Verdigraph

io.github.jdhart81由 GitHub 账号 jdhart81 发布v0.1.0更新于 Nov 2, 2026

Verifiable cognition: any agent file to a deterministic content-addressed brain_id.

发布方自己的服务：Energyai

已验证Streamable HTTP可网页运行开发者工具 AI 与机器学习

服务详情

标识符: io.github.jdhart81/verdigraph
传输: Streamable HTTP
运行时: Web executable
信任: Verified
发布于: Sep 16, 2026

安全提示

MCP 服务会接收对话中的工具调用请求，并可能执行外部操作。启用前请先了解该服务能做什么，并只授予它所需的权限。

注册中心已验证此条目所属的 GitHub 账号 jdhart81。这并不表明该账号就是产品方。

SourceWeft 的检查只涵盖条目是否完整、端点是否响应；不审查服务器的代码，也不审查其工具的行为。

在 README 中添加徽章

向读者展示该 MCP 服务已收录于 SourceWeft，徽章链接到本页。

[![Listed on SourceWeft](https://sourceweft.com/badge/mcp/io.github.jdhart81%2Fverdigraph.svg)](https://sourceweft.com/mcp/io.github.jdhart81%2Fverdigraph?utm_source=readme&utm_medium=badge)

概览

AI 生成的概览

远程 MCP 服务，可将智能体文件编译为确定性的、按内容寻址的 brain_id，并让助手重新核验该标识。

可以做什么: 识别给定内容属于哪种受支持的智能体文件格式
将智能体内容编译为确定性的、按内容寻址的 brain，并返回节点与边数量、不变量触发报告和来源信息
根据提交的内容重新计算 brain，并核对声称的 brain_id 或 content_hash 是否一致
返回该服务的能力说明与输入约定
适用场景: 当你需要为智能体定义获得可复现的指纹，或希望助手核实某个声称的 brain_id 或 content_hash 是否确实与提交内容相符，而不是仅凭标签相信时使用。
运行要求: 需要一个远程 streamable HTTP 端点；未声明任何软件包、环境变量或请求头。README 说明托管服务每个服务每天有 10 次调用的免费额度，核验始终免费。

安装前请注意

提交的内容会发送到第三方托管服务，请勿提交密钥或私密智能体文件。README 说明托管服务层（含支付、结算、签名权限与客户数据）为专有部分，与开源核心分开。

安装

在 SourceWeft 中

打开控制台中的 Verdigraph，将其添加到工作区。
为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体，或 SourceWeft 对话中。智能体会读取该服务器的安装指南，先向你展示发布者、端点和登录方式，经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.jdhart81%2Fverdigraph/install.md，并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前，先告诉我它的发布者、端点和登录方式，等我确认。不要把 API 密钥写进命令或我会提交的文件；问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "verdigraph": {
      "type": "http",
      "url": "https://mcp.viridisconservation.com/verdigraph/mcp"
    }
  }
}

README

Viridis Agent Fleet: open-source agents for the agent economy

Deterministic, auditable MCP agents: trust and settlement rails (identity, trust, escrow, metering, arbitration, notary, surety, provenance, covenant, offsets, compute-ledger) and priced-grade services (tax credits, GHG accounting, construction takeoff, measurement, compliance). Every core is pure Python with no LLM in the serving path, and every result is content-addressed so anyone can re-verify it.

Licensed Apache-2.0. Run any agent yourself, fork it, embed it.

Run an agent locally (free, forever)

bash

pip install -r requirements-dev.txtpython3 agent-escrow-agent/adapters/mcp_server.py          # stdio MCPpython3 run_fleet_tests.py                                 # every agent's invariant tests

pip install -r requirements-dev.txtpython3 agent-escrow-agent/adapters/mcp_server.py          # stdio MCPpython3 run_fleet_tests.py                                 # every agent's invariant tests

Or use the hosted fleet

https://mcp.viridisconservation.com runs all agents as one managed MCP service:

	Self-hosted (this repo)	Hosted by Viridis
Agent logic & verification	✅ identical	✅ identical
Viridis-signed receipts & notary chain	–	✅
Shared trust/reputation network data	–	✅
Maintained rule packs (tax, GHG factors, regs)	snapshot	✅ kept current
Payments (x402, card), seats, SLA	–	✅

Free tier: 10 calls/day per service; the rails (identity, escrow, metering…) are free forever. Verification is always free.

For agents

Agents and LLM tools can start from llms.txt, a generated index of every agent, and AGENTS.md, the rules for using and changing the fleet.

Part of the Viridis open-source commons

Building agents for conservation or energy work? VergeCommon and EnergyAI MCP also expose MCP tools, and ViridisOS serves its decision functions to agents over MCP.

This project is one of a connected set of open tools from Viridis. Each stands on its own; together they run from field evidence to checked decisions.

Conservation commons

VergeCommon: community conservation co-ops with consent-based land mapping, field evidence and group decisions
Dendritic Forest Management (DFM): compare forest-retention options around roads and waterways, with traceable inputs and portable reports
Global Soil Intelligence Project (GSIP): contribute soil observations and explore open soil maps with explicit uncertainty

Verifiable science

Viridis Canon: machine-checked Lean 4 mathematics for ecological decisions, with a DOI for every result
ViridisOS: open reference runtime for DOI-backed decision modules and recomputable results
Viridis Research: the nightly public research record, each claim labeled by what a verifier actually checked
ProofLens: see what a Lean statement means before reading the proof

Agent tools

Viridis Agent Fleet (this repository): open-source MCP agents with deterministic, auditable cores
EnergyAI MCP: energy-planning estimates and incentive sources for AI agents

Browse every project on the Viridis profile. If this one is useful to you, a star helps other people find it.

Boundary

This repo contains agent cores, MCP adapters, tests, standards and self-host tooling. The hosted service layer (gateway, payment and settlement, seats, signing authority, operations, telemetry and customer data) is proprietary and is not in this repo. Please don't open PRs that add it.

"Viridis", "Viridis Conservation" and the Viridis marks are trademarks of Viridis LLC; see TRADEMARKS.md.

来源：README.md，提交 350712c

工具

build_brain

Compile an agent file into a deterministic, content-addressed brain. content = the file as a string; format = verdigraph_genome | claude_project_export | openai_assistant | prompt_list | auto. Returns brain_id, content_hash, node/edge counts, the 9-invariant firing report, and provenance. Identical bytes always produce the identical brain_id (VB1). include_document=true returns the full graph.

输入参数formatcontent必填request_idinclude_document

verify_brain

Machine-check a cognition claim: recompute the brain from the submitted content and compare against the claimed brain_id and/or content_hash. valid=true iff every claimed identifier matches the deterministic recomputation (VB3).

输入参数formatcontent必填brain_idcontent_hash