Documentation Index
Fetch the complete documentation index at: https://docs.agno.com/llms.txt
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Ungrounded research is confident and wrong. A research agent needs to know the rules it operates under, the body of knowledge it can draw on, and what was decided before. Agno layers these so each agent carries the right context without carrying everything.
| Layer | Holds | Mechanism |
|---|
| Static context | The mandate, policy, and process that never change per query | Injected into every system prompt |
| Research library | Company profiles, sector analyses, source documents | RAG over a vector database |
| Prior work | Past decisions and memos | File navigation tools |
Layer 1: static context in the prompt
Rules that apply to every question belong in the prompt, not in retrieval. Load them once and inject them into every agent.
from pathlib import Path
CONTEXT_DIR = Path(__file__).parent
def load_context() -> str:
sections = [f.read_text() for f in sorted(CONTEXT_DIR.glob("*.md"))]
return "\n\n---\n\n".join(sections)
COMMITTEE_CONTEXT = load_context()
instructions = f"""\
You are the Risk Officer on a $10M investment team.
## Committee Rules (ALWAYS FOLLOW)
{COMMITTEE_CONTEXT}
## Your Role
Enforce position limits and sector caps on every recommendation.
"""
Layer 2: the research library in RAG
The corpus the agents reason over goes in a shared knowledge base, searched per query. Instantiate it once and import it everywhere, never recreate it per agent.
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
# Shared instance, imported from a settings module
from agents.settings import team_knowledge
analyst = Agent(
model=OpenAIResponses(id="gpt-5.5"),
knowledge=team_knowledge,
search_knowledge=True,
instructions="Search the research library before forming a view. Cite what you used.",
)
reply = analyst.run("What does our research say about semiconductor supply?").content
# Answers from the library and cites the profiles and analyses it pulled,
# instead of from the model's prior.
search_knowledge=True lets the agent pull the relevant profiles and analyses per question instead of carrying the whole library in context.
Layer 3: prior work on disk
Past memos and decisions are durable artifacts, not vectors. Give a read agent file tools so it can navigate the archive and ground new work in what was already concluded. The agent that writes new memos gets write access; the agent that reads them does not. Keep the boundary explicit.
Why three layers, not one
| If you only had | You would lose |
|---|
| The prompt | A corpus too large to inline |
| RAG | Hard rules that must apply to every query, verbatim |
| The archive | The reasoning trail behind past decisions |
Next steps
| Task | Guide |
|---|
| Make grounded research compound | Institutional learning |
| End in an auditable artifact | Structured deliverable |
Developer Resources
