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Kalaris Labs is an independent AI research lab, founded in India in 2026, building the infrastructure for the agentic era. Software is learning to act: agents now write code, transact, negotiate and coordinate for people and institutions, faster than anything beneath them can adapt. Everyone is building the agents. We are building what they will stand on.
Our motto: the rules have to exist before the actors do. The internet, the web and finance all got their infrastructure late, after the damage. When software acts at machine speed, this era does not get to wait.

Why the lab exists

The crowded bet is that more capable models will fix the problems of autonomous agents on their own. We think that bet is wrong. Scaling makes models more capable; it does not make them accountable. Capability without accountability is not progress. It is a liability with a network connection. Agents are entering codebases, markets and workflows without clear mandates. They talk to one another without shared protocols. They are graded by benchmarks they can learn to game. When many of them meet in the same system, they behave in ways no single evaluation was built to predict. Kalaris Labs exists to close that gap.

What we build

We also publish on efficient inference (Anudhi, Pipit, Banyan) and computational neuroscience (fly brain resuscitation). See all papers.

Principles

Safety is a design constraint, not a department

An agent should do what it was asked, within the limits it was given, and nothing more. Its actions should be traceable, its incentives readable, and its results verifiable by someone other than itself.
Infrastructure that only one company can read is not infrastructure; it is a toll booth. Our papers are open access and our code is on GitHub under open-source licenses.
India has shown what happens when a foundational layer is treated as a public good: a payments network that hundreds of millions of people use every day and that any bank or app can build on. The agentic era deserves rails like that.
Every paper page states its evidence level: proposal, partial implementation, simulation, experiment or position. We state limitations next to results and withdraw results that do not hold up, as with the PrincipalBench v1 rankings.
Revenue matters because it keeps the work going; impact is the point.
Read the full manifesto by founder Sayan Chowdhury.

People

sayan-chowdhury

Sayan Chowdhury

Founder of Kalaris Labs and agentic AI researcher; author of the lab’s preprints and most of the blog. LinkedIn, X, ORCID, GitHub, Hugging Face.
The lab is hiring a small applied AI research team. See careers.

Contact

Questions about the lab or its tools? See the FAQ.
Last modified on November 15, 2026