VEKTOR Slipstream v1.7.4: Effort Control & Real Memory Search | by Vektor Memory | Jul, 2026 | MediumSitemapOpen in appSign up<br>Sign in

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VEKTOR Slipstream v1.7.4: Effort Control & Real Memory Search

Vektor Memory

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Photo by Sindre Fjerdingby Korsviken PexelsAnd a Look at What’s Coming from OpenAI & Anthropic<br>If you’ve been running VEKTOR Slipstream for a while, you’ll know the last few releases have mostly been about defense. v1.7.3 brought Faraday, our MPC Prompt Injection Shield, the security proxy that scans every MCP tool call for threats before it touches your memory graph. Before that we were deep in causal inference and FadeMem decay layers, teaching the system to forget the right things at the right time.<br>v1.7.4 is a model-focused release, but it fixes something that’s been bugging me for months: the Desk agent had no way to actually search your memory. And it adds a feature that changes how you think about cost and latency when you’re running Claude models day to day. Here’s what’s new, why it matters, and a bit about where the model landscape is heading next.<br>The problem with one-size-fits-all inference<br>Every LLM call you make costs something, in tokens, in latency, in dollars. Most tools treat that as fixed. You pick a model, you pick a prompt, and whatever the model decides to do with its reasoning budget is what you get. If you’re running a quick fact lookup and a complex multi-step synthesis through the same model, they cost roughly the same to run, even though one of them barely needed to think at all.<br>Anthropic’s newer Claude models, Sonnet 5 & Fable 5, expose an effort parameter that lets you control this directly, through output_config.effort in the API. Instead of picking a different model for cheap tasks versus hard tasks, you keep the same model and just dial the reasoning effort up or down. Low effort for a quick tag suggestion. High or extended effort for something that actually needs the model to work through a problem.<br>Press enter or click to view image in full size

Sonnet 5 resultsv1.7.4 wires this straight into VEKTOR. There’s an EFFORT_CAPABLE map that knows which levels each Claude model supports, because not every tier goes all the way up to max. If you ask for a level a given model doesn't support, VEKTOR clamps it down to whatever that model's ceiling actually is rather than throwing an error at you. Ask for something that isn't a Claude model at all, and the parameter just gets quietly dropped, no errors, no dead code paths.<br>The practical bit: there’s a new effort pill row sitting right in the CONFIG panel under your Active Model card. Low, medium, high, xhigh, max, whichever your model supports. Pick one, it saves through the same config store everything else uses, and it applies whether you’re in the main chat path or running the Desk agent’s tool-calling loop. You set it once per session and both surfaces respect it.<br>This matters more than it sounds like on paper. If you’re running VEKTOR against a big batch job, like re-embedding a session transcript or doing a background REM cycle synthesis, you can drop effort down and save real money without switching to a weaker model entirely. And when you’re doing something that actually needs the model to reason carefully, you can bump it up without touching your provider config.<br>Press enter or click to view image in full size

Claude Sonnet 5 and Claude Fable 5 land in the model catalog<br>Alongside the effort work, the model catalog got a refresh. Claude Sonnet 5 and Claude Fable 5 are both in the CONFIG model list now, and the stale claude-sonnet-4-6 reference that had been floating around the codebase since the last naming cycle is finally gone. If you've been manually overriding your model string to point at Sonnet 5 already, you can drop that override and just pick it from the list.<br>Fable 5 is worth a quick note if you haven’t been following the naming changes on Anthropic’s side. It sits at the same tier as Mythos 5, with the difference being extra safety measures around biology, cybersecurity, and LLM R&D topics. For most VEKTOR use cases, agent memory, JOT synthesis, Desk chat, you won’t notice a difference day to day, but if you’re doing anything in those more sensitive domains it’s the variant you want configured.<br>Worth flagging: access to the Mythos-tier models is currently paused while Anthropic works through an export control matter, so if you go looking for Fable 5 or Mythos 5 in your provider dashboard and it’s not there yet, that’s why. It’s not a VEKTOR issue. Keep an eye on Anthropic’s announcements page if you want the exact timeline.<br>The Desk agent can finally search its own memory<br>This is the fix I’m most pleased about, mostly because it’s the kind of gap you don’t notice until it actively annoys you. The Desk chat agent, the one running at /api/desk/chat, is genuinely...