AI engineering tools,
compared honestly.

Conduct is an AI agent orchestration platform for engineering teams. It doesn't replace GitHub, Git, or your IDE — it wraps around your existing Git workflow to automate the repetitive parts: reviewing PRs, fixing labeled issues, triaging incoming bugs, diagnosing CI failures, patching dependencies, and responding to incidents. Every automation is a visual YAML playbook your team can read, fork, and customise. Human-in-the-loop approval gates are first-class blocks — nothing ships without a reviewer unless you explicitly remove the gate. Every run is fully audited with cost visibility. MIT licensed.

GitHub Copilot is the most widely adopted AI coding assistant, with 15M+ users. It works as an IDE extension (VS Code, JetBrains, Neovim) providing inline code completions, multi-file edits, and a chat interface. In 2024–2025 GitHub added agentic capabilities: Copilot can now be assigned GitHub issues, create branches, write code, and open PRs — all triggered from the GitHub web UI. The underlying model has expanded to include GPT-4o and Claude Sonnet. For teams already on GitHub Enterprise, it integrates natively with zero friction.

Devin is designed to be a fully autonomous software engineer — not a copilot. You give it a task in natural language and it independently: explores the codebase, creates a plan, writes code across multiple files, runs tests, debugs failures, and opens a pull request. It runs in its own isolated cloud environment with a browser, terminal, and editor. Devin is best described as a "junior engineer" you can hand full tasks to. It is the most capable fully-autonomous agent publicly available, and is priced accordingly for teams that need end-to-end task execution without hand-holding.

LinearB combines engineering intelligence (cycle time, deployment frequency, DORA metrics) with PR automation via its gitStream product. gitStream is a YAML-based automation engine that routes PRs, enforces review policies, assigns reviewers, and auto-merges safe changes. LinearB's AI layer adds code review suggestions and automated checks. It is primarily a tool for engineering managers who want visibility into team performance alongside automated PR governance. The metrics dashboards are among the best in the market for understanding eng team health.

Bito specialises in AI-powered code review with a strong emphasis on codebase context. Unlike tools that look at just the diff, Bito indexes your entire codebase and uses that context when reviewing PRs — catching issues that require understanding of how the changed code interacts with the broader system. It also integrates with Jira to read linked tickets and provide more relevant review comments. Bito offers an IDE plugin for real-time feedback and a CLI for CI integration.

Amazon Q Developer is AWS's answer to GitHub Copilot, with a stronger focus on AWS infrastructure and cloud-native development. Beyond code completions, it offers unique features: automated Java version upgrades, security vulnerability scanning, and agentic software development tasks. It integrates deeply with AWS services (CodeCatalyst, CodePipeline, CloudWatch) and is the strongest choice for teams heavily invested in the AWS ecosystem. It is available free on a basic tier and included in AWS Builder ID.

CodeRabbit is a focused, well-executed AI code review product. Install it as a GitHub or GitLab app, and it automatically reviews every PR — posting line-by-line comments, a summary, and a walkthrough of the changes. It supports custom review instructions, learning from dismissals, and integrates with Linear and Jira to read ticket context. The product is beloved for how little setup it requires — install in 60 seconds and it starts reviewing immediately. It has a generous free tier for open source projects.

Runtm is a sandbox aggregation layer for coding agents — an abstraction that sits above cloud execution backends (E2B, Modal, AWS EC2, Daytona, Vercel) and coding agents (Claude Code, OpenAI Codex, Cursor, Devin, Gemini, GitHub Copilot) alike. Rather than committing to a single cloud VM or container service, you connect Runtm once and get a unified API and dashboard across all of them. Agents run in isolated sandboxes, deploy to live HTTPS URLs, and emit session logs and cost metrics — regardless of which backend is powering the box. Where Conduct is the orchestration layer that decides what an agent should do and when to trigger it, Runtm is the aggregated runtime layer where execution happens. The integration story is multiplicative: adding a single Runtm adapter to Conduct's sandbox dispatch unlocks every backend Runtm already supports — E2B, Modal, Daytona, EC2, Vercel — without writing a separate adapter for each.

xHawk focuses on end-to-end SDLC automation: from issue creation through code generation, testing, code review, and deployment. It targets enterprise teams that want to automate the full software delivery pipeline with AI. xHawk is more opinionated about the SDLC process and less flexible for general-purpose agent workflows. Based on public information, it is primarily configured via code/API rather than a visual interface, and is positioned at larger engineering organisations.