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Chat history enables multi-turn conversations. Without it, each run is isolated—the agent has no idea what was said before. With a database and add_history_to_context=True, previous messages are automatically included in every request.

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Enable Chat History

Set add_history_to_context=True to include previous messages in every run: 
from agno.agent import Agent
from agno.db.sqlite import SqliteDb

agent = Agent(
    db=SqliteDb(db_file="agent.db"),
    add_history_to_context=True,
    num_history_runs=3,  # Include last 3 turns
)

agent.print_response("My name is Sarah", session_id="chat_123")
agent.print_response("What's my name?", session_id="chat_123")  # Agent knows: "Sarah"
Chat history requires a database. Without one, there’s nothing to retrieve.

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Control History Size

More history means more tokens. Use these parameters to control what gets included:
ParameterDescription
num_history_runsNumber of previous runs to include (default: 3)
num_history_messagesMaximum messages to include across all runs
max_tool_calls_from_historyLimit tool call messages in history
agent = Agent(
    db=SqliteDb(db_file="agent.db"),
    add_history_to_context=True,
    num_history_runs=5,
    num_history_messages=20,  # Cap at 20 messages total
)
Start with num_history_runs=3. Increase only if your agent needs more context. For long conversations, combine limited history with session summaries.

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On-Demand History Access

Instead of always including history, let the agent decide when to look it up: 
agent = Agent(
    db=SqliteDb(db_file="agent.db"),
    read_chat_history=True,  # Agent gets a get_chat_history() tool
)
The agent can call get_chat_history() when it needs context, rather than having history in every request. Useful for analytics, auditing, or when most queries don’t need prior context.

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Cross-Session History

Search across multiple sessions for context that spans conversations: 
agent = Agent(
    db=SqliteDb(db_file="agent.db"),
    search_session_history=True,
    num_history_sessions=2,  # Search last 2 sessions
)
Keep num_history_sessions low (2-3). Cross-session history can quickly fill your context window.

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Programmatic Access

Retrieve history directly in your code: 
# Get user-assistant message pairs
chat_history = agent.get_chat_history(session_id="chat_123")

# Get all messages from the session
messages = agent.get_session_messages(session_id="chat_123")

# Get the last run output with metrics
last_run = agent.get_last_run_output()
Use this for building custom UIs, debugging, or exporting transcripts.

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Team History

Teams support additional history sharing between members: 
team = Team(
    db=SqliteDb(db_file="team.db"),
    add_history_to_context=True,
    num_history_runs=3,
    add_team_history_to_members=True,  # Share history across team members
)
With add_team_history_to_members=True, member agents see the full team conversation, not just their own interactions.

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Workflow History

Workflows use add_workflow_history_to_steps to pass previous run results to steps: 
from agno.workflow import Workflow

workflow = Workflow(
    db=SqliteDb(db_file="workflow.db"),
    add_workflow_history_to_steps=True,
    num_history_runs=5,
    steps=[...],
)
Workflow history passes previous workflow outputs to steps, not conversation messages. See Workflow Sessions for details.

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Choosing a Pattern

ScenarioConfiguration
Chat-style productsadd_history_to_context=True, num_history_runs=3
Long conversationsLimited history + session summaries
Tool-heavy agentsAdd max_tool_calls_from_history to reduce noise
Cross-session recallsearch_session_history=True, num_history_sessions=2
Selective lookupread_chat_history=True (agent decides when to look up)
Custom UIsUse get_chat_history() programmatically

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Developer Resources