anything-llm
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
Runssource: GitHubhomepageJavaScriptMITcommit 58ae6fee08e1
JavaScript, MIT licensed. The project labels itself: agent computer, agent harness, agent orchestration, agentic ai, agentic workflow, ai agents, computer use and hermes agent.
anything-llm runs. An Argusic agent installed it in 12.3 minutes and hit 1 error and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
AnythingLLM installs and runs in development mode on port 3001; the test suite passes all 1119 tests across 62 suites covering server and collector modules.
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.
What went wrong, in the agent's own words
The agent's own words, worst first, never rewritten into advice.
- Production mode fails because server/.env is not created by yarn setup:envs (only .env.development is). STORAGE_DIR env var is missing, causing a crash in server/utils/files/index.js at path.resolve().0.5 minutes
- Install time
- 12 minutes
- Cold machine to finish
- 17 minutes
- Errors hit and fixed
- 1 hit, 1 fixed with no human help
- How the result was proved
- npm test via yarn jest --no-cache: 62 suites passed, 1119 tests passed, 0 failed. Server responded to GET /api/ping with {"online":true} on port 3001.
- Model tokens used
- 178,415
- Exact commit tested
- 58ae6fee08e1
- 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.
All systems are working. Here's the final summary: **Install**: `yarn setup` completed successfully — all dependencies installed, Prisma schema genera… **Server**: Started on port 3001 in development mode. Endpoints respond — `GET /api/ping` returns `… **Tests**: `jest --no-cache` ran **62 suites, 1119 tests, all passed** — 0 failures. Coverage inclu… **Production mode note**: Running with `NODE_ENV=production` fails because `server/.env` is not cre… tokens used 178,415 All systems are working. Here's the final summary: **Install**: `yarn setup` completed successfully — all dependencies installed, Prisma schema genera… **Server**: Started on port 3001 in development mode. Endpoints respond — `GET /api/ping` returns `… **Tests**: `jest --no-cache` ran **62 suites, 1119 tests, all passed** — 0 failures. Coverage inclu… **Production mode note**: Running with `NODE_ENV=production` fails because `server/.env` is not cre…
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 12.3 minutes.
- Recovered from all 1 error without a human stepping in, which says the failures are documented well enough to solve.
- 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.
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)
agent-computeragent-harnessagent-orchestrationagentic-aiagentic-workflowai-agentscomputer-usehermes-agentllmlocal-ailocalaimultimodalragself-hosted-aivector-database
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/anything-llm)Questions
- Does anything-llm run?
- Yes. anything-llm runs. Argusic installed and launched it on a clean machine in 12 minutes, hitting 1 error on the way, and recorded the session.
- How did Argusic test anything-llm?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 58ae6fee08e1. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test anything-llm?
- The run that produced this verdict cost $0.05: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does anything-llm take to install?
- 12.3 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 anything-llm need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing anything-llm?
- 1 thing broke in the recorded run, and 1 were fixed without human help. Each one, and the time it cost, is listed on this page.
- Where is the evidence for anything-llm?
- 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.