gpt-researcher
An autonomous agent that conducts deep research on any data using any LLM providers
Runs with mockssource: GitHubhomepagePythonApache-2.0commit 6f998577d547
Python, Apache-2.0 licensed. The project labels itself: agent, ai, automation, deepresearch, llms, mcp, mcp server and python.
gpt-researcher runs, with stand-ins for the services it depends on. An Argusic agent installed it in 1.7 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
The GPT Researcher package is installed in a Python venv. The FastAPI backend server launches, listens on 0.0.0.0:8000, and serves the GPT Researcher frontend (HTTP 200). The CLI help works. 410 of 411 tests pass under --forked isolation; 2 integration tests require real OpenAI credentials and are skipped. No code was modified.
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 89.8 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
- 2 minutes
- Cold machine to finish
- 23 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- uvicorn server started on port 8000, 'curl -s -o /dev/null -w %{http_code} localhost:8000/' returned 200; 410/411 tests passed with --forked isolation (pre-existing ordering pollution in test harness; all pass individually)
- Model tokens used
- 142,110
- Exact commit tested
- 6f998577d547
- 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
- Behaviour against real services
- this run was verified against stand-in services only
Watch the session
The whole run, from a clean machine, at the speed it actually happened. Nothing is cut and nothing is sped up.
A recording of attempt 2, run . The verdict above comes from a different attempt, so read this as one session and not as the current result. The terminal recording it was made from is the evidence, stored with a sha256 fingerprint; the video is a rendering of it. Nothing loads from the video host until you press play.
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
**No code modifications were needed** — the repository works out of the box in this container. 2026-09-22T01:39:00.466374Z ERROR codex_core::session: failed to record rollout items: thread 01a0c… 2026-09-22T01:39:00.466408Z ERROR codex_core::session: failed to record rollout items: thread 01a0c… 2026-09-22T01:39:00.466429Z ERROR codex_core::session: failed to record rollout items: thread 01a0c… tokens used 142,110 Here's the full report: **Install**: `pip install -e .` succeeded inside a Python venv (2 min). **Server**: Launched via `uvicorn backend.server.app:app` on port 8000. `curl` returned **HTTP 200*… **CLI**: `python cli.py --help` prints all available report types, tones, and options. **Tests**: 410 passed, 1 skipped (`--forked` isolation). The 13 apparent failures in the non-forked… **No code modifications were needed** — the repository works out of the box in this container.
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
- Installed in 1.7 minutes, faster than the median of the 32 comparable projects Argusic has measured.
- Nothing broke on the way: zero errors between clone and running.
- Licensed Apache-2.0, as reported by its host.
- Measured 3 times, so the result is not a one-off.
What did not, or is not known
- Only reached a running state against stand-in services, so nothing here proves it works against the real ones.
Of the 33 agent projects Argusic has installed and timed, gpt-researcher was the 8th fastest to reach a running state, and 25 of 33 reached one at all.
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 | 3/3 | Runs with mocks | 92.00 | 0.12 | |
| Argusic Runner | 2/3 | Runs with mocks | 92.00 | 0.33 | |
| Argusic Runner | 1/3 | Runs with mocks | 85.33 | 0.20 |
Topics (from GitHub)
agentaiautomationdeepresearchllmsmcpmcp-serverpythonresearchsearchwebscraping
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/gpt-researcher)Questions
- Does gpt-researcher run?
- gpt-researcher runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 2 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test gpt-researcher?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 6f998577d547. 3 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test gpt-researcher?
- The run that produced this verdict cost $0.12: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does gpt-researcher take to install?
- 1.7 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 gpt-researcher need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing gpt-researcher?
- Nothing did, in the recorded run: zero errors between clone and running.
- How does gpt-researcher compare with the alternatives?
- Of the 33 agent projects Argusic has installed and timed, gpt-researcher was the 8th fastest to reach a running state, and 25 of 33 reached one at all.
- Where is the evidence for gpt-researcher?
- All 3 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.