History Injection

Automatically inject session history into agent context for multi-turn conversations

Session history is preserved even across long conversations — see Retention Policies for how overflow is summarised and archived.

Overview

History injection automatically loads previous conversation messages from a session and includes them in the agent’s context. This enables:

• Multi-turn conversations across sessions
• Context continuity when users return
• Persistent memory without complex setup

from praisonaiagents import Agent

agent = Agent(
    name="assistant",
    instructions="Continue our conversation with prior turns in context",
    memory="history",
)

agent.start("What did we decide yesterday?")

The user returns to a session; prior messages are injected before the model runs.

Quick Start

1. Simple Usage

   Preset (Simple)

   from praisonaiagents import Agent
   
   # Enable history with defaults (last 10 messages)
   agent = Agent(
       name="assistant",
       instructions="Be helpful",
       memory="history"
   )

   Chat Preset

   from praisonaiagents import Agent
   
   # Conversational preset (last 20 messages)
   agent = Agent(
       name="assistant",
       instructions="Be helpful",
       memory="chat"
   )

   Custom Limit

   from praisonaiagents import Agent
   from praisonaiagents import MemoryConfig
   
   # Custom history limit
   agent = Agent(
       name="assistant",
       instructions="Be helpful",
       memory=MemoryConfig(history=True, history_limit=5)
   )

2. With Configuration

   Switch presets (memory=”chat”) or use a MemoryConfig for token limits and injection rules — see the remaining tabs.

Memory Presets

Enable history via memory presets:

# Enable history via memory preset
agent = Agent(
    name="assistant",
    memory="history"  # Enables history with file backend
)

# Alias for history preset
agent = Agent(
    name="assistant",
    memory="session"  # Same as "history"
)

# Conversational preset with higher limit
agent = Agent(
    name="assistant",
    memory="chat"  # history_limit=20
)

MemoryConfig

For full control, use MemoryConfig:

from praisonaiagents import Agent
from praisonaiagents import MemoryConfig

agent = Agent(
    name="assistant",
    instructions="Be helpful",
    memory=MemoryConfig(
        history=True,       # Enable history injection
        history_limit=15    # Last 15 messages
    )
)

With auto_save

When using auto_save, the session ID is automatically used for history:

from praisonaiagents import Agent
from praisonaiagents import MemoryConfig

agent = Agent(
    name="assistant",
    memory=MemoryConfig(auto_save="my-session", history=True)
)

agent.start("Hello!")

# Later, same session continues
agent2 = Agent(
    name="assistant",
    memory=MemoryConfig(auto_save="my-session", history=True)
)
agent2.start("What did I say before?")  # Has context!

from praisonaiagents import Agent

agent = Agent(
    name="assistant",
    auto_save="my-session",  # Deprecated standalone param
    memory="history"
)

How It Works

The user sends a message; the agent loads prior turns from the session store and prepends them to the messages array before calling the model.

1. Agent Initialization

   History settings are resolved from memory parameter: – memory="history" → enabled with limit=10 – memory="chat" → enabled with limit=20 – memory=MemoryConfig(history=True, history_limit=N) → custom limit

2. Message Building

   When _build_messages() is called: 1. System prompt is added 2. Session history is injected (if enabled) 3. In-memory chat history is added 4. User prompt is added

3. Session Store

   History is loaded from the session store using get_working_history(session_id, max_messages=limit) when the store supports it — this replays a compacted summary + retained tail if a compaction checkpoint exists. Stores that implement only get_chat_history fall back to raw history automatically. See Compacted Session Resume.

Best Practices

Comparison

Related