One API. 60+ Models.
Zero Framework Bloat.
Built from scratch. No LangChain, no N8N, no framework to fight. One API across OpenAI, Anthropic, Google, xAI, Meta — and open-weight models on your own hardware. When a model gets deprecated or repriced, you swap one line and keep shipping.
Source-available, self-hostable, yours to fork.
Start Building in Minutes
// Install the SDK
npm install @bike4mind/sdk
// Initialize with your API key
import { Bike4Mind } from '@bike4mind/sdk';
const bike4mind = new Bike4Mind({
apiKey: process.env.BIKE4MIND_API_KEY
});
// Generate with any model
const response = await bike4mind.generate({
model: 'gpt-4o',
messages: [
{ role: 'user', content: 'Build me a React component for a todo list' }
]
});
console.log(response.content);Built on Atomics, Not Stitching.
No LangChain. No N8N. No Haystack. No framework dependencies.
Every component in Bike4Mind is purpose-built from scratch.
When LangChain breaks, we don't break. When N8N changes their API, we don't notice.
How Everyone Else Builds
# The "modern" AI stack
langchain==0.1.20 # breaks monthly
n8n-workflow@1.30.0 # breaking changes quarterly
chromadb==0.4.22 # API rewrites yearly
llamaindex==0.10.12 # yet another abstraction
# Plus 200+ transitive dependencies
# you didn't choose and can't auditLangChain ships breaking changes monthly — your production code breaks with it
N8N workflow changes cascade through your entire automation layer
200+ transitive dependencies you didn't choose, can't audit, and pray don't conflict
How We Build Bike4Mind
// Our AI stack
// RAG pipeline — built from scratch
// Vector search — built from scratch
// Agent orchestration — built from scratch
// Auth & MFA — built from scratch
// File ingestion — built from scratch
// Streaming engine — built from scratch
// Zero framework lock-in.
// Zero upstream surprises.We upgrade on our timeline, not when a framework author pushes a breaking change
Every line of code is ours — fully auditable, fully explainable
When you deploy in your AWS, you get the full source — not a dependency tree you can't control
What "Built on Atomics" Means for You
Complete Transparency
Every line of business logic is ours. No black-box framework internals. You can read, audit, and modify everything.
Your Upgrade Timeline
We ship when we're ready, not when a dependency forces our hand. Your production stays stable because our foundation is ours.
Full Auditability
Regulated industries need to audit their AI stack. Good luck auditing LangChain's 847 files. Ours? One codebase, one team, full provenance.
Zero Lock-In
No framework means no framework lock-in. When you fork Bike4Mind, you own a complete, self-contained system — not a pile of wired-together libraries.
Write it. Run it. Have a frontier model read the results. One session.
A teaching-scale transformer — ~250 lines of from-scratch Python, deliberately small enough to read in one sitting. The Playground ran it on this machine, streamed the loss curve live, and Claude reviewed the run and suggested what to try next. No Colab, no kernel setup, no copy-pasting logs between tabs.
The model is a toy — on purpose. The loop is the product: code, run, analyze, iterate — one pane of glass, on your own machine.
From Simple Scripts to Real ML — All In-Browser
This Is What Happens When Your AI Platform Doesn't Make You Leave
Your code, your data, your models, your team's tools — CLI, GitHub, JIRA, Slack, Wolfram — all connected. Stay in the flow. No context-switching. No extra tabs. The same "built on atomics" philosophy that drives our RAG pipeline, agent orchestration, and vector search also means every tool in this platform talks to every other tool. One surface for everything.
What Developers Are Building
What you can build when the AI infrastructure is already done for you
Senior Full-Stack Developer
DevOps Engineer
Indie Developer
ML Engineer
Platform Engineer
Your IDE, One AI Backend
Transform your development workflow with context-aware AI assistance
Intelligent Code Generation
Connect your IDE to Bike4Mind's API for real-time suggestions
const response = await bike4mind.generate({
model: 'gpt-5.2',
messages: [{ role: 'system', content: codeContext }],
stream: true
});Result
AI understands your entire codebase context, not just the current file
Multi-Model Code Review
Run your PR through different AI models for diverse perspectives
const reviews = await Promise.all([
bike4mind.review(pr, { model: 'claude-4.6-sonnet' }),
bike4mind.review(pr, { model: 'o3' }),
bike4mind.review(pr, { model: 'gemini-3-pro' })
]);Result
Get security insights from Claude, logic review from o3, and performance tips from Gemini
Automated Documentation
Generate and maintain docs that stay in sync with code
const docs = await bike4mind.artifacts.create({
type: 'mermaid',
content: await bike4mind.generateDiagram(codeStructure)
});Result
Living documentation with diagrams, API specs, and examples
Key Features:
Impact:
75% faster development, 90% better documentation
Built for Developers, by Developers
Powerful APIs
RESTful & WebSocket
Standard REST for requests, WebSocket for real-time streaming
Batch Processing
Process thousands of requests efficiently with our batch API
Webhooks
Get notified when long-running tasks complete
GraphQL (Soon)
Query exactly what you need with our upcoming GraphQL API
Built for Scale
Auto-Scaling
Absorbs traffic spikes with load balancing and fallback routing
Global Edge Network
Low latency worldwide with CloudFront distribution
Intelligent Caching
Semantic cache reduces costs and improves speed
99.95% Uptime target
Redundancy and failover; SLA available on enterprise plans
SDKs for Every Stack
Official SDKs with full type safety, auto-retry, and intelligent error handling
Under the hood
No hand-waving. Here is the actual architecture — the agent loop, the artifact sandbox, retrieval, and how you deploy it.
The agent loop
Every agent runs the same ReAct cycle: observe a result, reason about it with the model, act by calling a tool, then observe again. Hard budget guards — max executions, sub-LLM call caps, a dollar ceiling — ring the whole loop, so an agent can't run away with your bill. Long-running instances are quests: durable, resumable, and fully inspectable.
Observe, reason, act, repeat — with hard budget guards on every run. A long-lived instance of this loop, reaching into the tool belt, is what we call a quest.
Artifacts that run where your data is
The model writes artifact code on our servers, and we version every revision by SHA-256. But it executes on your machine — in an opaque-origin iframe with no same-origin access and CSP connect-src set to 'none'. Python runs in a Pyodide WASM worker on your own CPU. Nothing the artifact produces can phone home.
Generated and versioned on our servers — but every artifact executes on your machine, in an opaque-origin sandbox with the network cut. The code runs where your data already is.
Retrieval-augmented, and multiplayer
Point Bike4Mind at your documents and they become a data lake: chunked, embedded, and indexed in a vector store. Retrieval pulls the relevant passages straight into the session context. And the session is multiplayer — your whole team works against the same notebook, prompts, and artifacts in real time.
Your documents become a searchable data lake, retrieved straight into a live session — and that session is multiplayer, so a whole team shares one context.
Run it wherever your data has to live
Take the hosted one-click deployment on our AWS, self-host the full source in your own AWS account where data never leaves your VPC, or — landing with the open-core release — run the entire stack locally with docker. Each mode draws its own trust boundary, so “where does my data live?” always has a one-line answer.
Run it hosted, in your own AWS, or entirely on your machine. The dashed boundary on each card is the whole answer to “where does my data live?”
Ready to Build Without the Glue Code?
Stop stitching together LangChain, vector databases, and prompt templates. We built all of that into one platform so you can focus on your product.
5-Minute Setup
OpenAPI Spec
Live Support