ava-whatsapp-agent-course
Meet Ava, the WhatsApp Agent
Runs with mockssource: GitHubPythonMITcommit 338fd6870df9
Python, MIT licensed. The project labels itself: agent, agent based, agentic workflow, agents, stt, tts and vector database.
ava-whatsapp-agent-course runs, with stand-ins for the services it depends on. An Argusic agent installed it in 17.4 minutes and hit 6 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
uv sync installs all dependencies, 30 Python source files compile, LangGraph workflow graph compiles with 9 nodes, FastAPI WhatsApp webhook answers GET verification with HTTP 200, Chainlit chat interface starts on port 8000 and serves HTTP 200, mock API keys produce 401 when POSTing messages as expected since real Groq/ElevenLabs/Together/Qdrant credentials are required for end-to-end operation
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 92 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.
- 'fastapi run' command fails with ModuleNotFoundError due to import path resolution2 minutes
- uv package manager not pre-installed1 minute
- 'source' not available in sh, can't activate venv1 minute
- .env file loaded by pydantic-settings but os.getenv() at module init time does not read it1 minute
- Default SHORT_TERM_MEMORY_DB_PATH=/app/data/memory.db not writable outside Docker1 minute
- Mock Groq/ElevenLabs/Together/Qdrant API keys cause 401/connection errors on POST
- Install time
- 17 minutes
- Cold machine to finish
- 19 minutes
- Errors hit and fixed
- 6 hit, 6 fixed with no human help
- How the result was proved
- curl GET /whatsapp_response?hub.verify_token=test-token&hub.challenge=12345 returned 200 with challenge body '12345'; curl GET http://localhost:8000 (Chainlit) returned HTTP 200; Python validation script passed: ALL 7 VALIDATIONS PASSED in 3.6s (settings load, graph compilation, edge routing, schedule generation, helper functions, exceptions, prompts). POST /whatsapp_response with mock keys returns 500 with expected groq.AuthenticationError from memory_extraction_node.
- Model tokens used
- 158,332
- Exact commit tested
- 338fd6870df9
- 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
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
13:16:43 Now: 13:34:08 Now I have all the data I need for the final report. exec /bin/sh -lc '# Final cleanup pkill -f chainlit 2>/dev/null; pkill -f uvicorn 2>/dev/null sleep 1 echo "done"' in /work/repo exited 143 in 0ms: tokens used 158,332
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
- Recovered from all 6 errors without a human stepping in, which says the failures are documented well enough to solve.
- Licensed MIT, as reported by its host.
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.
- Took 17.4 minutes to install, slower than the median of the 73 comparable projects Argusic has measured.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 74 agent projects Argusic has installed and timed, ava-whatsapp-agent-course was the 57th fastest to reach a running state, and 50 of 74 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 | 1/3 | Runs with mocks | 92.00 | 0.09 |
Topics (from GitHub)
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/ava-whatsapp-agent-course)Questions
- Does ava-whatsapp-agent-course run?
- ava-whatsapp-agent-course runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 17 minutes, hitting 6 errors on the way, and recorded the session.
- How did Argusic test ava-whatsapp-agent-course?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 338fd6870df9. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test ava-whatsapp-agent-course?
- The run that produced this verdict cost $0.09: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does ava-whatsapp-agent-course take to install?
- 17.4 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 ava-whatsapp-agent-course need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing ava-whatsapp-agent-course?
- 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.
- How does ava-whatsapp-agent-course compare with the alternatives?
- Of the 74 agent projects Argusic has installed and timed, ava-whatsapp-agent-course was the 57th fastest to reach a running state, and 50 of 74 reached one at all.
- Where is the evidence for ava-whatsapp-agent-course?
- 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.