@contextstream/mcp-server weekly downloads statistics

<!-- mcp-name: io.github.contextstream/mcp-server --> <p align="center"> <img src="assets/contextstream-logo.png" alt="ContextStream" width="64" /> </p> <h1 align="center">ContextStream MCP Server</h1> <p align="center"><strong>The context platform for AI agents.</strong></p>

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.

<a href="https://contextstream.io/benchmarks"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/contextstream/mcp-server/main/assets/benchmarks-dark.png"> <img src="https://raw.githubusercontent.com/contextstream/mcp-server/main/assets/benchmarks-light.png" width="100%" alt="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)"> </picture> </a>

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

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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

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

Windows 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.

Benchmark	ContextStream	Compared with
LongMemEval-S, all 500 questions	96.8% (484/500); 96.0% on the half held out from tuning	Above 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 questions	95.7% (1474/1540); 95.8% held out; 97.8% on the audited answer key	Above 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 queries	98.77% best answer in the top 10; 100% a useful file in the top 10	Embedding-only search 72.84% and ripgrep 42.59% on the same queries and judgments
Agent tasks, 8 tasks × 3 trials	23/24 passed (96%); 9/9 on tasks that need team knowledge	14/24 (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.…