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How Weaviate’s scores are calculated

The full audit trail, recomputed from the verdict data at build time through the same code that produced the leaderboard: verdict × quality × story weight per cell, cells sum to dimension scores, dimensions blend into the PA Score. Every number on the product page is reproducible from this page alone; for why the formula looks like this, see the methodology.

verdict factors: full ×1.0 · partial ×0.6 · disputed ×0.3 · none ×0.0 · n/a excluded from both sides · cell points = weight × quality × factor · cell max = weight × 10

PA Score29/100

Agent-ready 42.5 × 0.30 = 12.75

API quality 3.4 × 0.20 = 0.68

Openness 50.0 × 0.20 = 10.00

Built-in AI 33.6 × 0.15 = 5.04

Automation 6.0 × 0.15 = 0.90

(12.75 + 0.68 + 10.00 + 5.04 + 0.90) ÷ (0.30 + 0.20 + 0.20 + 0.15 + 0.15) = 29.37 ÷ 1.00 = 29.4

Scores are stored to 1 decimal; the product page’s pills round to whole numbers for display. Each dimension below shows the stories, verdicts, and cited evidence behind its number.

Agent-ready42.5/100×0.30 of the PA blend

Outside-in: can YOUR agent reach and drive this product — API, MCP, CLI, headless runs, agent docs.

Point an agent at llms.txt or agent-oriented docsweight 2

2 (weight) × 9 (quality) × 1.0 (full) = 18.0 of 20 max

  • [probe] https://docs.weaviate.io/llms.txt“PROBE llms.txt: HTTP 200 at https://docs.weaviate.io/llms.txt # Weaviate ## TL;DR Weaviate is an open-source vector database (Go) that stores objects, vectors, and inverted indexes”
  • [probe] https://github.com/weaviate/mcp-server-weaviate“official MCP server documented at https://github.com/weaviate/mcp-server-weaviate”
  • [claimed-docs] https://docs.weaviate.io“Enable and configure the Weaviate MCP server so LLMs and IDE assistants can interact with your Weaviate instance.”

Run the product headlessly / in CI for automationweight 2

2 (weight) × 6 (quality) × 0.6 (partial) = 7.2 of 20 max

  • [github] https://github.com/weaviate/weaviate“You can easily start Weaviate and a local vector embedding model with Docker.”
  • [github] https://github.com/weaviate/weaviate“Weaviate offers multiple installation and deployment options: Docker, Kubernetes, Weaviate Cloud”
  • [claimed-docs] https://docs.weaviate.io/weaviate/quickstart“Follow the instructions below to install one of the official client libraries, available in Python, JavaScript/TypeScript, Go, and Java.”

Plug MCP servers into this product so it can use their toolsweight 3

3 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 30 max

  • [claimed-docs] https://docs.weaviate.io“Enable and configure the Weaviate MCP server so LLMs and IDE assistants can interact with your Weaviate instance.”
  • [probe] https://github.com/weaviate/mcp-server-weaviate“official MCP server documented at https://github.com/weaviate/mcp-server-weaviate”

Connect an agent via an official MCP serverweight 3

3 (weight) × 8 (quality) × 1.0 (full) = 24.0 of 30 max

  • [claimed-docs] https://docs.weaviate.io“Enable and configure the Weaviate MCP server so LLMs and IDE assistants can interact with your Weaviate instance.”
  • [probe] https://github.com/weaviate/mcp-server-weaviate“official MCP server documented at https://github.com/weaviate/mcp-server-weaviate”

Use an official CLIweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [claimed-docs] https://docs.weaviate.io/weaviate/quickstart“Follow the instructions below to install one of the official client libraries, available in Python, JavaScript/TypeScript, Go, and Java.”
  • [probe] https://github.com/weaviate/mcp-server-weaviate“official MCP server documented at https://github.com/weaviate/mcp-server-weaviate”

Drive the product through a documented public APIweight 3

3 (weight) × 8 (quality) × 1.0 (full) = 24.0 of 30 max

  • [claimed-docs] https://docs.weaviate.io/weaviate/quickstart“Follow the instructions below to install one of the official client libraries, available in Python, JavaScript/TypeScript, Go, and Java.”
  • [claimed-docs] https://docs.weaviate.io“Enable and configure the Weaviate MCP server so LLMs and IDE assistants can interact with your Weaviate instance.”
  • [probe] https://github.com/weaviate/mcp-server-weaviate“official MCP server documented at https://github.com/weaviate/mcp-server-weaviate”
  • [github] https://github.com/weaviate/weaviate“Weaviate supports two approaches to store vectors: automatic vectorization at import using integrated models ... or direct import of pre-computed vector embeddings”
  • [claimed-docs] https://docs.weaviate.io/weaviate/model-providers“Import objects directly into Weaviate without having to manually specify embeddings”

Issue scoped/least-privilege API credentials for an agentweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [claimed-docs] https://docs.weaviate.io“Enable and configure the Weaviate MCP server so LLMs and IDE assistants can interact with your Weaviate instance.”
  • [probe] https://github.com/weaviate/mcp-server-weaviate“official MCP server documented at https://github.com/weaviate/mcp-server-weaviate”
  • [claimed-docs] https://docs.weaviate.io/weaviate/manage-collections/multi-tenancy“Multi-tenancy provides data isolation. Each tenant is stored on a separate shard. Data stored in one tenant is not visible to another tenant.”

Build against official SDKsweight 2

2 (weight) × 8 (quality) × 1.0 (full) = 16.0 of 20 max

  • [claimed-docs] https://docs.weaviate.io/weaviate/quickstart“Follow the instructions below to install one of the official client libraries, available in Python, JavaScript/TypeScript, Go, and Java.”
  • [community] https://news.ycombinator.com/item?id=37311394“Just migrated from Supabase + pgvector to Weaviate hoping to take advantage of langchain.retrievers.weaviate_hybrid_search.WeaviateHybridSearchRetriever. Unfortunately i can't get it work asynchronously :(”

Subscribe to events via webhooksweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Agent-ready = 89.2 ÷ 210 × 100 = 42.5

API quality3.4/100×0.20 of the PA blend

The programmable surface once an agent is there — machine-readable spec, interactive docs, sandbox, versioning discipline.

Explore an interactive API reference with runnable examplesweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Download a machine-readable API spec (OpenAPI or equivalent)weight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Test against a sandbox environment without touching production dataweight 1

1 (weight) × 4 (quality) × 0.6 (partial) = 2.4 of 10 max

  • [github] https://github.com/weaviate/weaviate“You can easily start Weaviate and a local vector embedding model with Docker.”
  • [github] https://github.com/weaviate/weaviate“Weaviate offers multiple installation and deployment options: Docker, Kubernetes, Weaviate Cloud”
  • [claimed-docs] https://weaviate.io/pricing“Always free — 1 cluster per user, upgrade to paid anytime.”
  • [claimed-docs] https://docs.weaviate.io/weaviate/manage-collections/multi-tenancy“Multi-tenancy provides data isolation. Each tenant is stored on a separate shard. Data stored in one tenant is not visible to another tenant.”

Rely on versioned APIs with a documented deprecation policyweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

API quality = 2.4 ÷ 70 × 100 = 3.4

Openness50.0/100×0.20 of the PA blend

Can you leave, inspect, or self-host — data export, open source, portability.

Do everything through the API that I can do in the UIweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Export all of my data in open formats and leaveweight 3

3 (weight) × 5 (quality) × 0.6 (partial) = 9.0 of 30 max

  • [claimed-docs] https://docs.weaviate.io/weaviate/configuration/backups“Seamless integration with widely-used cloud blob storage, such as AWS S3, GCS, or Azure Storage”
  • [claimed-docs] https://docs.weaviate.io/weaviate/configuration/backups“Backup and Restore between different storage providers”
  • [claimed-docs] https://docs.weaviate.io/weaviate/configuration/backups“Incremental backups that only store changed data, reducing backup and speeding up backup times”
  • [claimed-docs] https://docs.weaviate.io/weaviate/configuration/backups“Choice of backing up an entire instance, or selected collections only”
  • [claimed-docs] https://docs.weaviate.io/weaviate/quickstart“Follow the instructions below to install one of the official client libraries, available in Python, JavaScript/TypeScript, Go, and Java.”
  • [community] https://news.ycombinator.com/item?id=36774093“Weaviate is pretty cool IMO. It is open source and fairly easy to get running locally... You can even run Weaviate as an embedded python package rather than a separate process, so compare that to running an ES cluster and I think life gets a lot easier.”

Read the product's source under an open licenseweight 2

2 (weight) × 7 (quality) × 1.0 (full) = 14.0 of 20 max

  • [probe] https://docs.weaviate.io/llms.txt“PROBE llms.txt: HTTP 200 at https://docs.weaviate.io/llms.txt # Weaviate ## TL;DR Weaviate is an open-source vector database (Go) that stores objects, vectors, and inverted indexes”
  • [community] https://news.ycombinator.com/item?id=36774093“Weaviate is pretty cool IMO. It is open source and fairly easy to get running locally... You can even run Weaviate as an embedded python package rather than a separate process, so compare that to running an ES cluster and I think life gets a lot easier.”
  • [github] https://github.com/weaviate/weaviate“Weaviate offers multiple installation and deployment options: Docker, Kubernetes, Weaviate Cloud”

Self-host the core productweight 3

3 (weight) × 9 (quality) × 1.0 (full) = 27.0 of 30 max

  • [github] https://github.com/weaviate/weaviate“Weaviate offers multiple installation and deployment options: Docker, Kubernetes, Weaviate Cloud”
  • [probe] https://docs.weaviate.io/llms.txt“PROBE llms.txt: HTTP 200 at https://docs.weaviate.io/llms.txt # Weaviate ## TL;DR Weaviate is an open-source vector database (Go) that stores objects, vectors, and inverted indexes”
  • [community] https://news.ycombinator.com/item?id=36774093“Weaviate is pretty cool IMO. It is open source and fairly easy to get running locally... You can even run Weaviate as an embedded python package rather than a separate process, so compare that to running an ES cluster and I think life gets a lot easier.”
  • [claimed-docs] https://docs.weaviate.io/weaviate/quickstart“Get answers from your data by using a natural language prompt/question. Cloud only”
  • [github] https://github.com/weaviate/weaviate“You can easily start Weaviate and a local vector embedding model with Docker.”

Openness = 50.0 ÷ 100 × 100 = 50.0

Built-in AI33.6/100×0.15 of the PA blend

Inside-out: how agentic the product itself is for its users — built-in assistants, autonomous features.

Get AI-generated insights and suggestions from my data inside the productweight 2

2 (weight) × 6 (quality) × 0.6 (partial) = 7.2 of 20 max

  • [claimed-docs] https://docs.weaviate.io“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”
  • [claimed-docs] https://docs.weaviate.io“These agents can leverage semantic insights to make decisions or trigger actions based on the data stored in Weaviate.”
  • [claimed-docs] https://docs.weaviate.io/weaviate/quickstart“Get answers from your data by using a natural language prompt/question.”
  • [claimed-docs] https://docs.weaviate.io/weaviate/quickstart“Get answers from your data by using a natural language prompt/question. Cloud only”
  • [claimed-docs] https://docs.weaviate.io“Query Agent: Run agentic search over your Weaviate Cloud collections”
  • [github] https://github.com/weaviate/weaviate“It combines vector similarity search with keyword filtering, retrieval-augmented generation (RAG), and reranking in a single query interface.”

Set up automations that run autonomously in the backgroundweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [claimed-docs] https://docs.weaviate.io“Query Agent: Run agentic search over your Weaviate Cloud collections”
  • [claimed-docs] https://docs.weaviate.io“Enable and configure the Weaviate MCP server so LLMs and IDE assistants can interact with your Weaviate instance.”
  • [claimed-docs] https://docs.weaviate.io“These agents can leverage semantic insights to make decisions or trigger actions based on the data stored in Weaviate.”

Delegate tasks to a built-in AI assistant inside the productweight 3

3 (weight) × 5 (quality) × 0.6 (partial) = 9.0 of 30 max

  • [claimed-docs] https://docs.weaviate.io“Query Agent: Run agentic search over your Weaviate Cloud collections”
  • [claimed-docs] https://docs.weaviate.io“These agents can leverage semantic insights to make decisions or trigger actions based on the data stored in Weaviate.”
  • [claimed-docs] https://docs.weaviate.io/weaviate/quickstart“Get answers from your data by using a natural language prompt/question. Cloud only”

Operate the product with natural-language commandsweight 2

2 (weight) × 7 (quality) × 1.0 (full) = 14.0 of 20 max

  • [claimed-docs] https://docs.weaviate.io/weaviate/quickstart“Get answers from your data by using a natural language prompt/question.”
  • [claimed-docs] https://docs.weaviate.io/weaviate/quickstart“Get answers from your data by using a natural language prompt/question. Cloud only”
  • [claimed-docs] https://docs.weaviate.io“Enable and configure the Weaviate MCP server so LLMs and IDE assistants can interact with your Weaviate instance.”
  • [claimed-docs] https://docs.weaviate.io“Query Agent: Run agentic search over your Weaviate Cloud collections”
  • [probe] https://github.com/weaviate/mcp-server-weaviate“official MCP server documented at https://github.com/weaviate/mcp-server-weaviate”

Built-in AI = 30.2 ÷ 90 × 100 = 33.6

Automation6.0/100×0.15 of the PA blend

Depth of automation primitives — rules, scheduling, bulk operations, webhooks.

Perform bulk operations across many items at onceweight 2

2 (weight) × 3 (quality) × 0.6 (partial) = 3.6 of 20 max

  • [claimed-docs] https://docs.weaviate.io/weaviate/quickstart“Set up a collection - Create a collection and import data into it.”
  • [claimed-docs] https://docs.weaviate.io/weaviate/model-providers“Import objects directly into Weaviate without having to manually specify embeddings”
  • [github] https://github.com/weaviate/weaviate“Weaviate supports two approaches to store vectors: automatic vectorization at import using integrated models ... or direct import of pre-computed vector embeddings”

Define rules that trigger actions automatically on eventsweight 3

3 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 30 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Schedule recurring jobs or workflowsweight 2

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Version, review, and roll back my automationsweight 1

1 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 10 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Automation = 3.6 ÷ 60 × 100 = 6.0