Verdigraph

io.github.jdhart81由 GitHub 帳號 jdhart81 發布v0.1.0更新於 Nov 2, 2026

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

發布方自己的服務：Energyai

已驗證Streamable HTTP可網頁執行開發者工具 AI 與機器學習

服務詳細資訊

識別碼: io.github.jdhart81/verdigraph
傳輸: Streamable HTTP
執行環境: Web executable
信任: Verified
發布於: Sep 16, 2026

安全提示

MCP 服務會接收對話中的工具呼叫請求，並可能執行外部操作。啟用前請先了解該服務能做什麼，並只授予它所需的權限。

註冊中心已驗證此條目所屬的 GitHub 帳號 jdhart81。這並不表明該帳號就是產品方。

SourceWeft 的檢查只涵蓋條目是否完整、端點是否回應；不審查伺服器的程式碼，也不審查其工具的行為。

在 README 中新增徽章

向讀者展示該 MCP 服務已收錄於 SourceWeft，徽章連結到本頁。

[![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)

概覽

AI 產生的概覽

遠端 MCP 服務，可將代理程式檔案編譯為具決定性、以內容定址的 brain_id，並讓助理重新驗證該識別碼。

可以做什麼: 辨識給定內容屬於哪一種支援的代理程式檔案格式
將代理程式內容編譯為具決定性、以內容定址的 brain，並回傳節點與邊數量、不變量觸發報告與來源資訊
依提交的內容重新計算 brain，並核對宣稱的 brain_id 或 content_hash 是否相符
回傳此服務的能力說明與輸入契約
適用情境: 當你需要為代理程式定義取得可重現的指紋，或希望助理核實某個宣稱的 brain_id 或 content_hash 是否確實與提交內容相符，而不是只憑標籤相信時使用。
執行需求: 需要遠端 streamable HTTP 端點；未宣告任何套件、環境變數或標頭。README 指出託管服務每個服務每天有 10 次呼叫的免費額度，驗證則永遠免費。

安裝前請注意

提交的內容會傳送到第三方託管服務，請勿送出密鑰或私密代理程式檔案。README 說明託管服務層（含付款、結算、簽章權限與客戶資料）屬專有部分，與開源核心分開。

安裝

在 SourceWeft 中

開啟儀表板中的 Verdigraph，將其新增到工作區。
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.github.jdhart81%2Fverdigraph/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。

{
  "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.

來源：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.

輸入參數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).

輸入參數formatcontent必填brain_idcontent_hash