Memory Features

PraisonAI agents support several memory features that make conversations smarter over time. Each feature is opt-in — disabled by default for zero overhead.

Quick Comparison

Feature	What it does	Config
auto_save	Saves full conversation to disk	`auto_save="chat1"`
history	Injects past sessions into context	`history=True`
auto_memory	Extracts facts/preferences automatically	`auto_memory=True`
learn	Captures patterns and insights	`learn=True`

Auto-Save Sessions

Automatically saves the full conversation history to disk after each start() / run() call.

Simple
Preset

from praisonaiagents import Agent, MemoryConfig

agent = Agent(
    name="assistant",
    instructions="Be helpful",
    memory=MemoryConfig(auto_save="my_chat")
)

agent.start("Hello!")
# Session saved to ~/.praisonai/memory/sessions/my_chat.json

from praisonaiagents import Agent

# Use the "session" preset (auto_save + history)
agent = Agent(
    name="assistant",
    instructions="Be helpful",
    memory="session"
)

History Injection

Automatically loads past session history into context so the agent remembers previous conversations.

Basic
With Limit
Preset

from praisonaiagents import Agent, MemoryConfig

agent = Agent(
    name="assistant",
    instructions="Be helpful",
    memory=MemoryConfig(history=True)
)

# First run
agent.start("My name is Alice")

# Later run — agent remembers!
agent.start("What's my name?")
# → "Your name is Alice"

from praisonaiagents import Agent, MemoryConfig

agent = Agent(
    name="assistant",
    instructions="Be helpful",
    memory=MemoryConfig(
        history=True,
        history_limit=20,  # Load last 20 messages
        session_id="user_123"  # Explicit session ID
    )
)

from praisonaiagents import Agent

# "history" preset — history=True, limit=10
agent = Agent(name="assistant", memory="history")

# "chat" preset — history=True, limit=20
agent = Agent(name="assistant", memory="chat")

When history=True, a session ID is auto-generated per project — it combines the workspace (git repo root or the current directory) with the agent name. Two Agent(name="assistant") calls in different projects get separate history files and never mix.Set session_id="..." for explicit control over which session to resume, or opt back into name-only global sessions by exporting PRAISONAI_GLOBAL_SESSIONS=true.

Where sessions live

Every auto-generated session is scoped to your workspace, so the same agent name in a different project starts fresh instead of leaking history.

Signal	Used as workspace identity
Inside a git repo	The repo’s root-commit SHA (stable across clones and paths).
Not in a git repo	The absolute path of your current directory.
Neither works (rare)	The literal string `global`.

The final on-disk id is:

history_<sha256("<workspace_id>:<agent_name>")[:8]>.json

Stored under ~/.praisonai/sessions/ (or wherever PRAISONAI_HOME points). Read the resolved identity from Python:

from praisonaiagents.session import workspace_id

print(workspace_id())
# → "git:8f5c1e…" or "dir:/Users/me/projects/my-app" or "global"

Opt out with PRAISONAI_GLOBAL_SESSIONS

export PRAISONAI_GLOBAL_SESSIONS=true

Reverts to the pre-workspace behaviour: the id becomes history_<sha256("global:<name>")[:8]> and same-named agents in any project share one history file. Accepted values (case-insensitive): 1, true, yes.

Upgrading from name-only sessions

Existing history_<name-only>.json files are not adopted automatically when running in workspace scope — otherwise Project A’s history would leak into Project B.

To keep using an old file everywhere, set PRAISONAI_GLOBAL_SESSIONS=true.
To keep it in one specific project, rename it to the new workspace-scoped id (or set session_id="..." explicitly).
Otherwise, agents start fresh in each new workspace.

Explicit session_id still wins

Passing session_id="..." is unchanged — it always overrides the auto id and stays global, so use it for deliberate cross-project continuity.

Auto-Memory Extraction

Automatically extracts memorable facts (names, preferences, roles) from conversations and stores them in long-term memory.

Enable
How It Works

from praisonaiagents import Agent, MemoryConfig

agent = Agent(
    name="assistant",
    instructions="Be helpful",
    memory=MemoryConfig(auto_memory=True)
)

agent.start("My name is John and I prefer Python")
# Auto-extracted: name=John, preference=Python

Auto-memory uses lightweight regex-based extraction — no LLM calls needed. It only processes messages that contain personal information keywords.

Manual Memory Storage

You can also store memories manually using the store_memory() method.

from praisonaiagents import Agent

agent = Agent(name="assistant", memory=True)

# Store facts manually
agent.store_memory("User prefers dark mode", "long_term")
agent.store_memory("Current task: build a dashboard", "short_term")

# Context is auto-injected into system prompt
response = agent.start("What should I work on?")

Memory Presets

Use string shortcuts instead of MemoryConfig for common configurations.

Preset	Equivalent Config
`"file"`	`MemoryConfig(backend="file")`
`"sqlite"`	`MemoryConfig(backend="sqlite")`
`"redis"`	`MemoryConfig(backend="redis")`
`"history"`	`MemoryConfig(history=True, history_limit=10)`
`"session"`	`MemoryConfig(history=True, history_limit=10)`
`"chat"`	`MemoryConfig(history=True, history_limit=20)`
`"learn"`	`MemoryConfig(learn=True)`

from praisonaiagents import Agent

# These are equivalent:
agent1 = Agent(name="a", memory="history")
agent2 = Agent(name="a", memory=MemoryConfig(history=True))

Custom Memory Backend

Implement the AgentMemoryProtocol to create your own memory backend.

from praisonaiagents import Agent

class MyRedisMemory:
    """Custom memory backend."""
    
    def get_context(self, query=None):
        # Return stored context for system prompt
        return "User prefers Python"
    
    def save_session(self, name, conversation_history=None, metadata=None):
        # Persist session to your backend
        pass

agent = Agent(
    name="assistant",
    memory=MyRedisMemory()  # Pass custom instance
)

Any object with get_context() and save_session() methods works as a memory backend. No inheritance required — PraisonAI uses duck typing via AgentMemoryProtocol.

Architecture

FileMemory persists to ~/.praisonai/memory/. Writes are atomic — see Storage → Durability.

Session Persistence

Resume conversations across restarts

Storage Backends

File, SQLite, Redis, PostgreSQL, MongoDB

Advanced Memory

Long-term, short-term, entity memory

Graph Memory

Relationship-aware memory

Getting Started

Install

Core Concepts

Guides

Features

Models

Databases

Observability

Memory

Telemetry & Analytics

Knowledge

RAG

Persistence

Tools

Other Features

Developers

Configuration

Best Practices

Getting Started (No Code)

Memory Features

Memory Features

Quick Comparison

Auto-Save Sessions

History Injection

Where sessions live

Auto-Memory Extraction

Manual Memory Storage

Memory Presets

Custom Memory Backend

Architecture

Session Persistence

Storage Backends

Advanced Memory

Graph Memory

​Memory Features

​Quick Comparison

​Auto-Save Sessions

​History Injection

​Where sessions live

​Auto-Memory Extraction

​Manual Memory Storage

​Memory Presets

​Custom Memory Backend

​Architecture

Session Persistence

Storage Backends

Advanced Memory

Graph Memory

Memory Features

Quick Comparison

Auto-Save Sessions

History Injection

Where sessions live

Auto-Memory Extraction

Manual Memory Storage

Memory Presets

Custom Memory Backend

Architecture