u/Rough_Cell7187

Quick math: step 10 of your agent loop re-sends steps 1-9 in full. Every tool call, every retry, every reasoning trace, verbatim, every time. That's not a bug, that's just how context windows work - and it's why your bill grows faster than your agent gets smarter.

I built Traject to fix exactly this for LangChain agents.

• 3 lines, zero call-site changes. Patch your existing LLM object, keep coding.
• Compresses before the request goes out — dedupes repeated tool output, summarizes bulk noise (diffs, logs, file listings) while keeping error lines/file:line refs/SHAs intact, drops what's genuinely dead weight.
• Shadow mode by default. It watches and logs savings before it touches a single live request. You flip it live when you trust the numbers.
• Reversible. Anything dropped is recoverable via an MCP tool — nothing is gone for good.

Numbers, not vibes: 49 real SWE-bench agent trajectories, 43-45% token reduction, with a separate fact-preservation check so "compression" doesn't quietly mean "we deleted your error messages." Fully reproducible, dataset's public.

Self-hosted, MIT licensed, your data never leaves your infra.

What I actually want: LangChain users running this on real traffic in shadow mode (safe — it doesn't change anything until you say so) to tell me where it breaks. Which chains it mishandles, which tool outputs it butchers, whatever. One benchmark dataset doesn't prove it generalizes.

Repo: Traject — tear it apart; I'll be in the comments.