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AgentD: Coordinate Claude Code and Codex on One Project with LibraOS

AgentD: Coordinate Claude Code and Codex on One Project with LibraOS

Coding agents like Claude Code and Codex can already handle complex development tasks. But what happens when several agents need to work on the same application? Coordinating tasks, sharing project context, tracking file ownership and verifying completed work can quickly become a challenge of their own. AgentD brings multiple coding

How to Write a Janitor AI Character Card That Stays in Character

Janitor AI Character Card

How to Write a Janitor AI Character Card That Stays in Character

Most bots don't break character because the model is bad. They break because the card gives the model too little to hold on to, too much to sort through, or instructions that quietly fall out of its memory after twenty messages. This guide is about the parts of

Why Your AI Roleplay Bot Forgets: Context Windows Explained

Why Your AI Roleplay Bot Forgets: Context Windows Explained

Forty messages into a great roleplay, your character forgets your name, the promise they made two scenes ago, or the fact that they hate the sea. It feels like the bot stopped paying attention. What actually happened is simpler: that part of the story no longer fits in what the

Choosing the Right AI Model Setup with LibraOS: BYOK, Local & Hosted

Choosing the Right AI Model Setup with LibraOS: BYOK, Local & Hosted

Every AI app has to answer one question early: where does the model run, and who provides it? The answer decides where your prompts go, who bills you for inference, and how much infrastructure you operate. With LibraOS, the model is a choice you make separately from the rest of

Deploying AI in Customer Environments: A Checklist by LibraOS

Deploying AI in Customer Environments: A Checklist by LibraOS

Sooner or later, a customer you want to sign says: "We like it, but it has to run in our environment." A bank wants model traffic inside its network. A law firm wants client files on its own servers. A public-sector buyer has no internet connection at

RAG vs Agent Memory: Which One Does Your AI Assistant Need?

RAG vs Agent Memory: Which One Does Your AI Assistant Need?

An AI assistant can know things in two different ways. It can look facts up in your documents, or it can remember what happened in earlier conversations. The first is usually called retrieval-augmented generation, or RAG. The second is agent memory. They're often confused, and teams sometimes

Self-Hosted and Air-Gapped AI Agents with LibraOS

Self-Hosted and Air-Gapped AI Agents with LibraOS

Some teams can't send their data to someone else's cloud. A law firm wants client files to stay on its own servers. A bank wants model traffic inside its network. An operator in a sensitive environment has no internet connection at all. For them, an AI

How to Build Autonomous AI Agents with LibraOS: Practical Examples and Tutorials

How to Build Autonomous AI Agents with LibraOS: Practical Examples and Tutorials

"Autonomous" is an easy word to oversell. In this guide it means something specific: an agent that can take a goal, plan the steps, run them on its own, hand work between specialists and keep going while nobody watches, inside limits you set, with people deciding at the

7 AI Applications You Can Build with LibraOS: From Personal Knowledge Assistants to Workflow Automation

7 AI Applications You Can Build with LibraOS: From Personal Knowledge Assistants to Workflow Automation

The LibraOS Runtime runs the agents, memory, knowledge and tools inside your app. What you build on top of it is up to you, but it helps to see concrete starting points. The seven applications below come from two public places: the app ideas and interactive concepts on the LibraOS

How to Build Secure Private AI Applications with LibraOS

How to Build Secure Private AI Applications with LibraOS

An AI application handles some of the most sensitive data a company has: customer records, contracts, internal documents, and the prompts people type about all of them. "Private" means that data stays where you decide. "Secure" means the AI can only do what you allow, and

How to Deploy AI Agents in Production: Security, Permissions, and Execution Visibility

How to Deploy AI Agents in Production: Security, Permissions, and Execution Visibility

An agent that works in a demo has answered the easy question: can it do the task? Production asks harder ones. Where does the data go? What is the agent allowed to do on its own? Who approves the rest? When something goes wrong at 3 a.m., can anyone

AI Agent vs. AI Chatbot: What’s the Difference and When Should You Use Each?

AI Agent vs. AI Chatbot: What’s the Difference and When Should You Use Each?

A chatbot answers. An agent gets something done. Both use a language model, and both can look like a chat window. The difference is what happens after the user hits send. A chatbot replies with text and stops. An agent can look things up in your systems, work through several

Long-Running AI Agent Tasks: Background Jobs with LibraOS

Long-Running AI Agent Tasks: Background Jobs with LibraOS

Some agent work fits in a single request: look something up, draft a reply, answer a question. Other work doesn't. Reviewing a month of support tickets, preparing a research summary or working through a long list of records can take minutes or hours, and nobody wants to keep

How to Build an AI Agent Audit Trail with LibraOS

How to Build an AI Agent Audit Trail with LibraOS

An audit trail is the record that lets you answer one question after the fact: what did the agent do, and why? When an agent reads customer data, drafts a reply or proposes a refund, someone will eventually ask who approved it, what it was based on and what actually

How to Make Your AI Assistant Cite Its Sources with LibraOS

How to Make Your AI Assistant Cite Its Sources with LibraOS

An assistant that answers from your documents is only useful if people can trust the answer. Citations are how they check. When a reply points to the policy, the ticket or the contract it came from, a person can open the source and confirm in seconds. When it doesn'

Why Your AI Roleplay Forgets Things, and How an “AI Game Master” on DeepSeek-V3.2 Fixes It

Why Your AI Roleplay Forgets Things, and How an “AI Game Master” on DeepSeek-V3.2 Fixes It

Anyone who has played AI roleplay knows the moment. The story is going well, and then the AI forgets the sword you picked up, brings back a character who died three scenes ago, or gives you a lucky dice roll that never really happened. It’s not that the model

LibraOS Benchmark Results: How to Read Them

LibraOS Benchmark Results: How to Read Them

An AI runtime sits in the path of every request your app sends, so its own overhead matters. LibraOS publishes a controlled benchmark on its homepage: 244 runs per second at 256 concurrent runs, about 3.8 times Agno in the Minimal Runtime test, with 52 MB of idle memory

Human-in-the-Loop AI: Approve Agent Actions with LibraOS

Human-in-the-Loop AI: Approve Agent Actions with LibraOS

Human-in-the-loop AI means the agent drafts, and a person decides. The agent can read, search and suggest on its own, but actions that change data or reach the outside world wait for a human to approve them. LibraOS lets you choose where actions need approval. For the

What Is LibraOS? An AI Agent Runtime with Memory and Approvals

What Is LibraOS? An AI Agent Runtime with Memory and Approvals

Adding AI to an app is no longer the hard part. A model can answer questions, summarize documents and draft replies, and today's app builders turn an idea into a working prototype quickly. The hard part starts when real users arrive. The app now has to carry context

Benchmarking Open-Source vs. Proprietary Models for Roleplay/Chat Proxies

open-source vs proprietary models chat proxy

Benchmarking Open-Source vs. Proprietary Models for Roleplay/Chat Proxies

Chat and roleplay proxy applications have requirements that differ meaningfully from general-purpose assistants: sustained persona consistency across long conversations, tolerance for creative and open-ended content, cost efficiency at high message volume, and low latency to keep conversations feeling natural. Choosing between open-source and proprietary models for this

How to Benchmark Multi-Agent AI Systems: Frameworks, Metrics & Best Practices

Agent Benchmarks

How to Benchmark Multi-Agent AI Systems: Frameworks, Metrics & Best Practices

Benchmarking a single LLM is challenging enough: you define a prompt, establish a scoring rubric, and measure performance against a baseline. Benchmarking a multi-agent AI system introduces a different set of challenges. You need to evaluate not only whether the system produces the correct answer, but also whether agents

DeepSeek-V3.2 API for AI Roleplay & Agents: MegaNova Pricing Guide for US Developers

DeepSeek-V3.2 API for AI Roleplay & Agents: MegaNova Pricing Guide for US Developers

Looking for an affordable DeepSeek-V3.2 API for AI roleplay, character conversations, or autonomous agents? The cheapest API isn’t always the best choice. To find the right fit, compare token pricing alongside response quality, latency, and reliability. MegaNova offers DeepSeek-V3.2 inference with the following published specifications:

Kimi K3 at Scale: Inside Our Serving Stack

LLM Infrastructure

Kimi K3 at Scale: Inside Our Serving Stack

Moonshot AI shipped Kimi K3's weights on July 27, and the numbers alone explain why serving it well is a genuinely different engineering problem than serving most models that came before it: 2.8 trillion total parameters, with only 104 billion active on any given token. That sparsity

Your Intelligence. Your Infrastructure. Your Control.

AI Infrastructure

Your Intelligence. Your Infrastructure. Your Control.

Every time an application sends a prompt to a closed, third-party model API, it's making a quiet decision that compounds with every request that follows: someone else controls what that model costs next quarter, what data policies govern the request, which capabilities are available and which are

What Is a Digital Employee? How AI Agents Automate Real Business Workflows

what is a digital employee

What Is a Digital Employee? How AI Agents Automate Real Business Workflows

A digital employee is an AI agent assigned to a business role, such as claims intake or invoice processing, that completes multi-step work using your tools and escalates to a person when unsure. Digital employees take over repetitive, rules-based work, lower cost per task, run 24/7 and