AdaL Engineer is the only agent you need. It orchestrates multiple specialized workers with the right model to deliver your task end to end. Step in whenever you want. Higher quality work. No more babysitting.
Download the Desktop app (Preview) for macOS Apple Silicon.
Trusted by engineers from
AdaL brings the full life cycle of agentic work into one continuous session. Switch interfaces, route tasks to specialized agents, and rotate across leading model families without fragmenting your workflow.
AdaL worker agents can run on Claude, GPT, Gemini, Kimi, DeepSeek, GLM, MiniMax, and more. Each worker brings the right model and toolchain into one agentic engineering workflow.
One subscription, accessible to all frontier models.
Students get 50% off. Learn more about student discounts.
Explore AdaL during your first week.
Perfect for short coding sprints in small codebases.
Great value for power users with the most access to all models.
For growing teams that need tailored plans, custom controls, onboarding, and enterprise-ready deployment support.
Book Demo
Founder of DataExpert
“As one of the earliest AdaL users, I’ve used it for months to solve hard and daunting tasks smoothly. We greatly enjoy the flexibility of model switching — AdaL plays the role of both designer and engineer on our team.”
Founder, Morphic
“Adal has singlehandedly accelerated our product roadmap by months”
CTO, Shor
“AdaL stays oriented in our codebase. It helps us move through boilerplate and edge-case-heavy contract logic without turning the work into a week-long slog.”
CTO, GrowthMax Inc.
“AdaL stayed stable across releases and kept adding useful features like model switching, multiple providers, and sub-agents. I’ve used it for full-stack platforms, iOS prototypes, compilers, and a command-line coding agent I use regularly.”
Senior Data Scientist, Micron Technology
“AdaL feels like a careful engineering partner. It explains its approach, shares useful context, and gives implementation insights that make development more enjoyable. Its deep research mode is especially valuable for understanding unfamiliar domains before building.”
CEO, Build Club
“AdaL helps our team build our flagship product, Solaris, and supports our AI community at hackathons and in their everyday work.”
Engineer
“As a small team of 2 developers, using AdaL has allowed us to increase our work capacity and move quickly. The ability to choose between multiple frontier models, easily keep context through multiple sessions, and knowing my data is private is what keeps me using AdaL.”
Engineer
“AdaL feels fundamentally different from other AI coding agents. It understands intent well, orchestrates tasks effectively through sub-agents, and delivers responses noticeably faster in complex workflows. It was the only agent that successfully navigated multiple roadblocks autonomously and completed the task end-to-end.”
Engineer, Siemens
“I chose AdaL because it was the only agent that truly grasped the engineering problem, not just the last prompt. It stayed focused across the entire codebase, caught edge cases I would have overlooked, and delivered something I was genuinely proud to ship. It even helped me submit a hackathon project on a tight deadline, which I couldn't have accomplished alone. If you've been disappointed by agents that generate code that doesn't hold together, AdaL is genuinely different.”
Professor, UT Austin
“I use AdaL to prototype research papers - the stage where the spec is half-formed, the codebase doesn't exist yet, and most AI tools collapse. AdaL holds up. It lets me take a paper-stage idea to a working experiment in hours instead of a week, which means I kill bad hypotheses earlier and put real compute behind the ones that survive. For research velocity, that's the only metric that matters.”
Founder, Morphic
“Adal has singlehandedly accelerated our product roadmap by months”
CTO, Shor
“AdaL stays oriented in our codebase. It helps us move through boilerplate and edge-case-heavy contract logic without turning the work into a week-long slog.”
CTO, GrowthMax Inc.
“AdaL stayed stable across releases and kept adding useful features like model switching, multiple providers, and sub-agents. I’ve used it for full-stack platforms, iOS prototypes, compilers, and a command-line coding agent I use regularly.”
Senior Data Scientist, Micron Technology
“AdaL feels like a careful engineering partner. It explains its approach, shares useful context, and gives implementation insights that make development more enjoyable. Its deep research mode is especially valuable for understanding unfamiliar domains before building.”
CEO, Build Club
“AdaL helps our team build our flagship product, Solaris, and supports our AI community at hackathons and in their everyday work.”
Engineer
“As a small team of 2 developers, using AdaL has allowed us to increase our work capacity and move quickly. The ability to choose between multiple frontier models, easily keep context through multiple sessions, and knowing my data is private is what keeps me using AdaL.”
Engineer
“AdaL feels fundamentally different from other AI coding agents. It understands intent well, orchestrates tasks effectively through sub-agents, and delivers responses noticeably faster in complex workflows. It was the only agent that successfully navigated multiple roadblocks autonomously and completed the task end-to-end.”
Engineer, Siemens
“I chose AdaL because it was the only agent that truly grasped the engineering problem, not just the last prompt. It stayed focused across the entire codebase, caught edge cases I would have overlooked, and delivered something I was genuinely proud to ship. It even helped me submit a hackathon project on a tight deadline, which I couldn't have accomplished alone. If you've been disappointed by agents that generate code that doesn't hold together, AdaL is genuinely different.”
Professor, UT Austin
“I use AdaL to prototype research papers - the stage where the spec is half-formed, the codebase doesn't exist yet, and most AI tools collapse. AdaL holds up. It lets me take a paper-stage idea to a working experiment in hours instead of a week, which means I kill bad hypotheses earlier and put real compute behind the ones that survive. For research velocity, that's the only metric that matters.”
Our Thesis
The crisis is not that AI writes bad code. It is that teams confuse code generation with engineering progress.
Agentic engineering brings that 80% back into the workflow. Agents execute with rigor, share long-term memory, and bring humans in for the decisions that shape the system.
Stay in control, understand faster, and ship with confidence.