Team DeadReckoning took 3rd place at the Agents & Knowledge Graphs Hackathon in London, hosted by LangChain Community and SurrealDB. 🏆 Congrats to Júlia Sala Bayo, PhD and Archie Marshall, who built a navigation tool for developers exploring an unfamiliar codebase. Want to learn more? Check out the GitHub repo. 👉 https://lnkd.in/edjsAKKE LangChain OSS
About us
SurrealDB is an AI-native, multi-model database built in Rust designed to unify multiple data models into a single, powerful engine. It combines document, graph, relational, time-series, geospatial and key-value data types natively, with powerful search and retrieval (vectors, full-text, hybrid) and built-in real-time and event-driven capabilities. By using SurrealDB, companies can drastically simplify their tech stack, reduce complexity and operational overhead, and build new applications faster. SurrealDB is designed as a single data and logic layer for AI agents, knowledge graphs, real-time applications (e.g. recommendation engines, fraud detection systems), and OLTP applications requiring multiple data types. SurrealDB can also be used as a backend-as-a-service thanks to its support for direct user authentication. Given that it’s a single Rust binary, SurrealDB can run embedded, in the edge, as single backend node, or fully scaled to a distributed cluster - all with the same API.
- Website
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https://surrealdb.com/
External link for SurrealDB
- Industry
- Software Development
- Company size
- 11-50 employees
- Headquarters
- London
- Type
- Privately Held
- Founded
- 2022
- Specialties
- Distributed Database, Cloud Native, High Performance, Multi-Cloud, Hybrid Cloud, Globally Distributed, Distributed SQL, Analytics, Web Database, Graph Database, Cloud Database, Serverless, Artificial Intelligence, Graph RAG, RAG, Vector Database, and Multi-Model Database
Products
![Click here to view SurrealDB]()
SurrealDB
NoSQL Database Software
SurrealDB is the ultimate multi-model database for AI applications. One database, infinite memory. Scale from edge devices to petabyte clusters. SurrealDB unifies vectors, graphs, documents, time-series and files, so your applications can learn, link and act in real time.
Locations
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Primary
Get directions
London, GB
Employees at SurrealDB
Updates
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Ready to get hands-on with SurrealDB? This tutorial gives you the practical skills to build a movie database while mastering our multi-model database and SurrealQL. Start building today. 👉 https://sdb.li/462wmCa
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Did you know that in SurrealQL, an object can be merged with another by using the + operator, and fields can be removed by calling the .remove() method? Check out more examples in our docs. 👉 https://sdb.li/47yymTn
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What if one database could handle different data models? More companies are ditching the complexity of managing multiple databases and are not looking back. Find out why. 👉 https://sdb.li/4d9N473
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What if the in-memory storage engine you use offered optional versioning and Redis-like persistent storage? SurrealDB's high-performance in-memory engine, known as SurrealMX, has been doing exactly this with your instances since 3.0 beta. Learn more from David MacLeod. 👉 https://sdb.li/4bLKfXv
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Team Synapse took 2nd place at the LangChain Community & SurrealDB Agents and Knowledge Graphs Hackathon in London! 🏆 Congrats to Anshul Yadav, who built Synapse: a memory layer that, based on your conversations with AI, builds pluggable memories that any agent can use to provide them with personal context. Check out Anshul's GitHub: https://lnkd.in/eKi_6v53 LangChain OSS
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SurrealQL is our powerful, SQL-like query language that provides developers with an intuitive way to interact with our multi-model database. Start your journey of learning how to query data across multiple data models. 👉 https://sdb.li/4p9A5p8
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Next stop Amsterdam! We're at KubeCon + CloudNativeCon, booth #885. Come meet CEO & Co-founder Tobie Morgan Hitchcock, chat with Matthew Penaroza & Ignacio Paz about why SurrealDB is the database for AI agents: core state, contextual memory, and connected data in one place. Catch Martin Schaer for a knowledge graph demo. 👋
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Our first San Francisco developer meetup set the bar high. 70+ attendees, three sessions, and a room of builders focused on giving production AI agents the right context. Thanks to Samuel Colvin (Pydantic) and Linghua Jin (CocoIndex) for their sessions, and to Amazon Web Services (AWS) Builder Loft for hosting us.
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