orqo already engineers it.
The limit everyone ran into this year wasn't model quality — it was context: how much an AI can hold in mind at once, and whether it's holding the right things. orqo is built to manage context, not just spend it.
Frontier models got good enough that quality stopped being the thing that broke. What broke instead was context — the working memory each agent carries while it runs.
A long job accumulates: every tool result, every message between teammates, every intermediate finding piles into the window. Past a point, the raw conversation overflows the model's context and the run stalls — not because the model got worse, but because it's drowning in its own history. And when many agents work as one team, that same history can live and grow in many windows at once, which is where the budget impact shows up — token economics
So the question that decides whether a multi-agent run finishes — and what it costs — isn't "which model." It's whether the platform manages context or merely spends it. orqo treats that as a first-class job, broken into three: compact what's stale, allocate it per agent, and remember what matters past the run.
Context management isn't one trick. It's three distinct disciplines, each handled on purpose.
As a job runs, each agent's working memory is compacted continuously — so it keeps going long past the point where the raw conversation would overflow the model's window and stall. The archivist remembers everything; the worker travels light.
Each agent owns its own context. For every tool, you decide whether its results auto-share with all agents in the stage or stay with the agent that called it — so context spreads on purpose, not by accident. And when two agents need to go deep, they break out into a side conversation the rest of the team never has to pay for — the team gets the conclusion, not the transcript.
Compaction keeps a single run alive; memory carries findings past it. Conclusions land in a knowledge graph the next run can navigate — not a transcript it has to re-read. See the knowledge graph
Most platformsThey compact by summarizing the whole conversation into a paragraph — and quietly lose the detail you needed three steps later.
orqoCompaction here is built not to. Three properties make the difference.
It compacts by how much each piece still matters to the work at hand, not simply by what's oldest — so the thread the job depends on stays put while the noise falls away.
History is condensed with its meaning intact, instead of blended into one lossy blur. And a bulky tool output — a search that returned fifty results when the agent needed two facts — collapses to a stub the agent can expand back to full whenever it needs it.
What finally ages out of the window doesn't vanish — it becomes long-term memory in the knowledge graph, still navigable later. That's the line between a lean window and catastrophic forgetting.
Each agent carries its own preset, from Aggressive to None — the archivist keeps more, the worker travels light. The net effect, measured on long-running research workflows: up to 4× more work from the same token budget. See token economics
Compact and allocate keep a run from stalling. Remember is about everything that should outlive the run — and it's a deliberately separate system.
The deep version of remember — the ontology, the typed relations, the causal navigation that goes a real step beyond RAG — has its own page, because it earns one. This page hands off to it: the knowledge graph
The platforms that hit the wall in 2026 weren't running worse models — they were spending context blindly and paying for it twice, in stalled runs and inflated bills. orqo compacts what's stale, allocates it per agent, and remembers what matters — so three people can run like thirty without the window, or the budget, getting in the way. Claim your slot