OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
Agent Memory & RAG

Anda Db

Anda Db is a modular Rust library and workspace for building durable AI memory systems. Its embedded, schema-aware document database provides B-tree filtering, BM25 full-text search and HNSW vector similarity, with persistence built on the object_store abstraction.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Anda Db — OpenViST project profile in Agent Memory & RAG.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Anda Db when a Rust application needs durable agent memory and retrieval embedded in its process, or when its documented KIP and service components fit a larger memory system.

Who it is for

  • Rust developers adding persistent memory and retrieval to AI-agent applications.
  • Teams embedding structured, lexical and vector search inside Rust services.
  • Developers building KIP-based cognitive-memory systems or optional database service layers.

Documented uses

  • Persist agent facts, observations, preferences, events and embeddings.
  • Retrieve stored knowledge using exact-match or range filters, full-text search and vector similarity.
  • Combine lexical and vector retrieval while filtering results against structured fields.
  • Choose local or enabled cloud object storage for the database persistence layer.
Before you choose

Requirements & limits

The repository is public and identifies the project as MIT-licensed. The documented embedded setup requires Rust 1.95 or newer and edition 2024.

  • The library combines B-tree, BM25 and HNSW retrieval rather than offering only one search mode.
  • The core database is embedded; optional server and shard-proxy components are separate deployment choices.
  • Persistence uses object_store, with local filesystem and several cloud backends described in the README.
  • The README identifies breaking changes in the 0.13-to-0.14 upgrade path; check the changelog and each crate's Cargo.toml before upgrading.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up an embedded local database

Connect Anda DB to local filesystem-backed storage and prepare a collection with retrieval indexes.

Before you start

  • Rust 1.95 or newer, using edition 2024
  • A Rust project with Tokio and Serde
  • The documented dependencies: anda_db, anda_object_store and object_store with its fs feature

Steps

  1. Add the documented database, object-store, Tokio and Serde dependencies to the Rust project.

  2. Create the local database directory and construct a LocalFileSystem store wrapped with MetaStoreBuilder.

  3. Connect AndaDB using DBConfig and the configured storage.

  4. Define a Serde-compatible record schema and open or create a collection; configure B-tree, BM25 and HNSW indexes as needed.

Expected outcome

A local embedded database connection and a schema-defined collection prepared for exact, full-text and vector retrieval.

Guide limitations

  • The example uses local filesystem storage; cloud object-store backends require the application to enable the relevant backend.
Documentation 1 ↗Documentation 2 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
ldclabs/anda-db · Official homepage title · retained in its source language
How we review sourcesSuggest a correction