Ogcode
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 and call graph via memory_recall, fetching code-structure context on demand, 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.
This is unique to Ogcode
No other coding agent on the market does this. Claude Code, Cursor, Copilot, Aider — every one of them replays the full conversation every turn. Token efficiency, in those tools, is an afterthought at best. Ogcode is the only agent engineered, at the agent-loop level, to conserve tokens and to curate context per turn — because it believes the real lever is context engineering: how efficiently and how relevantly you prepare the context for a given task.
Context engineering lets smaller models punch above their weight
This is the deeper payoff. If the only thing context engineering did was save money, it would still be worth it — but it does more: it extracts capability even from lower-end models. Keep the context relevant, limited, and short, and a mid-tier model (Claude Sonnet, a local Llama, a smaller GPT) can reason just as clearly — and sometimes outperform — a frontier model that's been handed a bloated, noisy transcript. The frontier model isn't smarter about your code; it just has more raw capacity to dig itself out of the irrelevant history you buried it under. Give either model a clean, on-point context window and the gap narrows dramatically — often to zero.
In the end, it's all about context engineering. Ogcode is brilliant at this, which is why it simultaneously cuts token cost and increases the accuracy of the task outcome. Cheaper and better — not a tradeoff.
Infinite Context — never hit a model's limit
Every model has a context window. Every other agent eventually slams into it. With Ogcode you never do — chat forever, on any model, no matter how small its window.
This is the part that genuinely changes the game. Every LLM ships with a fixed context limit — 8K, 128K, 200K, a million tokens — and every other coding agent creeps toward that wall as the session grows, until the model starts dropping the start of the conversation or simply refuses to continue. Ogcode removes the ceiling entirely.
The reason is Agentic Session Memory. Because Ogcode recalls the few facts relevant to the current turn from a persistent knowledge graph — instead of replaying the entire transcript — the prompt it sends stays flat no matter how long the conversation runs. A session that's 50 messages deep and one that's 5,000 messages deep hand the model the same compact, on-point context window. The conversation is unbounded; the per-turn context is not. So you can talk to a model forever and never reach its limit — and that holds for any model, whatever the size of its native window.
Three wins from one idea
- Infinite conversations, on any model. Drive Ogcode with a frontier model or a tiny local Llama with an 8K window — it makes no difference. The knowledge graph keeps every turn small, so the model's own context limit is never the bottleneck. You get effectively limitless session length even on models that could never sustain it alone.
- Far lower cost. A flat per-turn prompt means you stop paying the linearly-growing bill of full-transcript replay — over 70% fewer tokens on long sessions. See Context Engineering and Token Efficiency.
- Higher accuracy. The model reasons over only the facts that matter for the turn in front of it, not a wall of stale history — so it drifts less and stays on-target. Cleaner context is more accurate, not just cheaper.
Never hit a context limit, spend far fewer tokens, and get more accurate results — on whatever model you choose. That is the true beauty of Agentic Session Memory. (For the knowledge-graph internals, see Agentic Session Memory.)
Plan Mode + Parallel PRs — ship features, not just suggestions
Other agents suggest code. Ogcode plans the feature, decomposes it, executes the pieces in parallel, and raises the pull requests for you.
Most coding agents stop at "here's the code for the file you asked about." Ogcode's Plan Mode turns a one-line goal into a shipping feature. You describe what you want to build; the planning agent reads your codebase, discusses the approach with you, and — once you lock the plan — it becomes Ogcode's responsibility to break that feature into smaller, implementation-ready tasks, run them, and open the pull requests.
Plan once, ship the whole feature
┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ 1. Describe │ → │ 2. Lock │ → │ 3. Review │ → │ 4. Execute │
│ your goal │ │ the plan │ │ Kanban board │ │ in parallel │
└──────────────┘ └──────────────┘ └──────────────┘ └──────┬───────┘
│
┌──────────────┐ ┌──────────────┐ ┌───────▼──────┐
│ 6. Retry │ ← │ 5. Complete │ ← │ Task runs │
│ if needed │ │ auto-PR │ │ in isolated │
└──────────────┘ └──────────────┘ │ git branch │
└──────────────┘
- Describe — Open a plan and describe your goal. The planning agent reads your codebase and refines the approach with you in conversation.
- Lock — When you're happy, lock the plan. Ogcode runs a breakdown agent that turns the agreed plan into a structured task list with effort estimates (S/M/L/XL), complexity scores, and a dependency graph.
- Review — Tasks land on a visual Kanban board. Inspect, reorder, or kick off tasks individually.
- Execute — Each task gets its own git branch and isolated agent session. Tasks with no dependency on each other run in parallel — multiple agents coding at the same time.
- Complete — Finished tasks auto-commit, push, and open pull requests directly against your GitHub repo.
- Retry — A failed task is retried from a clean slate: the stale branch is deleted and the task starts fresh.
How Ogcode breaks the feature down
The breakdow