Best vector database for RAG and AI agents
Pinecone scores 90 on AgentReady, the top vector database for RAG in the StackResolve registry, followed by Chroma at 81.
Pinecone is the top-scoring vector database for RAG in the StackResolve registry, with an AgentReady score of 90. That score measures how well an AI agent can find, read, install, and operate the tool on its own. Below are the top seven vector databases in the vector-databases category, ranked by that score, each with the numbers pulled live from our API.
#The ranking
The AgentReady score is a blend of four sub-scores: discovery (can an agent find the docs), understanding (are the docs machine-readable and clear), adoption (how hard is install and first call), and operability (auth, errors, rate limits, observability). A high headline score with weak operability means an agent gets started fast but hits friction in production.
| Rank | Tool | AgentReady | Discovery | Understanding | Adoption | Operability |
|---|---|---|---|---|---|---|
| 1 | Pinecone | 90 | 93 | 92 | 100 | 76 |
| 2 | Chroma | 81 | 93 | 92 | 80 | 59 |
| 3 | Supabase | 74 | 100 | 67 | 63 | 67 |
| 4 | Weaviate | 74 | 100 | 62 | 80 | 53 |
| 5 | MongoDB Atlas | 62 | 93 | 38 | 80 | 35 |
| 6 | Qdrant | 58 | 80 | 46 | 80 | 24 |
| 7 | turbopuffer | 53 | 80 | 23 | 60 | 47 |
#One-line takes
Pinecone (90). A perfect adoption score of 100 and 92 on understanding. An agent can read the docs, get a key, and write its first upsert with almost no guesswork. Operability at 76 is the highest in the set, so it holds up past the demo.
Chroma (81). Matches Pinecone on discovery and understanding but scores 59 on operability. It shines for local RAG prototypes where you run the store in-process and skip auth setup.
Supabase (74). Perfect 100 on discovery. This is Postgres with pgvector, so an agent already fluent in SQL reuses what it knows. Understanding sits at 67 because the vector path is one feature inside a broad platform.
Weaviate (74). Also 100 on discovery, with an 80 adoption score. Understanding at 62 reflects a larger API surface an agent has to map before it commits to a schema.
MongoDB Atlas (62). Strong discovery and adoption, but understanding drops to 38. Vector search rides on the document model, so an agent spends more tokens working out how index and query fit together.
Qdrant (58). Solid 80 on both discovery and adoption. Operability is the low mark at 24, so expect more work wiring up auth, error handling, and limits for a production agent.
turbopuffer (53). Higher operability (47) than several tools ranked above it, but understanding sits at 23. The docs are thinner for an agent reading cold, which drags the headline number down.
#How to read these for RAG
Adoption and understanding matter most when an agent builds the pipeline. Operability matters most when the agent runs it under load. Pinecone leads because it is strong on both halves. Chroma and Supabase are the picks when you want an easy start and already run local or Postgres.
Before you pick a store, decide whether you need retrieval at all. Some agent tasks call a tool directly and skip the index. Our guide on MCP vs RAG walks through when each pattern fits, so you do not stand up a vector database for a job a tool call handles in one step.
Scores update as we re-audit each tool. Open any registry profile to see the sub-score breakdown and what moved it.