big-AGI
AI suite powered by state-of-the-art models and providing advanced AI/AGI functions. Includes AI personas, AGI functions, world-class Beam multi-model chats, text-to-image, voice, response streaming, code highlighting and execution, PDF import, presets for developers, much more. Deploy on-prem or in the cloud.
Runssource: GitHubhomepageTypeScriptMITcommit 007150498197
TypeScript, MIT licensed. The project labels itself: agi, ai agents, ai suite, ai workspace, anthropic api, deepseek api, gemini api and gpt.
big-AGI runs. An Argusic agent installed it in 9 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
All npm commands (install, test, build, tscheck, lint) pass cleanly on Node.js v22.14.0. The test suite runs 25 subtests: 4 pass (including a live OpenRouter model listing returning 388 models), 21 skip due to absent API keys, 0 fail.
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 100 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
- Install time
- 9 minutes
- Cold machine to finish
- 9 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- npm test exited 0 (4 passed, 21 skipped, 0 failed); npm run tscheck exited 0; npm run lint exited 0; npm run build exited 0 with production build
- Model tokens used
- 27,811
- Exact commit tested
- 007150498197
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
**Verification**: - `npm test` exited 0 — 25 subtests, 4 passed (Argusic live listing returned 388 models, Perplexity… - `npm run tscheck` exited 0 — zero type errors - `npm run lint` exited 0 — zero lint violations - `npm run build` exited 0 — production build compiled successfully, 16 static pages generated **Environments**: Project requires Node.js ^22 || ^24 || ^26; the container ships v18. I downloaded… **Note**: 27 npm audit vulnerabilities reported (1 critical, 20 high), all requiring `--force` to f… ``` Node.js v22.14.0 used (system had v18); install minutes ≈ 9 npm test: 0 fail (4 pass, 21 skip — API-key gated) tscheck: 0 errors | lint: 0 violations | build: compiled ```
Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Reached a running state on a clean machine, with the session recorded.
- Installed in 9 minutes.
- Nothing broke on the way: zero errors between clone and running.
- Ran without a GPU, so it does not need one to start.
- Licensed MIT, as reported by its host.
What did not, or is not known
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
What it is a reasonable choice for
- Trying it on a laptop or a small server: it reached a running state without a GPU.
- A quick evaluation: it was running 9 minutes after the clone.
- Following its own README: the documented path worked with nothing to fix.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
Topics (from GitHub)
agiai-agentsai-suiteai-workspaceanthropic-apideepseek-apigemini-apigptgpt-5librechatmulti-modalmulti-modelnano-bananaopenai-responses-apiopenwebuiperplexity-apisonnet-4-5xai-api
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/big-agi)Questions
- Does big-AGI run?
- Yes. big-AGI runs. Argusic installed and launched it on a clean machine in 9 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test big-AGI?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 007150498197. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test big-AGI?
- The run that produced this verdict cost $0.04: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does big-AGI take to install?
- 9 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does big-AGI need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing big-AGI?
- Nothing did, in the recorded run: zero errors between clone and running.
- Where is the evidence for big-AGI?
- The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.