Quntur
Graduate-level AI research collaborator for advanced quantum chemistry.
About · El Agente UK
Autonomous agents for scientific discovery
From quantum chemistry and materials to structural biology and beyond — a UK node of the El Agente network, contributing research, infrastructure, and open tooling to a growing community of agentic-science labs.
- >95%
- Task success rate
- 6
- Specialized agents
- ∞
- Self-healing workflows
The framework
A multi-agent ecosystem for advanced research
El Agente is a state-of-the-art, large language model–powered multi-agent framework designed to automate and accelerate advanced scientific research. Developed by the Matter Lab and the Acceleration Consortium, it operates as a hierarchical ecosystem of specialized AI research collaborators across chemistry, materials science, quantum simulation, and laboratory operations.
At its core, El Agente democratizes computational science through a natural-language interface that lets researchers define sophisticated tasks without requiring extensive programming or software expertise.
The family
Specialized agents
Each agent focuses on a distinct research domain, collaborating through a shared reasoning fabric.
Cuántico
Automates quantum simulation workflows and quantum dynamics studies.
Sólido
First-principles solid-state materials modeling and battery materials research.
Estructural
AI-powered molecular editor for structural design, geometry manipulation, and chemical transformations.
Forjador
Task-oriented forging agent that autonomously generates, validates, and reuses computational tools.
Seguro
Laboratory safety agent that identifies hazards, evaluates risks, and generates SOPs.
Reasoning-driven
Self-healing scientific workflows
Unlike traditional automation frameworks, El Agente reasons directly over scientific literature, software documentation, and computational outputs to design, execute, and validate its own research pipelines.
This architecture enables autonomous error recovery and in-situ debugging that resolves syntax errors, workflow failures, and numerical convergence issues with minimal human intervention.
Across a wide range of scientific benchmarks, El Agente achieves task success rates of >95% — letting scientists focus on discovery, not infrastructure.