AI coding is powerful. Managing it shouldn't be guesswork.
crewkit is the management layer for AI-assisted engineering. We give teams the skills, context, planning, and iteration tools to turn ad-hoc AI usage into a repeatable, optimizable practice.
The problem
AI coding assistants are the biggest productivity unlock in a generation. But without structure, they create new problems.
Engineering leaders adopting these tools hit the same walls:
Black-box sessions
No one knows what sessions cost, which agents succeed most often, or how junior developers are being guided. Data stays locked on individual machines.
Zero governance
Every developer has their own agent prompts, skills, and rules. There is no single source of truth, no role-based controls, and no compliance trail.
No way to improve
You can tweak a prompt, but you cannot measure whether the change helped. Without data, improving agent performance is guesswork -- not engineering.
Why now
AI coding assistants went from experimental to essential in under 18 months. Engineering teams are spending real budgets, shipping real code, and scaling real workflows with AI -- but the tooling to manage this shift does not exist yet.
Spend is scaling fast
Teams are going from a few hundred dollars to five and six figures in AI coding costs per month. Visibility is no longer optional.
Compliance demands governance
Regulated industries need audit trails, role-based controls, and enforceable coding standards -- even when the code is AI-assisted.
Fragmentation is the default
Every developer configures agents differently. Without centralized management, consistency is impossible as teams grow.
Performance is unmeasured
No one can say which agent configuration produces the best results. The feedback loop that makes software engineering work is missing.
Our approach
crewkit is built around three pillars that turn ad-hoc AI usage into a managed, measurable engineering practice.
Observe
Track every session, conversation, and task across the organization. See costs, token usage, success rates, and tool invocations in one dashboard.
Govern
Enforce consistent agent configurations across the team with 3-tier inheritance. Role-based modifiers give juniors coaching and seniors autonomy.
Improve
A/B test agent configurations with real traffic. Statistical analysis tells you which version performs better, then deploy the winner with one click.
What sets crewkit apart
We are not another dashboard. crewkit is an operational platform that integrates into where developers already work -- the terminal.
Resource marketplace
A curated library of agents, skills, playbooks, and commands. Install proven configurations in seconds. Share what works across your organization.
3-tier inheritance
Platform, organization, and project-level configurations that compose and override cleanly. Change a skill once at the org level and it propagates everywhere.
Context-aware agents
Agents receive project context, team playbooks, and conventions automatically. Every session starts with the right knowledge.
Repeatable workflows
Skills and commands codify how work gets done. Instead of ad-hoc prompting, teams build and share reusable patterns that deliver consistent results.
Data-driven optimization
Every resource version is tracked with performance metrics. A/B test configurations, measure impact, and deploy the version that works best.
Continuous iteration
Session analytics surface what is working and what is not. Coaching insights help developers improve over time. The feedback loop is built in.
How it works
Install the CLI
A single install command gets your team up and running. Works with any AI coding assistant.
Run crewkit code
Launch sessions from the CLI. Agents, skills, and configurations sync automatically.
Team configs propagate
Resources from the marketplace and your org settings are applied to every session.
Data flows to your dashboard
Session telemetry, costs, and outcomes appear in real time. Iterate on what works.
Our vision
Every engineering team will use AI assistants. The question is whether they use them well. Today, most teams treat AI coding tools as individual developer utilities -- no shared configurations, no measurement, no improvement loop.
crewkit exists to close that gap. We believe AI-assisted engineering should be a managed practice, the same way CI/CD transformed deployment and observability transformed operations. Teams that instrument their AI workflows will build faster, ship better code, and outpace those running blind.
We are building the platform that makes that possible -- starting with the developer tools they already use, and extending into planning, artifact management, and cross-team optimization.
Open source CLI
The crewkit CLI is open source. Inspect the code, contribute improvements, or report issues directly on GitHub.
Built in Rust for speed and reliability. The dashboard and API are hosted services designed for teams that need governance and analytics at scale.
karibew/crewkit-cli on GitHub