Neurogenesis

io.github.jdhart81由 GitHub 账号 jdhart81 发布v0.2.0更新于 Nov 9, 2026

Developmental agents for the agent economy: create an agent from a digital genome, then evolve it wi

发布方自己的服务:Energyai

已验证Streamable HTTP可网页运行云端与基础设施数据与分析AI 与机器学习

概览

AI 生成的概览

托管型 MCP 服务:从数字基因组创建发育型智能体,通过评估演化其认知图谱,并按成本、延迟与能耗为任务选择路由。

可以做什么
  • 创建、列出、查看、描述、导出、导入和删除由数字基因组构建的发育型智能体,包含节点、适应度指标、成长规则与安全公理
  • 提交任务结果以强化或削弱图谱边权重,驱动成长与剪枝,并读取只追加的发育账本
  • 获取从指定节点出发的最强下一步认知步骤路由建议
  • 注册调用方自有的计算配置,并在质量、可靠性、本地性、成本、延迟与能耗约束下选择负担最小的可用路由
  • 记录实际路由结果,并读取免费的效率报告,对比预测与实际成本、延迟和能耗
适用场景
适合希望让助手跨任务构建并演化持久智能体状态,或在本地规则、工具、本地模型与云端模型之间做出可审计路由决策并核算成本与能耗的场景。更偏向智能体记忆与计算效率实验,而非通用工具。
运行要求
远程 streamable HTTP 端点 mcp.viridisconservation.com;未声明认证、环境变量或请求头。README 另说明可从开源仓库用 Python 与 pip 自托管。托管版列出每项服务每天 10 次调用的免费额度。
安装前请注意
智能体存储在托管挂载上,delete_agent 会将其永久删除;import_state 可从导出文档重建智能体。账本与路由结果为只追加且哈希绑定,已记录的评估与结果无法更正。托管服务包含支付、席位与签名权限,属专有部分,不在开源仓库中。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Neurogenesis,将其添加到工作区。
  2. 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%2Fneurogenesis/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": {
    "neurogenesis": {
      "type": "http",
      "url": "https://mcp.viridisconservation.com/neurogenesis/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

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

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

14

create_agent

Create a developmental agent from a digital genome: {agent_name, purpose, initial_nodes (unique, >=1), fitness_metrics (>=1), optional growth_rules / safety_axioms}. Returns agent_id + initial graph summary.

输入参数genome必填request_id

submit_evaluation

Evolve an agent with one task outcome: {task_id, task_type, success_score in [0,1], optional accuracy/user_satisfaction/ cost_efficiency/safety_score/notes/used_nodes/used_edges}. Success strengthens the used edges, failure weakens them; growth and pruning follow the genome's rules under its safety axioms (NG1). Returns the new developmental-ledger events.

输入参数agent_id必填evaluation必填request_id

get_agent

Full current state: genome, cognitive graph (nodes/edges with weights and trust), and summary counts.

输入参数agent_id必填

list_agents

All developmental agents on this mount, with summary counts.

无需输入参数。

best_next_steps

Routing recommendation: the strongest next cognitive steps from a given node, by learned edge weight and trust.

输入参数limitagent_id必填from_node必填

get_ledger

The append-only developmental ledger: every growth, pruning, and evaluation event with reasons (NG3 — returned verbatim).

输入参数limitagent_id必填

export_state

Portable state document for an agent (import_state recreates it anywhere — including a self-hosted verdigraph-neurogenesis).

输入参数agent_id必填

import_state

Recreate an agent from an export_state document.

输入参数state必填request_id

delete_agent

Remove a developmental agent from this mount.

输入参数agent_id必填request_id

register_compute_profile

Register a caller-owned Wu Wei execution profile: reuse/cache, deterministic rule, local model, tool/workflow, or cloud model. Profiles can declare quality, reliability, capabilities, token costs, latency, locality, energy rates or average power, carbon intensity, and cache confidence/age. Routing never invents capacity or energy evidence.

输入参数profile必填request_id

route_task

Choose the least-burden eligible route for a task. Hard constraints include quality, reliability, locality, capabilities, context, cost, latency, and energy. Optional baselines quantify predicted savings. Explicit allow_defer/value/urgency fields may produce a no-work decision; no result is then claimed. Returns a hash-bound decision receipt.

输入参数task必填request_id

record_route_outcome

Attach one observed outcome to a Wu Wei decision. Actual cost, latency, and energy are optional and remain explicitly unknown when omitted. One append-only, hash-bound outcome is allowed per decision.

输入参数notesrequest_iddecision_id必填success_score必填actual_cost_usdactual_energy_whactual_latency_ms

compute_efficiency_report

Free read: decision/outcome receipts, route modes, compute avoided, predicted savings, predicted-vs-observed cost/latency/energy coverage, and Landauer-floor context. Estimates and observations stay distinct.

输入参数limit

describe_agent

Return capabilities and input contract.

无需输入参数。

版本历史

1
  1. v0.2.0最新Sep 16, 2026

评分

已满足适用检查项的 100%

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  • 近 30 天内可连通必需25 分
  • 已声明认证方式必需10 分
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  • 描述必需15 分
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  • 文档15 分
  • 许可证10 分

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