DB-GPT
open-source agentic AI data assistant for the next generation of AI + Data products.
Runssource: GitHubhomepagePythonMITcommit ca9f014cb3ea
Python, MIT licensed. The project labels itself: agents, bgi, database, deepseek, gpt, gpt 4, hacktoberfest and llm.
DB-GPT runs. An Argusic agent installed it in 3.4 minutes and hit 4 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
DB-GPT 0.8.2 is installed, all 7 workspace packages build and install via uv sync, the CLI (`dbgpt --help`) prints all 14 commands, the web server starts on port 5670 and serves HTTP endpoints (verified 200 on root and the hello AWEL trigger), 641 core tests pass, and the first-time setup wizard successfully writes an OpenAI profile config.
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 96 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.
- 22 litellm tests fail: litellm 1.72.6 uses `aiohttp.ConnectionTimeoutError` but aiohttp 3.8.4 (pinned by the project) does not expose that attribute2 minutes
- uv sync failed: `default-groups = ["dev"]` in dbgpt-core/pyproject.toml references a `dev` dependency group that is not defined in `[dependency-groups]`0.5 minutes
- provider_test.py error: missing `provider` fixture — this test expects to be parametrized externally0.3 minutes
- Non-fatal: 'libro' binary not found — start libro exception: No such file or directory0.1 minutes
- Install time
- 3 minutes
- Cold machine to finish
- 5 minutes
- Errors hit and fixed
- 4 hit, 4 fixed with no human help
- How the result was proved
- `uv run pytest --pyargs dbgpt -q --no-header --ignore=packages/dbgpt-core/src/dbgpt/model/proxy/tests --ignore=packages/dbgpt-core/src/dbgpt/model/utils` exited 0, 641 passed, 1 skipped; `uv run dbgpt start webserver -y --api-key sk-mock` started Uvicorn on http://0.0.0.0:5670; `curl http://127.0.0.1:5670/` returned HTTP 200; `curl 'http://127.0.0.1:5670/api/v1/awel/trigger/examples/hello?name=World'` returned '"Hello, World, your age is 18"'; `uv run dbgpt --version` printed 'dbgpt, version 0.8.2'
- Model tokens used
- 181,253
- Exact commit tested
- ca9f014cb3ea
- 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.
aiohttp version: 3.8.4 exec /bin/sh -lc 'export PATH="$HOME/.local/bin:$PATH" && cd /work/repo && echo "=== Server verification… exited 7 in 0ms: === Server verification === Root endpoint: HTTP 000 Here's my comprehensive report: tokens used 181,253 Here's my comprehensive report:
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 3.4 minutes, faster than the median of the 37 comparable projects Argusic has measured.
- Recovered from all 4 errors 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.
- Measured 2 times, so the result is not a one-off.
Of the 38 agents projects Argusic has installed and timed, DB-GPT was the 16th fastest to reach a running state, and 28 of 38 reached one at all.
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 3.4 minutes after the clone.
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
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 2/3 | Runs | 100.00 | 0.02 | |
| Argusic Runner | 1/3 | Runs with mocks | 92.00 | 0.10 |
Topics (from GitHub)
agentsbgidatabasedeepseekgptgpt-4hacktoberfestllmprivateragsecurityvicuna
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/db-gpt)Questions
- Does DB-GPT run?
- Yes. DB-GPT runs. Argusic installed and launched it on a clean machine in 3 minutes, hitting 4 errors on the way, and recorded the session.
- How did Argusic test DB-GPT?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit ca9f014cb3ea. 2 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test DB-GPT?
- The run that produced this verdict cost $0.02: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does DB-GPT take to install?
- 3.4 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 DB-GPT need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing DB-GPT?
- 4 things broke in the recorded run, and 4 were fixed without human help. Each one, and the time it cost, is listed on this page.
- How does DB-GPT compare with the alternatives?
- Of the 38 agents projects Argusic has installed and timed, DB-GPT was the 16th fastest to reach a running state, and 28 of 38 reached one at all.
- Where is the evidence for DB-GPT?
- All 2 recorded runs are on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.