Four steps from message to optimal context
Drift detection
BRANCH / STAY / ROUTE
Build optimal context
Full audit trail
Get started with a few lines of code
import { createDriftClient } from '@driftos/client';
const drift = createDriftClient('http://localhost:3000');
// Route a message
const result = await drift.route('conv-123', 'I want to plan a trip to Paris');
console.log(result.branchTopic); // "Paris trip planning"
// Get context for LLM
const { system, messages } = await drift.buildPrompt(result.branchId);
// Use with OpenAI
const response = await openai.chat.completions.create({
model: 'gpt-4o',
messages: [
{ role: 'system', content: system },
...messages,
{ role: 'user', content: 'What hotels do you recommend?' }
]
});Real engineering, not just marketing
From chatbots to enterprise AI
Users switch topics mid-conversation. Your bot forgets everything.
Agent #3 doesn't know what Agent #1 decided. Context lost.
Scroll through 200 messages to find that one decision? Never again.
Auditor asks why the AI said that. You shrug.
Core routing and embedding functionality available under MIT license
LLM Based Routing Engine
Embedding Based Routing Engine
JavaScript SDK
Enterprise features (advanced NLP, compliance tools) available separately
Interested in DriftOS for your project? Let us know.