The database for AI applications
Structured storage, search, and auto-embeddings in one place
Schema to working API in 10 minutes. No separate vector DB, no embedding pipeline, no sync scripts.
Works with any LLM, agent framework, or application
AI applications need more from their database
Every AI app needs to store data, search it, and keep it in sync. That usually means stitching together a database, vector store, search engine, and embedding pipeline. FoxNose replaces all of it.
Four services, four bills, and sync scripts to keep them all consistent.
Define a schema, get a full API with search built in. Store data, query it, see what your AI retrieves — one service.
Hybrid search, auto-embeddings, and structured storage — built in
Hybrid Search API
Semantic search, keyword matching, and structured filters in one query. Built into the database — no separate search engine.
Auto-Embeddings
Mark fields as vectorizable. Embeddings generate on save. No embedding pipeline, no external API.
Real-Time Indexing
Data is searchable moments after write. No cron jobs, no reindex commands, no sync lag.
AI Knowledge Governance
Schema versioning, audit trails, RBAC. Debug what your AI retrieved and when.
One database, many AI use cases
Build RAG pipelines, power AI-native content, give agents persistent memory, or use it as the primary database for any AI-powered product.
Build RAG Apps
Production RAG pipeline without managing vector databases, embedding APIs, or sync scripts. Python and JavaScript SDKs included.
Headless CMS for AI
A headless CMS with built-in vector search, auto-embeddings, and LangChain integration. A Contentful alternative with AI native search.
AI Agent Memory
Persistent knowledge base for AI agents. Read-write API, schema versioning, and native LangChain agent tools. A Pinecone alternative that simplifies the stack.
LLM Database
The primary database for AI applications. Structured storage with built-in search, auto-embeddings, and multi-tenant isolation.
One API from schema to search
Define your data model, store records, and query with hybrid search. Auto-embeddings and indexing happen behind the scenes.
from foxnose_sdk.management import ManagementClient
mgmt = ManagementClient(...)
folder = mgmt.get_folder("knowledge-base")
# Create a new schema version
version = folder.create_version()
# Add fields — vectorizable fields get auto-embeddings
version.create_field(body={
"key": "title", "name": "Title", "type": "text", "required": True
})
version.create_field(body={
"key": "body", "name": "Body", "type": "text", "vectorizable": True
})
# Publish when ready
version.publish()
# → API endpoints ready. Embeddings on every write.Define your content model and mark fields for vector search. FoxNose generates embeddings automatically — no external embedding API or pipeline needed. Knowledge base versioning →
Start building
Create your first database in minutes. Free during beta.