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Agent Memory & RAG

Longbow

Longbow is a Go-based distributed vector engine that provides clustered vector search and shared memory infrastructure for AI-agent applications through Apache Arrow Flight.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Longbow — 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 Longbow when an application needs a self-operated, clustered vector engine for agent memory or retrieval, particularly when Arrow Flight access, metadata filters or distributed membership matter.

Who it is for

  • Teams building AI-agent applications that need shared vector memory.
  • Engineers operating clustered vector retrieval for AI workloads.
  • Developers integrating Arrow Flight-based vector search into a service.

Documented uses

  • Store and retrieve dense or sparse vectors with HNSW search.
  • Apply metadata predicates during searches and scans.
  • Share vector-memory infrastructure across loosely coupled nodes.
  • Use supported distance metrics and remove vectors through tombstones.
Before you choose

Requirements & limits

  • The primary interface is Apache Arrow Flight over gRPC/HTTP2, with separate data and metadata servers.
  • The README lists vector data types and dimensions from 128 through 3,072.
  • Linux is recommended for best performance; GPU acceleration needs platform-specific builds.
  • Storage options described include ephemeral in-memory storage, WAL and Parquet snapshots, with optional io_uring WAL support.

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

Build Longbow and start a local cluster

Prepare the Longbow binary and launch the documented local cluster.

Before you start

  • Go 1.25 or later
  • Linux or macOS; Linux is recommended for best performance

Steps

  1. Clone the Longbow repository and change into its directory.

  2. Build the standard binary with the command below.

    Documented command
    go build -o bin/longbow ./cmd/longbow

  3. Run ./scripts/start_local_cluster.sh.

Expected outcome

A locally built Longbow binary and a started local cluster.

Guide limitations

  • The README does not specify cluster topology, configuration values or a client-side test procedure in this setup sequence.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Vector search

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
LongbowStore and retrieve dense or sparse vectors with HNSW search.Server
KektordbSave and retrieve semantic memories across agent sessions.Self-hosted, ServerThe README describes self-hosted use and displays an Apache 2.0 license badge.
Memento MCPAdd persistent knowledge-graph memory to an MCP-compatible client such as Claude Desktop, Cursor or GitHub Copilot.Cloud, Self-hosted, Server
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
23skdu/longbow · Official homepage title · retained in its source language
How we review sourcesSuggest a correction