Sales Agent
An agent that qualifies leads, follows up, and books meetings — remembers every conversation, never repeats itself.
Infrastructure for AI agents that cannot afford to forget
KrishnaLabs builds production AI agents with memory, provenance, contradiction detection, and audit trails. Most agents break when they leave a demo. Ours don't.
agent.js — powered by Kalairos
// Three calls. init, remember, query.
const kalairos = require("kalairos")
await kalairos.init({ embedFn: myEmbedder })
await kalairos.remember("Q3 revenue target is $10M")
await kalairos.remember("Q3 revenue target revised to $12M")
// Time-travel: what did we believe last week?
const past = await kalairos.queryAt(
"Q3 revenue", Date.now() - 7 * 86400_000
)Studio
Don't want to build it yourself?
KrishnaLabs takes on 2–3 agent builds per quarter. Fixed scope, fixed price, delivered in 6 weeks. You get Biraj — not a junior dev.
Operations Agent
Automates internal workflows end-to-end. Reads from your tools, acts on your data, logs every decision for audit.
Customer Support Agent
Handles support with full context. Remembers the customer's history, escalates intelligently, never forgets.
Starting at $8,000 · 6-week delivery
Fixed scope. No hourly billing. No surprises. Powered by Kalairos for production-grade memory.
Open Source
Kalairos Platform
The infrastructure stack that powers every KrishnaLabs agent build. Available open source, MIT-licensed.
Core
Kalairos
Persistent, versioned memory for AI agents — automatic version history, time-travel recall, contradiction detection, and provenance. Open source, MIT-licensed, local-first.
- Versioned long-term memory
- Time-travel recall (asOf)
- Contradiction detection
- BYO embedder · MCP-compatible
Infrastructure
Agent Runtime
A high-performance runtime environment for deploying and scaling memory-aware AI agents in production.
- Auto-scaling
- Low latency execution
- Multi-model support
- Real-time monitoring
Storage
Knowledge Base
An agent-ready knowledge layer built for hybrid retrieval, provenance, and evolving business memory.
- Vector embeddings
- Hybrid search
- Enterprise controls
- Seamless integration
Platform Capabilities
Everything you need to build, deploy, and scale production AI agents
Lightning Fast
Sub-second memory retrieval with optimized vector, keyword, graph, and recency scoring.
Enterprise Security
Classification, provenance, deletion workflows, and controls for sensitive agent memory.
Local First
Deploy without a hosted vector database and keep your agent memory close to your app.
Multi-Model
Works with OpenAI, Cohere, local embedders, and any function that returns a number array.
Version Control
Track memory changes with full history, deltas, timestamps, and contradiction detection.
Analytics
Understand memory usage, recall quality, token budgets, and agent context behavior.
Founder
Hi, I'm Biraj Thapa
I founded KrishnaLabs LLC in California because I kept seeing the same problem: companies would demo an AI agent that looked incredible, then deploy it and watch it fall apart in production. It forgot context. It contradicted itself. It had no audit trail. Nobody could explain what it did or why.
So I built Kalairos — the memory layer I needed but couldn't find. Versioned recall, time-travel queries, contradiction detection, a full trust graph and audit trail. The primitives that make an AI agent reliable enough to actually run your business.
KrishnaLabs is the studio where I deploy that infrastructure for you. I take on a small number of builds per quarter so every client gets my full attention. Read the source on GitHub or install Kalairos from npm.
Mission
To give AI agents the memory and accountability they need to work in the real world — not just in a demo.
53/53
goal assertions
100%
temporal accuracy
100%
cross-session recall
100%
contradiction checks
Biraj Thapa
Founder & CEO · KrishnaLabs LLC · California
Coming Soon
Kalairos API — Direct Access
Want to integrate Kalairos memory directly into your product without building it yourself? We're opening the hosted API to external developers. Join the waitlist — no spam, just a ping when it's ready.
From the blog
Notes on building agent memory — benchmarks, design decisions, and tutorials.
Benchmarking memory poisoning defense in AI agents
We ran MINJA-class injection attacks against Kalairos with trust scoring on and off. Here is what we learned about provenance, author-class tagging, and detection rates.
Why versioned memory is non-negotiable for long-running agents
Stateless retrieval breaks the moment a fact changes. We walk through the Kalairos bitemporal model — event time, ingest time, valid intervals — and why time-travel queries matter for legal, medical, and finance agents.
Give your agent memory in three calls
init, remember, query. A 5-minute walkthrough of the Kalairos flat API, with a working example you can copy into your own agent today.
Let's talk
Have an agent build in mind? Want direct API access to Kalairos? Just want to ask Biraj a question? Send a message — I read every one.