whatisgithub

What is since?

lnshrivas/since — explained in plain English

Analysis updated 2026-05-18

1PythonAudience · developerComplexity · 2/5Setup · easy

In one sentence

A lightweight Python tool that helps AI coding agents detect when files have changed on disk since the agent last read them, preventing stale overwrites and giving chat agents time-awareness between messages.

Mindmap

mindmap
  root((repo))
    What it does
      Tracks file changes
      Prevents stale overwrites
      Reports drifted files
      Adds time context to chat
    Tech stack
      Python
      MCP server
      Zero dependencies
    Use cases
      Safe AI file editing
      Multi-agent coordination
      Time-aware chat bots
    Audience
      AI tool builders
      Agent developers
    Setup
      pip install pysince
      Add to MCP client config
      Python 3.10 or later

Code map

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What do people build with it?

USE CASE 1

Stop your AI coding agent from overwriting files that changed since the agent last read them.

USE CASE 2

Coordinate multiple agents editing the same files by alerting each one to external changes.

USE CASE 3

Give a chatbot the ability to recognize time gaps and greet users naturally after absences.

USE CASE 4

Build safer long-running coding tasks where formatters or teammates modify files mid-task.

What is it built with?

PythonMCP

How does it compare?

lnshrivas/since0xustaz/streamgatea-bissell/unleash-lite
Stars111
LanguagePythonPythonPython
Setup difficultyeasyhardhard
Complexity2/54/54/5
Audiencedeveloperdeveloperresearcher

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

Install with pip install pysince (not 'since') and add it to your MCP client config, requires Python 3.10+ with zero other dependencies.

No license information was provided in the repository explanation.

So what is it?

Coding agents like Claude Code, Cursor, and Copilot read a file, reason about it across many steps, then write changes back. But the file may have changed on disk in the meantime: a formatter ran, a teammate pushed code, another agent edited the same file. The agent never notices and overwrites the newer version with full confidence. This is called the stale world model problem, and it accounts for a significant share of agent failures on long tasks. since is a lightweight Python package that solves this. It fingerprints every file the agent reads by recording the file's modification time and a content hash. On every subsequent tool call, it compares those stored fingerprints against the current state of the file on disk. If something changed, since reports it before the agent acts, so the agent re-reads the file instead of writing from a stale copy. There is no background process or polling. It just checks disk at the next turn. The package runs as a local MCP server, which is a standard way for agent tools to communicate. You add it to your client's configuration and add one line to your agent's instructions telling it when to call the provided tools. The main tool is check_staleness, which reports not just the file you asked about but every tracked file that has drifted. This means the agent cannot stay blind to a change it did not think to check. The package has zero dependencies and requires Python 3.10 or later. Beyond file checking, since also offers temporal context for chat applications. You wrap your chat function with a decorator, and the model receives a small time block before each turn showing when the conversation started, how long the gaps were between messages, and whether context has gone stale. This lets the model say "welcome back, it has been about six hours since we last spoke" instead of claiming it has no memory of previous interactions. The package is installed with pip install pysince. The PyPI name is pysince because the name since was already taken on the package index.

Copy-paste prompts

Prompt 1
I want to prevent my AI coding agent from overwriting files that changed on disk since the agent last read them. Help me install pysince, configure it as an MCP server in my agent setup, and add the right instruction line so the agent calls check_staleness before writing files.
Prompt 2
I have multiple AI agents editing the same codebase and they keep clobbering each other's changes. Show me how to use pysince's check_staleness tool to make each agent aware of files modified by others before it writes, and walk me through the MCP client configuration.
Prompt 3
I want my chatbot to recognize when a user has been away and say something like 'welcome back, it's been six hours.' Help me use pysince's chat decorator to add temporal context to my chat function so the model knows about message time gaps and conversation staleness.
Prompt 4
Help me understand which files pysince is tracking and how check_staleness reports every drifted file, not just the one I ask about. Show me how this prevents my agent from staying blind to changes it didn't think to check.

Frequently asked questions

What is since?

A lightweight Python tool that helps AI coding agents detect when files have changed on disk since the agent last read them, preventing stale overwrites and giving chat agents time-awareness between messages.

What language is since written in?

Mainly Python. The stack also includes Python, MCP.

What license does since use?

No license information was provided in the repository explanation.

How hard is since to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is since for?

Mainly developer.

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