Up-to-date documentation for LLMs and AI code editors. A project by @upstash team
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Software factory is the next level of coding with AI. You're the CEO, agents do the work.
Watch @leonvz build one you can copy. Agents pick up tasks, write code and ship, each in its own Upstash Box sandbox.
piped.video/AsvzMlLyQ38
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docs7 deploy
One command.
Your docs are hosted on *.docs7.io
Agents can read them through Context7.
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Introducing Context7 Search:
a grounding API for coding agents.
One GET request, get the docs snippets.
Unlike web search:
• Official docs, managed by library owners
• Scanned for prompt injection and malware
• Fast and token efficient
Blog:
upstash.com/blog/context7-se…
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Upstash now supports Redis Arrays.
Not a list. Index = address, not position.
- O(1) random access (lists walk O(N))
- Sparse slots cost nothing
- Delete doesn't shift
- ARRING: ring buffer in one command, 2x RPUSH+LTRIM
upstash.com/blog/redis-array…
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We benchmarked Docs7 vs Mintlify on Upstash docs.
Docs7 won 33 of 34 Pagespeed comparisons.
Page load on mobile: 3.3s vs 9.7s
Static HTML at build time, served from Cloudflare. Raw data in the post.
upstash.com/blog/docs7-vs-mi…
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Docs7 is faster than Mintlify.
According to Google PageSpeed.
upstash.com/blog/docs7-vs-mi…
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We tested Jev against the models we use inside Context7's parsing pipeline (Gemini Flash, DeepSeek).
5 classification tasks. Results:
- 3 ties: query relevance, duplicate detection, website suitability
- 1 win: page classification — 85% vs 56%
- 1 loss: crawl-root selection — 27% vs 93%
- 10-170x faster, 3-20x cheaper
Tagging a single page: Jev wins.
Reasoning about a whole site's structure: it doesn't.
Crawl-root selection: given a URL + site nav, pick which section to crawl.
Query relevance: is the user's question about this library or something else.
Duplicate detection: are two snippets the same example.
Website suitability: is this site technical docs worth indexing.
Page classification: does this doc page have code, API ref, or info.
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Context7 now indexes DeepWiki pages too.
Before: "the lifecycle of a user message in Chainlit" → nothing useful, because no docs page says it.
After: real answer, pulled from the repo's DeepWiki.
Your agent can now ask how things work.
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Every Context7 doc is scanned before your agent sees it
Your AI agent reads docs. Docs can carry prompt injections.
Context7 scans every doc before indexing. A custom classifier flags injection attempts and malware patterns.
context7.com/docs/security/d…
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Someone asked Context7 about EVAL on Upstash Redis REST API.
Our docs didn't cover it. Docs7 agents noticed the gap, wrote the docs, opened a PR. We reviewed and merged it.
Docs that fix themselves when users hit a gap. That's the goal.
github.com/upstash/docs/pull…
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Box is now part of the Upstash MCP.
Any agent gets a remote sandbox, your GitHub repos, a browser, file storage. On the subscription you already pay for.
We tried it: 12 PRs across 4 repos from one Claude chat message. Nothing built on my laptop.
upstash.com/blog/turn-any-ag…
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we are not great at hype. stable growth is peaceful.
I don't see enougn hype around the @Context7AI mcp. It's a staple in every project I work on. Thanks for the good work guys
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Every Docs7 site serves WebMCP out of the box. No setup.
And the dashboard shows what AI agents are actually searching for in your docs.
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Doc7 shows you which AI agents read your docs.
Every request from ChatGPT, Claude, Meta, etc. is a row: agent, page, country, time.
Screenshot is from Upstash docs, right now. Turns out agents read a lot of ratelimit and QStash pages.
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we paid mintlify $1000+/month just for AI chat usage.
we built Docs7. no usage fees for AI docs chat, included in the $200 pro plan.
context7.com/docs7
we've learned the hard way that usage pricing needs to be predictable and results oriented
so we’re moving to fixed, outcome-based pricing
you pay for completed outcomes, and we take on the underlying compute variability
this means that
→ 100% of costs can be forecasted in advance
→ ~96% of teams will spend less on the AI assistant
→ self-updating content costs ~70% less on average
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Context7 retweeted
if you want ai agents to recommend your product, you should start with SEO
new big number @Context7AI
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Context7 is now an OpenCode plugin.
opencode plugin @upstash/context7-opencode
MCP server + a skill that auto-triggers doc lookups. One command, no config.
context7.com/docs/clients/op…
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