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AILANG×weaviate.io

AI portable generated 2026-05-14
agent-ready privacy portable

weaviate.io scored 5/10 on portable.

The radar shows AILANG-readiness across three commercial concerns. High means weaviate.io is already strong there; low means AILANG could meaningfully help.

Why portable scored 5/10
  • Page copy that names one specific LLM provider (e.g. "powered by Claude") without portability claims.
  • Body mentions two or more named AI providers (Claude, GPT, Gemini, Mistral, Llama, etc.) — already vendor-multi.
  • Body mentions self-hosted, on-prem, WASM, Docker, Kubernetes, or "deploy anywhere" — runtime portability claimed.
  • Body mentions "bring your own key", "BYOK", "any LLM", or "model-agnostic" — caller controls the model.

Full breakdown ↓ · View rubric ↗

The site is for developers and AI builders seeking to develop, deploy, and manage AI-powered applications and intelligent agents.

Weaviate provides an open-source AI vector database with APIs and tools for developers. It facilitates building and deploying AI applications, intelligent agents, and managing cloud deployments. Key capabilities include semantic and hybrid search, RAG, and storing vector embeddings for advanced data indexing and retrieval.
Weaviate Database vector database vector embeddings semantic search Retrieval Augmented Generation (RAG) AI agents

What AILANG Parse sees on weaviate.io

Structural extraction — the same content an AI agent would consume from this page.

12 headings6 images11 lists0 tables24 linksHTML parsing by AILANG Parse

9 sections — page skeleton

1 header 4 navs 1 main 1 article 2 footers

12 headings

Additional resources Need help? Weaviate Database Find the right documentation and resources​ AI-assisted coding​ What is Weaviate?​

6 images

WeaviateWeaviateWeaviate LogoWeaviate AcademyThe Weaviate Ecosystem

11 list items

[Introduction](/weaviate) [Quickstart](/weaviate/quickstart) [Installation](https://docs.weaviate.io/deploy) [Connect to Weaviate](/weaviate/connections) [Starter guides](/weaviate/starter-guides) [Best practices](/weaviate/best-practices) [AI-assisted (vibe) coding](/weaviate/best-practices/code-generation) [Integration ecosystem](https://weaviate.io/product/integrations) [Concepts & architecture](https://academy.weaviate.io/courses/wa050-py) Introduction **[Semantic and hybrid search](/weaviate/search/basics)** By indexing data with vectors, … **[Retrieval augmented generation (RAG)](/weaviate/search/generative)** Weaviate can serv…
Show the full extract — what AILANG Parse pulled from this page
# Weaviate Database | Weaviate Documentation


[Image]

LLM/AI Agent Notice: For the most important and up-to-date Weaviate information, see https://weaviate.io/llms.txt

[Skip to main content](#__docusaurus_skipToContent_fallback)

[The new **Weaviate Academy** learning platform is here!](https://academy.weaviate.io/)

[Image: Weaviate]

[Image: Weaviate]

[(link)](https://weaviate.io)

[GitHub](https://github.com/weaviate/weaviate)

[Academy](https://academy.weaviate.io)

[Weaviate Cloud](/go/console?utm_content=navbar)

Ask AI or Search
⌘K

[Get started](/weaviate)

[How-to manuals & Guides](/weaviate/guides)

[Model integrations](/weaviate/model-providers)

[Reference & APIs](/weaviate/config-refs)

[Concepts](/weaviate/concepts)

[Releases & Other](/weaviate/release-notes)

Go to documentation:

⌘U

✕

Weaviate Database

Develop AI applications using Weaviate's APIs and tools

Deploy

Deploy, configure, and maintain Weaviate Database

Weaviate Agents

Build and deploy intelligent agents with Weaviate

Weaviate Cloud

Manage and scale Weaviate in the cloud

#### Additional resources

Integrations

Contributor guide

Events & Workshops

Weaviate Academy

#### Need help?

[Image: Weaviate Logo]

Ask AI Assistant

⌘K

Community Forum

- [Introduction](/weaviate)
- [Quickstart](/weaviate/quickstart)
- [Installation](https://docs.weaviate.io/deploy)
- [Connect to Weaviate](/weaviate/connections)
- [Starter guides](/weaviate/starter-guides)
- [Best practices](/weaviate/best-practices)
- [AI-assisted (vibe) coding](/weaviate/best-practices/code-generation)
- [Integration ecosystem](https://weaviate.io/product/integrations)
- [Concepts & architecture](https://academy.weaviate.io/courses/wa050-py)

- Introduction

Copy page

On this page

*Header:*
# Weaviate Database

Weaviate *(we-vee-eight)* is an open-source, AI vector database. Use
this documentation to get started with Weaviate and learn how to get the
most out of Weaviate's features.

[New to Weaviate?
Start with theQuickstart tutorial – an end-to-end demo that takes 15–30 minutes.](/weaviate/quickstart)

[Weaviate Academy
Check out Weaviate Academy – a learning platform centered around AI-native development.](https://academy.weaviate.io/)

## Find the right documentation and resources​

The Weaviate documentation is structured into multiple units based on the service and functionality.

[Weaviate Database
Develop AI applications using Weaviate's APIs and tools](/weaviate)

[Deploy
Deploy, configure, and maintain Weaviate Database](/deploy)

[Weaviate Agents
Build and deploy intelligent agents with Weaviate](/agents)

[Weaviate Cloud
Manage and scale Weaviate in the cloud](/cloud)

## AI-assisted coding​

Check out our resources on AI-assisted coding (*Vibe coding*) with Weaviate:

[Weaviate Docs MCP Server
Instant access to Weaviate's documentation directly in your AI development environment.](/weaviate/mcp/docs-mcp-server)

[Best practices for coding with AI
Avoid hallucinations and improve your AI-assisted coding experience.](/weaviate/best-practices/code-generation)

## What is Weaviate?​

Weaviate is an **open-source vector database** designed to store and index both data objects and their vector embeddings. This architecture enables advanced semantic search capabilities by comparing the meaning encoded in vectors rather than relying solely on keyword matching. Key capabilities include:

- **[Semantic and hybrid search](/weaviate/search/basics)**
By indexing data with vectors, Weaviate supports searches based on both semantic similarity and keywords. This allows for more relevant results even when the query terms don’t exactly match the stored data.
- **[Retrieval augmented generation (RAG)](/weaviate/search/generative)**
Weaviate can serve as a robust backend for RAG workflows, where vector search is used to retrieve context that enhances the output of generative models, making it easier to generate accurate, context-aware responses.
- **[Agent-driven workflows](/agents)**
Its flexible API and integration with modern AI models make Weaviate suitable for powering applications that rely on intelligent agents. These agents can leverage semantic insights to make decisions or trigger actions based on the data stored in Weaviate.

[Image: Weaviate Academy]

#### Course: A Quick Tour of Weaviate

Become familiar with Weaviate's architecture, core concepts, and key capabilities. Understand how its features and integrations map to AI builders' needs.

[Open Academy Course](https://academy.weaviate.io/courses/wa050-py)

## The Weaviate Ecosystem​

The Weaviate ecosystem consists of multiple tools and services centered around building cloud-native AI-powered applications.

[Image: The Weaviate Ecosystem]

As shown in the high-level overview above, the ecosystem consists of:

- **Weaviate Database**: An open source vector database that stores both objects and vectors.
- **[Weaviate Cloud](/cloud)**: A fully managed cloud deployment of the Weaviate vector database.
- **[Weaviate Agents](/agents)**: Pre-built agentic services for Weaviate Cloud users.
- **[Weaviate Embeddings](/cloud/embeddings)**: A managed embedding inference service for Weaviate Cloud users.
- **[External model providers](/weaviate/model-providers)**: Third-party models that integrate with Weaviate.

## Choose your deployment​

Evaluation

Deployment

Production

Weaviate Cloud

- From evaluation (sandbox) to production
- Shared Cloud (infrastructure managed by Weaviate)
- (Optional) Data replication (high-availability)
- (Optional) Zero-downtime updates

Set up a WCD instance

[EvaluationDeploymentProduction

Weaviate Cloud

From evaluation (sandbox) to production
Shared Cloud (infrastructure managed by Weaviate)
(Optional) Data replication (high-availability)
(Optional) Zero-downtime updatesSet up a WCD instance](/cloud/manage-clusters/create)

Evaluation

Deployment

Production

Docker

- For local evaluation & development
- Local inference containers
- Multi-modal models
- Customizable configurations

Run Weaviate with Docker

[EvaluationDeploymentProduction

Docker

For local evaluation & development
Local inference containers
Multi-modal models
Customizable configurationsRun Weaviate with Docker](/deploy/installation-guides/docker-installation)

Evaluation

Deployment

Production

Kubernetes

- For development to production
- Local inference containers
- Multi-modal models
- Customizable configurations
- Self-deploy or Marketplace deployment
- (Optional) Zero-downtime updates

Run Weaviate with Kubernetes

[EvaluationDeploymentProduction

Kubernetes

For development to production
Local inference containers
Multi-modal models
Customizable configurations
Self-deploy or Marketplace deployment
(Optional) Zero-downtime updatesRun Weaviate with Kubernetes](/deploy/installation-guides/k8s-installation)

Evaluation

Deployment

Production

Embedded Weaviate

- For basic, quick evaluation
- Conveniently launch Weaviate directly from Python or JS/TS

Run Embedded Weaviate

[EvaluationDeploymentProduction

Embedded Weaviate

For basic, quick evaluation
Conveniently launch Weaviate directly from Python or JS/TSRun Embedded Weaviate](/deploy/installation-guides/embedded)

## Community & support​

[Questions
Please visit our forum. The Weaviate team and our awesome community can help.](https://forum.weaviate.io/c/support/)

[Open-source on GitHub
Give us a star on GitHub to support our work.](https://github.com/weaviate/weaviate)

*Footer:*
[Edit this page](https://github.com/weaviate/docs/tree/main/docs/weaviate/index.mdx)

*Footer:*
##### Documentation

- [Weaviate Database](/weaviate)
- [Deployment documentation](/deploy)
- [Weaviate Cloud](/cloud)
- [Weaviate Agents](/agents)

##### Support

- [Forum](https://forum.weaviate.io/c/support/)

- Find the right documentation and resources
- AI-assisted coding
- What is Weaviate?
- The Weaviate Ecosystem
- Choose your deployment
- Community & support

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weaviate.io scored 5/10 on portable. AILANG opportunity is therefore 5/10. Here's where it would land first.

Same module, any LLM — picked at the CLI

Provider selection isn't a code edit — it's a flag on the run command. The exact same compiled .ail file talks to Anthropic, Google, OpenAI, OpenRouter or local Ollama depending on what you pass to `--ai`. Vendor lock-in becomes a shell-history concern.

# Same chat.ail, three vendors — no source change.
ailang run --ai claude-haiku-4-5  chat.ail
ailang run --ai gemini-2.5-flash chat.ail
ailang run --ai gpt-5.1-nano     chat.ail
# std/ai dispatches to each provider's native API.
→ AILANG docs

Structured output, portable across providers

callJson(prompt, schema) maps to each provider's native structured-output primitive — responseSchema for Gemini, response_format for OpenAI, forced-tool for Anthropic. Your schema, their plumbing.

let result = callJson(prompt, intentSchema);
-- same AILANG code, four different provider paths underneath.
→ AILANG docs

OpenRouter routing with replayable resolution

Reach SOTA open-source models through OpenRouter; the resolved model ID is logged so the eval is replayable months later, even if the upstream router has moved on.

call(prompt, model = "openrouter/meta-llama/llama-4-405b");
-- the eval harness pins the exact resolved model ID.
→ AILANG docs

How this page was made

func sketchSite(url: string<pii>, topic: Topic) -> Sketch
  ! {Net @limit=1, AI @limit=5, FS @limit=4, Process, Declassify}
SignalTopicResultPointsAILANG primitive
agent.json referencedagent-ready0/1ailang serve-api generates A2A agent cards automatically — bonus if you're an early adopter
openapi.json referencedagent-ready0/2ailang serve-api generates OpenAPI 3.1 from Hindley-Milner type signatures
MCP endpoint referencedagent-ready2/2ailang serve-api --mcp-http exposes typed functions as MCP tools
Public API docs linkedagent-ready0/2ailang serve-api hosts Swagger + ReDoc at /api/_meta/ by default
Webhooks documentedagent-ready0/2ailang serve-api handles webhooks as typed handler functions with effect-tracked side effects
Rate limits documentedagent-ready0/2Capability budgets — Net @limit=N is the symmetric server-side primitive for what agents see as rate limits
Streaming / SSE endpointagent-ready0/2std/stream — ssePost and Stream effect handle event-source endpoints with typed event types
Sandbox / test environment offeredagent-ready2/2ailang --ai-stub plus mock effect handlers — deterministic, capability-scoped fakes for any effect, including Net and AI
Authentication documentedagent-ready0/2std/jwt for verification, IFC labels (string / string) to keep credentials out of public sinks at the type level
Idempotency keys documentedagent-ready0/2Pure functions are idempotent by construction; requires/ensures contracts express idempotence as a static guarantee
AG-UI streaming protocolagent-ready0/1std/stream — the AG-UI event lifecycle (RUN_STARTED → TEXT_MESSAGE_CONTENT → TOOL_CALL_RESULT → RUN_FINISHED) is a textbook sum type. ADTs + exhaustive pattern matching make every event-type branch a compile error to skip.
HTTP 402 agent payments (x402 / pay-per-crawl)agent-ready0/1Net @endpoint-scoped capability budgets bound payment destinations; requires { amount <= budget } gates the payload; IFC labels keep the signed payment key out of public sinks. Same primitives cover x402 payload signing and Cloudflare's crawler-price negotiation.
AP2 Agent Payments Protocolagent-ready0/1Mandates ARE contracts. requires { intent.price <= mandate.maxPrice } + ensures { cart.total <= intent.price } is a one-to-one translation of an Intent/Cart Mandate into AILANG. Z3 can verify the bounds at compile time.
UTCP tool-calling protocolagent-ready0/1Typed function signatures are the manifest. ailang serve-api emits the same metadata as a UTCPManual (name, input/output schema, native endpoint) — direct-call discovery without a proxy server.
End-to-end encryption documentedprivacy0/2IFC labels (string) force decryption to flow through a typed boundary; the compiler refuses to publish sealed values without explicit declassification
Compliance certifications citedprivacy0/2requires/ensures contracts express machine-verifiable claims; capability budgets bound audit-trail effects; effect rows leave nothing un-declared
Data minimisation languageprivacy0/2Capability scoping — each Net call declares its endpoint in the effect row, so "doesn't sell" becomes a type-system-enforceable claim, not a marketing one
Third-party domains restrainedprivacy0/2Capability scoping — each Net call declares its endpoint in the effect row
Data residency / on-prem languageprivacy0/2Three-runtime deploy — same module runs in WASM (browser), Cloud Run, and native CLI
Single-vendor LLM languageportable2/2std/ai multi-provider — switch from Anthropic to Gemini to OpenAI without rewriting
Multiple AI providers citedportable0/2std/ai — one Step API across Anthropic, OpenAI, Gemini, OpenRouter, Ollama, and custom-package providers
Cross-runtime / deployment portabilityportable2/2Effect handlers as runtime adapters — same .ail runs as WASM in the browser, a Cloud Run container, and a native CLI; only the handlers change
BYO key / model-agnosticportable0/2AILANG WASM — the full interpreter ships as a browser bundle, so caller-held keys (BYOK), offline apps, and embedded demos all work client-side