Introducing Rohrpost: Context Compression for LLM Workflows

If you’ve spent time building with large language models, you’ve hit the wall: conversations grow, context windows fill up, and suddenly your AI assistant has forgotten everything you discussed twenty minutes ago.

The problem

Every LLM interaction carries a token budget. Once you exceed it, you’re forced to choose between:

• Truncating history — losing earlier decisions and context
• Summarizing manually — time-consuming and error-prone
• Starting fresh — repeating yourself from scratch

None of these options are great when you’re deep in a multi-day coding session or managing a complex project.

Our approach

Rohrpost takes a different path. Instead of fighting context limits, we work with them. The system continuously distills your conversation history into compact, structured capsules that preserve:

• Architectural decisions and their rationale
• Code references and file relationships
• Project status and open questions
• Key entities and their connections

When you need context for your next LLM call, Rohrpost recalls the most relevant capsules and assembles a token-efficient brief — typically 60–80% smaller than the raw conversation history.

How it works

Raw conversation (8,000 tokens)
    ↓ distill
Capsule (1,800 tokens)
    ↓ recall + rank
Brief for next prompt (1,200 tokens)

The compression isn’t just truncation. Rohrpost uses a hybrid extraction pipeline combining heuristic pattern matching with LLM-based summarization to identify what actually matters in your context.

What’s next

We’re actively developing Rohrpost in the open. Check the changelog for the latest releases, or dive into the documentation to try it yourself.