data

Knowledge provides agents with pre-loaded reference information from files, URLs, or documents. It uses RAG (Retrieval Augmented Generation) to find relevant information.

Diagram

Knowledge Code Example

python

from praisonaiagents import Agent

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

# Agent automatically searches knowledge for relevant info
response = agent.start("How do I reset my password?")

Supported File Types

Documents

PDF, DOC, DOCX, PPT, PPTX, XLS, XLSX

Text

TXT, CSV, JSON, XML, MD, HTML

Media

JPG, PNG, GIF, MP3, WAV (with transcription)

How Knowledge Works

Load Documents
Files are read and converted to text using MarkItDown.
Chunk Content
Text is split into smaller chunks (default: 512 tokens, 50 overlap).
Generate Embeddings
Each chunk is converted to a vector embedding.
Store in Vector DB
Embeddings are stored in ChromaDB for fast similarity search.
Query at Runtime
When agent needs info, query is embedded and similar chunks are retrieved.

When to Use Each

Diagram

Use Context When:

Agent-to-agent data flow – Passing results between sequential agents
Tool results – Using output from tool calls
Simple workflows – No external reference data needed
Zero dependencies – Want to avoid extra packages

Use Knowledge When:

Reference documents – Manuals, FAQs, documentation
Large content – Too much to fit in prompt
Semantic search – Need to find relevant sections
RAG applications – Question answering over documents

Using Both Together

The most powerful pattern combines both: Knowledge for reference data + Context for workflow data.

python

from praisonaiagents import Agent, AgentTeam

# Agent with knowledge base
researcher = Agent(
    name="Researcher",
    instructions="Research using the knowledge base",
    knowledge=["research_papers/"]  # Pre-loaded documents
)

# Agent receives context from researcher
writer = Agent(
    name="Writer",
    instructions="Write article based on research findings"
    # No knowledge - uses context from researcher
)

team = AgentTeam(
    agents=[researcher, writer],
    process="sequential"
)

# Researcher searches knowledge, passes findings via context to writer
result = team.start("Write about quantum computing advances")

Diagram

Knowledge Configuration

Basic Configuration

python

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    knowledge=["docs/"]  # Directory of files
)

Advanced Configuration

python

from praisonaiagents import Agent, Knowledge

# Create shared knowledge instance
knowledge = Knowledge(config={
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "my_docs",
            "path": ".praison"
        }
    },
    "embedder": {
        "provider": "openai",
        "config": {
            "model": "text-embedding-3-small"
        }
    }
})

# Add documents
knowledge.add("docs/manual.pdf")
knowledge.add("https://example.com/faq")

# Share across agents
agent1 = Agent(name="Agent1", knowledge=knowledge)
agent2 = Agent(name="Agent2", knowledge=knowledge)

Performance Comparison

Operation	Context	Knowledge
Setup	Instant	~1-5s per document
Query	~0ms	~50-200ms
Dependencies	None	chromadb
Storage	Memory	Disk (persistent)

Summary

Context

Runtime data flow between agents. Fast, zero dependencies, ephemeral. Use for passing agent outputs.

Knowledge

Pre-loaded reference data with semantic search. Persistent, RAG-enabled. Use for documents and FAQs.

Rule of thumb: Use context for workflow data flow. Add knowledge when you need semantic search over documents.