OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Developer Tools & SDKs

Anchoring AI

Anchoring AI is an open-source, no-code tool for teams to build, evaluate, host and share applications powered by GPT and other large language models. Its features include drag-and-drop component chaining, prompt management, batch processing, budget management, customizable team authentication and LangChain integration for Python.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Anchoring AI — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Anchoring AI when a team wants a shared no-code environment for assembling and evaluating LLM applications, with documented options for hosting, batch jobs and budget management.

Who it is for

  • Teams assembling LLM-powered applications without a code-first workflow.
  • Teams that need to evaluate apps, manage prompts or run repeatable batch workloads.
  • Developers and operators seeking a self-hosted deployment route documented with Docker.

Documented uses

  • Assemble language-model applications by chaining components in a drag-and-drop interface.
  • Manage prompts and chains, and evaluate LLM applications collaboratively.
  • Host and share team-built applications.
  • Run batch processing for evaluations or repetitive tasks.
  • Manage budgets and team access for LLM application work.
Before you choose

Requirements & limits

The project is open source under the Apache-2.0 license. The README links to an alpha release of its web platform and documents Docker deployment and manual installation.

  • The interface is described as drag-and-drop, with modular support for models, datasets and extensions.
  • LangChain integration is specified for Python.
  • The documented Docker route requires Docker; the manual installation guide lists MySQL 8.0, Redis 5.0.7, Node.js 18.16.0 and Python 3.8.10.
  • The manual installation guide is primarily designed for Linux and macOS and describes adjustments for Windows, including Redis through WSL.

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

Start Anchoring AI with Docker Compose

Build and start the application containers using the repository’s documented Docker workflow.

Before you start

  • Docker installed on the system.

Steps

  1. Clone the Anchoring AI repository.

  2. Change to the project root directory.

  3. Build the Docker image with docker-compose build.

  4. Start the containers with docker-compose up.

Expected outcome

The README says the application should be accessible at localhost:3000.

Guide limitations

  • This guide covers the documented Docker startup sequence; it does not detail application configuration or use after startup.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
AnchoringAI/anchoring-ai · Official homepage title · retained in its source language
How we review sourcesSuggest a correction