Coverage-aware agents
Starts from real coverage output, then focuses agent effort on gaps that can improve the suite.
AgenticTesting measures your repository's coverage, finds meaningful test gaps, plans the next focused task, and lets AI agents improve tests through constrained local tools.
Instead of staring at coverage reports and guessing where to write the next test, AgenticTesting turns the process into a repeatable local workflow.
Starts from real coverage output, then focuses agent effort on gaps that can improve the suite.
Pick a repository, set a target, choose a provider/model, inspect runs, and reuse saved settings.
Agents operate through relative file tools and command allowlists rather than unrestricted shell access.
The product is built around one clear cycle: measure, analyze, plan, implement, review, repeat.
Run the configured coverage command for the selected project.
Find files, branches, or behavior that need useful tests.
Select one focused testing task instead of trying to fix everything.
Let the agent add or improve tests through constrained tools.
Check the result and continue until the target or limit is reached.
The primary workflow is the local UI. API keys can be saved encrypted locally, and local model options are supported for laptop-first usage.
git clone https://github.com/sl-badcoder/AgenticTesting.git
cd AgenticTesting
python -m venv .venv
source .venv/bin/activate
pip install pytest pytest-cov cryptography
python -m src.frontend.debug_ui
# open http://127.0.0.1:8765
AgenticTesting gives AI a narrow, practical job: inspect coverage, create targeted tests, and keep iterating with guardrails.
Explore the repository