Knowledge Agents

Engineering and operations teams use knowledge agents to answer questions from product documentation, policies, and internal procedures. Agno provides ingestion, retrieval, metadata filtering, corpus isolation, and source updates. AgentOS exposes the knowledge base through its API and Control Plane.

knowledge_agent.py

from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.chroma import ChromaDb
from agno.vectordb.search import SearchType

knowledge = Knowledge(
    name="Company handbook",
    vector_db=ChromaDb(
        collection="company_handbook",
        path="tmp/chromadb",
        persistent_client=True,
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

knowledge.insert(
    name="Travel policy",
    text_content=(
        "Domestic travel under $2,000 requires manager approval. "
        "International travel and travel costing $2,000 or more require VP approval."
    ),
    skip_if_exists=True,
)

agent = Agent(
    name="Policy Agent",
    model="openai:gpt-5.5",
    knowledge=knowledge,
    search_knowledge=True,
    instructions=[
        "Use the knowledge base for policy questions.",
        "Say when the answer is not in the knowledge base.",
    ],
)

agent.print_response("When does travel need VP approval?", stream=True)

Create a virtual environment, install the OpenAI and ChromaDB integrations, and set OPENAI_API_KEY before running the agent:

uv venv --python 3.12
uv pip install -U "agno[chromadb,openai]"
export OPENAI_API_KEY="your_openai_api_key"
uv run python knowledge_agent.py

The agent decides when to search the indexed policy. ChromaDB persists the embedded content under tmp/chromadb for later runs.

Choose the information path

Information	Pattern
Policies, manuals, and product documentation	Index the corpus with Knowledge.
Current account, order, or operational data	Query a context provider, tool, or MCP server at run time.
Policies plus current records	Retrieve the rules from Knowledge, then call a tool for the current record.
Typed fields extracted from incoming files	Use Document Processing.

Knowledge owns indexed content and retrieval. Context providers and tools query live systems. Customer-facing agents add sessions, authentication, and interfaces around either pattern.

Choose retrieval behavior

Behavior	Configuration	Use when
Agentic search	`search_knowledge=True`	The agent may need several searches or no search.
Automatic context	`add_knowledge_to_context=True`, `search_knowledge=False`	Each string input should receive retrieved context before the model runs.
Custom retrieval	Set a custom `knowledge_retriever`	An existing search service owns query rewriting, ranking, or access checks.

Start with agentic search. Add automatic context when each string input depends on the corpus. Use a custom retriever when retrieval policy lives outside Agno.

Protect corpus boundaries

Requirement	Control
Several named corpora share one vector database	Give each `Knowledge` instance a unique name and set `isolate_vector_search=True`.
Results should match document attributes	Insert metadata and apply knowledge filters.
Tenant or user authorization	Authenticate and authorize the caller before retrieval. Apply metadata filters from the verified identity to narrow results.
Users can submit remote URLs	Configure reader `allowed_hosts` and follow the SSRF hardening pattern.

Reindex older content before enabling isolated vector search if its vectors do not contain linked_to metadata.

Production path

Need	Guide
Ingest files, URLs, and text	Knowledge quickstart
Track content state and updates	Contents database and knowledge lifecycle example
Choose a production vector database	Vector stores
Choose vector, keyword, or hybrid retrieval	Search and retrieval
Tune how documents are split	Chunking
Isolate customer corpora	Multi-tenant example
Manage content through AgentOS	Manage knowledge
Catch retrieval or answer regressions	Agent Evaluation

Next steps

Task	Guide
Build your first indexed agent	Knowledge quickstart
Combine several source types	Multi-source RAG
Share retrieval across specialists	Knowledge for teams
Query live systems instead of an index	Connecting your data

Welcome

Get Started

Features

Use Cases

Knowledge Agents

Choose the information path

Choose retrieval behavior

Protect corpus boundaries

Production path

Next steps

​Choose the information path

​Choose retrieval behavior

​Protect corpus boundaries

​Production path

​Next steps

Choose the information path

Choose retrieval behavior

Protect corpus boundaries

Production path

Next steps