# LangGraph Redis
This repository contains Redis implementations for LangGraph, providing both Checkpoint Savers and Stores functionality.
## Overview
The project consists of two 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
## Dependencies
The project requires the following main dependencies:
- `redis>=5.2.1`
- `redisvl>=0.3.7`
- `langgraph-checkpoint>=2.0.10`
## 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 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 RedisStore.from_conn_string("redis://localhost:6379", index=index_config) as store:
store.setup()
# Use the store with vector search capabilities...
```
### Async Implementation
```python
from langgraph.store.redis.aio import AsyncRedisStore
async def main():
async with AsyncRedisStore.from_conn_string("redis://localhost:6379") as store:
await store.setup()
# Use the store asynchronously...
asyncio.run(main())
```
## Implementation Details
### Indexing
The Redis implementation creates these main indices:
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
## Contributing
We welcome contributions! Here's how you can help:
### Development Setup
1. Clone the repository:
```bash
git clone https://github.com/langchain-ai/langgraph
cd langgraph
```
2. Install dependencies:
```bash
poetry install
```
### Available Commands
The project includes several make commands for development:
- **Testing**:
```bash
make test # Run all tests
make test_watch # Run tests in watch mode
```
- **Linting and Formatting**:
```bash
make lint # Run all linters
make lint_diff # Lint only changed files
make lint_package # Lint only the package
make lint_tests # Lint only tests
make format # Format all files
make format_diff # Format only changed files
```
### 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.
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"description": "# LangGraph Redis\n\nThis repository contains Redis implementations for LangGraph, providing both Checkpoint Savers and Stores functionality.\n\n## Overview\n\nThe project consists of two main components:\n1. **Redis Checkpoint Savers**: Implementations for storing and managing checkpoints using Redis\n2. **Redis Stores**: Redis-backed key-value stores with optional vector search capabilities\n\n## Dependencies\n\nThe project requires the following main dependencies:\n- `redis>=5.2.1`\n- `redisvl>=0.3.7`\n- `langgraph-checkpoint>=2.0.10`\n\n## Installation\n\nInstall the library using pip:\n\n```bash\npip install langgraph-checkpoint-redis\n```\n\n## Redis Checkpoint Savers\n\n### Important Notes\n\n> [!IMPORTANT]\n> When using Redis checkpointers for the first time, make sure to call `.setup()` method on them to create required indices. See examples below.\n\n### Standard Implementation\n\n```python\nfrom langgraph.checkpoint.redis import RedisSaver\n\nwrite_config = {\"configurable\": {\"thread_id\": \"1\", \"checkpoint_ns\": \"\"}}\nread_config = {\"configurable\": {\"thread_id\": \"1\"}}\n\nwith RedisSaver.from_conn_string(\"redis://localhost:6379\") as checkpointer:\n # Call setup to initialize indices\n checkpointer.setup()\n checkpoint = {\n \"v\": 1,\n \"ts\": \"2024-07-31T20:14:19.804150+00:00\",\n \"id\": \"1ef4f797-8335-6428-8001-8a1503f9b875\",\n \"channel_values\": {\n \"my_key\": \"meow\",\n \"node\": \"node\"\n },\n \"channel_versions\": {\n \"__start__\": 2,\n \"my_key\": 3,\n \"start:node\": 3,\n \"node\": 3\n },\n \"versions_seen\": {\n \"__input__\": {},\n \"__start__\": {\n \"__start__\": 1\n },\n \"node\": {\n \"start:node\": 2\n }\n },\n \"pending_sends\": [],\n }\n\n # Store checkpoint\n checkpointer.put(write_config, checkpoint, {}, {})\n\n # Retrieve checkpoint\n loaded_checkpoint = checkpointer.get(read_config)\n\n # List all checkpoints\n checkpoints = list(checkpointer.list(read_config))\n```\n\n### Async Implementation\n\n```python\nfrom langgraph.checkpoint.redis.aio import AsyncRedisSaver\n\nasync def main():\n write_config = {\"configurable\": {\"thread_id\": \"1\", \"checkpoint_ns\": \"\"}}\n read_config = {\"configurable\": {\"thread_id\": \"1\"}}\n\n async with AsyncRedisSaver.from_conn_string(\"redis://localhost:6379\") as checkpointer:\n # Call setup to initialize indices\n await checkpointer.asetup()\n checkpoint = {\n \"v\": 1,\n \"ts\": \"2024-07-31T20:14:19.804150+00:00\",\n \"id\": \"1ef4f797-8335-6428-8001-8a1503f9b875\",\n \"channel_values\": {\n \"my_key\": \"meow\",\n \"node\": \"node\"\n },\n \"channel_versions\": {\n \"__start__\": 2,\n \"my_key\": 3,\n \"start:node\": 3,\n \"node\": 3\n },\n \"versions_seen\": {\n \"__input__\": {},\n \"__start__\": {\n \"__start__\": 1\n },\n \"node\": {\n \"start:node\": 2\n }\n },\n \"pending_sends\": [],\n }\n\n # Store checkpoint\n await checkpointer.aput(write_config, checkpoint, {}, {})\n\n # Retrieve checkpoint\n loaded_checkpoint = await checkpointer.aget(read_config)\n\n # List all checkpoints\n checkpoints = [c async for c in checkpointer.alist(read_config)]\n\n# Run the async main function\nimport asyncio\nasyncio.run(main())\n```\n\n### Shallow Implementations\n\nShallow Redis checkpoint savers store only the latest checkpoint in Redis. These implementations are useful when retaining a complete checkpoint history is unnecessary.\n\n```python\nfrom langgraph.checkpoint.redis.shallow import ShallowRedisSaver\n# For async version: from langgraph.checkpoint.redis.ashallow import AsyncShallowRedisSaver\n\nwrite_config = {\"configurable\": {\"thread_id\": \"1\", \"checkpoint_ns\": \"\"}}\nread_config = {\"configurable\": {\"thread_id\": \"1\"}}\n\nwith ShallowRedisSaver.from_conn_string(\"redis://localhost:6379\") as checkpointer:\n checkpointer.setup()\n # ... rest of the implementation follows similar pattern\n```\n\n## Redis Stores\n\nRedis Stores provide a persistent key-value store with optional vector search capabilities.\n\n### Synchronous Implementation\n\n```python\nfrom langgraph.store.redis import RedisStore\n\n# Basic usage\nwith RedisStore.from_conn_string(\"redis://localhost:6379\") as store:\n store.setup()\n # Use the store...\n\n# With vector search configuration\nindex_config = {\n \"dims\": 1536, # Vector dimensions\n \"distance_type\": \"cosine\", # Distance metric\n \"fields\": [\"text\"], # Fields to index\n}\n\nwith RedisStore.from_conn_string(\"redis://localhost:6379\", index=index_config) as store:\n store.setup()\n # Use the store with vector search capabilities...\n```\n\n### Async Implementation\n\n```python\nfrom langgraph.store.redis.aio import AsyncRedisStore\n\nasync def main():\n async with AsyncRedisStore.from_conn_string(\"redis://localhost:6379\") as store:\n await store.setup()\n # Use the store asynchronously...\n\nasyncio.run(main())\n```\n\n## Implementation Details\n\n### Indexing\n\nThe Redis implementation creates these main indices:\n\n1. **Checkpoints Index**: Stores checkpoint metadata and versioning\n2. **Channel Values Index**: Stores channel-specific data\n3. **Writes Index**: Tracks pending writes and intermediate states\n\nFor Redis Stores with vector search:\n1. **Store Index**: Main key-value store\n2. **Vector Index**: Optional vector embeddings for similarity search\n\n## Contributing\n\nWe welcome contributions! Here's how you can help:\n\n### Development Setup\n\n1. Clone the repository:\n ```bash\n git clone https://github.com/langchain-ai/langgraph\n cd langgraph\n ```\n\n2. Install dependencies:\n ```bash\n poetry install\n ```\n\n### Available Commands\n\nThe project includes several make commands for development:\n\n- **Testing**:\n ```bash\n make test # Run all tests\n make test_watch # Run tests in watch mode\n ```\n\n- **Linting and Formatting**:\n ```bash\n make lint # Run all linters\n make lint_diff # Lint only changed files\n make lint_package # Lint only the package\n make lint_tests # Lint only tests\n make format # Format all files\n make format_diff # Format only changed files\n ```\n\n### Contribution Guidelines\n\n1. Create a new branch for your changes\n2. Write tests for new functionality\n3. Ensure all tests pass: `make test`\n4. Format your code: `make format`\n5. Run linting checks: `make lint`\n6. Submit a pull request with a clear description of your changes\n7. Follow [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0/) for commit messages\n\n## License\n\nThis project is licensed under the MIT License.",
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