Documentation Index
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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"
}
{
"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
