ContextStream

io.contextstream由 contextstream.io 發布官方發布者v0.5.35更新於 Nov 2, 2026

Persistent memory and cross-session learning for AI coding assistants (hosted remote MCP).

已驗證Streamable HTTP可網頁執行知識與記憶溝通與協作媒體與設計

概覽

AI 產生的概覽

託管式記憶伺服器,讓 AI 程式助理找到相關檔案、回想已儲存的專案決策,並在不同工作階段與工具間延續先前的成果。

可以做什麼
  • 找出與某次變更相關的檔案並引用來源
  • 儲存專案決策及其理由,並在後續工作階段重新取回
  • 為已連接的程式碼、文件與對話建立索引以供託管搜尋
  • 透過 studio 工具生成品牌圖片、音效與產品影片,包含規劃、製作、狀態、結果與修改步驟
  • 透過團隊自己的 Resend 帳號草擬、預覽、測試與寄送電子郵件活動,並管理聯絡人與退訂
  • 可選擇在安裝指令執行前,對照公開套件庫檢查指令中提到的套件
適用情境
適合希望助理在工作階段與工具之間保留脈絡的情況,例如回想某項決策的原因,或在大型程式庫中定位正確的檔案。也適合想在同一工作區內生成品牌素材或執行郵件活動的團隊。
執行需求
遠端 Streamable HTTP 端點,引導流程中以 OAuth 登入;免費帳號每月 10,000 點數,不需信用卡。也可改用本機 npm 啟動器(Node.js 20+),它會下載原生二進位檔。腳本化安裝會讀取 CONTEXTSTREAM_API_KEY,並可能需要工作區 ID。郵件功能需要連接 Resend 帳號。
安裝前請注意
建立索引會把符合條件的來源內容傳送到 ContextStream 進行託管搜尋,且對話紀錄儲存與本機 Git 中繼資料擷取預設為開啟。studio 的 make 與郵件寄送會消耗點數或寄出郵件,設計上需要你明確同意;刪除聯絡人無法復原。連接敏感專案前請先了解資料處理方式。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 ContextStream,將其新增到工作區。
  2. 連接 所需的 憑證。它們會被私密儲存,絕不會在公開頁面上展示。
  3. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.contextstream%2Fmcp/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。 客戶端會在首次連接時完成 OAuth 登入。

{
  "mcpServers": {
    "contextstream": {
      "type": "http",
      "url": "https://mcp.contextstream.io/mcp?default_context_mode=fast&src=registry"
    }
  }
}

README

[ContextStream]

ContextStream MCP Server

The context platform for AI agents.

Your agent knows code. Give it the decisions behind yours.

Connect your code, docs, and conversations so your agents can find the right files, recall saved decisions, and build on past work across sessions and tools.

[ContextStream benchmarks: LongMemEval-S 96.8% (484 of 500), LoCoMo 95.7%, code search 98.77% best answer in the top 10, agent tasks 96% (23 of 24, up from 58% without it)]

The numbers, their sources, and what they do not claim are under Benchmarks.

Start free with MCP

10,000 monthly credits. No credit card required.

Works with Claude Code, Cursor, Codex, GitHub Copilot, Gemini CLI, Qwen Code, Kimi Code, Muse Code, ZCode, Zed, and more MCP clients, on Claude, GPT, Gemini, Kimi, GLM, Qwen, and other models. Create your account or sign in during MCP onboarding. No separate website signup needed.

macOS and Linux

sh
curl -fsSL https://contextstream.io/scripts/mcp.sh | bash

Windows PowerShell

powershell
irm https://contextstream.io/scripts/mcp.ps1 | iex

Paste the command into your terminal and follow onboarding to connect your project and supported editor. Restart your editor after setup.

What gets installed and indexed? · Supported clients · Setup guide · Prefer to start on the web?

Make the next session useful

Ask your connected agent:

Use ContextStream to find the files relevant to my next change. Cite the sources and retrieve any saved project decisions that should guide the work.

Then ask it to save a real decision and its reason. Start a new session and retrieve that decision without explaining it again. Indexing supplies code context; it cannot recover every undocumented decision.

Benchmarks

Each benchmark runs through the production API, is registered before its results are read, and is published with its method, confidence interval, and losses.

BenchmarkContextStreamCompared with
LongMemEval-S, all 500 questions96.8% (484/500); 96.0% on the half held out from tuningAbove Mastra (94.87%) and supermemory (85.4%) on the same official GPT-4o judge. Level with agentmemory's self-reported 96.2%, which is inside our 95% interval
LoCoMo, all 1,540 questions95.7% (1474/1540); 95.8% held out; 97.8% on the audited answer keyAbove every system in the EverMemOS paper's same-judge table: EverMemOS 93.05%, Zep 85.22%, MemOS 80.76%, MemU 66.67%, Mem0 64.20%
Code search, 177 judged developer queries98.77% best answer in the top 10; 100% a useful file in the top 10Embedding-only search 72.84% and ripgrep 42.59% on the same queries and judgments
Agent tasks, 8 tasks × 3 trials23/24 passed (96%); 9/9 on tasks that need team knowledge14/24 (58%) and 0/9 with the repository alone

61 ms median server-side search time (127 ms p95) in the October 27 production run. End to end it was 223 ms, because the client ran in a different region from the one serving the workspace. Your latency depends on your distance from that region.

Competitor figures are as each vendor or the EverMemOS paper published them; we did not re-run them, and readers and judges differ across rows. The code-search baselines come from the August 1 run on the same queries. The agent-task result uses one repository and one agent model, so treat it as directional. Results, methodology, and limitations.

ContextStudio

For people ContextStream Create offers one-prompt making to, the server adds one tool, studio: ask for a film, a product explainer filmed from your connected product, an image, sound or music in plain words, and ContextStudio's producer plans the pieces from what your team knows (brand kit, docs, decisions, connected products). There are no models to pick.

  • Brand work by name. Brand images (social sets in each network's size, ad sets, hero and product shots, posters and flyers with print PDFs, email headers, brand graphics) and brand sounds (sonic logos, music beds, loops, effect packs, voice-overs, podcast intros and outros): pass form, with sizes (up to 6, such as instagram-post or linkedin-post) or a voice, and the plan keeps to it.
  • Plan, then price. plan returns the pieces and one price ("up to N credits, likely about M") and spends nothing. Pass what your agent knows that the workspace may not (the diff it just made, release notes, the doc it has open) as context; the producer reads it next to the team's own context.
  • Your go-ahead. make spends at most the plan's price, and only after you agree: a client that can ask you (MCP elicitation, or a stateless input_required result) asks you itself; otherwise the agent passes the price you accepted.
  • Following it. status reports each piece while it waits (with notifications/progress where the call asks for them); a product film takes 13 to 21 minutes. result shows images inline and links video and sound (signed links valid about 15 minutes), with each piece's placard. Brand work comes as files, each with its own link: every size as PNG, JPG and WebP with a ZIP of the set, a print PDF, or WAV and MP3 for the web with a broadcast master and stems, plus alt text, a caption and the checks it was held to.
  • Revising. revise starts from a made image or sound: a variation (more like it), a revision (it, changed as you say) or a resize (an image in new sizes). Also stop, library, brand and products.
  • Email, where Create offers it. Campaigns sent through your team's own Resend account. email_draft writes one in your brand from a prompt ("announce our winter sale to customers") and sends nothing; email_audiences, email_campaigns, email_test (to your own address or Resend's test addresses), email_cancel and email_report (a campaign's delivery, opens, clicks, bounces and complaints, or the workspace's trends). email_send goes only with your go: the client asks you when it can; otherwise the agent shows you the campaign and its recipient count and passes the count you agreed to. email_preview gives a link to share that shows the email in Gmail, Outlook and Apple Mail mockups (desktop, tablet and phone, light and dark, images on or off), with the inbox line and what to check. email_revise changes a draft from plain words ("shorter subject") or sets its subject and preview line; email_duplicate makes a draft copy. email_contacts lists, adds (with where they agreed to hear from you) or unsubscribes contacts; erasing one can't be undone, so it is the delete tool's (studio.email_contact_erase), after your go; email_unsubscribes shows the suppression list with why and when, and takes an address off it only with your go. Resend is connected in the studio, never through an agent: the tool refuses anything shaped like a Resend key.
  • Connector. The hosted path /studio serves only this tool, for Claude.ai or ChatGPT connectors, with OAuth tokens bound to that resource (read:studio, write:studio).

Data controls

Scoped access. Traceable sources. Configurable capture. Indexing sends eligible source contents to ContextStream for hosted search. Transcript saving and local Git metadata capture are on by default and can be turned off. Review data handling and controls before connecting sensitive projects.

Other installation options and troubleshooting

npm (Node.js 20+)

bash
npx -y @contextstream/mcp-server@latest setup

The npm launcher downloads the native Rust binary for your platform and verifies its SHA-256 checksum. Pin an exact package version for production automation.

Hosted MCP without a local process

For clients supporting Streamable HTTP and OAuth, the hosted endpoint is:

text
https://mcp.contextstream.io/mcp?default_context_mode=fast

Supported clients lists one-line add commands for Claude Code, Codex, Gemini CLI, Qwen Code, Copilot CLI, and Droid, and one-click install for VS Code. Use the MCP documentation for other clients and stdio alternatives. A hosted connection alone does not sync your local checkout.

Preview or diagnose setup

bash
contextstream-mcp setup --dry-runcontextstream-mcp doctorcontextstream-mcp doctor --repair

Scripted and CI setup

For automation, wrap the install in pipefail so it fails when the download fails, instead of reporting success after installing nothing:

bash
bash -o pipefail -c 'curl -fsSL https://contextstream.io/scripts/mcp.sh | bash'

setup --yes reads the key from CONTEXTSTREAM_API_KEY or saved credentials, and needs --workspace-id when the account has more than one workspace:

bash
export CONTEXTSTREAM_API_KEY=...   # or: printf %s "$KEY" | contextstream-mcp configure --api-key-stdincontextstream-mcp setup --yes --editors=claude --workspace-id=<UUID> --project-path=.

Package Bouncer (opt-in)

A hook for Claude Code and Cursor that checks the packages an install command names against their public registry before the command runs. It covers npm, pnpm, yarn, bun, npx, pip, uv, uvx, pipx, poetry and cargo, including claude mcp add ... -- npx ....

  • It denies a package the registry does not have, naming the popular package it looks like. An agent may have invented the name, and anyone can register it.
  • It asks before a barely downloaded lookalike of a popular package.
  • It allows everything else, including whenever a lookup fails or takes too long.

It skips projects and commands that use a private registry (.npmrc, pip.conf, uv or poetry sources, --registry, --index-url). CONTEXTSTREAM_PACKAGE_BOUNCER=off turns it off.

Claude Code: add this entry to PreToolUse in ~/.claude/settings.json, next to the hooks setup installed. update-hooks leaves it alone.

json
{ "matcher": "Bash", "hooks": [{ "type": "command", "command": "contextstream-mcp hook package-bouncer", "timeout": 8 }] }

Cursor (~/.cursor/hooks.json):

json
{ "version": 1, "hooks": { "beforeShellExecution": [{ "command": "contextstream-mcp hook package-bouncer" }] } }

Open source and contributing

This repository contains the MIT-licensed Rust MCP server, client, editor setup, and release tooling. The hosted backend is separate and is not included here. See the architecture, release integrity, and latest release.

bash
cargo build --locked -p mcp-server --bin contextstream-mcpcargo test --locked --workspace

Use the pinned Rust toolchain. For contribution checks and commit sign-off, read CONTRIBUTING.md. Report vulnerabilities using SECURITY.md. See LICENSE, NOTICE, and GOVERNANCE.md for licensing, trademarks, and project governance.


Let humans be human.

Docs · Pricing · Integrations · Benchmarks

Optional deep project learning

Deep project learning starts off for new accounts. Interactive setup offers it once per account (or once when terms change) and remembers if you decline; plain contextstream-mcp setup --yes preserves your current choice.

bash
contextstream-mcp configure --account-learning status   # show current consent statecontextstream-mcp configure --account-learning on       # open privacy page in browser (or print URL)contextstream-mcp configure --account-learning off      # withdraw consent and queue deletion

--deep-learning is accepted as an alias for --account-learning. Only a person can grant consent by ticking the unchecked box on the signed-in privacy page (https://contextstream.io/dashboard/account/privacy); agents and API keys cannot consent for you. Doctor and init show status only. See data handling and controls.

來源:README.md,提交 0f8ff20

工具

0
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版本歷史

1
  1. v0.5.35最新Sep 16, 2026

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