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Lamdis Exchange

作者:lamdis-ai版本 0.1.0i★ –i在云端运行
注册名称ai.lamdis/exchange

安装

概览

作者提供的说明(英文原文)

Pay people nearby for physical work: find out if something is true, or have it done.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在云端运行
  • 传输方式: Streamable HTTP
  • 分类: 其他
  • 你需要填写:Authorization

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。

你需要

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

这个地址需要在请求的 header 中放入密钥,而添加连接器的菜单无法设置。请改用 Claude Code、Cursor 或 VS Code。

在终端中运行这条命令

claude mcp add --transport http exchange https://exchange.lamdis.ai/mcp --header 'Authorization: your-value'

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "exchange": {
      "url": "https://exchange.lamdis.ai/mcp",
      "headers": {
        "Authorization": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "exchange": {
      "type": "http",
      "url": "https://exchange.lamdis.ai/mcp",
      "headers": {
        "Authorization": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个地址需要在请求的 header 中放入密钥,而添加连接器的菜单无法设置。请改用 Claude Code、Cursor 或 VS Code。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • Authorization 机密 HTTP header
    Optional. Bearer lam_sk_... agent key for the full buyer side, or an operator session token for the supply side. Leave it off for the eight guest tools.

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

我们还没有读取这个服务器的工具列表;我们会轮流检查各个端点。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过域名 lamdis.ai 验证(注册中心会核实域名所有权)

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    端点有响应

    尚未检查;我们会轮流检查各个端点

  • … 尚未检查

    使用 OAuth 登录

    尚未检查;我们会轮流检查各个端点

  • … 尚未检查

    工具描述中没有隐藏指令

    尚未检查;我们会在检查端点时读取工具列表

与“其他”中的其他服务器比较i

  • 星标数多于此分类中其他 32545 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 32545 个服务器的 32% 更新
  • 基础检查结果等于或优于此分类中其他 32545 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Lamdis Exchange 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

你需要填写:Authorization

它在我的电脑上运行还是在云端?

它在作者的云端运行。通过互联网连接,无需安装,但你发送的内容会经过作者的服务器。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.1.0)于2个月前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接,所以接受远程 MCP 服务器的应用也可以使用,例如开启开发者模式的 ChatGPT。

有什么可以替代它?

同一分类中有:Spec Driven Development, Remote Desktop Commander, Python MCP

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

作者:formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新
安装

Remote Desktop Commander

作者:desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211昨天更新
安装

Python MCP

作者:Files-com

Local-only Python MCP server for the Files.com API.

在你的电脑上运行今天更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

lamdis

Escrow and proof of completion for work that happens outside software — and shared, permissioned context for the agents doing it.

State what should become true. Hold the money against it. Nothing moves until evidence satisfies what was stated. Who does the work is deliberately not the interesting part: an executor authenticates with a hosted identity or a signed keypair, so a person, another agent, a robot or a vendor's own crew are the same thing to the protocol.

Your agent, in your threads, in one command

curl -fsSL https://lamdis.ai/install | sh

cd your-project lamdis "add a zero guard to Div() and verify it" # the agent, in this repo lamdis # interactive lamdis serve # the app, for your threads

At the terminal, lamdis is a coding agent: six bounded tools (read, list, search, edit, write, run), a repo map up front so the prompt prefix stays stable, distilled command output, and a verify step it is told to run. Every task is a thread named after the repository, and every run is recorded there: what it read, what it changed, what it ran, what it cost. Any model OpenRouter serves, or a local one with -url http://localhost:11434/v1.

A thread is a workspace shared between you, your agent, and whoever you choose. Everything anyone writes there, the agent included, is the record. You decide who sees which part, and the agent keeps working there when you are not.

curl -fsSL https://lamdis.ai/install | sh

lamdis serve # opens the app claude mcp add lamdis -- lamdis mcp # lets Claude Code read and write your threads too

One static binary. No runtime, no toolchain, no account. The node generates its own keypair on first run and keeps everything in ~/.lamdis. Put an OpenRouter key in ~/.lamdis/.env as LAMDIS_OPENROUTER_KEY and the agent is on.

What the agent is:

  • Chat is the record. Ask it something and both the question and the answer are signed entries in the thread, next to your notes.
  • It has its own key. Your key signs a delegation for it in every thread it writes to, so anyone reading the thread can tell you from your agent without trusting a label. It can never share or grant access.
  • It works on its own. Give a thread standing instructions and it runs when a new entry arrives (yours, a collaborator's, another agent's) or on a schedule. Every run leaves a record: what it read, which tools it called, what it fetched, what it cost.
  • It stops and asks. When a choice is yours, it writes a question with options into the thread and waits. Your answer resumes it.
  • Outside reach is opt-in and recorded. On its own it fetches only from domains you list and calls only the MCP tools you allowlist in ~/.lamdis/agent.json; tools you mark confirm wait for your yes each time. Private and local addresses are always refused. Pages and tool results reach the model as data, never as instructions.

Threads carry lanes, so you can grant somebody else's agent the summary lane while keeping content to yourself — they see "migration on track, two blockers", not "vendor stuck on clause 7". Permissions are a deterministic fold over a signed control lane: deny by default, and only person-signed entries change anything.

The exchange

The settlement half runs at exchange.lamdis.ai. No account is needed for a first job.

Try it in ten seconds, with nothing

No account, no key, no card. This posts a job into the sandbox and it runs the real machinery — claimed, evidence submitted, verified, settled, receipt — in about ten seconds.

curl -sX POST https://exchange.lamdis.ai/v1/tasks \

-H 'content-type: application/json' \ -d '{"kind":"observe","predicate":"the sign is up at the front", "lat":42.3314,"lon":-83.0458,"radius_m":150, "fee_minor":800,"sandbox":true}'

That returns a job and a token. Ten seconds later:

curl -s https://exchange.lamdis.ai/v1/jobs/$JOB/receipt -H "authorization: Bearer $TOKEN"

The receipt says "sandbox": true, "evidence_synthetic": true, "paid_minor": 0, and states in words that nobody was dispatched and nothing was paid. A sandbox job never reaches the public board, never touches the ledger, and is never anchored. It exists so you can build the whole integration before a single real person is involved.

Integrations

Already using an agent framework? Each of these installs straight from this repository — nothing is on npm or PyPI — and gives the agent the same three tools: lamdis_check_feasible, lamdis_run_job (sandbox by default) and lamdis_job_status. None needs an account, and the sandbox costs nothing.

frameworkinstall
LangChainpip install "git+https://github.com/lamdis-ai/lamdis-protocol.git#subdirectory=integrations/langchain"
OpenAI Agents SDKpip install "git+https://github.com/lamdis-ai/lamdis-protocol.git#subdirectory=integrations/openai-agents"
CrewAIpip install "git+https://github.com/lamdis-ai/lamdis-protocol.git#subdirectory=integrations/crewai"
Vercel AI SDKpnpm add "github:lamdis-ai/lamdis-protocol#path:integrations/vercel-ai" — npm: npx degit lamdis-ai/lamdis-protocol/integrations/vercel-ai vendor/lamdis then npm install ./vendor/lamdis
from lamdis_langchain import lamdis_tools      # or lamdis_openai_agents, lamdis_crewai

llm.bind_tools(lamdis_tools)

import { lamdisTools } from "@lamdis/ai-sdk-tools";

generateText({ model, tools: lamdisTools, prompt: "..." });

Each package README has a ten-line example that runs against the live sandbox and the exact JSON every tool returns; see integrations/.

One line, from any agent

claude mcp add --transport http lamdis https://exchange.lamdis.ai/mcp

Or install the Claude Code plugin, which adds the server and a skill that teaches the agent how to use it honestly — feasibility first, sandbox to try, hand the person the pay link, never claim work is arranged until it is taken:

claude plugin marketplace add lamdis-ai/lamdis-plugins

claude plugin install lamdis@lamdis-plugins

The same skill ships here at skills/lamdis/SKILL.md. Agents that discover by well-known paths find an A2A card at /.well-known/agent-card.json and the OpenAPI at /openapi.yaml.

Any MCP client works, and no credential is needed for the guest tools: check_feasible, observe_world, do_in_world, find_out, job_status, job_receipt, job_evidence, list_bids. Pass sandbox: true to any of them to run against the sandbox.

A real job

A job posted for real comes back with a pay_at link and a token: send the person the pay link. Their card is authorised for the job's ceiling, not charged; the job goes on the board when that lands, and the card is charged once, at the end, for exactly what was paid out on proof.

Coverage today is zero. Nobody has registered as an operator yet, so check_feasible will honestly tell you a real job would sit unclaimed, and it records where the work was asked for so supply can be recruited there — see /v1/demand. If you can do physical work somewhere, say where. That is the constraint, and it is not hidden anywhere in this repository.

The TypeScript and Python clients live in sdk/ and are not published to npm or PyPI yet; use them from source, or curl, or the MCP endpoint. See examples/ for a first job from Claude Code, the OpenAI Agents SDK, LangChain, the Vercel AI SDK, n8n and the shell.

Where to look

agents.mdthe exchange written for an agent: the anonymous flow, the token, the money rules, the operator side
spec/openapi.yamlthe REST surface, OpenAPI 3.1, every field from the Go
sdk/typescript · sdk/pythonlamdis clients, zero dependencies
examples/one file per framework, each under forty lines
exchange.lamdis.ai/docs · /llms.txtthe live reference

How the money works

Amounts are integer minor units, USD. The exchange currently keeps nothing from what a worker earns. Posting from an account holds the job's ceiling in escrow; posting anonymously authorises a card for it instead, and only proof captures. An observation pays for honest evidence whichever way the answer turns out, so a "no" is worth as much as a "yes"; a do-job pays on completion, with an attempt fee for a documented failed trip. Earnings wait 24 hours for the buyer to look — release early, or hold on a named ground and a panel that is neither party decides within seven days. Every receipt is signed and states its confidence ceiling honestly (0.85 with a capture location, 0.72 without), because capture is not yet attested in hardware.

The supply side is the same endpoint: an operator's agent, signed in with the operator's own session, can find_work, take_job, place_bid, set_capacity and be pushed signed offers to a webhook.

The protocol underneath

Permissioned shared context for AI agents.

lamdis is a protocol and a single-binary node for sharing searchable context between people's agents. Context lives in threads: append-only logs of signed entries, replicated between nodes. Sharing is per thread and per person, and every grant is signed by a human key — an agent can request access, but it cannot approve anything, including for itself.

Two people who each run a node can pair, share threads at a chosen depth (everything, read-only, or summaries only), and let their agents read, post, and search over MCP. Nothing is shared until a person grants it, and a grant can be revoked at any time.

Install

Download a binary from releases (macOS, Linux, Windows; no dependencies), or build from source:

cd node && go build -o lamdis ./cmd/lamdis
Quick start
lamdis init                                # create your identity (a keypair)

lamdis thread new "pool project" lamdis post pool "pump arrived, sitting in the garage" lamdis search pump

Search is full-text by default. For semantic search, point the node at any OpenAI-compatible embeddings endpoint:

export LAMDIS_EMBED_URL=http://localhost:11434/v1   # e.g. Ollama

export LAMDIS_EMBED_MODEL=nomic-embed-text

Sharing with another person

Each person runs their own node. Pair once by URL; identities are exchanged automatically:

lamdis serve                                     # both sides keep this running

lamdis peer add jane http:// :8420

lamdis grant payments jane contribute,read,search # full collaboration lamdis grant payments jane summary,search # or: the gist only lamdis access payments # who sees this thread lamdis revoke payments jane

Commands take a thread's title (or a unique fragment of it) and a peer's name. The other side runs lamdis sync (or sync -watch 30s) to exchange entries.

Scopes:

scopegrants
contributeappend entries
readreplicate and read the whole thread
summaryreplicate the summary lane only; raw entries are never transmitted
searchquery; results are filtered to the holder's read level

The summary scope is enforced at the sender: entries a peer is not entitled to are not filtered on arr

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