Neurogenesis

io.github.jdhart81Published by GitHub account jdhart81v0.2.0Updated Oct 24, 2026

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

The publisher's own server: Energyai

VerifiedStreamable HTTPWeb executableData & AnalyticsAI & ML

Overview

AI-generated overview

Hosted MCP service for creating developmental agents from a digital genome, evolving their cognitive graphs through evaluations, and routing tasks by cost…

What you can do
  • Create, list, inspect, describe, export, import and delete developmental agents built from a digital genome with nodes, fitness metrics, growth rules and safety axioms
  • Submit task outcomes to strengthen or weaken graph edges and drive growth and pruning, then read the append-only developmental ledger
  • Get routing recommendations for the strongest next cognitive steps from a given node
  • Register caller-owned compute profiles and route tasks to the least-burden eligible route under quality, reliability, locality, cost, latency and energy constraints
  • Record observed route outcomes and read a free efficiency report comparing predicted and observed cost, latency and energy
When to use it
Useful when you want an assistant to build and evolve persistent agent state across tasks, or to make auditable routing decisions between local rules, tools, local models and cloud models with cost and energy accounting. It suits experimentation with agent memory and compute-efficiency tracking rather than general-purpose tooling.
Requirements
Remote streamable HTTP endpoint at mcp.viridisconservation.com; no authentication, environment variables or headers are declared. The README also describes a self-hosted option from the open-source repository using Python and pip. The hosted tier lists a free allowance of 10 calls per day per service.
Before you install
Agents are stored on the hosted mount, and delete_agent removes one permanently; import_state recreates agents from an exported document. The ledger and route outcomes are append-only and hash-bound, so recorded evaluations and outcomes cannot be corrected. The hosted service adds payments, seats and signing authority that are proprietary and not in the open repository.

Installation

In SourceWeft

  1. Open Neurogenesis in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.

Use with an agent

Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.

Read https://sourceweft.com/mcp/io.github.jdhart81%2Fneurogenesis/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.

Read the install guide the agent follows

Other MCP clients

Add this to your client's mcpServers config.

{
  "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.

Source: README.md at commit 350712c

Tools

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.

Parametersgenomerequiredrequest_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.

Parametersagent_idrequiredevaluationrequiredrequest_id

get_agent

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

Parametersagent_idrequired

list_agents

All developmental agents on this mount, with summary counts.

Takes no parameters.

best_next_steps

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

Parameterslimitagent_idrequiredfrom_noderequired

get_ledger

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

Parameterslimitagent_idrequired

export_state

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

Parametersagent_idrequired

import_state

Recreate an agent from an export_state document.

Parametersstaterequiredrequest_id

delete_agent

Remove a developmental agent from this mount.

Parametersagent_idrequiredrequest_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.

Parametersprofilerequiredrequest_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.

Parameterstaskrequiredrequest_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.

Parametersnotesrequest_iddecision_idrequiredsuccess_scorerequiredactual_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.

Parameterslimit

describe_agent

Return capabilities and input contract.

Takes no parameters.

Version history

1
  1. v0.2.0LatestSep 16, 2026

Score

100% of the applicable checks met

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  • Reached in the last 30 daysRequired25 pts
  • Auth declaredRequired10 pts
  • Tools listedRequired15 pts
  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

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