◌ reasoning your agents can reuse
Delapan models a domain's reasoning once, reproducibly — then any agent injected with it performs the task better, on grounding it can show its work for.
◌ 01 — the problem
Every run starts from scratch. Your agents explore the domain, reconstruct the structure, re-derive the understanding — then throw it all away. Same task, same agent, same expensive reasoning loop. Run twice, ground differently. Can't be reproduced, can't be audited, can't move to another model.
tokens to ground one agent run — less to read, because the reasoning was modeled once. (illustrative; the ~1,750 figure is the engine's real hard limit.)
◌ 02 — how it works
You build the reasoning model once. Delapan keeps it, tracks its provenance, and injects it into any agent — a cheap lookup instead of an expensive exploration loop. Same grounding every run, reproducible and auditable on demand.
Build the reasoning model once — the expensive understanding, done once and kept with full provenance.
Any agent reads the model — a cheap lookup instead of re-deriving. Same grounding every run.
Every output is grounded in reproducible, provenance-tracked context — replayable on demand.
Every tap returns one of three verdicts before any computation runs —
› /delapan:tap "who competes for the AI 'memory layer' — and what do they miss?" ◌ delapan · kb: context-layer-landscape Grounded: 6 findings (rich coverage). # reasoning already modeled Mem0 and Letta own "memory layer" [#2a1c]; Zep/Graphiti pitch a temporal "context platform" [#5f0a]. They all store & fetch — agents still re-reason over those facts. Delapan's lane: the reasoning itself, modeled once [#9b3d].
◌ 03 — building the model
Building the reasoning model means doing the expensive part once. Delapan plans searches, reads sources, extracts single checkable facts, scores each one, and merges the duplicates — all live, in seconds. The result is a modeled, provenance-tracked representation of how to reason over the domain.
Each item in the model is one claim, one source, one confidence score. It reads a whole page to write a single sentence — so the cost of modeling is paid once, not every time an agent runs.
Tell it what you're working on, and it plans a whole library of knowledge bases, shows you the plan, and — once you say go — builds the reasoning model across all of them at once. One real session went from empty to 5 modeled knowledge bases in about two minutes.
◌ 04 — plug it in
Modeled once, the same reasoning injects everywhere — your coding assistant and your deployed product inherit the exact same grounding, reproducible on every call.
Every message to your coding assistant already carries the modeled reasoning, quietly, in the background. You don't lift a finger — and the model deepens as the domain grows.
Publish it as a simple API behind your own key. One request gives your app a ready-to-inject reasoning context to drop straight into its prompt — same grounding every call.
# inject the modeled reasoning — not a document dump ctx = requests.get( "https://api.delapan.ai/v1/preamble", params={"q": user_question}, headers={"Authorization": "Bearer dlp_live_…"}, ).json() # ctx["preamble"] → drop into your system prompt # ctx["coverage"] → already-knows | mostly | brand-new
◌ 05 — why it's leverage
The leverage is in what gets amortized: the expensive part — exploring the domain, understanding its structure, deriving how to reason over it — happens once. Every agent after that reads a cheap lookup. The cost is fixed; the reuse compounds.
Whether one agent or a hundred tap the model, each reads the same cheap lookup. Modeling cost is fixed; reuse is free at scale.
Outputs cite the exact model items they used by ID — the reasoning is traceable, replayable, and auditable without bloating the prompt.
The modeling cost is a one-time write. Every agent run after that is a read — and reads stay cheap no matter how deep the model grows.
Pay once to model it. Inject it everywhere, reproducibly. That's how modeled reasoning ends up cheaper — and more trustworthy — than re-deriving from scratch.
cost per agent run, normalized to re-derive baseline. model build amortizes across all future runs — delapan reaches steady-state around run 10.
◌ 06 — where it fits
/delapan:build "your domain" → /delapan:tap "your question" → /delapan:ship