3 posts tagged with "tutorial"

Claude Code is one of the best coding assistants available. But what if you want to use it with GPT-4o, Qwen, Llama, or a model running on your own hardware? OGX makes that possible. A single command connects Claude Code to your OGX server, auto-discovers your models, and maps them to Claude's haiku/sonnet/opus tiers.

This post walks through the setup, explains how the translation works under the hood, and shows how to configure multi-provider routing so different Claude Code model tiers hit different backends.

OpenAI's Codex CLI is a terminal-native coding agent. It reads your codebase, proposes changes, runs commands, and iterates, all from your shell. The problem: it only talks to OpenAI's API.

OGX fixes that. By placing OGX between Codex and your inference provider, you get Codex's coding workflows with any model OGX supports: Llama via Ollama, Claude via Bedrock, Mistral via vLLM, or OpenAI itself with conversation compaction on top.

This post walks through setup, configuration, and what to expect from this alpha integration.

What if your AI agent could improve itself? Most agent tutorials show a single loop — user asks a question, the agent calls some tools, returns an answer. But what happens when you need to systematically improve your agent's behavior over time?

In this post, we build a ResearchAgent that answers questions from an internal engineering knowledge base — and gets better at it automatically. The agent uses the Responses API agentic loop with file_search and client-side tools to research questions, and it owns its own system prompt. Every N calls, it benchmarks itself by using a different model to judge the results, and rewrites its own prompt via the Prompts API.

This is literally self-referential: a OGX agent evaluating and improving itself using the Responses API, Prompts API, and Vector Stores as its toolkit.