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
Preview-then-store
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

from praisonaiagents.memory import FileMemory, AutoMemory

base = FileMemory(user_id="user123")
auto = AutoMemory(base, enabled=True)

# Preview — extraction only, no state change
auto.process_interaction("I prefer dark mode.", store=False)

# Real store — writes because the preview did not consume dedup
auto.process_interaction("I prefer dark mode.", store=True)

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

Previews don’t consume dedup — a process_interaction(..., store=False) call can be followed by store=True for the same text and the real store still writes. See Previews and retry semantics for the full contract.

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")`
`"chroma"`	`MemoryConfig(backend="chroma")`
`"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))

You can add provider-specific settings with config= — the outer backend= still takes effect: MemoryConfig(backend="sqlite", config={"short_db": "./mem/short.db"}) builds a sqlite store. Only a provider/backend key inside config= overrides it. See Memory → Combining backend and config.

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, ChromaDB, mem0, MongoDB, Dakera, in-memory

Advanced Memory

Long-term, short-term, entity memory

Graph Memory

Relationship-aware memory

Getting Started

Install

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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