Context Engineering

Context engineering controls what a model sees when an agent or team runs. Product teams use it to give the model the right instructions and application data while keeping each request focused.

uv pip install -U agno openai sqlalchemy

support_agent.py

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses

agent = Agent(
    model=OpenAIResponses(id="gpt-5.4-mini"),
    db=SqliteDb(db_file="tmp/support.db"),
    instructions=[
        "Answer product questions clearly and concisely.",
        "The customer's plan is {plan}.",
    ],
    session_state={"plan": "enterprise"},
    add_history_to_context=True,
    num_history_runs=3,
)

agent.print_response(
    "Which support channels can I use?",
    user_id="customer-42",
    session_id="support-thread-7",
)

The system message carries the agent’s instructions and resolved plan. The user message carries the current request. Up to three previous runs from the same stored session can also enter the model context.

Sources of Context

Source	What it contributes	Use it for
Description and instructions	Stable role, behavior, and constraints	Product behavior that applies across runs
Run input	The current user request	The task to complete now
Knowledge	Retrieved content from documents and data	Domain grounding and source-backed answers
Memory	Persistent facts associated with a user	Preferences and details that cross sessions
Chat history	Messages from earlier runs in one session	Multi-turn continuity
Session state	Application data stored with the session	Carts, task progress, plans, and counters
Dependencies	Static or callable values resolved at run time	Request-specific application data
Tool definitions and results	Available operations and their outputs	Reading data and taking actions
`additional_context` and `additional_input`	Explicit system or message context	Few-shot examples and custom context blocks

Agno can assemble these sources for each run. Enable only the sources the model needs for the current use case.

Control Context Size

Requirement	Configuration
Include recent conversation turns	`add_history_to_context=True` with `num_history_runs` or `num_history_messages`
Condense a long conversation	Session summaries
Reduce stored tool-result context	Context compression
Retrieve relevant domain content	Knowledge search
Add runtime values to the user message	`add_dependencies_to_context=True`
Add session state as a context block	`add_session_state_to_context=True`

Start with the smallest set that supports the task. Inspect model messages in debug mode when behavior suggests the model received missing, stale, or conflicting context.

Context Caching

Some model providers cache repeated prompt prefixes. Provider requirements and pricing differ. Keep stable instructions consistent between requests, place changing data in the appropriate runtime fields, and verify the selected provider’s caching behavior.

Next Steps

Agent Context

Configure system messages and instructions for agents.

Team Context

Configure context for a leader and its members.

Chat History

Select previous messages for model context.

Get Started

Basics

Advanced

Production

Providers

Other

Additional Resources

Context Engineering

Sources of Context

Control Context Size

Context Caching

Next Steps

Agent Context

Team Context

Chat History

​Sources of Context

​Control Context Size

​Context Caching

​Next Steps

Agent Context

Team Context

Chat History

Sources of Context

Control Context Size

Context Caching

Next Steps