This is first in a series based on real production patterns of Kindo customers.

A few of our users have told us they like to use LibreChat as their chat UI, and Kindo as the secure engine behind it. And that's great, that’s exactly how we like to work here at Kindo, directly with the tools your teams already use.

This piece is based on this customer pattern, showing how you could use LibreChat for the conversation layer and pair it with Kindo for policy, DLP, audit trails, and agentic actions across your stack. We’ll introduce both tools first, and then present a side by side comparison table. By the end, you’ll see that Kindo and LibreChat address different needs, and that using them in tandem can give you the best of both worlds.

As you also many know, we also have deep roots in the open source community. Deep Hat, our LLM, is available on Hugging Face, and Kindo is model agnostic so you can bring OpenAI, Anthropic, Google, Mistral, or your own models. So supporting OSS is in our DNA too.

Kindo - AI Native Security & Ops Platform

Kindo is the AI native terminal for technical operations. It’s often described as the agentic command center for SecOps, DevOps, ITOps, and even red teams.

Unlike a simple chat tool, Kindo acts as a centralized hub that connects to your existing systems and drives end-to-end processes that previously used many separate scripts and tools. Kindo’s AI isn’t just answering questions; it’s taking action in your environment.

Privacy and Deployment

A standout feature of Kindo is its emphasis on enterprise grade privacy and control. Kindo delivers a fully self managed AI solution that runs securely on your own infrastructure (on premises or private cloud). It even includes a custom large language model (LLM) called Deep Hat, made for DevSecOps scenarios and trained with real world adversarial techniques. All of this means you get AI automation behind your firewall, avoiding the risk of sending sensitive data to third party services.

Integration and Automation

Kindo plugs directly into the tools and workflows you already use, from Kubernetes clusters and cloud APIs to CI/CD pipelines, ITSM ticketing systems, SIEMs, and more. This deep integration lets Kindo’s AI agents not only analyze information but also trigger actions in those systems. For example, if Kindo’s AI detects a misconfiguration or a security alert, it could open a ticket, roll back a deployment, or update a policy automatically. Kindo essentially becomes a smart orchestrator for your SecOps/DevOps tasks.

Multi-Model AI with Governance

Under the hood, Kindo is AI agnostic. It can interface with popular AI providers like OpenAI, Google, Anthropic, Cohere, IBM, and even open source models from Hugging Face. This means you aren’t limited to one model, you can choose the right AI for each job. Kindo gives IT and security teams a central control plane over all these AI models. You can govern who can use which model on what data, set guardrails, and log all AI usage centrally. Built-in data loss prevention (DLP) filters can automatically redact or block sensitive info, ensuring that, say, an engineer doesn’t accidentally send secret keys to an external AI service.

User Interface and Agents

Even though Kindo is doing heavy lifting in the background, it provides a friendly front-end. Kindo ships an enterprise chatbot interface that any employee can use to interact with AI models in a safe, consistent way. Whether the bot is powered by GPT-4 or a local model, the user experience is unified. Non-developers can ask questions or request automations through chat, without worrying about which model or data source is handling it, Kindo routes the request appropriately. For more technical users, Kindo offers a no-code agent builder. This lets teams create custom AI workflows by chaining prompts, tools, and data sources, all without writing code.

Security Superpowers

Kindo’s platform encourages an adversarial mindset, meaning it helps your team think like an attacker to improve defense. In practice, this means Kindo’s agents can simulate many tasks a hacker would do, but for your benefit. For instance, Kindo can comb through your code repositories to find any leaked secrets (API keys, credentials) in seconds. It can enumerate your attack surface by monitoring things like new subdomains or open ports, so you catch an exposed server before cybercriminals do. Kindo’s AI can even generate exploit proof-of-concepts from newly disclosed vulnerabilities, helping you test and patch weak spots faster.

LibreChat - Open Source AI Chat Platform

If Kindo is the all-in-one brain, then LibreChat is a flexible interface that lets users tap into that intelligence. LibreChat is a free, open-source AI chat platform that you can self host and fully control. It provides a user-friendly web interface for interacting with AI models, but unlike proprietary apps, it isn’t tied to a single provider, you decide which models to connect, whether from OpenAI, Anthropic, local deployments, or other sources.

Open Source and Multi Provider

As an open source project under the MIT license, LibreChat can be used and customized without fees or restrictions. This has made it popular among developers and enterprises alike, indeed, thousands of organizations (including some big names) are using LibreChat as a part of their AI toolkit. The platform supports a wide range of AI providers out-of-the-box: you can plug in OpenAI’s GPT-4, Anthropic’s Claude, Google’s PaLM, Azure OpenAI, local HuggingFace models, and more. In practice, LibreChat acts as a unified chat hub: your team can have one interface and easily switch the underlying AI model depending on the task.

Feature Rich Chat UI

One reason LibreChat is often called an open source chat app is because of its rich set of user centric features. It’s designed to feel very much like ChatGPT’s interface (so users find it intuitive), but with power user enhancements. You can have multiple chat threads, rename them, and even fork a conversation to try different prompts or what-if scenarios from the same starting context. This is great for analysts who want to explore different approaches without losing their original chat. LibreChat also supports multimodal inputs. You can upload images or files and have the AI analyze them (with capable models). It even has an integrated code interpreter (sometimes called code assistant) that lets you run code within the chat environment.

Customization and Extensibility

Because it’s open source, LibreChat is highly extensible. Teams can modify the UI and features to their needs, whether it’s adding custom plugins, integrating internal databases, or tweaking the interface. LibreChat comes with built-in support for retrieval augmented generation (RAG) workflows: it can be connected to a document database (using tools like LangChain with a PGVector-backed Postgres) so that the AI can fetch answers from your specific company data. There are also community contributed plugins for things like web search or YouTube transcription, which can enrich the AI’s capabilities for users. LibreChat’s authentication system is also enterprise ready: out-of-the-box it supports OAuth logins (e.g. via GitHub, Discord) and single sign-on with Azure AD or AWS Cognito for company deployments.

Data Privacy and Control

One of the main reasons organizations turn to LibreChat is to keep their data in house. By self hosting LibreChat on your own servers (or cloud), you ensure that chat data isn’t being sent to a third party SaaS by default. You have the option to use local models for sensitive data or route requests through an API gateway of your choice. This addresses both privacy concerns and cost control. Essentially, LibreChat gives you a ChatGPT-like experience on your terms. However, it’s worth noting that LibreChat by itself is an interface. If you connect it to an external API like OpenAI, your data will go to that API (unless you tokenize/anonymize it). The power is that you decide which model to use for which task.

Kindo & LibreChat Feature Comparison
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Open source gives you speed and choice. Kindo adds the guardrails and the execution you need in production. If your teams already love LibreChat, keep it, then plug in Kindo to move from chat to change with enterprise controls. We’ll keep spotlighting real customer builds that mix OSS tools with Kindo to deliver outcomes. If you want to be featured in the next installment, tell us how you’re using Kindo with LibreChat, Deep Hat, or your favorite AI and DevSecOps tools.