Big-AGI
The multi-model AI workspace for experts: Beam runs models in parallel to de-hallucinate answers, with self-hosting, local data ownership, and no vendor lock-in.
- Source repo
- enricoros/big-AGI
- Stars
- ★ 7.2k
- Last updated
- 1d ago
- License
- MIT
- Primary language
- TypeScript
- FA score
- 57/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platformOpenAI API · Claude API
- Cost
- Free tier plus a paid hosted plan
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- An engineer architecting a system uses Beam to query Opus, GPT, and Gemini simultaneously, comparing and merging answers to cross-validate the design
- Not a fit if
- Teams that need fully offline operation with no cloud model calls
- Users wanting a turnkey tool with no API keys of their own
- Self-hosters who need cross-device sync without a Pro subscription
- Source review
- 57/100 · Major gaps
What does this agent do, and when should you use it?
Big-AGI is an open-source, multi-model AI workspace (repository enricoros/big-AGI, MIT license) aimed at engineers, founders, and researchers who need to think broader and decide faster. Its signature Beam feature sends one prompt to multiple LLMs and merges the results to defeat hallucinations, across 500+ models from 20+ providers including OpenAI, Anthropic, Google Gemini, DeepSeek, xAI, Groq, Mistral, Ollama, LocalAI, and any OpenAI-compatible endpoint. Beyond chat it ships AI personas, voice calls, text-to-image (Nano Banana, gpt-image-1), cited web search, PDF import, code highlighting and execution, and an AI Inspector for request inspection. The app is local-first and heavily optimized for near-zero latency, stores data locally, uses your own API keys, and imposes no vendor lock-in. Deployment options are Docker self-hosting, one-click Vercel deploy, or the hosted big-agi.com; Pro subscriptions at $10.99/mo fund development of this independent, non-VC-funded project.
Big-AGI provides a web interface where you configure provider API keys and then converse with 500+ models: OpenAI (GPT and o-series), Anthropic Claude, Google Gemini, DeepSeek, Grok, Kimi, and more. Core flow: send a message for a single-model reply, or launch Beam to query several models in parallel and Merge their outputs for de-hallucination. It supports response streaming, chain-of-thought reasoning models (o3, DeepSeek R1, etc.), web search with citations, image generation and editing, multi-vendor speech synthesis and real-time voice calls, PDF import, code highlighting and execution, and request inspection via AI Inspector. Chats are stored locally, exportable to Markdown and CSV, and organized with folders, split-screen multi-chat, and starred messages. Deployment boundary: Docker container (big-agi image on GHCR), Vercel one-click deploy, local development, or the hosted big-agi.com service.
- An engineer architecting a system uses Beam to query Opus, GPT, and Gemini simultaneously, comparing and merging answers to cross-validate the design
- A researcher validates hypotheses with cited web search and resumable Deep Research, checking model claims against sources
- A self-hoster or IT department deploys via Docker on their own infrastructure for maximum control, using their own API keys with no vendor lock-in
- A user who needs to chat with videos and documents uses the YouTube Transcriber persona, PDF import, and camera OCR for multimodal content
- A creator generates and edits images inside the workspace using Nano Banana or gpt-image-1
- A budget-conscious user picks the free hosted tier for the full core experience, supplying their own keys without usage-based platform charges
How do you install or deploy this agent?
Three paths:
- Self-host: follow docs/installation.md to deploy with Docker; the image is published on GHCR (github.com/enricoros/big-AGI/pkgs/container/big-agi); README estimates 5-30 min setup;
- Vercel one-click: use the Deploy on Vercel button in the README to clone the repo; optionally set backend API key env vars such as OPENAI_API_KEY (see docs/environment-variables.md); backend keys may be overridden in the UI;
- Hosted: go to big-agi.com, ready in about 2 minutes.
How do you use this agent?
On first use, enter your own provider API keys in settings (OpenAI, Anthropic, Google Gemini, DeepSeek, xAI, etc., or any OpenAI-compatible endpoint — models, pricing, and capabilities are auto-detected). Then create a chat and pick a model; for Beam, switch to Beam mode in the chat to generate with several models in parallel and merge results. Use personas, voice calls, image generation, web search (requires configuring Google CSE or similar), and PDF import from the UI. Chats are stored locally; save/load with Ctrl+S / Ctrl+O or export to Markdown. The free hosted tier is fully featured but BYO keys; Pro adds cross-device sync and 1GB storage.
What are this agent's strengths and limitations?
- Beam multi-model parallel generation with Merge is a genuine differentiator; the README reports ~35% of users run it daily for cross-model comparison and de-hallucination
- Extremely broad model coverage: 20+ providers, 500+ models, including local models via Ollama/LocalAI and any OpenAI-compatible endpoint, with no vendor lock-in
- Local-first architecture: data stored locally, your own API keys, and the platform neither charges for model usage nor limits access
- Well-trodden self-hosting paths (Docker, Vercel, local dev) backed by an actively maintained project (README cites 5,000+ commits and frequent model-support updates)
- You must supply your own provider API keys and bear the underlying model usage costs; hosted free tier includes no keys
- Pro-only features (cross-device sync, 1GB storage) require a $10.99/mo subscription; the self-hosted open-source edition has no cloud sync
- Some features have external dependencies: speech synthesis relies on ElevenLabs/Inworld, web search needs Google CSE configuration, and web browsing depends on Browserless/Puppeteer
- The README positions this as an expert tool and explicitly says it is not for users wanting a toy UI or clone — setup and learning costs are nontrivial for beginners
How does this agent compare with similar options?
The repository's topic tags name LibreChat and Open WebUI (librechat, openwebui), which occupy the same open-source self-hosted AI chat frontend space. Big-AGI's claimed differentiators are Beam multi-model validation, the AI Inspector for request inspection, and local-first zero-latency performance; weigh these against those alternatives when choosing.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Big-AGI This agent | 57 · Major gaps | Self-hosted serviceFreemium | ★ 7.2k | 1d ago | TypeScript | OpenAI API · Claude API |
| Iva Personal Assistant | 85 · Good | Chat botFree + model costs | ★ 266 | 1d ago | TypeScript | ChatGPT · Codex |
| Event Planning Multi-Agent System | 62 · Some gaps | CLIFree + model costs | ★ 115 | 6mo ago | Python | — |
| Presenton – Open-Source AI Presentation Generator & API | 56 · Major gaps | Self-hosted serviceFreemium | ★ 11k | 3d ago | TypeScript | — |
How does FollowAgents rate this agent?
Why each dimension lost points
The claude-dm workflow has owner gating, a tool allowlist, and a minimal permissions block; ci.yml pins actions by SHA — credit due. However, claude-dm itself is not SHA-pinned, posthog telemetry dependencies exist in package. with no data-flow disclosure in the supplied files, and user confirmation, sensitive-data handling, and rollback rest only on README marketing claims (local-first, no lock-in) without code evidence.
CI runs typecheck, lint, and offline tests with npm ci and an .nvmrc pinned Node version — reasonable self-consistency. But failure-message quality and network-test gating are described only in comments, not evidenced, so partial scores.
The README clearly targets engineers, founders, researchers, self-hosters, and IT departments; 20+ providers and Docker/Vercel deployment paths are laid out. But trigger precision (when/how the agent fires) and capability boundaries lean on external links not supplied.
Full MIT license with clear copyright (top marks); detailed release history from 2.1.1 back to 1.10 with a structured changelog; linked docs structure. Missing known-limitations section, examples/FAQ are marketing badges only, and maintenance is concentrated in a single author (enricoros), a sustainability risk.
Beam multi-model comparison, 500+ model support, and multi-vendor de-hallucination are genuine differentiators; the free self-host plus Pro model is explicit. But output-usability evidence is thin in static files, and cost features are mere references.
The README mixes facts with unverifiable or dubious marketing claims ('Opus 5', 'GPT 5.6', 'zero-latency', '850k pulls'); screenshots and praise claims cannot be corroborated; the project itself does not separate fact from promotion.
- posthog telemetry (posthog-js/posthog-node) is integrated by default; verify its outbound data scope before self-hosting and disable via environment variables.
- The claude-dm workflow grants contents:write and allows Bash commands (cat/cp/mkdir etc.); despite owner gating, pin third-party actions by SHA and audit the tool allowlist.
- Model names and performance claims in the README (e.g., 'Opus 5', 'zero-latency') are unverified — do not treat them as fact in assessment.
- The project is driven by a single author; evaluate continuity risk before adoption. MIT license carries no warranty.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
-
Release notes excerpt
Big-AGI 2.1.0 - Weights Dust Six new vendors. Cerebras, NVIDIA NIM, Sakana AI, Cohere, Modular and Nous Research, each with a setup guide. Plus model updates across Anthropic, OpenAI, Gemini, xAI, Moonshot and Z.ai, a multi-vendor transcription framework, explicit reasoning-effort and mode controls, and code sandboxes that persist across turns. 628 commits since 2.0.5. <img width="1536" height="1024" alt="210-d4" src="https://github.com/enricoros/big-AGI/releases/download/v2.1.0/210-d4-FINAL.png"> New Vendors …View on GitHub ↗ -
Release notes excerpt
Big-AGI 2.0.5 - Roberto > Named after my father, on his birthday. Resilience, persistence, excellence. Long-running AI sessions get a serious upgrade. Anthropic Containers give you persistent files, Skills, and bash sessions that span turns. Gemini Deep Research is now fully resumable - reconnect after pauses, recover from server cuts, cancel cleanly. Add to that Opus 4.7 with 1M tokens, GPT-5.5, DeepSeek V4, and two new vendors. <img width="1536" height="1024" alt="205-b" src="https://github.com/user-attachments/assets/11e6052a-6dc9-4ca4-9a65-aa7f6846790a" /> Models …View on GitHub ↗ -
Release notes excerpt
Big-AGI 2.0.4 - Hyper Params Precision matters. This release is about getting models right: accurate parameter ranges for reasoning effort, verbosity, and temperature across providers and through 3rd parties such as OpenRouter and AWS. Plus the latest frontier models, a new cloud platform, and Anthropic going deeper. …View on GitHub ↗