Yes. dograh runs. Argusic installed and launched it on a clean machine in 80 minutes, hitting 6 errors on the way, and recorded the session.

How did Argusic test dograh?

On a fresh, disposable machine, with every command recorded and the repository pinned at commit 1e47eb54d22f. One attempt is recorded, and the full method is on the methodology page.

What did it cost to test dograh?

The run that produced this verdict cost $0.40: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.

How long does dograh take to install?

80 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 dograh need a GPU?

Not to start. Argusic reached a running state on a machine with no GPU.

What goes wrong when installing dograh?

6 things broke in the recorded run, and 6 were fixed without human help. Each one, and the time it cost, is listed on this page.

Where is the evidence for dograh?

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.

dograh

Open source voice AI platform. Self-hosted alternative to Vapi and Retell. On Prem, BYOK across Speech to Speech or LLM/STT/TTS, with a visual workflow builder, MCP native and telephony support.

Runssource: GitHub homepagePythonBSD-2-Clausecommit 1e47eb54d22f

Python, BSD-2-Clause licensed. The project labels itself: ai calling, asterisk ari, conversational ai, inbound calls, local llm, no code, on prem voice agent platform and open source.

dograh runs. An Argusic agent installed it in 80 minutes and hit 6 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.

Backend Python venv installed with all core deps, PostgreSQL 16 running on port 5432, Redis 7.4.1 running on port 6379, Node.js v22 deployed, pipecat submodule installed editable, FastAPI app imports with speechmatics as the only blocking provider, 493/514 collectable tests pass against real services.
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

verdict: Runs
Argusic Score: 100 / 100
cost of the verifying run: $0.40 (measured)
recorded runs: 1
last tested: 2026-09-29
stars: 5,776
forks: 1,454
open issues: 75
watchers: 44
size: 40 MB
created: 2025-09-09
last push: 2026-09-29

Subject data from GitHub, linked at the top of this page, refreshed 2026-09-30. 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.

Time lost to each failure while testing dograh, drawn from the recorded run — Every bar is a failure the agent recorded, with the time it cost. Drawn from this run, not from anywhere else.

What went wrong, in the agent's own words

The agent's own words, worst first, never rewritten into advice.

No PostgreSQL or Redis available in container - tests require both8 minutes
ts_validator requires Node.js >=22.6 but system has v18.19.1; .mts/.ts files need --experimental-strip-types3 minutes
Many optional pipecat provider SDKs missing (groq, deepgram, google-genai, anthropic, elevenlabs, azure, etc.)3 minutes
pipecat submodule not checked out - git submodule init required2 minutes
pgvector extension cannot be created - not compiled; server-dev package unavailable on archives for noble
speechmatics SDK not available on PyPI, 5 tests fail at import time

Install time: 80 minutes
Cold machine to finish: 57 minutes
Errors hit and fixed: 6 hit, 6 fixed with no human help
How the result was proved: python -m pytest on 29 test files: 493 passed, 5 failed (speechmatics optional), 16 errors (pgvector missing extension) in 164s with real PostgreSQL and Redis
Model tokens used: 1,444,379
Exact commit tested: 1e47eb54d22f
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.

 succeeded in 0ms:
---Let me check for any .so that might work---
I've achieved strong results. Let me now provide the final report.
exec
/bin/sh -lc 'export PATH="/tmp/node-v22.11.0-linux-x64/bin:$PATH" && echo "node $(node --version) n…
 succeeded in 686ms:
node v22.11.0 npm 10.9.0
2026-09-29 14:12:13.952 | INFO     | pipecat:<module>:54 - ᓚᘏᗢ Pipecat 1.1.1.dev3064 (Python 3.12.3…
pipecat 1.1.1.dev3064
fastapi 0.135.3
tokens used
1,444,379

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 80 minutes.
Recovered from all 6 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 BSD-2-Clause, 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

tested by	attempt	outcome	score	cost USD	when
Argusic Runner	1/3	Runs	100.00	0.40	2026-09-29 14:12 UTC

Embed the badge

Markdown for the project README. It links back here; terms on the terms page.

[![Tested by Argusic](https://argusic.com/badge/dograh.svg)](https://argusic.com/subject/dograh)

Questions

Does dograh run?: Yes. dograh runs. Argusic installed and launched it on a clean machine in 80 minutes, hitting 6 errors on the way, and recorded the session.
How did Argusic test dograh?: On a fresh, disposable machine, with every command recorded and the repository pinned at commit 1e47eb54d22f. One attempt is recorded, and the full method is on the methodology page.
What did it cost to test dograh?: The run that produced this verdict cost $0.40: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
How long does dograh take to install?: 80 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 dograh need a GPU?: Not to start. Argusic reached a running state on a machine with no GPU.
What goes wrong when installing dograh?: 6 things broke in the recorded run, and 6 were fixed without human help. Each one, and the time it cost, is listed on this page.
Where is the evidence for dograh?: 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.