Kronvex — EU-Native Memory API for AI Agents

Persistent, semantically searchable memory.
Three endpoints. GDPR-compliant. Data stays in Europe.

Why Kronvex?

Every time a user opens a new session with your AI agent, it starts from scratch. No context, no history, no user preferences. You end up injecting entire conversation histories into every prompt — expensive, slow, and context-window-limited.

Kronvex gives your agent persistent, semantically searchable memory across sessions. Store interactions, recall relevant context by meaning, inject a ready-to-use context block before each LLM call — and keep all data in Europe.

Performance

99.9% uptime · EU Frankfurt · GDPR-compliant · pgvector cosine similarity · 1536-dim embeddings

Quick Start

1. Get a free API key

curl -X POST https://api.kronvex.io/auth/demo \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Alice",
    "email": "alice@company.com",
    "usecase": "Customer support bot with memory"
  }'

{
  "full_key": "kv-xxxxxxxxxxxxxxxx",
  "agent_id": "uuid-of-your-first-agent",
  "memory_limit": 100,
  "message": "Ready! Your API key and first agent are set up."
}

2. Store a memory

curl -X POST https://api.kronvex.io/api/v1/agents/{agent_id}/remember \
  -H "X-API-Key: kv-xxxxxxxxxxxxxxxx" \
  -H "Content-Type: application/json" \
  -d '{"content": "Alice is a Premium customer since January 2023."}'

3. Inject context before each LLM call

curl -X POST https://api.kronvex.io/api/v1/agents/{agent_id}/inject-context \
  -H "X-API-Key: kv-xxxxxxxxxxxxxxxx" \
  -H "Content-Type: application/json" \
  -d '{"message": "I still have that billing issue"}'

{
  "context_block": "[KRONVEX CONTEXT]\n- Alice is a Premium customer since Jan 2023 (similarity: 0.94)",
  "memories_used": 1
}

SDKs

Python

pip install kronvex

from kronvex import Kronvex

kx = Kronvex("kv-your-api-key")
agent = kx.agent("your-agent-id")

await agent.remember("User prefers concise answers")
context = await agent.inject_context("How should I format this?")

Node.js / TypeScript

npm install kronvex

import { Kronvex } from "kronvex";

const kx = new Kronvex("kv-your-api-key");
const agent = kx.agent("your-agent-id");

await agent.remember("User prefers concise answers");
const context = await agent.injectContext("How should I format this?");

MCP (Claude Desktop)

{
  "mcpServers": {
    "kronvex": {
      "command": "npx",
      "args": ["kronvex-mcp"],
      "env": { "KRONVEX_API_KEY": "kv-your-api-key" }
    }
  }
}

→ Python SDK on PyPI · Node SDK on npm

How It Works

Memories are ranked by a composite confidence score:

confidence = similarity × 0.6 + recency × 0.2 + frequency × 0.2

• Similarity: pgvector cosine similarity on 1536-dim OpenAI embeddings
• Recency: sigmoid with 30-day inflection point
• Frequency: log-scaled access count

Self-Hosting

# Requires Docker
cp .env.example .env
# Edit .env with your OPENAI_API_KEY and DATABASE_URL
docker-compose up --build

API available at http://localhost:8000 · Docs at http://localhost:8000/docs

Endpoints

Full interactive docs: api.kronvex.io/docs

Pricing

→ See full pricing

Contributing

See CONTRIBUTING.md.

Built in Paris · kronvex.io · hello@kronvex.io