---
title: "cognigy"
description: "NiCE Cognigy Plugin: create, test, and manage AI Agents on the NiCE Cognigy platform"
canonical: https://agentpluginsdirectory.com/plugins/cognigy
last-updated: 2026-09-16
---

# cognigy
NiCE Cognigy Plugin: create, test, and manage AI Agents on the NiCE Cognigy platform
- Slug: cognigy
- Publisher: Cognigy
- Repository: https://github.com/Cognigy/cognigy-plugin
- Manifest: plugin/plugin.json
- Version: 1.19.0
- License: MIT
- Category (editorial): other
- Skills: 15 (agent-creation, agent-red-team, docs-lookup, flow-nodes, knowledge-setup, llm-providers, package-management, settings, snapshot-backups, tools-setup, troubleshooting, voice-gateway-setup, voice-go-live-checklist, webchat-setup, xapps)
- MCP servers: 2 (docs, platform)
- Stars: 13
- Repository created: 2026-06-18
- Repository last pushed: 2026-09-15
- Publisher type: Organization
- Listing: https://agentpluginsdirectory.com/plugins/cognigy
- Schema: https://agent-plugins.org/schemas/1.0.0/plugin.schema.json

## What cognigy does, in the publisher's words

> Distributed exclusively through each client's native plugin mechanism, a plugin on Claude Code, Claude Desktop, ChatGPT + Codex, and Antigravity, a plugin on Cursor, and an Agent Plugins-standard plugin on any conformant host (Kiro, VS Code + Copilot, …), with more clients to come. Each package installs the server engine and ships skills + agents.

A plugin that connects your AI assistant to the Cognigy.AI REST API. Create, test, and improve LLM-based AI Agents through a self-improvement loop, without leaving your client.

- 17 workflow tools for agent creation, deployment, packaging, backup, and voice setup
- One-call agent setup: creates Agent + Flow + AI Agent Job Node + REST Endpoint automatically

From the project README, punctuation lightly normalized. Full text: https://raw.githubusercontent.com/Cognigy/cognigy-plugin/HEAD/README.md

## Skills

- agent-creation: Use when the user wants to build, create, or set up a new Cognigy AI Agent from scratch, covers listing projects, ensuring an LLM exists, creating the agent, testing it, and refining persona/job fields.
- agent-red-team: Use when the user wants to red-team, adversarially test, jailbreak-test, or probe the guardrails of a Cognigy AI Agent, covers scoping consent, deriving the agent's policy surface from its config, generating targeted probes, scoring verdicts, and producing a standardised findings report.
- docs-lookup: Use when the user asks how a Cognigy.AI feature, node, endpoint, setting, or concept works, asks for Cognigy documentation, best practices, or release/version behavior, or when you are unsure of platform behavior or valid configuration values and need to consult the official docs before answering o…
- flow-nodes: Use when adding custom logic inside a Cognigy tool branch with manage_flow_nodes, or when rendering/visualizing a flow as a diagram, supported node types, config schemas, placement rules, the tool-first workflow, and the render operation.
- knowledge-setup: Use when the user wants to add knowledge, RAG, or a knowledge store/source to a Cognigy agent, covers embedding vs Knowledge Search models, Knowledge AI settings, ingesting sources, and attaching knowledge as a tool.
- llm-providers: Use when configuring or choosing an LLM for a Cognigy agent, valid provider names (openAI, anthropic, azureOpenAI, google, mistral, openAICompatible, awsBedrock), model strings, connection types, credential resolution, OpenAI-compatible endpoints (vLLM, Hugging Face, LiteLLM, Azure AI Foundry, self…
- package-management: Use when exporting, importing, uploading, inspecting, or downloading Cognigy package zip files, including reusing an LLM plus its connection across projects.
- settings: Use when configuring project-level Cognigy settings: voice preview / speech provider configuration and Knowledge AI settings.
- snapshot-backups: Use when backing up a Cognigy project before changing an existing AI Agent, rolling a project back to a previous state, undoing agent changes, or working with Cognigy Snapshots, create, restore, list, delete.
- tools-setup: Use when creating or configuring Cognigy agent tools, choosing the tool type (tool, http, mcp, knowledge, send_email) and their configuration schemas.
- troubleshooting: Use when a Cognigy agent returns empty responses, a tool call or create_ai_agent fails, a resource is not found, setup_llm fails, or you need to diagnose a Cognigy MCP problem.
- voice-gateway-setup: Use when the user wants to set up a voice agent or a Voice Gateway endpoint with WebRTC for browser-based voice interaction.
- voice-go-live-checklist
- webchat-setup
- xapps

Descriptions come from the frontmatter of each SKILL.md, punctuation lightly normalized.

## MCP servers

- docs: transport: streamable-http; url: https://docs.cognigy.com/mcp
- platform: transport: stdio; command: npx -y -p cognigy-engine@npm:@cognigy/plugin-engine@1.19.0 cognigy-mcp

Read from the plugin's own mcp.json. Environment variable names only, never values.