A visual, modular AI-agent builder — compose agents from drag-and-drop blocks for decision-making, research and action, backed by Pydantic and OpenAI/Groq LLMs with parallel tasking and workflow memory.
Teams wanted the power of autonomous AI agents without hiring an ML team to hand-code every workflow. They needed something a domain expert could assemble visually — and trust in production.
The challenge
Most agent frameworks are code-first and brittle: a single prompt change ripples through the whole pipeline, there's no memory between runs, and nothing is reusable. Non-engineers were locked out entirely.
Our approach
We built an n8n-style canvas where each capability — decision-making, research, tool calls, action execution — is a typed block. Pydantic schemas keep data flowing between blocks safe, LLM calls route across OpenAI and Groq for cost and speed, and a memory layer lets workflows carry state across steps and runs. Blocks are versioned and reusable across projects.
Under the hood
- Builder — n8n-style visual canvas, typed drag-and-drop blocks
- Validation — Pydantic schemas between every block
- Models — OpenAI + Groq LLMs, routed per task
- Runtime — Parallel tasking with persistent workflow memory
What we built
- A drag-and-drop canvas for composing multi-step agents
- Typed blocks for decisions, research, tool use and actions
- Parallel execution with memory that persists across runs
- A reusable block library shared across client workflows
Results
- Domain experts ship agents without writing code
- Workflows are versioned, observable and reusable
A Lightning Leap case study — one of the AI systems we've designed, built, and shipped.