Compatible with every major AI agent and IDE
Create index on OpenSearch Vector
knn: true` and mapping a rigid dynamic dense vector field optimized for cosine similarity. Create a new native OpenSearch KNN index ready for vector embeddings
Delete document on OpenSearch Vector
Delete an explicit vector document bounding from OpenSearch
Get index on OpenSearch Vector
Retrieve explicit OpenSearch index mapping and settings
Index document on OpenSearch Vector
This executes a fast transactional atomic insertion into the embedding space. Upsert a singular vector document directly into an OpenSearch KNN index
List indexes on OpenSearch Vector
List all explicit indexes residing on the OpenSearch cluster
Search on OpenSearch Vector
Provide the exact index name and a JSON-stringified dense float vector array to find conceptually similar embeddings natively. Execute a K-Nearest Neighbors (k-NN) vector search against OpenSearch
How Vinkius protects your data
Can I audit what my AI agents are doing with this integration?
Yes, Vinkius provides an immutable, HMAC-chained audit log. Every tool execution, payload, and response is tracked in real-time on your dashboard, giving you complete visibility into your agent's actions.
What happens if the underlying API rate limits my agent?
Our edge infrastructure automatically handles backoffs, queueing, and throttling. If an AI agent sends too many erratic requests, Vinkius manages the rate limits gracefully, ensuring your backend doesn't crash.
How does the AI access my passwords and credentials?
It simply doesn't. On Vinkius, your passwords, API keys, and login details are kept in a secure vault. The AI (like ChatGPT or Claude) merely "asks" Vinkius to perform the task. Vinkius opens the door, does the work, and hands the result back to the AI. Your credentials are never seen, read, or learned by the artificial intelligence.
Does this work with Amazon OpenSearch Service (managed)?
Yes. Provide the Amazon OpenSearch Service endpoint as the host (e.g., https://search-xxx.us-east-1.es.amazonaws.com) along with the master username and password. The integration uses standard REST APIs that work identically on managed and self-hosted clusters.
What can AI Agents do with OpenSearch Vector?
The OpenSearch Vector integration provides comprehensive execution endpoints, allowing AI models to orchestrate tasks reliably.
Automating vector database with AI
Use the OpenSearch Vector server to execute vector database operations from your AI agent. The protocol manages state and authentication for continuous loved by devs workflows.
Cursor Copilot for k nn
Integrate OpenSearch Vector for AI-driven k nn management. The MCP server structures the outputs required for Claude to analyze loved by devs data.
OpenSearch Vector. Runs on everything.
From IDE to framework. Every connection governed by Vinkius.
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
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