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The Knowledge class gives agents the ability to search a corpus of documents for information beyond their model’s training data and cut-off date. Instead of hardcoding information in prompts or relying solely on their model’s training set, agents can dynamically retrieve the most relevant and latest information. The Knowledge class uses vector embeddings to find the most relevant document chunks based on semantic similarity.
Create a searchable knowledge base and give an agent access to it as a tool: 
from autonomy import Agent, Knowledge, KnowledgeTool, Model, Node

async def main(node):
  # Create searchable knowledge base
  knowledge = Knowledge(
    name="legal_statutes",
    searchable=True,
    model=Model("embed-english-v3"),  # Embedding model
    max_results=5,  # Top 5 most relevant chunks
    max_distance=0.3  # Similarity threshold (0.0 = exact match, 1.0 = very different)
  )
  
  # Add legal documents
  await knowledge.add_text(
    document_name="contract_law",
    text="""
    Contract Formation Requirements:
    1. Offer: Clear proposal of terms
    2. Acceptance: Unequivocal agreement to terms
    3. Consideration: Exchange of value between parties
    4. Capacity: Legal ability to enter contract
    5. Legality: Lawful purpose and terms
    """
  )
  
  await knowledge.add_text(
    document_name="tort_law",
    text="""
    Elements of Negligence:
    - Duty of care owed to plaintiff
    - Breach of that duty
    - Causation (actual and proximate)
    - Damages suffered by plaintiff
    """
  )
  
  # Give agent access to the knowledge base as a tool
  await Agent.start(
    node=node,
    name="henry",
    instructions="""
    You are Henry, an expert legal assistant.
    Use search_statutes tool to find relevant legal information.
    """,
    model=Model("claude-sonnet-4-v1"),
    tools=[
      KnowledgeTool(knowledge=knowledge, name="search_statutes")
    ]
  )

Node.start(main)

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Adding Documents

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From Text

Add text directly to the knowledge base: 
await knowledge.add_text(
  document_name="statute_330",
  text="# 15 USC 330: Weather Modification\n\nAny person engaging in weather modification activities..."
)

​
From URLs

Load documents from the web: 
await knowledge.add_document(
  document_name="sec_330",
  document_url="https://raw.githubusercontent.com/AlextheYounga/us-federal-code/refs/heads/master/usc/title-15-commerce-and-trade/chapter-9a-weather-modification-activities-or-attempts%3B-reporting-requirement/sec-330.md",
  content_type="text/markdown"  # Optional, auto-detected if omitted
)
Supported formats (via text extraction):
  • Plain text
  • Markdown
  • HTML
  • PDF (if markitdown is installed)
  • Word documents (if markitdown is installed)
  • Many more formats via markitdown

​
Configuration Options

knowledge = Knowledge(
  name="docs",                     # Unique name for this knowledge base
  searchable=True,                 # Enable vector search
  model=Model("embed-english-v3"), # Model for generating embeddings
  max_results=10,                  # Max search results to return
  max_distance=0.2,                # Max cosine distance (0.0-1.0, lower = more similar)
  max_knowledge_size=4096,         # Max characters to include in context
  chunker=NaiveChunker(            # How to split documents
    max_characters=256,            # Chunk size
    overlap=16                     # Overlap between chunks
  )
)
Key parameters:
  • model - Embedding model for vector search. Default: embed-english-v3
  • max_results - Number of top results to return. Default: 10
  • max_distance - Similarity threshold (0.0 = identical, 1.0 = completely different). Default: 0.2
  • max_knowledge_size - Maximum characters in the returned context. Default: 4096
  • chunker - Strategy for splitting documents into chunks

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Storage Backends

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In-Memory Storage (Default)

Stores everything in memory. Simple but not persistent: 
from autonomy import InMemory, Knowledge

knowledge = Knowledge(name="docs", searchable=True, storage=InMemory())

​
Database Storage

For persistent storage across restarts: 
from autonomy import Database, Knowledge

# Initialize database connection
db = await Database.from_environment()
knowledge = Knowledge(name="docs", searchable=True, storage=db)

​
Use Knowledge as a Tool

Give agents explicit control over when to search: 
from autonomy import KnowledgeTool

await Agent.start(
  node=node,
  name="henry",
  instructions="""
  You are Henry, an expert legal assistant.
  Use search_statutes when you need to reference legal code.
  """,
  model=Model("claude-sonnet-4-v1"),
  tools=[KnowledgeTool(knowledge=knowledge, name="search_statutes")]
)