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Heimdall

Heimdall is a command-line tool that builds persistent, cross-repository memory for AI coding agents and provides ranked search over prior work.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Heimdall — 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 Heimdall when coding work spans multiple repositories and an agent or developer needs a local, searchable record of earlier work rather than repeating manual searches.

Who it is for

  • Developers who work across repositories and want to retrieve earlier solutions or project knowledge.
  • Users of the coding-agent harnesses named in the README, including Claude Code, pi, Codex, Cursor and OpenCode.

Documented uses

  • Search across repositories for a previously implemented solution.
  • Record reusable work as a titled note with descriptive text and keywords.
  • Connect the memory workflow to a supported coding-agent harness.
  • Check whether indexed results still correspond to files on disk.
Before you choose

Requirements & limits

The repository is public and MIT-licensed. The documented installation is through npm; setup uses local backend components. The README specifies macOS launchd management and Linux manual daemon management.

  • The documented stack uses Node.js 22.5 or later, bash and Python 3.
  • Search and health checks require the Graft backend; setup may need operating-system build packages.
  • Symbol-depth indexing depends on a tree-sitter-capable Python environment and installed grammars; without them, indexing may remain at file depth.
  • The README describes CPU-based local indexing and locally computed embeddings, with optional GPU acceleration.
  • Search statuses distinguish STRONG, WEAK, REBUILT and STALE results, but do not guarantee that a result is useful for every query.

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

Install Heimdall and connect a coding harness

Install the CLI, configure the local backend and model, and wire Heimdall into a supported harness.

Before you start

  • Node.js 22.5 or later, bash and Python 3
  • For automatic backend builds, platform-specific build prerequisites and git; the README lists packages for macOS, Debian/Ubuntu, and Fedora/Amazon Linux
  • macOS for launchd daemon management, or Linux with manual daemon management

Steps

  1. Install the package globally with the command below.

    Documented command
    npm i -g @arihantdeva/heimdall

  2. Run heimdall setup to detect hardware, configure the backend, obtain the model if needed and install the daemon.

  3. Run heimdall init --harness claude-code, substituting another listed harness such as pi, Codex, Cursor or OpenCode when appropriate.

  4. Run heimdall doctor to check daemon and index health.

Expected outcome

A configured local Heimdall setup with a selected harness connection; the documented next stage is to use search and record reusable work.

Guide limitations

  • If build prerequisites are missing during installation, the package installation can finish while reporting setup needed; run heimdall setup later.
  • The README describes macOS launchd management and Linux manual daemon management.
  • This is setup guidance from project documentation, not a human-run verification.
Documentation 1 ↗
Source-based practical guide

Record and search reusable work

Add a reusable work note to Heimdall's knowledge base and retrieve it with a ranked search.

Before you start

  • A configured Heimdall installation and working Graft backend
  • A title, descriptive body and optional keywords for the reusable work

Steps

  1. Run heimdall insert with a title, body and keywords describing the work.

  2. Run heimdall search with terms that describe the prior solution or task.

  3. Review the ranked results and their verification labels before using a result.

Expected outcome

A recorded knowledge item can be surfaced through ranked search, with result labels indicating the documented verification state.

Guide limitations

  • The README's examples do not establish that search results are always correct or that every result will be STRONG.
  • Search requires the Graft backend.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for AI coding assistance

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
HeimdallSearch across repositories for a previously implemented solution.CLIThe repository is public and MIT-licensed. The documented installation is through npm; setup uses local backend components. The README specifies macOS launchd management and Linux manual daemon management.
Continuum — pouyahasanamrejiStore project lessons so coding agents can retrieve them across conversations.CLI, Self-hosted, ServerThe repository documents local Docker Compose and bare-metal development setups. Bare-metal startup requires Node 22.12.0 or later and pnpm 10.33 or later; the Compose path skips these host prerequisites.
HaftRecord the problem a project is solving and the options considered.CLI, Self-hosted
Evidence and transparency

Sources & review

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