SurrealDB seamlessly integrates with popular AI and data frameworks, enabling you to leverage SurrealDB’s powerful features like vector search, graph relationships, and structured data storage. These integrations make it easy to build sophisticated applications combining LLMs, agents, data pipelines and more - all while using familiar tools and frameworks.
| Integration | Description |
|---|---|
| Camel | A Python framework for building multi-agent LLM systems with SurrealDB vector storage capabilities. |
| CrewAI | A framework for orchestrating role-playing AI agents with SurrealDB for entity and short-term memory. |
| Dagster | A data orchestration framework with SurrealDB vector search integration for ML pipelines. |
| DeepEval | A testing framework for LLM systems that uses SurrealDB’s vector capabilities to evaluate RAG pipeline quality. |
| Dynamiq | Dynamiq is a Python framework for building multi-agent LLM systems with SurrealDB vector storage capabilities. |
| Feast | A feature store for ML pipelines with SurrealDB vector search integration. |
| Google Agent | A framework for building and deploying intelligent agents in Google Cloud with SurrealDB vector storage for RAG. |
| LangChain | A framework for building LLM based applications. |
| Llama Index | A framework for building RAG pipelines with SurrealDB’s native HNSW vector index as the backing store. |
| Smol Agents | A complete walkthrough for building a code-generating AI agent that recommends grocery items by querying SurrealDB’s HNSW vector index. |
