Open-source tools and infrastructure for AI agents. Built by a practitioner, not a lab. Designed for the teams that actually deploy agents in production and need them to work reliably overnight without babysitting.
The current AI agent stack is held together with duct tape. Agents talk to the web through browsers built for humans. They receive raw HTML when they need structured data. They execute multi-page tasks as sequential scripts when they need declarative workflows. They run in isolation when they should share resources.
Rushd Labs builds the missing layer between AI agents and the systems they interact with. Not the models. Not the frameworks. The practical infrastructure that turns "it works in a demo" into "it runs unattended overnight."
Every project starts from the same question: what does an agent actually need here, and what is it currently forced to do instead?
The gap between those two answers is where the product lives. Wraith exists because agents need structured data from web pages but get raw HTML. BabyGPT exists because people are told LLMs are magic when every layer can be written in plain NumPy and understood from first principles.
Everything ships open source. Paid tiers cover hosting and custom configurations, not the core tools.
An orchestration layer that sits on top of headless browsers and turns dumb page loads into smart, targeted extractions. Agents get clean JSON instead of raw HTML. Multi-page workflows run as config files, not scripts.
Define what you need in TOML. Wraith blocks the junk, extracts the signal, and returns structured data your agent can reason over immediately.
A complete GPT implementation built from scratch using only NumPy. No PyTorch, no TensorFlow. Every layer of the transformer is written in plain Python so you can see the maths, trace the gradients, and understand what's actually happening inside a language model.
Includes a React + FastAPI web UI for training, attention visualisation, LoRA fine-tuning, and model export. Models from 420K to 124M parameters, all trainable on a laptop CPU.
An AI-driven financial risk engine that simulates the UK pension LDI crisis using autonomous agents. Unlike static backtesting, the system uses generative AI to invent market scenarios, detect insolvency risks in real-time, and execute strategic trades to preserve solvency.
A 30-day simulation loop runs autonomously: a Market Agent moves the gilt yield curve, a Valuation Engine recalculates present values, an Ops Agent issues margin calls, and a Portfolio Manager Agent decides whether to sell, repo, or hold.
Analyses GitHub repositories for rug-pull patterns in Solidity contracts. Submit a repo URL and the system clones it, runs static analysis via Slither, applies custom detection rules across three categories, and returns a risk score from 0 to 100.
Built as a full-stack application with async job processing. Analyses run in background workers while the dashboard updates in real time. Dockerised for one-command deployment.
All Labs projects are open source and welcome contributions. If you're building agent infrastructure or want to collaborate, get in touch.