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| 1 | +# DynamoDB Store for LangGraph |
| 2 | + |
| 3 | +A DynamoDB-backed store implementation for LangGraph that provides persistent key-value storage with hierarchical namespaces. |
| 4 | + |
| 5 | +## Features |
| 6 | + |
| 7 | +- ✅ **Persistent Storage**: Durable storage using AWS DynamoDB |
| 8 | +- ✅ **Hierarchical Namespaces**: Organize data with multi-level namespaces |
| 9 | +- ✅ **TTL Support**: Automatic item expiration with configurable time-to-live |
| 10 | +- ✅ **Filtering**: Basic filtering capabilities for search operations |
| 11 | +- ✅ **Batch Operations**: Efficient batch processing of multiple operations |
| 12 | +- ✅ **Cost-Effective**: Pay-per-request billing for unpredictable workloads |
| 13 | + |
| 14 | +## Installation |
| 15 | + |
| 16 | +```bash |
| 17 | +pip install langgraph-checkpoint-aws |
| 18 | +``` |
| 19 | + |
| 20 | +## Quick Start |
| 21 | + |
| 22 | +```python |
| 23 | +from langgraph_checkpoint_aws import DynamoDBStore |
| 24 | + |
| 25 | +# Create a store instance |
| 26 | +store = DynamoDBStore(table_name="my-store-table") |
| 27 | + |
| 28 | +# Setup the table (creates it if it doesn't exist) |
| 29 | +store.setup() |
| 30 | + |
| 31 | +# Store and retrieve data |
| 32 | +store.put(("users", "123"), "prefs", {"theme": "dark"}) |
| 33 | +item = store.get(("users", "123"), "prefs") |
| 34 | +print(item.value) # {"theme": "dark"} |
| 35 | +``` |
| 36 | + |
| 37 | +## Basic Usage |
| 38 | + |
| 39 | +### Storing Documents |
| 40 | + |
| 41 | +```python |
| 42 | +# Store a document with hierarchical namespace |
| 43 | +store.put( |
| 44 | + ("documents", "user123"), |
| 45 | + "report_1", |
| 46 | + { |
| 47 | + "text": "Machine learning report on customer behavior analysis...", |
| 48 | + "tags": ["ml", "analytics", "report"], |
| 49 | + "author": "data_scientist" |
| 50 | + } |
| 51 | +) |
| 52 | +``` |
| 53 | + |
| 54 | +### Retrieving Documents |
| 55 | + |
| 56 | +```python |
| 57 | +# Get a specific document |
| 58 | +item = store.get(("documents", "user123"), "report_1") |
| 59 | +print(f"Text: {item.value['text']}") |
| 60 | +print(f"Created: {item.created_at}") |
| 61 | +print(f"Updated: {item.updated_at}") |
| 62 | +``` |
| 63 | + |
| 64 | +### Searching |
| 65 | + |
| 66 | +```python |
| 67 | +# Search all documents in a namespace |
| 68 | +results = store.search(("documents", "user123")) |
| 69 | + |
| 70 | +# Search with filter |
| 71 | +results = store.search( |
| 72 | + ("documents", "user123"), |
| 73 | + filter={"author": "data_scientist"} |
| 74 | +) |
| 75 | +``` |
| 76 | + |
| 77 | +### Deleting Items |
| 78 | + |
| 79 | +```python |
| 80 | +store.delete(("documents", "user123"), "report_1") |
| 81 | +``` |
| 82 | + |
| 83 | +## Advanced Features |
| 84 | + |
| 85 | +### Time-To-Live (TTL) |
| 86 | + |
| 87 | +Configure automatic item expiration: |
| 88 | + |
| 89 | +```python |
| 90 | +store = DynamoDBStore( |
| 91 | + table_name="my-store-table", |
| 92 | + ttl={ |
| 93 | + "default_ttl": 60, # 60 minutes default TTL |
| 94 | + "refresh_on_read": True, # Refresh TTL on reads |
| 95 | + } |
| 96 | +) |
| 97 | +store.setup() |
| 98 | + |
| 99 | +# Item will expire after 60 minutes |
| 100 | +store.put(("temp", "session_123"), "data", {"value": "temporary data"}) |
| 101 | + |
| 102 | +# Custom TTL for specific item (30 minutes) |
| 103 | +store.put( |
| 104 | + ("temp", "session_123"), |
| 105 | + "short_lived", |
| 106 | + {"value": "expires soon"}, |
| 107 | + ttl=30 |
| 108 | +) |
| 109 | +``` |
| 110 | + |
| 111 | +### Listing Namespaces |
| 112 | + |
| 113 | +```python |
| 114 | +# List all namespaces |
| 115 | +namespaces = store.list_namespaces() |
| 116 | + |
| 117 | +# List with prefix filter |
| 118 | +user_namespaces = store.list_namespaces(prefix=("users",)) |
| 119 | + |
| 120 | +# Limit depth |
| 121 | +shallow_namespaces = store.list_namespaces(max_depth=2) |
| 122 | +``` |
| 123 | + |
| 124 | +### Batch Operations |
| 125 | + |
| 126 | +```python |
| 127 | +from langgraph.store.base import PutOp, GetOp |
| 128 | + |
| 129 | +# Batch put operations |
| 130 | +ops = [ |
| 131 | + PutOp(("batch",), "item1", {"value": 1}, None, None), |
| 132 | + PutOp(("batch",), "item2", {"value": 2}, None, None), |
| 133 | + PutOp(("batch",), "item3", {"value": 3}, None, None), |
| 134 | +] |
| 135 | +results = store.batch(ops) |
| 136 | + |
| 137 | +# Batch get operations |
| 138 | +get_ops = [ |
| 139 | + GetOp(("batch",), "item1", False), |
| 140 | + GetOp(("batch",), "item2", False), |
| 141 | +] |
| 142 | +items = store.batch(get_ops) |
| 143 | +``` |
| 144 | + |
| 145 | +### Context Manager |
| 146 | + |
| 147 | +```python |
| 148 | +with DynamoDBStore.from_conn_string("my-store-table") as store: |
| 149 | + store.setup() |
| 150 | + store.put(("test",), "example", {"data": "value"}) |
| 151 | + item = store.get(("test",), "example") |
| 152 | +``` |
| 153 | + |
| 154 | +## Configuration Options |
| 155 | + |
| 156 | +### Constructor Parameters |
| 157 | + |
| 158 | +- `table_name` (str): Name of the DynamoDB table |
| 159 | +- `region_name` (str, optional): AWS region name |
| 160 | +- `boto3_session` (boto3.Session, optional): Custom boto3 session |
| 161 | +- `ttl` (TTLConfig, optional): TTL configuration |
| 162 | +- `max_read_capacity_units` (int, optional): Max read capacity (default: 10) |
| 163 | +- `max_write_capacity_units` (int, optional): Max write capacity (default: 10) |
| 164 | + |
| 165 | +### TTL Configuration |
| 166 | + |
| 167 | +```python |
| 168 | +ttl = { |
| 169 | + "default_ttl": 60, # Default TTL in minutes |
| 170 | + "refresh_on_read": True, # Refresh TTL when items are read |
| 171 | +} |
| 172 | +``` |
| 173 | + |
| 174 | +## DynamoDB Table Schema |
| 175 | + |
| 176 | +The store uses a single DynamoDB table with the following structure: |
| 177 | + |
| 178 | +- **PK** (Partition Key, String): Namespace joined with ':' |
| 179 | +- **SK** (Sort Key, String): Item key |
| 180 | +- **value** (Map): The stored dictionary |
| 181 | +- **created_at** (String): ISO format timestamp |
| 182 | +- **updated_at** (String): ISO format timestamp |
| 183 | +- **expires_at** (Number, optional): Unix timestamp for TTL |
| 184 | + |
| 185 | +## AWS Configuration |
| 186 | + |
| 187 | +Ensure you have proper AWS credentials configured through: |
| 188 | + |
| 189 | +- Environment variables (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`) |
| 190 | +- AWS credentials file (`~/.aws/credentials`) |
| 191 | +- IAM role when running on AWS services |
| 192 | + |
| 193 | +Required IAM permissions: |
| 194 | + |
| 195 | +```json |
| 196 | +{ |
| 197 | + "Version": "2012-10-17", |
| 198 | + "Statement": [ |
| 199 | + { |
| 200 | + "Effect": "Allow", |
| 201 | + "Action": [ |
| 202 | + "dynamodb:CreateTable", |
| 203 | + "dynamodb:DescribeTable", |
| 204 | + "dynamodb:PutItem", |
| 205 | + "dynamodb:GetItem", |
| 206 | + "dynamodb:Query", |
| 207 | + "dynamodb:Scan", |
| 208 | + "dynamodb:DeleteItem", |
| 209 | + "dynamodb:UpdateItem", |
| 210 | + "dynamodb:UpdateTimeToLive" |
| 211 | + ], |
| 212 | + "Resource": "arn:aws:dynamodb:*:*:table/your-table-name" |
| 213 | + } |
| 214 | + ] |
| 215 | +} |
| 216 | +``` |
| 217 | + |
| 218 | +## Comparison with Other Stores |
| 219 | + |
| 220 | +### DynamoDB Store vs Valkey Store |
| 221 | + |
| 222 | +| Feature | DynamoDB Store | Valkey Store | |
| 223 | +|---------|---------------|--------------| |
| 224 | +| Vector Search | ❌ No | ✅ Yes | |
| 225 | +| High Performance | ✅ Good | ✅ Excellent | |
| 226 | +| TTL Support | ✅ Yes | ✅ Yes | |
| 227 | +| Cost | Pay-per-request | Infrastructure cost | |
| 228 | +| Best For | Simple storage, managed infra | Vector search, high performance | |
| 229 | + |
| 230 | +Use **DynamoDB Store** when: |
| 231 | +- You need a fully managed solution |
| 232 | +- You don't require vector search capabilities |
| 233 | +- You want pay-per-request pricing |
| 234 | +- Your workload is unpredictable |
| 235 | + |
| 236 | +Use **Valkey Store** when: |
| 237 | +- You need vector search capabilities |
| 238 | +- You require ultra-low latency |
| 239 | +- You can manage your own infrastructure |
| 240 | +- You have consistent, predictable workloads |
| 241 | + |
| 242 | +## Limitations |
| 243 | + |
| 244 | +- **No Vector Search**: This store does not support semantic/vector search |
| 245 | +- **Scan Cost**: Listing namespaces uses DynamoDB Scan which can be expensive |
| 246 | +- **Filter Limitations**: Basic filtering only (equality checks) |
| 247 | +- **No Transactions**: Operations are not transactional across multiple items |
| 248 | + |
| 249 | +## Examples |
| 250 | + |
| 251 | +See the [example notebook](../../samples/memory/dynamodb_store.ipynb) for comprehensive usage examples. |
| 252 | + |
| 253 | +## Contributing |
| 254 | + |
| 255 | +Contributions are welcome! Please see the main [CONTRIBUTING.md](../../libs/langgraph-checkpoint-aws/CONTRIBUTING.md) for guidelines. |
| 256 | + |
| 257 | +## License |
| 258 | + |
| 259 | +This package is part of the `langgraph-checkpoint-aws` project. See [LICENSE](../../LICENSE) for details. |
| 260 | + |
| 261 | +## Related Resources |
| 262 | + |
| 263 | +- [LangGraph Documentation](https://langchain-ai.github.io/langgraph/) |
| 264 | +- [AWS DynamoDB Documentation](https://docs.aws.amazon.com/dynamodb/) |
| 265 | +- [BaseStore Interface](https://langchain-ai.github.io/langgraph/reference/store/) |
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