"Found in dsh's plugin marketplace": a context-compression plugin built by one person, 4,000 stars in three months

billion-context is an open-source plugin by a Chinese solo developer, sitting between a coding agent and the model. It lets the model decide when to fold past conversation into summaries. The pitch is "100K window is enough, save 5x tokens, run one session for a month". It took off because three things coincided: the wave of DeepSeek Harness opening its plugin marketplace, one package connecting to about 25 clients, and an iteration speed that doesn't look like a single person's work. The category itself is already crowded, so what's worth learning is its timing for the listing and its approach to reaching clients.

A post from Oct 15

On the morning of Oct 15, 2026, someone posted a short thread on linux.do titled "Anyone using billion-context?" The body was three lines: "Saw it in dsh's plugin marketplace. It supports a lot of harnesses. Would using it in pi or omp lower the quality of the output?" The post later got 638 views and 12 replies.

The replies are almost a thumbnail of the project. One person says "just downloaded it yesterday, I use pi, the principle seems sound". Another says "used it for a while, turned it off now, because the compression is too aggressive and it easily loses important context memory". Someone else is torn between magic-context and this one. On Oct 18, one person wrote "I use magic-context, but billion's star count is rising very fast".

That week, it had just reached GitHub's TypeScript weekly trending chart. On Oct 9 it had 698 stars, and on Nov 6 it had 4,111 (daily star counts from GitHub). It gained about 3,400 over four weeks, with a single-day peak of +237 (10-25).

What it actually does

As a coding agent conversation runs longer, the context keeps piling up. You either hit the window limit, or re-send tens of thousands of tokens on every turn and pay per token. Every client has built-in compression, but it works by summarizing the entire conversation in one shot once a threshold is reached. DeepSeek Harness (dsh)'s default threshold doesn't trigger until the context grows to about 678k tokens (Tencent Cloud developer community, 9-28). The tokens burned in between are a widely recognized pain point.

billion-context takes a different approach: it's a proxy sitting between the client and the model API. It gives the model extra tools (compress, decompress, search_context) so the model itself picks which parts of the history are done, folds them into layered summaries, and can unfold them again later. The author says a healthy session's prefix cache hit rate stays at 95–97%, with compression overhead under 2%. The repo also ships a preprint: 4.5 months, 174,327 model calls, 18.8B input tokens cumulatively, and the 204,800 window never overflowed.

It targets a very specific crowd: the many developers in China running cheap small-window models (128K DeepSeek Flash, various coding plans). Someone on linux.do reported: "Context stays basically under 100k, even with a total consumption of several hundred million."

The author, ranxianglei, registered their GitHub account 2015 years ago and still has only 68 followers. Fully free and open source, no paid version.

Where the stars came from

Point by point.

The dsh plugin store was the first entry point. deepseek-harness open-sourced in Aug 13, now at 274k stars, and a wave of companion plugins flooded in (dsh-desktop 35k, awesome-dsh-plugin 23k). In the same wave, dsh-context, which does "context viewing," also climbed to 4.2k, almost identical to billion-context, which suggests demand for context-type plugins among dsh users is real. The billion-context repo has only one topic tag: dsh-plugin.

The charts amplified word of mouth in a small circle. For the first two months it only gathered two or three hundred to seven hundred stars, almost all within Chinese developer circles. The repo was created in Oct 5; by Oct 9, after 60 days, it reached the weekly chart, and for the following month it was almost always on the weekly or monthly chart. The 4,000 stars were mostly gained in this period. Timing-wise, it looks like it was surfaced once it crossed some chart threshold (inference).

Each client added brings another community. One package covers about 25 clients including pi, omp, opencode, dsh, kimi, claude, codex, gemini, qwen, copilot, amp, kiro, goose, aider, and zed; even closed-source binary clients can plug in via certificate MITM. Longtime Pi community users moved over directly (someone on Reddit r/PiCodingAgent said it had "quite a lot of downloads on pi.dev"), and OpenCode and dsh users each found it in their own stores.

It moves faster than a single person could. Within 92 days of creation, issue and PR numbers reached #5820, the author committed 3,597 times, and npm got 1,208 stable releases (plus auto-published preview packages for each PR, over 8,000 versions in total). On Nov 6 alone, more than a dozen bug-fix PRs landed in a single day. The author also has a project called ework, described as a "self-hosted AI development fleet driven by issues." This repo is likely being maintained by that agent fleet (inference). Users' takeaway is "file an issue and it gets fixed the same day," so they keep filing.

It backs its credibility with papers and benchmarks. This category is full of "saves 95%" slogans. It published a preprint and a cross-benchmark evaluation over 1,500 tasks (LongMemEval-S, ContextBench, MemoryAgentBench), and the CSDN comparison and several blogs cite these numbers directly. Not everyone is convinced, though: someone on linux.do said "the evaluation in the preprint is poorly done."

No Hacker News thread, Product Hunt launch, or paid promotion found. On X, only one recommendation with 408 views from @8-31 turned up.

A caveat: the 3.77M npm downloads since August shouldn't be read as user count. The package auto-updates, and each PR publishes a preview package, which inflates downloads many times over.

Who it's competing with

"Context compression" was a hot category in 2026 years, and the leaders are an order of magnitude bigger. It holds a narrow niche: compresses the entire session, the model decides itself, and works across all clients.

Reviews are split. The clearest comment came from a user on linux.do who compared it in Pi: "Cache hit rate is a bit better with billion; for very long tasks billion works better. But it feels like billion has higher latency per response? And billion has no long-term project memory, only memory within a single session." Another post was titled outright "Is billion-context really that good to use?" The poster switched over from magic-context: "Lately there have been acp problems all the time. I thought it was my gateway network jittering, and after digging, it turned out to be this plugin."

Copycats are already here: a dsh port Tyan66666/billion-context-dsh (168 stars, created the day dsh went open source), a Rust rewrite billion-context-rs (10-26 published to crates.io), a LiteLLM callback version, and a dozen or so 0-star derivative libraries. None at the same scale yet.

If it were you

My take: Don't build another general-purpose context compressor. Copy its two moves: when a new agent platform opens a plugin marketplace, be in the first batch listed; and make one core that plugs into N clients. This specific niche already has it, magic-context, and a pile of big players, and the dsh wave was three months ago.

Who it suits: individual developers who already have an agent plugin or MCP tool and use an agent to maintain their own code; people already active in a particular client community (Pi, OpenCode, dsh, Kimi, Qwen).

Who it doesn't suit: anyone wanting to go head-on against headroom or context-mode on compression algorithms; anyone without time to triage issues every day. Much of its retention comes from "fixed the same day," so if you can't match that pace, don't touch this kind of tool that sits inside users' workflows.

How to do it: three paths, ordered by confidence. ① Watch the next agent client that opens a plugin marketplace, and within the week of opening ship a plugin that fixes the most-complained-about default setting (for dsh it was the compression threshold set too high; the next one may be something else), tagged with the platform's required topic tag. ② Do what it doesn't do well: it gets criticized for "compressing too hard and losing context," "no cross-session memory," and "higher latency." A small tool that shows what changed before and after compression, or that turns session summaries into project memory, has ready-made users looking for it. ③ Go after a different audience: as of Nov 6 it had only just gotten its first YouTube video, and English READMEs, English tutorials, and deep adaptations for English-language clients (Cursor, Windsurf, Cline) are still thin.

How to get users: claim a spot in the platform plugin store and awesome lists first; in Chinese, post on linux.do, the Juejin series, and Zhihu; in English, post on r/LocalLLaMA, the subreddits and Discords of the relevant clients; publish numbers you've measured yourself (how many tokens saved, how many days it ran), which work better than slogans. Spend the first two months building word of mouth and daily star growth, then ride the wave when the chart placement comes.

When it needs to happen: within two weeks after the next plugin marketplace opens; if you take path ③, get ahead of the big English accounts writing about it, which probably means the next month or two.

What would show you read this wrong: ① Over these months, the major clients change their built-in compression to incremental, reversible versions, and this class of plugin loses its reason to exist as a whole; ② billion-context keeps gaining a hundred or two hundred stars a day with no new chart exposure, which would mean users value the product itself rather than the timing, and the room for path ② is smaller than you think; ③ on the next platform that opens a plugin marketplace, no context-type plugin in the first batch passes 1,000 stars in a month, which would mean the "first batch listed" move no longer works.

Data and sources: GitHub repo page and daily star counts (10-09 to 11-06), GitHub weekly and monthly charts; npm registry and download stats (8-01 to 11-05); dsh-plugin.org listing page; linux.do threads "Anyone using billion-context?" (10-15) and "Is billion-context really that good to use?" and others; Tencent Cloud Developer Community 9-28 article; Zhihu 10-22 weekly star-gain chart; CSDN 10-16 comparison; Reddit r/PiCodingAgent; YouTube AICodeKing (11-06); MindStudio blog (11-06); GitHub pages of each competitor.