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Vet is an AI-powered code review tool that automatically checks code changes and agent behavior for correctness and goal adherence. It runs standalone via CLI, integrates with CI/CD pipelines, and supports multiple LLM providers with your own API keys.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | imbue-ai/vet |
| Owner | imbue-ai |
| Primary language | Python |
| License | AGPL-3.0 — OSI-approved |
| Stars | 653 |
| Forks | 17 |
| Open issues | 2 |
| Latest release | v0.2.15 (2026-06-10) |
| Last updated | 2026-06-10 |
| Source | https://github.com/imbue-ai/vet |
What vet is
Vet snapshots diffs, optionally adds conversation context, runs LLM-based checks for logic errors and security issues, then deduplicates findings. Built in Python, licensed AGPL-3.0, with CLI, GitHub Actions, and agent skill integrations; supports OpenAI, Anthropic, Gemini, and custom OpenAI-compatible endpoints.
Get the vet source
Clone the repository and explore it locally.
git clone https://github.com/imbue-ai/vet.gitcd vet# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Install via pip/pipx/uv; requires Python runtime and API keys (ANTHROPIC_API_KEY, OPENAI_API_KEY, etc.) in environment or `.vet/` config.
- Configure model via `.vet/models.json` (repo) or `~/.config/vet/models.json` (user); remote registry available via `--update-models`.
- Use `--history-loader` option to inject agent conversation—note: this executes shell commands as the current user; validate before sharing config presets.
- Exit codes: 0 (no issues), 1 (runtime error), 2 (usage error), 10 (issues found); integrate with CI workflows accordingly.
- Output formats (text, json, github) support different downstream consumers; JSON output recommended for programmatic CI integration.
When to avoid it — and what to weigh
- Proprietary Codebase with AGPL Concerns — AGPL-3.0 requires source code disclosure for derivative works in network services. Review licensing constraints before embedding in commercial products.
- Air-Gapped Environments — Vet requires direct API calls to external LLM providers (Anthropic, OpenAI, etc.). Not suitable for fully offline or isolated network deployments.
- Enterprise Compliance with Data Residency — Code and conversation history are sent to external inference providers. Unsuitable if code must never leave corporate infrastructure.
- Need for 24/7 Commercial SLA Support — This is a community-driven open-source project. No commercial SLA, dedicated support, or guaranteed response times are provided.
License & commercial use
AGPL-3.0 (GNU Affero General Public License v3.0). This is a copyleft license requiring source code disclosure for derivative works or network services that expose its functionality to external users. Permits non-commercial and internal use; commercial use requires careful review.
AGPL-3.0 is not a standard permissive license (unlike MIT, Apache 2.0). Commercial deployment or integration into closed-source products triggers disclosure obligations. If you modify Vet or use it as a backend service accessible to customers, you likely must release source code. Consult legal counsel before commercial deployment. Internal or tool-only use within a company may be safer, but networked exposure complicates this.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Needs review |
| Deployment complexity | Low |
| DEV.co fit | Good |
| Assessment confidence | High |
Code and conversation history are transmitted to external LLM providers; ensure API keys are restricted and rotated. The `--history-loader` option executes arbitrary shell commands—validate before sharing config presets or using untrusted loaders. AGPL licensing means modification and network distribution create compliance obligations. No hardening guidance provided for sensitive code environments.
Alternatives to consider
GitHub Copilot for code review
Integrated into GitHub, no CLI setup, but less flexible model choice and no agent skill support; proprietary, not open-source.
Coderabbit / Review agents (permissive license)
Commercial SaaS alternatives with PR integration; avoid AGPL licensing but require vendor lock-in and data handling trade-offs.
Local llama.cpp / open model review tools
Air-gapped, self-hosted models avoid external API calls and licensing concerns, but require local hardware and accept less capable review quality.
Build on vet with DEV.co software developers
Install Vet via pip, configure your LLM provider, and start catching issues automatically. Free, open-source, and no vendor lock-in.
Talk to DEV.coRelated on DEV.co
Explore the category and the services that help you build with it.
vet FAQ
Can I use Vet in a commercial product?
Does Vet store my code or conversations?
Can I use Vet offline?
What models does Vet support?
Software developers & web developers for hire
vet rarely ships on its own. As a software development company, DEV.co handles the surrounding work — data modeling, service integration, deployment, and the ai coding agents glue code that production actually requires.
Integrate AI Code Review Into Your Workflow
Install Vet via pip, configure your LLM provider, and start catching issues automatically. Free, open-source, and no vendor lock-in.