Inference memory for ML platform teams

Reuse the context your agents already paid for.

Evanitas gives long-context agent systems a memory layer between the runtime and model serving stack: stable-prefix reuse, KV cache coordination, trace compilation, and task-level cost diagnostics.

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For
Long-context agents
Works with
vLLM, TensorRT-LLM, SGLang
Optimizes
Prefill, KV memory, token waste
82% cache hit rate
41% TTFT reduction
128k tokens avoided
Status Private alpha

Design partners for agent-heavy inference workloads.

Category Inference memory

Context reuse, trace compression, and KV cache efficiency.

Boundary Below the agent

Not a model, not a chat app, not a generic request proxy.

Review surface Cost per task

Report completed agent work instead of isolated model calls.

The production problem

Agent systems recompute the same memory again and again.

Long tasks repeat system prompts, tool schemas, retrieval context, repo maps, policy documents, and trace history. The result is avoidable prefill latency, GPU memory pressure, and token spend that is difficult to explain outside the infrastructure team.

Repeated region Failure mode Runtime pressure Evanitas action
System and policy prompts Stable context charged every turn Prefill latency Promote to reusable prefix memory
Tool schemas and examples Repeated schema injection Input tokens and cache churn Compile into cache-friendly layout
Retrieval and repo context Minor changes invalidate reuse KV memory pressure Separate stable source from dynamic query
Agent trace history Growing context window Cost per completed task Fold repeated traces into memory regions

Platform

A memory layer below the agent and above the serving runtime.

Evanitas treats context as structure. Stable prefixes, dynamic turns, retrieval fragments, and reusable KV blocks are separated before they hit the expensive path.

Cache-aware inference, not another model endpoint.

Evanitas attaches to existing inference stacks and makes repeated computation visible, reusable, and governed by policy.

  • Compile raw traces into stable and dynamic regions.
  • Coordinate KV reuse with runtime memory budgets.
  • Diagnose cache misses and repeated prefill.
  • Report savings at the task, workflow, and team level.
Stable prefix base with dynamic branching memory structures

Products

Three product surfaces. One inference memory model.

Each product surface has a clear role in production: reuse memory, compile context, or explain where inference is being wasted.

KV cache matrix with illuminated reusable nodes
Runtime layer

Evanitas KV Fabric

A low-level memory layer for KV block reuse, prefix-aware scheduling, and long-context serving efficiency.

Input
Stable prefixes, KV blocks, runtime events
Output
Reusable cache regions and memory policy
Improves
TTFT, cache hit rate, GPU memory pressure
Chaotic trace particles compiled into ordered light planes
Compiler layer

Evanitas Context Compiler

Turns raw agent traces into cache-friendly layouts with stable-prefix extraction, trace segmentation, and tool schema reuse.

Input
Agent traces, tools, retrieval, history
Output
Stable context regions and dynamic turn boundaries
Improves
Context stability, cache reuse, long-task cost
Analytical laser plane scanning a complex geometric data structure
Diagnostic layer

Evanitas Profiler

Trace-level token waste analysis, cache miss diagnosis, and cost-per-task reporting for agent workloads.

Input
Trace segments, token counts, cache state
Output
Miss diagnosis, recommendations, task reports
Improves
Cost visibility, review readiness, team accountability

How it works

From raw agent trace to reusable inference memory.

The workflow starts observationally. Teams can inspect trace waste first, then move into compilation and KV memory coordination once the savings path is clear.

01

Observe traces

Collect sample agent runs and identify repeated system, tools, retrieval, and history regions.

02

Compile context

Separate stable prefixes from dynamic turns so small changes do not break cache reuse.

03

Attach KV fabric

Coordinate reusable memory regions with the existing serving stack and runtime budget.

04

Report task cost

Measure cache hit rate, recompute avoided, TTFT pressure, and cost per completed task.

Production telemetry

Metrics that make inference waste reviewable.

Illustrative sample fields. Real deployments should use reproducible benchmark methodology.

82%Cache hit rate

Repeated context reaching reusable memory.

41%TTFT reduction

Prefill pressure removed from long-context runs.

128kTokens avoided

Repeated input tokens avoided in one trace family.

37%KV pressure reduced

Memory pressure shifted from recompute to reuse.

Runtime compatibility

Built to fit the stack teams already operate.

Evanitas should sit inside the infrastructure review process, not force teams to replace the serving stack they already know how to run.

vLLM

Prefix-aware serving and long-context workloads.

TensorRT-LLM

GPU-optimized inference and memory-sensitive deployments.

SGLang

Structured generation systems and agent-serving experiments.

Custom serving

Internal runtimes, private clusters, and enterprise AI platforms.

Security and operations

Designed around private traces and production review.

Inference memory depends on traces, and traces can be sensitive. Evanitas is structured around privacy, deployment control, access boundaries, and auditability.

No training on customer traces

Diagnostic trace data stays separate from model training workflows.

Private deployment path

VPC, private cloud, and self-hosted runtime integration conversations.

Retention and redaction controls

Mask sensitive trace regions and set retention windows by environment.

Audit-ready reporting

Reports for cost review, cache policy changes, and operational approvals.

Solutions

Built for traces that keep coming back.

Coding agents

Reuse repo maps, policy prompts, tool schemas, and recurring dependency context.

Research agents

Compile recurring source material and citation context into cache-friendly memory.

Support agents

Separate stable policy and product knowledge from dynamic customer turns.

Enterprise workflows

Reduce repeated tool and workflow schemas across long-running operational agents.

Resources

Technical notes for teams reducing inference waste.

Public material should make the company feel legible before a sales call: what is measured, how cache-aware context works, and how teams can evaluate the savings.

Technical brief

Cache-aware context compilation for agents

Stable prefixes, dynamic turns, and trace segmentation.

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Benchmark note

Measuring recompute avoided

Task-level cost, TTFT pressure, and KV memory utilization.

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Engineering log

Designing trace profilers

Profiler UI, event schemas, and cache miss diagnosis.

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Company

A systems company for the agent era.

Evanitas is building the memory layer that lets AI systems reuse context instead of recomputing it. The work sits between model serving, agent runtimes, observability, and cost control.

The name Evanitas points at what the product does: it makes unnecessary inference disappear from the system without making the system less capable.

Private alpha

Bring inference memory into your agent stack.

We are working with teams running long-context, agent-heavy workloads where repeated context is becoming a measurable infrastructure cost.

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