Big-AGI: Local-First AI Chat Workspace for Experts

Who needs to compare model outputs

Big-AGI is a multi-model AI workspace for experts: engineers architecting systems, founders making decisions, and researchers validating hypotheses. The project is independent and non-VC-funded, with development supported by optional Pro subscriptions at $10.99 per month. The philosophy is simple: AI should elevate you, not replace you. Big-AGI is built to be local-first and optimized for zero-latency, launched multi-model first to defeat hallucinations, designed with humans in the loop, and architectured so you are not locked into a vendor. The README emphasizes this is a powerful tool, not a toy UI or clone. You need a tool that scales with your thinking, not a generic chatbot. The last push was on 2026-09-25.

How Beam compares models in parallel

The core feature is Beam, which runs the same prompt across multiple models simultaneously and displays the outputs side by side. This is how you defeat hallucinations: ask the same question of Opus 5, GPT-5.6, Gemini 3.7, and Kimi K3 at once, then pick the best response or synthesize from all four. Beam also includes Merge mode for combining outputs into a single response. The interface is designed around flow state, with highly customizable personas that shape how each model responds to your prompts. The README emphasizes flow-state interface, highly customizable design, and best-in-class UX as design priorities. You can inspect every request that the app makes via the AI Inspector to understand exactly what the models saw and how they responded. The interface is local-first, meaning your chat history stays on your machine until you choose to enable sync. The app runs on TypeScript and is deployed as a Next.js application, taking advantage of modern web standards and component-driven architecture.

How to self-host Big-AGI

Big-AGI runs as a Docker container or on Vercel. For self-hosting, use Docker:

docker-compose up

The docker-compose.yaml file pulls the latest image from ghcr.io/enricoros/big-agi and exposes port 3000. The app opens at http://localhost:3000. For development or custom builds, clone the repository and run the dev server with npm scripts. The package.json includes predev hooks that generate workspace configuration and LLM definitions. Build the production image with npm run build, then run with npm start or Docker. All environment variables are optional and may be overridden in the UI, so you can set OPENAI_API_KEY and other provider keys at runtime. Configuration requires your API keys for the models you want to use. The built-in Dockerfile uses a multi-stage build with Node 24 on Alpine, optimizing for size and security. Self-hosting gives you complete control over where your data flows and which models you enable.

Models and integrations

Big-AGI supports models from multiple vendors. Recent release notes mention Opus 5, GPT-5.6, Gemini 3.7, Kimi K3, Grok 4.6, and DeepSeek V4 as available options. The framework integrates with AWS Bedrock, Google Drive, and speech synthesis from multiple vendors. It also supports Anthropic Containers and Skills, and resumable Deep Research mode. The application generates definitions for available models at build time via the gen-llms-defs script. Version 2.1.1 added Weights Dust feature, version 2.0.5 added Roberto and Opus 4.7 1M, and version 2.0.4 added Hyper Params. This rapid release cadence reflects active development.

Free tier versus Pro subscription

Big-AGI comes in three tiers. Big-AGI Open is the self-hosted version with full control and privacy and maximum feature parity with the cloud version. big-agi.com Free is the online version with the full core experience, improved Beam, new Personas, and the best UX, requiring only 2 minutes to set up your API keys. Big-AGI Pro at $10.99 per month adds Sync across unlimited devices and 1GB storage. The project emphasizes that it does not charge for model usage or limit your access; you control your own API keys and pay only for what you use. Pro subscriptions fund development for everyone, including the free tier. As an independent project, this is the business model.

Local-first architecture and privacy

Big-AGI is built on the principle that your data stays with you. Chat history is stored locally in the browser or on your self-hosted instance. You own your conversations and can export them. The app does not send your data to a third-party server unless you choose to use the sync feature (Pro only). Every API request is inspectable via the AI Inspector, so you can see exactly what the app sends to each model and what the models return. This transparency means you are not trusting a black box; you can verify that your prompts and responses are not logged or monitored by Big-AGI itself. Self-hosting on your own infrastructure gives you maximum control.

Deployment and scaling

Big-AGI is a Next.js application, so it scales horizontally. The Docker image is built for production use and available at ghcr.io/enricoros/big-agi. You can deploy on Vercel with one click using the provided template, or on any platform that runs Node.js containers. For development, the predev script generates workspace configuration and LLM definitions. The build process is transparent: npm run build compiles TypeScript and Next.js, npm prune removes development dependencies, and the Docker image is ready to ship. The application is stateless, so you can scale it behind a load balancer if needed. Environment variables control API keys and build options.

Editorial conclusion

Big-AGI is for engineers and researchers who need to run multiple AI models in parallel, own their data, and avoid vendor lock-in. Start with the free online version to explore Beam and personas, then self-host if you need maximum privacy and control. Verify that your API keys are configured correctly and test a Beam comparison before committing to production use.

Frequently asked questions

Official sources

1. Official documentation
2. Official README
3. Project repository
4. Release notes