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.

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.

Context Code Example

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.

Knowledge Code Example

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

1. Load Documents

   Files are read and converted to text using MarkItDown.

2. Chunk Content

   Text is split into smaller chunks (default: 512 tokens, 50 overlap).

3. Generate Embeddings

   Each chunk is converted to a vector embedding.

4. Store in Vector DB

   Embeddings are stored in ChromaDB for fast similarity search.

5. Query at Runtime

   When agent needs info, query is embedded and similar chunks are retrieved.

---

When to Use Each

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.

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")

---

Knowledge Configuration

Basic Configuration

from praisonaiagents import Agent

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

Advanced Configuration

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)

Knowledge has higher setup cost but enables semantic search over large document collections that wouldn’t fit in context.

---

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.