duncatzat/vigils
vigils
A local control plane for AI agents — see what they do, approve what matters, keep secrets out. Rust + Tauri + Chrome MV3.
Usage guide
vigils is an open-source project around agent-security, ai-agents, audit-log with 205 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in Rust, useful for judging integration effort in a similar stack.
- GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating vigils for Rust AI workflows.
- Comparing a GitHub project with 205 stars and current repository activity.
Pros
- vigils has visible GitHub traction with 205 stars. Topics: agent-security, ai-agents, audit-log.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the Apache-2.0 terms fit your use case.
Production readiness
vigils should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
Apache-2.0 is reported by GitHub; review the repository license before redistribution or commercial use.
vigils architecture preview
vigils's main path starts at the entry surface, runs through Agent orchestration runtime, combines LLM / model client, Runtime context, External tool adapters, and returns Assistant response / action result.
Entry
CLI / terminal entry
vigils is primarily entered through a developer command or terminal workflow.
cargo test --workspace
Runtime
Agent orchestration runtime
The orchestration layer plans tasks, calls tools, manages context, and decides the next action.
agent workflow
Model
LLM / model client
The project connects its core runtime to local models or hosted AI APIs when model inference is required.
model signal
Context
Runtime context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Tools
External tool adapters
Tool adapters let the runtime act outside the model through External tool adapters.
tool signal
Output
Assistant response / action result
The final result is a response, action, or task completion returned through the active channel.
assistant output
Install tutorial
Before you install
- Local build tools for compiling the project
- A clean working directory for the first test run
Check the runtime environment
vigils may require a local build toolchain. Check the compiler, package manager, and system dependencies first.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/duncatzat/vigils.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ cargo test --workspaceTroubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented vigils example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is vigils?
vigils is an open-source ai agents project. A local control plane for AI agents — see what they do, approve what matters, keep secrets out. Rust + Tauri + Chrome MV3.
How do I install vigils?
Start with the official README. The first detected setup step is: git clone https://github.com/duncatzat/vigils.git.
Is vigils beginner-friendly?
If you already know the Rust ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can vigils be used commercially?
GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does vigils need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt vigils?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.