HASH

This is HASH's public monorepo which contains our public code, docs, and other key resources.

  What is HASH?

HASH supports turning raw information into knowledge and process graphs, helping create graph-backed world models that power process automation, optimization, decision making, and AI. In essence, HASH is a self-building, open-source database which grows, structures and checks itself. Integrating data in (near-)realtime, it provides a powerful set of interfaces so that information can be understood and used in any context. Intelligent, autonomous agents can be deployed to grow, check, and maintain the database, collating and structuring information from the public internet — as well as your own connected private sources — in a standardized semantic form. Users, including those who are non-technical, are able to visually browse and manage both entities (data) and types (schemas). HASH acts as a source of truth for critical data, no matter its source, and provides a platform for high-trust, safety-assured decision-making. Read our blog post →

Many of HASH's features are unique to a platform of its kind, including its support for bitemporality, which provides first-order support for recording and querying both when something happened and, separately, when it became known. This capability is central to our support for better process modeling.

  Getting started

Instructions for using HASH can be found in the HASH Developer Docs.

Create an account

Create an account to get started.

Sign in

Sign in to access your account.

Skip the queue

When you first create an account you may be placed on a waitlist. To jump the queue, once signed in, follow the instructions shown in your HASH dashboard. All submissions are reviewed by a member of the team.

Running HASH locally

These instructions set up HASH for working on the codebase — the app services run natively with hot-reload against containerised infrastructure. To run the whole stack from container images instead (no toolchain required on the host), follow the setup guide.

Prerequisites

Install these on the host first:

• Git (≥ 2.17)
• Docker (≥ 20.10), including:
  • Docker Compose (≥ 2.17) — orchestrates the stack
  • Docker Buildx (≥ 0.10) — builds the images
• mise (≥ 2026.10.17) — installs and pins Node, Rust, protoc and the other required tool versions

Give Docker at least 8 GB RAM (Preferences → Resources) and keep ~15 GB of disk free for build artefacts, images and volumes.

On Windows, run the setup and development commands in Git Bash. Repository scripts call Bash and use POSIX shell commands. PowerShell can resolve bash to the Windows Subsystem for Linux executable instead. Activate mise in Git Bash using the linked instructions before running yarn; then check yarn --version in that shell.

Check your versions:

git --version            # ≥ 2.17
docker --version         # ≥ 20.10
docker compose version   # ≥ 2.17
docker buildx version    # ≥ 0.10
mise --version           # ≥ 2026.10.17

Setup

1. Clone the repository and enter it:

   git clone https://github.com/hashintel/hash.git && cd hash
   

2. Install the pinned toolchains (mise trust is required once per clone), then activate mise in your shell:

   mise trust && mise install
   

3. Install JavaScript dependencies:

   yarn install
   

4. Create a .env.local in the repository root. Real keys are only needed for AI features — dummy values work otherwise:

   OPENAI_API_KEY=dummy
   ANTHROPIC_API_KEY=dummy
   HASH_TEMPORAL_WORKER_AI_AWS_ACCESS_KEY_ID=dummy
   HASH_TEMPORAL_WORKER_AI_AWS_SECRET_ACCESS_KEY=dummy
   

   .env.local is git-ignored and overrides .env and .env.development. Don't edit the other .env files unless you mean to change the defaults.

5. Start the containerised infrastructure:

   yarn compose up -d
   

This is the beginning of the README. Read the rest at the original. Read the full README ↗