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I've been building SimplAI for the past several months — it's a platform for building, testing, and deploying LLM-powered agents and multi-step workflows.
The problem I kept running into: spinning up an AI agent pipeline means stitching together prompt management, tool calling, memory, evals, and deployment — often from scratch every time. SimplAI tries to be the layer that handles all of that so you can focus on what your agent actually does.
What it does: - Visual + code-first workflow builder for chaining LLM calls, tools, and APIs - Built-in prompt versioning and A/B testing - Supports multiple LLM providers (OpenAI, Anthropic, Gemini, etc.) - Evaluation and observability built in, not bolted on - Deploy agents as APIs in one click
It's not trying to be LangChain or LlamaIndex — the focus is on speed to production and giving non-ML engineers a sane path forward.
Happy to answer questions about the architecture, design decisions, or anything else. Critical feedback especially welcome.
