- Category
- Developer Tools
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Previous survey · No. 1865 ·
- Pricing
- Open Source
- Type
- TOOL
- Use case
- Data, Retrieval & Knowledge
- Interfaces
- MCP
- GitHub
- 5 stars
- Date
About
AgeDB is an open-source analytical database built for AI agents to store and query context directly, using columnar storage, a vectorized query engine, and MCP as a first-class interface. Agents can create tables and ask questions in plain English without writing SQL, and data one agent collects can be reused by another agent even across different model providers.
At a glance
AgeDB gives agents a local database for records they collect. It turns simple questions into a query plan and returns that plan alongside the answer. Consider it when you need to check how an agent counted, filtered or ranked records.
Good fit
Developers collecting structured research or operations data whose questions fit one table at a time.
Consider something else when
Use your existing database if joins are part of the daily work or you need a mature distributed system.
Start here
Start with the documented leads example. Compare the ranking question with the returned plan and check the grouping and aggregation. Then look at how the example handles a question spanning two tables.
Cost and requirements
Apache 2.0. Building the documented Rust workspace requires Rust 1.90 or later. The local MCP setup uses a client that supports stdio.
Limits to know
The documented release is single-node and refuses queries that need joins. Its deterministic parser can interpret a question more narrowly than intended, so read the plan before acting on the number.
Sources checked 2026-11-05. Source-based assessment.
Why it made the leaderboard
AgeDB gives agents a shared, queryable memory store across providers and sessions, reachable directly over MCP instead of through a bolted-on SQL layer.
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Editorially curated, with community endorsements as a secondary signal. Corrections welcome.