Redis

Redis is used for state management, caching, and vector search.

Installation

pip install "praisonaiagents[tools]"

Docker Setup

docker run -d --name praison-redis -p 6379:6379 redis:7

Quick Start

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant.",
    memory={
        "backend": "redis",
        "db": "redis://localhost:6379",
        "session_id": "my-session"
    }
)

response = agent.start("Hello!")
print(response)

Connection String Format

redis://localhost:6379
redis://:password@host:port/db_number

Environment Variables

export PRAISON_STATE_URL="redis://localhost:6379"

import os
from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    memory={"db": os.getenv("PRAISON_STATE_URL")}
)

CLI

# Validate connection
praisonai persistence doctor \
    --state-url "redis://localhost:6379"

Use Cases

Session state caching
Rate limiting
Temporary data storage
Vector search (Redis Stack)

When to Use

✅ Good for:

Session caching
Rate limiting
Real-time features
Pub/sub messaging

❌ Not recommended for:

Long-term storage
Complex queries
Large datasets

Storage Backend (Advanced)

For training data, sessions, and general persistence, use the RedisBackend:

from praisonaiagents.storage import RedisBackend, BaseJSONStore

backend = RedisBackend(
    url="redis://localhost:6379",
    prefix="praison:",
    ttl=3600  # 1 hour TTL
)
store = BaseJSONStore("session.json", backend=backend)
store.save({"messages": ["Hello"]})
data = store.load()

from praisonai.train.agents.storage import TrainingStorage
from praisonaiagents.storage import RedisBackend

with TrainingStorage(
    session_id="train-123",
    backend=RedisBackend(url="redis://localhost:6379", prefix="train:", ttl=86400),
) as storage:
    storage.save_iteration(...)
# Redis connection closed automatically at the end of the with block

from praisonai.cli.state.sessions import SessionManager
from praisonaiagents.storage import RedisBackend

backend = RedisBackend(url="redis://localhost:6379", prefix="session:", ttl=3600)
manager = SessionManager(backend=backend)
sessions = manager.list(limit=10)

from praisonai.recipe.history import RunHistory
from praisonaiagents.storage import RedisBackend

backend = RedisBackend(url="redis://localhost:6379", prefix="runs:")
history = RunHistory(backend=backend)

from praisonai.mcp_server.tool_index import MCPToolIndex
from praisonaiagents.storage import RedisBackend

backend = RedisBackend(url="redis://localhost:6379", prefix="mcp:")
index = MCPToolIndex(backend=backend)

TrainingStorage is a context manager. Using with (or calling .close() explicitly) releases the underlying Redis connection at the end of the block — best practice for long-running processes. On backends without an open resource (e.g. FileBackend) .close() is a safe no-op.

CLI Usage

# Training with Redis backend
praisonai train agents --input "Hello" --storage-backend redis://localhost:6379

# Session list with Redis backend
praisonai session list --storage-backend redis://localhost:6379

RedisBackend Features

Feature	Description
TTL Support	Automatic expiration with `ttl` parameter
Key Prefixing	Namespace isolation with `prefix` parameter
Sub-ms Latency	High-speed caching
Distributed	Shared state across nodes

Production notes

RedisStateStore.scan_prefix(prefix) iterates the keyspace with a non-blocking SCAN cursor (batches of 500) instead of the O(N) blocking KEYS command. Prefer it over keys(pattern) when listing many keys on a production Redis instance, so a single read never freezes other clients on the single-threaded server.

from praisonai.persistence import create_state_store

store = create_state_store("redis", url="redis://localhost:6379")
recent = store.scan_prefix("session:")

See Storage Backends for more details.

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)

Redis

Redis

Installation

Docker Setup

Quick Start

Connection String Format

Environment Variables

CLI

Use Cases

When to Use

Storage Backend (Advanced)

CLI Usage

RedisBackend Features

Production notes

​Redis

​Installation

​Docker Setup

​Quick Start

​Connection String Format

​Environment Variables

​CLI

​Use Cases

​When to Use

​Storage Backend (Advanced)

​CLI Usage

​RedisBackend Features

​Production notes

Redis

Installation

Docker Setup

Quick Start

Connection String Format

Environment Variables

CLI

Use Cases

When to Use

Storage Backend (Advanced)

CLI Usage

RedisBackend Features

Production notes