Read, try, share and contribute
Vons explores compact local decisions inside agent workflows. A planner supplies state and bounded candidates; the host retains execution, policy and consent. The community should help people understand this boundary, try the software and report what works or fails.
Start here
| Your goal | Entry point | Current availability |
|---|---|---|
| Understand Vons | README and model card | Included in this source preview |
| Read the research | Paper and Tech Report | Reviewed PDFs and exact verification scope are included |
| Try without weights | The walkthrough below | Deterministic contract and synthetic data only |
| Run model inference | TypeScript SDK | Requires separately obtained, reviewed model/tokenizer assets |
| Share experience | Experience report | GitHub account required to submit |
| Reproduce a result | Reproduction report | Form included; evidence reviewed separately |
| Improve Vons | Contributing | Documentation, tests and scoped code changes welcome |
The destinations are GitHub: inlevel9-com/Vons and Hugging Face: INLEVEL9/Vons. The source preview includes documentation and GitHub issue forms for feedback. The paper and Tech Report are included as versioned PDFs; their index preserves exact hashes, evidence boundaries and arXiv status. A source upload does not provide hosted model inference. The current software terms require a separate agreement for enterprise/commercial use, including enterprise evaluation. Reading the paper under its article terms is separate from software use.
A five-minute first exploration
Five minutes is a usability target, not a measured onboarding result. Start in the root of a source checkout with Python 3.10+. This path needs no model assets, GPU, API token or hosted inference account. Python packaging may need internet access to install its build tools.
python3 -m venv .venv
. .venv/bin/activate
python -m pip install -e .
python -m vons.cli generate-smoke --output data/generated/community-smoke.jsonl
python -m vons.cli validate-data --input data/generated/community-smoke.jsonl
On Windows PowerShell use .venv\Scripts\Activate.ps1 for activation. The
generator reports four synthetic rows. Validation prints their data manifest;
this checks the data contract, not model quality.
Run this Python example in the same environment:
from vons import KASIAdapter, KASIProposal
adapter = KASIAdapter(tool_policy={"read_weather": "low"})
for tool in ("read_weather", "unknown_tool"):
proposal = KASIProposal.from_mapping({
"calls": [{"name": tool}],
"confidence": 0.9,
"risk": "low",
})
decision = adapter.decide(proposal)
print(tool, decision.action.value, decision.reason)
Expected output:
read_weather call approved_by_host_policy
unknown_tool refuse unregistered_or_no_proposed_call
This is the deterministic KASI adapter, not a learned Vons prediction. The word
call is a returned decision; the example never fetches weather or executes a
tool. The host supplies the tool policy. The literal confidence is sample input,
not a calibrated probability. See the general scope and
KASI integration before building an integration.
Share an honest experience
Use a report form with the source revision or release-manifest digest, scenario, OS/device, runtime/provider, exact command, expected behavior and observed behavior. Mark whether you tried the contract, a synthetic example or real model inference. Include attempted, successful, abstained and failed counts with definitions; explain any overlapping categories. Report unavailable quantities as unavailable and keep errors in the denominator.
A short negative report is useful. Personal experience and measured benchmarks serve different purposes: user ratings, stars and downloads do not establish accuracy, safety or independent replication. Public reports are not automatically training data or promotional testimonials. Link only safe evidence you have reviewed and have permission to share. No name, employer or private workflow is required beyond the hosting service's account requirements.
A practical platform roadmap
These are proposed acceptance criteria, not completed or measured milestones.
- Reading and participation: publish a reviewed source preview, readable research summary and accurate availability notices. A signed-out visitor can read the documentation; a signed-in contributor can submit each issue form and find the contribution rules. Check keyboard navigation and narrow screens.
- First use: test the no-weight walkthrough from a clean checkout. Invite voluntary reports of completion, failure and time spent. Keep counts and denominators; do not infer actual use from page views or stars.
- Real inference: offer a versioned demo only after permitted model assets, tokenizer, notices, provider evidence and numerical parity are reviewed. Clearly label unsupported requests and known failures. Host policy still controls actions. Do not present static or simulated results as inference.
- Community learning: triage reports, publish reproducible improvements, credit contributions and keep corrections visible. Record submitted reports, independently reproduced reports and accepted contributions separately.
Start with the existing GitHub and Hugging Face channels. A dedicated website, review database, accounts and telemetry need a separate implementation decision; none is required for the first source preview. Reading should remain available without a Vons account. Follow the contribution and moderation rules.