# LangGraph Redis

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This repository contains Redis implementations for LangGraph, providing both Checkpoint Savers and Stores functionality.

## Overview

The project consists of three main components:

1. **Redis Checkpoint Savers**: Implementations for storing and managing checkpoints using Redis
2. **Redis Stores**: Redis-backed key-value stores with optional vector search capabilities
3. **Redis Middleware**: LangChain agent middleware for semantic caching, tool caching, and conversation memory

## Dependencies

### Python Dependencies

The project requires the following main Python dependencies:

- `redis>=5.2.1`
- `redisvl>=0.5.1`
- `langgraph-checkpoint>=2.0.24`

### Redis Modules Requirements

**IMPORTANT:** This library requires Redis with the following modules:

- **RedisJSON** - For storing and manipulating JSON data
- **RediSearch** - For search and indexing capabilities

#### Redis 8.0+

If you're using Redis 8.0 or higher, both RedisJSON and RediSearch modules are included by default as part of the core
Redis distribution. No additional installation is required.

#### Redis < 8.0

If you're using a Redis version lower than 8.0, you'll need to ensure these modules are installed:

- Use [Redis Stack](https://redis.io/docs/stack/), which bundles Redis with these modules
- Or install the modules separately in your Redis instance

Failure to have these modules available will result in errors during index creation and checkpoint operations.

### Azure Managed Redis / Azure Cache for Redis / Redis Enterprise Configuration

If you're using **Azure Managed Redis**, **Azure Cache for Redis** (especially Enterprise tier) or **Redis Enterprise**, there are important configuration considerations:

#### Client Configuration

Azure Managed Redis, Azure Cache for Redis and Redis Enterprise use a **proxy layer** that makes the cluster appear as a single endpoint. This requires using a **standard Redis client**, not a cluster-aware client:

```python
from redis import Redis
from langgraph.checkpoint.redis import RedisSaver

# ✅ CORRECT: Use standard Redis client for Azure/Enterprise
client = Redis(
    host="your-cache.redis.cache.windows.net",  # or your Redis Enterprise endpoint
    port=6379,  # or 10000 for Azure Managed Redis / Azure Enterprise with TLS
    password="your-access-key",
    ssl=True,  # Azure/Enterprise typically requires SSL
    ssl_cert_reqs="required",  # or "none" for self-signed certs
    decode_responses=False  # RedisSaver expects bytes
)

# Pass the configured client to RedisSaver
saver = RedisSaver(redis_client=client)
saver.setup()

# ❌ WRONG: Don't use RedisCluster client with Azure/Enterprise
# from redis.cluster import RedisCluster
# cluster_client = RedisCluster(...)  # This will fail with proxy-based deployments
```

#### Why This Matters

- **Proxy Architecture**: Azure Managed Redis, Azure Cache for Redis and Redis Enterprise use a proxy layer that handles cluster operations internally
- **Automatic Detection**: RedisSaver will correctly detect this as non-cluster mode when using the standard client
- **No Cross-Slot Errors**: The proxy handles key distribution, avoiding cross-slot errors

#### Azure Specific Settings

For Azure Managed Redis & Azure Cache for Redis Enterprise tier:

- **Port**: Use port `10000` with TLS, or `6379` for standard
- **Modules**: RediSearch and RedisJSON need to be selected at creation
- **SSL/TLS**: Always enabled, minimum TLS 1.2

Example for Azure Managed Redis, Azure Cache for Redis Enterprise:

```python
client = Redis(
    host="your-host-endpoint",
    port=10000,  # Enterprise TLS port
    password="your-access-key",
    ssl=True,
    ssl_cert_reqs="required",
    decode_responses=False
)
```

## Installation

Install the library using pip:

```bash
pip install langgraph-checkpoint-redis
```

## Redis Checkpoint Savers

### Important Notes

> [!IMPORTANT]
> When using Redis checkpointers for the first time, make sure to call `.setup()` method on them to create required
> indices. See examples below.

### Standard Implementation

```python
from langgraph.checkpoint.redis import RedisSaver

write_config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
read_config = {"configurable": {"thread_id": "1"}}

with RedisSaver.from_conn_string("redis://localhost:6379") as checkpointer:
    # Call setup to initialize indices
    checkpointer.setup()
    checkpoint = {
        "v": 1,
        "ts": "2024-07-31T20:14:19.804150+00:00",
        "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
        "channel_values": {
            "my_key": "meow",
            "node": "node"
        },
        "channel_versions": {
            "__start__": 2,
            "my_key": 3,
            "start:node": 3,
            "node": 3
        },
        "versions_seen": {
            "__input__": {},
            "__start__": {
                "__start__": 1
            },
            "node": {
                "start:node": 2
            }
        },
        "pending_sends": [],
    }

    # Store checkpoint
    checkpointer.put(write_config, checkpoint, {}, {})

    # Retrieve checkpoint
    loaded_checkpoint = checkpointer.get(read_config)

    # List all checkpoints
    checkpoints = list(checkpointer.list(read_config))
```

### Async Implementation

```python
from langgraph.checkpoint.redis.aio import AsyncRedisSaver


async def main():
    write_config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
    read_config = {"configurable": {"thread_id": "1"}}

    async with AsyncRedisSaver.from_conn_string("redis://localhost:6379") as checkpointer:
        # Call setup to initialize indices
        await checkpointer.asetup()
        checkpoint = {
            "v": 1,
            "ts": "2024-07-31T20:14:19.804150+00:00",
            "id": "1ef4f797-8335-6428-8001-8a1503f9b875",
            "channel_values": {
                "my_key": "meow",
                "node": "node"
            },
            "channel_versions": {
                "__start__": 2,
                "my_key": 3,
                "start:node": 3,
                "node": 3
            },
            "versions_seen": {
                "__input__": {},
                "__start__": {
                    "__start__": 1
                },
                "node": {
                    "start:node": 2
                }
            },
            "pending_sends": [],
        }

        # Store checkpoint
        await checkpointer.aput(write_config, checkpoint, {}, {})

        # Retrieve checkpoint
        loaded_checkpoint = await checkpointer.aget(read_config)

        # List all checkpoints
        checkpoints = [c async for c in checkpointer.alist(read_config)]


# Run the async main function
import asyncio

asyncio.run(main())
```

### Shallow Implementations

Shallow Redis checkpoint savers store only the latest checkpoint in Redis. These implementations are useful when
retaining a complete checkpoint history is unnecessary.

```python
from langgraph.checkpoint.redis.shallow import ShallowRedisSaver

# For async version: from langgraph.checkpoint.redis.ashallow import AsyncShallowRedisSaver

write_config = {"configurable": {"thread_id": "1", "checkpoint_ns": ""}}
read_config = {"configurable": {"thread_id": "1"}}

with ShallowRedisSaver.from_conn_string("redis://localhost:6379") as checkpointer:
    checkpointer.setup()
    # ... rest of the implementation follows similar pattern
```

## Redis Checkpoint TTL Support

Both Redis checkpoint savers and stores support automatic expiration using Redis TTL:

```python
# Configure automatic expiration
ttl_config = {
    "default_ttl": 60,  # Expire checkpoints after 60 minutes
    "refresh_on_read": True,  # Reset expiration time when reading checkpoints
}

with RedisSaver.from_conn_string("redis://localhost:6379", ttl=ttl_config) as saver:
    saver.setup()
    # Checkpoints will expire after 60 minutes of inactivity
```

When no TTL is configured, checkpoints are persistent (never expire automatically).

### Removing TTL (Pinning Threads)

You can make specific checkpoints persistent by removing their TTL. This is useful for "pinning" important threads that should never expire:

```python
from langgraph.checkpoint.redis import RedisSaver

# Create saver with default TTL
saver = RedisSaver.from_conn_string("redis://localhost:6379", ttl={"default_ttl": 60})
saver.setup()

# Save a checkpoint
config = {"configurable": {"thread_id": "important-thread", "checkpoint_ns": ""}}
saved_config = saver.put(config, checkpoint, metadata, {})

# Remove TTL from the checkpoint to make it persistent
checkpoint_id = saved_config["configurable"]["checkpoint_id"]
checkpoint_key = f"checkpoint:important-thread:__empty__:{checkpoint_id}"
saver._apply_ttl_to_keys(checkpoint_key, ttl_minutes=-1)

# The checkpoint is now persistent and won't expire
```

When no TTL configuration is provided, checkpoints are persistent by default (no expiration).

This makes it easy to manage storage and ensure ephemeral data is automatically cleaned up while keeping important data persistent.

## Redis Stores

Redis Stores provide a persistent key-value store with optional vector search capabilities.

### Synchronous Implementation

```python
from langgraph.store.redis import RedisStore

# Basic usage
with RedisStore.from_conn_string("redis://localhost:6379") as store:
    store.setup()
    # Use the store...

# With vector search configuration
index_config = {
    "dims": 1536,  # Vector dimensions
    "distance_type": "cosine",  # Distance metric
    "fields": ["text"],  # Fields to index
}

# With TTL configuration
ttl_config = {
    "default_ttl": 60,  # Default TTL in minutes
    "refresh_on_read": True,  # Refresh TTL when store entries are read
}

with RedisStore.from_conn_string(
        "redis://localhost:6379",
        index=index_config,
        ttl=ttl_config
) as store:
    store.setup()
    # Use the store with vector search and TTL capabilities...
```

### Async Implementation

```python
from langgraph.store.redis.aio import AsyncRedisStore


async def main():
    # TTL also works with async implementations
    ttl_config = {
        "default_ttl": 60,  # Default TTL in minutes
        "refresh_on_read": True,  # Refresh TTL when store entries are read
    }

    async with AsyncRedisStore.from_conn_string(
            "redis://localhost:6379",
            ttl=ttl_config
    ) as store:
        await store.setup()
        # Use the store asynchronously...


asyncio.run(main())
```

## Redis Middleware for LangChain Agents

Redis middleware provides semantic caching, tool result caching, conversation memory, and semantic routing for LangChain agents. These middleware components integrate directly with `langchain.agents.create_agent()`.

### Key Features

- **SemanticCacheMiddleware**: Cache LLM responses by semantic similarity, reducing costs and latency
- **ToolResultCacheMiddleware**: Cache expensive tool executions (API calls, computations)
- **ConversationMemoryMiddleware**: Inject semantically relevant past messages into context
- **SemanticRouterMiddleware**: Route requests based on semantic matching

### Quick Start

```python
import ast
import operator as op

from langchain.agents import create_agent
from langchain_core.messages import HumanMessage
from langchain_core.tools import tool
from langgraph.middleware.redis import (
    SemanticCacheMiddleware,
    SemanticCacheConfig,
    ToolResultCacheMiddleware,
    ToolCacheConfig,
)

# Safe math expression evaluator (no arbitrary code execution)
SAFE_OPERATORS = {
    ast.Add: op.add, ast.Sub: op.sub, ast.Mult: op.mul,
    ast.Div: op.truediv, ast.Pow: op.pow, ast.USub: op.neg,
}

def _eval_expr(node):
    if isinstance(node, ast.Constant):
        return node.value
    elif isinstance(node, ast.BinOp) and type(node.op) in SAFE_OPERATORS:
        return SAFE_OPERATORS[type(node.op)](_eval_expr(node.left), _eval_expr(node.right))
    elif isinstance(node, ast.UnaryOp) and type(node.op) in SAFE_OPERATORS:
        return SAFE_OPERATORS[type(node.op)](_eval_expr(node.operand))
    raise ValueError(f"Unsupported expression")

def safe_eval(expr: str) -> float:
    return _eval_expr(ast.parse(expr, mode='eval').body)

# Define tools with cacheability metadata
@tool
def calculate(expression: str) -> str:
    """Evaluate a math expression."""
    return str(safe_eval(expression))

calculate.metadata = {"cacheable": True}  # Deterministic - safe to cache

@tool
def get_stock_price(symbol: str) -> str:
    """Get current stock price."""
    return fetch_price(symbol)

get_stock_price.metadata = {"cacheable": False}  # Temporal - don't cache

# Create middleware
semantic_cache = SemanticCacheMiddleware(
    SemanticCacheConfig(
        redis_url="redis://localhost:6379",
        name="llm_cache",
        distance_threshold=0.15,
        ttl_seconds=3600,
        deterministic_tools=["calculate"],  # Safe to cache after these tools
    )
)

tool_cache = ToolResultCacheMiddleware(
    ToolCacheConfig(
        redis_url="redis://localhost:6379",
        name="tool_cache",
        ttl_seconds=1800,
    )
)

# Create agent with middleware
agent = create_agent(
    model="gpt-4o-mini",
    tools=[calculate, get_stock_price],
    middleware=[semantic_cache, tool_cache],
)

# Use async invocation (middleware is async-first)
result = await agent.ainvoke({"messages": [HumanMessage(content="Calculate 25 * 4")]})
```

### Tool Cacheability

The tool cache uses a priority chain (inspired by SQL function volatility and MCP `ToolAnnotations`) to decide whether a tool call should be cached. The checks are evaluated in order — the first match wins:

| Priority | Check | Result |
|----------|-------|--------|
| 1 | `metadata["cacheable"]` is set | Use its boolean value (highest priority) |
| 2 | `metadata["destructive"] == True` | Never cache |
| 3 | `metadata["volatile"] == True` | Never cache |
| 4 | `metadata["read_only"] and metadata["idempotent"]` | Cache |
| 5 | Tool name matches a side-effect prefix | Never cache |
| 6 | Call args contain a volatile arg name | Never cache |
| 7 | Config whitelist / blacklist | Existing fallback |

#### Tool Metadata

Control cacheability per-tool using LangChain's native metadata:

```python
# Explicit cacheable flag (priority 1 — overrides everything)
@tool
def search(query: str) -> str:
    """Search the web."""
    return web_search(query)

search.metadata = {"cacheable": True}

# MCP-style annotations (priorities 2–4)
@tool
def send_email(to: str, body: str) -> str:
    """Send an email."""
    return smtp_send(to, body)

send_email.metadata = {"destructive": True}  # Never cached

@tool
def get_exchange_rate(pair: str) -> str:
    """Get live exchange rate."""
    return fetch_rate(pair)

get_exchange_rate.metadata = {"volatile": True}  # Never cached

@tool
def lookup_zip(code: str) -> str:
    """Look up a ZIP code."""
    return zip_db.get(code)

lookup_zip.metadata = {"read_only": True, "idempotent": True}  # Cached

# StructuredTool with metadata
from langchain_core.tools import StructuredTool

get_weather = StructuredTool.from_function(
    func=fetch_weather,
    name="get_weather",
    description="Get current weather",
    metadata={"cacheable": False},  # Real-time data
)
```

#### Advanced Cache Key Configuration

`ToolCacheConfig` supports three opt-in fields for fine-grained cache key control. All default to `None` (disabled), so existing behavior is unchanged.

```python
from langgraph.middleware.redis import (
    ToolResultCacheMiddleware,
    ToolCacheConfig,
    DEFAULT_VOLATILE_ARG_NAMES,
    DEFAULT_SIDE_EFFECT_PREFIXES,
)

tool_cache = ToolResultCacheMiddleware(
    ToolCacheConfig(
        redis_url="redis://localhost:6379",
        name="tool_cache",
        ttl_seconds=1800,

        # --- Volatile arg names (priority 6) ---
        # Tool calls containing these arg names at any nesting depth
        # are never cached. Useful for temporal arguments.
        # Use the built-in defaults or provide your own set.
        volatile_arg_names=DEFAULT_VOLATILE_ARG_NAMES,
        # DEFAULT_VOLATILE_ARG_NAMES includes:
        #   "timestamp", "current_time", "now", "date",
        #   "today", "current_date", "current_timestamp"

        # --- Ignored arg names ---
        # These arg names are stripped from the cache key before
        # serialization. Two calls differing only in ignored args
        # will share the same cache entry.
        ignored_arg_names={"request_id", "trace_id", "correlation_id"},

        # --- Side-effect prefixes (priority 5) ---
        # Tool names starting with these prefixes are never cached.
        # Use the built-in defaults or provide your own tuple.
        side_effect_prefixes=DEFAULT_SIDE_EFFECT_PREFIXES,
        # DEFAULT_SIDE_EFFECT_PREFIXES includes:
        #   "send_", "delete_", "create_", "update_", "remove_",
        #   "write_", "post_", "put_", "patch_"
    )
)
```

**Volatile arg names** — When a tool call's arguments contain a key matching one of these names (checked recursively at any nesting depth), the call is never cached. This prevents stale results for time-dependent queries like `{"query": "weather", "timestamp": 1709827200}`.

**Ignored arg names** — Per-request noise like `request_id` or `trace_id` inflates cache misses without affecting the tool's output. Stripping them from the cache key means two otherwise-identical calls will share a cache entry regardless of their tracking IDs.

**Side-effect prefixes** — Tools whose names start with mutating prefixes (e.g., `send_email`, `delete_record`, `create_user`) are never cached, since their results represent actions that should always execute. This can be overridden per-tool with `metadata["cacheable"] = True`.

### Middleware Composition

Combine multiple middleware using `MiddlewareStack` or factory functions:

```python
from langgraph.middleware.redis import MiddlewareStack, from_configs

# Option 1: Create stack directly
stack = MiddlewareStack([
    SemanticCacheMiddleware(SemanticCacheConfig(redis_url="redis://localhost:6379", name="llm_cache")),
    ToolResultCacheMiddleware(ToolCacheConfig(redis_url="redis://localhost:6379", name="tool_cache")),
])

# Option 2: Use from_configs factory (shares Redis connection)
stack = from_configs(
    configs=[
        SemanticCacheConfig(name="llm_cache", ttl_seconds=3600),
        ToolCacheConfig(name="tool_cache", ttl_seconds=1800),
    ],
    redis_url="redis://localhost:6379",
)

agent = create_agent(model="gpt-4o-mini", tools=tools, middleware=[stack])
```

### Connection Sharing with Checkpointer

Share Redis connections between middleware and checkpointer:

```python
from langgraph.checkpoint.redis.aio import AsyncRedisSaver
from langgraph.middleware.redis import IntegratedRedisMiddleware

# Create checkpointer
checkpointer = AsyncRedisSaver(redis_url="redis://localhost:6379")
await checkpointer.asetup()

# Create middleware that shares the connection
middleware = IntegratedRedisMiddleware.from_saver(
    checkpointer,
    configs=[
        SemanticCacheConfig(name="llm_cache"),
        ToolCacheConfig(name="tool_cache"),
    ],
)

agent = create_agent(
    model="gpt-4o-mini",
    tools=tools,
    checkpointer=checkpointer,
    middleware=[middleware],
)
```

### Example Notebooks

See the `examples/middleware/` directory for detailed notebooks:

- `middleware_semantic_cache.ipynb`: LLM response caching with semantic matching
- `middleware_tool_caching.ipynb`: Tool result caching with metadata-based control
- `middleware_conversation_memory.ipynb`: Semantic conversation history retrieval
- `middleware_composition.ipynb`: Combining middleware with checkpointers

## Examples

The `examples` directory contains Jupyter notebooks demonstrating the usage of Redis with LangGraph:

### Checkpoint and Store Examples

- `persistence_redis.ipynb`: Demonstrates the usage of Redis checkpoint savers with LangGraph
- `create-react-agent-memory.ipynb`: Shows how to create an agent with persistent memory using Redis
- `cross-thread-persistence.ipynb`: Demonstrates cross-thread persistence capabilities
- `persistence-functional.ipynb`: Shows functional persistence patterns with Redis

### Middleware Examples (`examples/middleware/`)

- `middleware_semantic_cache.ipynb`: LLM response caching with semantic similarity matching
- `middleware_tool_caching.ipynb`: Tool result caching with metadata-based cacheability control
- `middleware_conversation_memory.ipynb`: Semantic conversation history and context injection
- `middleware_composition.ipynb`: Combining multiple middleware with shared Redis connections

### Running Example Notebooks

To run the example notebooks with Docker:

1. Navigate to the examples directory:

   ```bash
   cd examples
   ```

2. Start the Docker containers:

   ```bash
   docker compose up
   ```

3. Open the URL shown in the console (typically <http://127.0.0.1:8888/tree>) in your browser to access Jupyter.

4. When finished, stop the containers:

   ```bash
   docker compose down
   ```

## Implementation Details

### Redis Module Usage

This implementation relies on specific Redis modules:

- **RedisJSON**: Used for storing structured JSON data as native Redis objects
- **RediSearch**: Used for creating and querying indices on JSON data

### Indexing

The Redis implementation creates these main indices using RediSearch:

1. **Checkpoints Index**: Stores checkpoint metadata and versioning
2. **Channel Values Index**: Stores channel-specific data
3. **Writes Index**: Tracks pending writes and intermediate states

For Redis Stores with vector search:

1. **Store Index**: Main key-value store
2. **Vector Index**: Optional vector embeddings for similarity search

### TTL Implementation

Both Redis checkpoint savers and stores leverage Redis's native key expiration:

- **Native Redis TTL**: Uses Redis's built-in `EXPIRE` command for setting TTL
- **TTL Removal**: Uses Redis's `PERSIST` command to remove TTL (with `ttl_minutes=-1`)
- **Automatic Cleanup**: Redis automatically removes expired keys
- **Configurable Default TTL**: Set a default TTL for all keys in minutes
- **TTL Refresh on Read**: Optionally refresh TTL when keys are accessed
- **Applied to All Related Keys**: TTL is applied to all related keys (checkpoint, blobs, writes)
- **Persistent by Default**: When no TTL is configured, keys are persistent (no expiration)

## Contributing

We welcome contributions! Here's how you can help:

### Development Setup

1. Clone the repository:

   ```bash
   git clone https://github.com/redis-developer/langgraph-redis
   cd langgraph-redis
   ```

2. Install dependencies:

   ```bash
   `poetry install --all-extras`
   ```

### Available Commands

The project includes several make commands for development:

- **Testing**:

  ```bash
  make test           # Run all tests
  make test-all       # Run all tests including API tests
  ```

- **Linting and Formatting**:

  ```bash
  make format        # Format all files with Black and isort
  make lint          # Run formatting, type checking, and other linters
  make check-types   # Run mypy type checking
  ```

- **Code Quality**:

  ```bash
  make test-coverage    # Run tests with coverage reporting
  make coverage-report  # Generate coverage report without running tests
  make coverage-html    # Generate HTML coverage report (opens in htmlcov/)
  make find-dead-code   # Find unused code with vulture
  ```

- **Redis for Development/Testing**:

  ```bash
  make redis-start   # Start Redis Stack in Docker (includes RedisJSON and RediSearch modules)
  make redis-stop    # Stop Redis container
  ```

### Contribution Guidelines

1. Create a new branch for your changes
2. Write tests for new functionality
3. Ensure all tests pass: `make test`
4. Format your code: `make format`
5. Run linting checks: `make lint`
6. Submit a pull request with a clear description of your changes
7. Follow [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0/) for commit messages

## License

This project is licensed under the MIT License.
