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Workflow

Developer Tooling

Code analysis agents, schema inference, test generation, API scaffolding, no-code agent builders, and CLI tools.

The problem

Developers lose time to boilerplate: docs, tests, schemas, and refactors that should follow the codebase but rarely get prioritized.

  • The README describes an API that changed three sprints ago.
  • Everyone agrees a module needs refactoring; nobody has a sprint for it.
  • A CI failure sits red because the person who understands the pipeline is out.

How NEO helps

NEO meets engineers in the repo: generating tests, inferring schemas, scaffolding APIs, and shipping small tools so the team moves faster with less toil.

  • Keeps docs synchronized with code via diff-aware regeneration, opened as a PR
  • Diagnoses CI failures into root-cause categories and auto-patches the low-risk ones
  • Refactors codebases with AST analysis and verifies every change against your test suite
  • Answers plain-language questions about any public repo via map-reduce analysis

Scenarios (4)

Autonomous Code Refactoring Agent
35% complexity reduction

Developer Tooling

Autonomous Code Refactoring Agent

Three-stage pipeline: AST parsing + static analysis, LLM-powered fixes, test validation. 35% complexity reduction on 10k+ line codebases.

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Autonomous Documentation Generator
4 languages, diff-aware

Developer Tooling

Autonomous Documentation Generator

Analyzes code structure, function signatures, and runtime behavior to generate README files, API docs, and inline comments.

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Conversational Codebase Q&A
Any public repo, plain-language Q&A

Developer Tooling

Conversational Codebase Q&A

Clones any public GitHub repo, analyzes architecture via map-reduce, and answers questions about the codebase in plain language.

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Self-Healing CI/CD Agent
5-way root-cause classification

Developer Tooling

Self-Healing CI/CD Agent

Monitors pipeline failures, diagnoses root causes with LLM analysis, auto-applies fixes for dependencies, configs, and flaky tests.

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Run this workflow in your workspace

Install NEO in VS Code or Cursor and ship training, evals, and deployment from chat.

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eval_pipeline.py
  def run_eval(model, tasks):-     results = model.predict(tasks)+     results = batch_eval(model, tasks)+     log_regression(results)      return score(results)