Persistence Module

Database persistence and session management for PraisonAI

Persistence Module

The Persistence module provides database adapters and session management for storing agent conversations, runs, and traces.

Installation

pip install praisonai

# For specific database support
pip install "praisonai[postgres]"
pip install "praisonai[redis]"

Quick Start

The recommended way to enable persistence is via the memory={} parameter:

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    instructions="You are helpful.",
    memory={
        "db": "postgresql://localhost/mydb",
        "session_id": "my-session"
    }
)

response = agent.start("Hello!")

Supported Databases

Conversation Stores

Database	Install	URL Format
PostgreSQL	pip install "praisonai[postgres]"	postgresql://user:pass@host/db
MySQL	pip install "praisonai[mysql]"	mysql://user:pass@host/db
SQLite	Built-in	sqlite:///path/to/db.sqlite
JSON	Built-in	json:///path/to/data.json

Knowledge Stores (Vector)

Database	Install
Qdrant	pip install "praisonai[qdrant]"
Chroma	pip install "praisonai[chroma]"
Pinecone	pip install "praisonai[pinecone]"
PGVector	pip install "praisonai[pgvector]"

State Stores

Database	Install
Redis	pip install "praisonai[redis]"
MongoDB	pip install "praisonai[mongodb]"

Database Examples

PostgreSQL

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    memory={
        "db": "postgresql://user:pass@localhost/mydb",
        "session_id": "user-123"
    }
)

SQLite

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    memory={
        "db": "sqlite:///./agent.db",
        "session_id": "local-session"
    }
)

Redis

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    memory={
        "backend": "redis",
        "db": "redis://localhost:6379",
        "session_id": "redis-session"
    }
)

Session Management

Sessions allow you to maintain conversational context:

from praisonaiagents import Agent

# Create agent with session
agent = Agent(
    name="Assistant",
    memory={"db": "sqlite:///data.db", "session_id": "user-123"}
)

# Conversations persist across runs
response1 = agent.start("Remember my name is Alice")
# Later...
response2 = agent.start("What's my name?")  # Remembers "Alice"

Data Models

DbMessage

Stored message format:

{
    "id": "msg-uuid",
    "session_id": "session-123",
    "agent_id": "agent-name",
    "role": "user",  # or "assistant"
    "content": "Message content",
    "timestamp": "2024-01-01T00:00:00Z"
}

DbRun

Execution run record:

{
    "id": "run-uuid",
    "session_id": "session-123",
    "agent_id": "agent-name",
    "status": "completed",
    "started_at": "2024-01-01T00:00:00Z",
    "completed_at": "2024-01-01T00:00:01Z"
}

DbToolCall

Tool execution record:

{
    "id": "tool-uuid",
    "run_id": "run-uuid",
    "tool_name": "search",
    "arguments": {"query": "AI news"},
    "result": "Search results...",
    "timestamp": "2024-01-01T00:00:00Z"
}

See Also

• Database Module - Core database adapters
• Session Module - Session management
• Memory Module - Agent memory