All Open Source Apps

AI-Native Distributed SQL Database for Agentic Workloads

Distributed SQL database for real-time analytics at scale

Open-source enterprise data warehouse unifying analytics, vector search, full-text search, and AI agent orchestration in a single Rust-built engine on S3.

Open-source vector database and search engine built in Rust for production-grade AI applications — from semantic search to RAG pipelines and recommendation systems.

A high-performance, open-source time-series database built for financial market data, IoT telemetry, and real-time analytics, combining a zero-GC Java/C++ core with SIMD-accelerated SQL and a WAL-to-Parquet storage engine.

Open-source vector database combining semantic search, hybrid queries, RAG, and image search in a single cloud-native system built for production scale.

The SQL database you can branch, merge, diff, and clone — Git for your data, MySQL-compatible and ready for multi-agent AI workflows.

Open-source, embedded vector database built on the Lance columnar format for fast multimodal search across billions of vectors, backed by Y Combinator (W23).

Open-source no-code platform to build databases, apps, automations, and AI agents — self-hosted or cloud, with full data ownership.

Born out of Y Combinator's S2023 batch, ParadeDB is a Postgres extension that delivers Elasticsearch-quality BM25 search and real-time analytics without a separate search cluster to manage.

Open Source realtime backend in 1 file — embedded SQLite, auth, file storage, and admin UI as a single Go binary.

An open source, Google Zanzibar-inspired authorization database that models permissions as relationships and evaluates fine-grained access checks at massive scale with single-digit millisecond latency.

Postgres-native ETL that streams change data capture in real time to Snowflake, BigQuery, ClickHouse, S3, and Kafka — up to 10x faster than general-purpose pipelines, managed through a familiar Postgres SQL interface.

Fast, lightweight, schema-less search backend in Rust — microsecond queries, 30MB RAM, no document storage required.

A temporal, verifiable graph database with git-like branching, integrated vector/text/geo search, and RDF/SPARQL/JSON-LD/openCypher support — benchmarked at 10.4x faster than the next database on the full Wikidata dump.

The programmable open source core ledger for fintech — build money-moving applications with atomic multi-posting transactions, account-based modeling, and Numscript, a built-in DSL for financial logic.

Embedded KV store built on object storage with zero replication cost

A graph-vector database built from scratch in Rust that unifies graph traversal, vector search, key-value, and relational storage into a single platform for AI applications.

An open-source context database that gives AI agents a unified filesystem for memory, resources, and skills with hierarchical tiered retrieval.

Open-source time-series database built for real-time ingest, fast SQL queries, and embedded Python automation — powered by Apache Arrow and Parquet.

A single graph+vector+temporal database for AI workloads — Neo4j-compatible, sub-millisecond hybrid search, and built-in memory decay.

The AI Workspace for Finance: Connect Data, Run AI Agents, Build Analytics

A RocksDB-based NVMe/S3 cache purpose-built for AI inference workloads — a faster Redis replacement optimized for batch, low-latency, zero-copy reads and writes between data pipelines and inference apps.

Federated AI search and RAG across 100+ enterprise sources—no data extraction, no vector database required.