The token-efficient agentic coding workbench.
Built for a future where every token counts. Ogcode curates the relevant context for each turn — not the full transcript — so it cuts 70%+ of tokens on long sessions, sharpens accuracy, and lets even lower-end models outperform frontier ones. And because it recalls instead of replays, your conversations run effectively forever — you never hit a model's context limit, on any model, frontier or local. All while planning with you, remembering your codebase, and shipping features in parallel from a single binary that never leaves your machine.
Context Engineering · Infinite Context · Plan Mode & Parallel PRs · Quick Start · Why Ogcode · Documentation · Discord
Context Engineering — the real differentiator
Every other coding agent resends your entire conversation history on every turn. Ogcode doesn't.
Most coding agents operate on a naive replay loop: each turn, they bundle up the full transcript so far — every prior message, every tool result, every tangent — and ship it back to the model. That has two costs, and only one of them is money.
1. It burns tokens. The prompt grows linearly with the session, so a 200-message task can cost 5× more than a 20-message one even if the new work is trivial. On a fixed monthly budget this caps how much you can actually ship.
2. It hurts accuracy — and this matters more than the money. An LLM can only act on what's in its context window. When you flood that window with stale, unrelated chatter from earlier in the session, the signal gets buried in noise: the model loses sight of the current task, drifts toward half-remembered earlier decisions, and reasons against context that was relevant then but isn't relevant now. The older the conversation, the more the historical turns actively distract from the turn in front of the model.
Ogcode does the opposite. For each turn it extracts only the context that is actually relevant to the task at hand — pulling precise facts from a persistent knowledge graph via memory_recall and compacting stale history instead of replaying it verbatim. The model receives a short, sharp, on-point context window. Less history, fewer tokens — and better outcomes, because the model isn't wading through a hundred old messages to find the three facts it needs right now.
Saving tokens isn't only about cost — it's about accuracy. A smaller, more relevant context window lets the model focus, so it produces more correct, more on-target results per turn. The two goals reinforce each other.
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