Reasoning infrastructure2026Active

Smriti

Checkpointed reasoning state for long-running, multi-model work.

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At a glance

Problem

Long-running reasoning becomes brittle when usable state only exists inside transcript text.

Approach

Checkpoint, branch, and restore reasoning state across tools and model switches.

Why it matters

The core object is not the chat log. It is the state you want to preserve and recover.

System sketch

01Capture

Freeze decisions, assumptions, open questions, and artifacts into an explicit checkpoint.

02Branch

Fork from a clean checkpoint to compare alternatives without contaminating the base line of thought.

03Restore

Re-enter a prior reasoning state without dragging in later turns that do not belong there.

04Compare

Make divergent reasoning paths inspectable so the workflow stays model-agnostic and legible.

Design notes

State over transcript

The important unit is the reasoning state, not the transcript around it.

Model switches should be cheap

Changing tools or models should not require rebuilding context by hand.

Branches clarify thought

Alternative lines of reasoning become more useful when they are explicit and revisitable.

Question

I built Smriti after repeatedly hitting the same failure mode in multi-model work: the reasoning was good, but the usable state was brittle.

Once tools changed, models switched, or a thread was revisited later, too much of the structure had to be reconstructed from prose. The goal is to make that structure explicit enough to save, branch, restore, and inspect.

Approach

  • Checkpoint decisions, assumptions, open questions, and artifacts as structured state rather than as implicit chat context.
  • Restore a clean reasoning context without leaking in later turns that no longer belong.
  • Branch from a checkpoint to compare alternatives without overwriting the base line of thought.
  • Treat the model as a renderer of reasoning state, not the only place where that state lives.

Open questions

  • What is the right granularity for a checkpoint: message-level, task-level, or decision-level?
  • How should persistent memory differ from short-horizon reasoning state?
  • How much structure is enough before the system becomes heavier than the reasoning workflow itself?

Project spec

RoleConcept, architecture, and implementation.
StatusActive
TypeReasoning infrastructure
UpdatedApr 2026
Primary objectCheckpoints and branches for reasoning state.
System boundaryCapture, restore, and compare state across tools and models.
Current artifactPublic repository and demo.
Pressure pointCheckpoint granularity versus workflow overhead.

Topics

reasoning stateagentsmulti-model workflowscontext management

Where it is now

  • The public repository and demo are already live.
  • The implementation currently centers on capture, restore, and branching across multi-model workflows.
  • The next real decision is how much structure a checkpoint should carry before it slows the workflow down.