AI-Engineering-Coach vs Guildly

AI-Engineering-Coach and Guildly are both coding assistants tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

AI-Engineering-Coach

The extension passively analyzes AI coding assistant activity across your workspace and surfaces usage metrics, prompt patterns, and code generation volume in a single dashboard — without requiring any API or cloud dependency. It covers any AI coding harness, not just Copilot, so teams running a mix of tools get consolidated signal instead of siloed logs. The anti-pattern detection flags weak prompting habits before they calcify across the team. Where it breaks: this is a read-only observer, not an enforcer. The docs describe an 'agentic readiness audit' framing, but no task is executed on your behalf — you get diagnostics, not automation.

Guildly

Each agent has a fixed role: PM writes PRDs, Manager routes tickets, SDEs work in isolated git worktrees, Reviewer signs off before anything merges. Every action traces back through a chain — line of code to ticket, ticket to PRD, PRD to the #general message that started it. The audit trail isn't a report you run after the fact; it's the structure the system runs on. That structure is also the ceiling: teams needing agents to adapt their process mid-sprint, or handle workflows that don't fit the six-role model, will hit the playbook's edges before long. The tool is in beta, with no API and no self-hosted option, so the surface you can extend is narrow.

Attribute	AI-Engineering-Coach	Guildly
Pricing	Free	Free
Free trial	No	No
Open source	Yes	No
Has API	No	No
Self-hosted option	Yes	No
Platforms	VS Code	macOS
Pros	Vendor-agnostic log analysis covers any AI coding assistant in the workspace, so teams running Copilot alongside other tools get one consolidated view instead of reconciling separate dashboards. Passive observation with no API dependency means no credentials to rotate and no outbound data flow to clear with security — which removes the procurement blocker that stalls most analytics tool rollouts. Anti-pattern detection surfaces weak prompt habits at the team level, so tech leads can address systemic issues in code review rather than catching them one pull request at a time. Repeated prompt discovery and skill promotion gives teams a path from scattered individual prompts to a shared, reusable prompt library without leaving VS Code. Self-hosted deployment is supported, so organizations with strict data-residency requirements can run the analytics stack inside their own infrastructure rather than accepting a SaaS data-sharing agreement.	Deterministic six-role workflow (PM → Manager → SDE → Reviewer) means agents don't improvise or skip steps, so you're not debugging a PR that nobody remembers creating. Full audit chain from code line to PRD to originating conversation, which means tracing a regression takes seconds instead of a git-blame session that still doesn't explain the why. Git worktree isolation per SDE ticket, so parallel agents working the same repo don't stomp each other's files mid-sprint. Model-agnostic agent identity — swapping the underlying LLM doesn't wipe team history or personality, so a model deprecation doesn't mean starting over. Per-agent token dashboard with cost limits on autopilot, so you can walk away without discovering in the morning that the overnight run cost more than you planned.
Cons	The tool produces diagnostics only — no enforcement, no automated feedback loop, and no way to block a weak prompt or flag a pattern before it hits the repository. Teams that need behavior change rather than measurement end up building a separate enforcement layer, at which point they are maintaining two systems. Because the extension reads local workspace logs passively, cross-team aggregation at the organization level is constrained by how logs are collected and shared. Teams operating across many repos or distributed environments report that assembling org-wide signal requires additional scripting — the extension's dashboard does not natively federate across workspaces. There is no API surface. Teams that want to pipe usage metrics into an existing observability stack — Datadog, Grafana, internal BI tooling — cannot pull data out programmatically. Organizations with mature engineering metrics programs that need AI coding data as a first-class signal alongside DORA metrics will move to a platform that exposes an API or native integration.	The six-role org chart is fixed — if your project needs a different shape (a dedicated QA agent, a data engineer, a second PM), the structure doesn't bend. Teams with non-standard workflows end up either forcing their process into the existing roles or looking at tools that let them define their own agent topology. No API means you cannot plug Guildly into an existing CI/CD pipeline, a Jira board, or a monitoring stack. Teams that need agents embedded in broader toolchains hit a dead end and move to a framework they can integrate themselves. Beta-only availability with no self-hosted option means you cannot deploy Guildly in an air-gapped or regulated environment. Any team with data residency requirements is blocked entirely.

Bottom line

AI-Engineering-Coach is open source. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between AI-Engineering-Coach and Guildly?

AI-Engineering-Coach is Free and open source, while Guildly is Free. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is AI-Engineering-Coach better than Guildly?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.