Smriti: A Git-like system for managing LLM reasoning state to prevent conversation drift

Smriti is an open-source system that addresses the problem of LLM conversation drift in long workflows. Instead of treating interactions as one long chat history, it treats them as reasoning state that can be managed like code in Git.

Core Problem

In long AI workflows, conversations often drift subtly over time through small changes: assumptions shift, side paths get explored, interpretations change, or the model starts reasoning from a slightly off state. This leads to plausible but misaligned responses. The traditional options—trying to steer back or starting over—both have drawbacks.

How Smriti Works

The system provides Git-like operations for reasoning state:

• Save a good state before things drift
• Restore that state later without later drift leaking in
• Branch into different directions from the same point
• Compare two reasoning paths
• Carry state across different models instead of re-explaining everything from scratch

Recent Additions

• Assumptions as first-class part of state
• Checkpoint review to surface contradictions and hidden assumptions
• Artifacts so checkpoints can include actual plans, snippets, or outputs being reasoned about, not just chat summaries

Key Difference

This isn't just saving markdown files or chat transcripts. The system controls what state the model sees next, enabling:

• Clean returns to known good states
• Later turns kept outside the current reasoning path
• Alternative exploration without contaminating the original thread
• Moving forward from structured state rather than one messy transcript

The creator notes this approach becomes more useful as workflows get more agentic, since long-running systems need ways to make reasoning state inspectable, recoverable, and less chaotic over time.