Neo — AI/ML Engineering

io.github.NeoAIResearchPublished by GitHub account NeoAIResearchv0.5.5Updated Nov 3, 2026

AI/ML engineering MCP server for Neo — submit tasks, track execution, and retrieve outputs.

VerifiedSTDIODesktop onlyDeveloper ToolsAI & ML

Overview

AI-generated overview

Connects AI coding editors to Neo, an autonomous AI/ML engineer that plans, runs and evaluates ML tasks and writes results into your repo.

What you can do
  • Submit AI/ML tasks to Neo and list, pause, resume or stop them
  • Read live task status, full task output and hash-chained local execution evidence
  • Answer Neo's questions when a task is waiting for feedback
  • Verify local artifacts with file assertions and acceptance commands
  • Register, list, test and remove third-party API keys (GitHub, HuggingFace, Anthropic, OpenRouter and others) for use as env vars
When to use it
Useful when you work in Claude Code, Cursor, VS Code, Windsurf, Zed, Continue or Codex and want ML, LLM, data-science, vision or audio work handled by a specialized agent without leaving the editor. It suits data scientists, ML engineers and analysts who want generated code, models, metrics and reports written into their own project.
Requirements
Local Python 3.11+ on Linux, macOS or Windows via WSL (native Windows shells unsupported); installed from PyPI as neo-mcp and run over stdio. Requires a Neo API key in NEO_SECRET_KEY. Optional NEO_ENVIRONMENT, NEO_WORKSPACE_DIR (git/project root), NEO_READ_ONLY and timeout variables. A local daemon must start for files to be written.
Before you install
NEO_SECRET_KEY is a paid-service credential; quota or trial limits can block tasks. Tasks are described as locally destructive and write files into the workspace, and stop permanently cleans up a task. Integration tools store third-party API keys on the machine, and testing an integration calls the provider's API. Task status is backend-reported and not independent proof; use verification tools before trusting outputs.

Installation

In SourceWeft

  1. Open Neo — AI/ML Engineering in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

Neo MCP — AI engineering, without leaving your editor

[PyPI] [Python] [License: MIT] [Downloads]

Stay in Claude Code, Cursor, or Codex and hand your AI/ML work to Neo — an autonomous AI engineer that plans, builds, runs, and evaluates entire workflows from a plain-English request. neo-mcp is the Model Context Protocol server that connects Neo to the AI coding tools you already use, so you never switch tabs: ask your agent to send a task to Neo, and a local daemon writes the resulting code, models, metrics, and reports straight into your repo — on your machine, nothing stored remotely.

Because Neo is purpose-optimized for AI engineering — not a general-purpose coding assistant — it goes deeper on ML, LLM, and data workflows than a general coding agent can.

📚 Docs: https://docs.heyneo.com/neo-mcp · 🔑 Get an API key: Neo dashboard

What MCP unlocks

  • 🧩 Stay in your editor — drive Neo from Claude Code, Cursor, VS Code (Copilot), Windsurf, Zed, Continue, or Codex. No new app, no context switching.
  • 🔬 More depth — autonomous planning, experiments, evaluation, and iteration tuned for AI/ML, beyond what a generic coding agent attempts.
  • 💾 Local-first — every output file is written to your machine; nothing is stored remotely.

What you can build with Neo

  • 🤖 Generative AI & LLMs — RAG & semantic search, agents & chatbots, fine-tuning (Llama, Qwen, Gemma), document analysis
  • 🧠 ML & deep learning — PyTorch / TensorFlow / scikit-learn training, architecture search, evaluation
  • 📊 Data science & analytics — EDA, feature engineering, forecasting, segmentation, A/B testing, reporting
  • 👁️ Computer vision — image classification, object detection, OCR
  • 🎤 Speech & audio — speech-to-text, text-to-speech, audio classification
  • 🔌 Bring your own keys — GitHub, HuggingFace, Anthropic, OpenRouter, OpenAI, AWS S3, Weights & Biases, Kaggle — stored locally, injected as env vars

For data scientists, ML & LLM engineers, analysts, researchers, and PMs who want results, not boilerplate.


Install

bash
pip install neo-mcp

Requires Python 3.11+ on Linux, macOS, or Windows through WSL. Native Windows cmd.exe/PowerShell execution is intentionally unsupported because Neo commands use POSIX shell and container-path semantics.

Tip: use pipx install neo-mcp to install in an isolated environment and avoid conflicts with your project's virtualenv.


Use Neo from your editor

Neo runs in every major MCP-enabled AI editor — set it up once below. Replace sk-v1-YOUR_KEY with your actual key.


Claude Code

bash
claude mcp add --scope user neo \  -e NEO_SECRET_KEY=sk-v1-YOUR_KEY \  -- neo-mcp

Open a new Claude Code session after running this. Neo tools load at session start.

Scope options: --scope user (global, recommended) · --scope project (writes .mcp.json in current repo) · --scope local (this machine only)

Verify it registered:

bash
claude mcp list

Cursor

~/.cursor/mcp.json:

json
{  "mcpServers": {    "neo": {      "command": "neo-mcp",      "env": {        "NEO_SECRET_KEY": "sk-v1-YOUR_KEY"      }    }  }}

Restart Cursor after editing.


Windsurf

~/.codeium/windsurf/mcp_config.json:

json
{  "mcpServers": {    "neo": {      "command": "neo-mcp",      "env": {        "NEO_SECRET_KEY": "sk-v1-YOUR_KEY"      }    }  }}

Restart Windsurf after editing.


VS Code (GitHub Copilot)

.vscode/mcp.json in your workspace root (requires VS Code 1.99+):

json
{  "servers": {    "neo": {      "type": "stdio",      "command": "neo-mcp",      "env": {        "NEO_SECRET_KEY": "sk-v1-YOUR_KEY"      }    }  }}

Zed

~/.config/zed/settings.json:

json
{  "context_servers": {    "neo": {      "source": "custom",      "command": {        "path": "neo-mcp",        "args": [],        "env": {          "NEO_SECRET_KEY": "sk-v1-YOUR_KEY"        }      }    }  }}

Continue.dev

~/.continue/config.json:

json
{  "mcpServers": [    {      "name": "neo",      "transport": {        "type": "stdio",        "command": "neo-mcp",        "env": {          "NEO_SECRET_KEY": "sk-v1-YOUR_KEY"        }      }    }  ]}

OpenAI Codex CLI

~/.codex/config.json:

json
{  "mcpServers": {    "neo": {      "command": "neo-mcp",      "env": {        "NEO_SECRET_KEY": "sk-v1-YOUR_KEY"      }    }  }}

Tools

ToolDescription
neo_submit_taskSubmit a locally destructive AI/ML task; one active owner per workspace.
neo_list_tasksList running and recent tasks — reconnects pollers automatically.
neo_task_statusCompact live backend-reported status; telemetry is not independent proof.
neo_get_messagesRead full task output when COMPLETED. Capped at ~20 000 tokens.
neo_send_feedbackReply when Neo asks a question (WAITING_FOR_FEEDBACK).
neo_pause_taskPause a running task.
neo_resume_taskResume a paused task.
neo_stop_taskStop and clean up a task permanently.
neo_list_integrationsList stored third-party API keys (names only — never the value).
neo_add_integrationRegister a GitHub PAT / HuggingFace token / Anthropic key / OpenRouter key so Neo tasks can use it as an env var.
neo_test_integrationCall the provider's API to confirm a stored key is still valid.
neo_remove_integrationDelete a stored key from this machine.
neo_get_execution_evidenceRead bounded, hash-chained local execution observations.
neo_verify_taskVerify local artifacts with explicit file assertions and acceptance commands.

Integration tools store credentials locally (file 0o600 under ~/.neo/integrations/, or OS keyring with NEO_INTEGRATIONS_BACKEND=keyring). Keys never leave your machine. See the full guide at docs/INTEGRATIONS.md.


Environment variables

VariableRequiredDescription
NEO_SECRET_KEYYesYour Neo API key (sk-v1-...) from the Neo dashboard
NEO_DEPLOYMENT_IDNoPin a specific deployment UUID (auto-generated and persisted by default)
NEO_WORKSPACE_DIRNoOverride working directory (useful in Docker)
NEO_READ_ONLYNotrue — expose only status, messages, task/evidence reads, integration-test/list, and BYOK-list tools; reject every mutation and verification command
NEO_INIT_CHAT_TIMEOUT_SECONDSNoSubmit timeout in seconds; defaults to 60 to match the production VS Code client
NEO_POLLER_READY_TIMEOUT_SECONDSNoMaximum pre-submit wait for the local daemon to establish its backend poll connection; defaults to 15

Troubleshooting

SymptomFix
neo-mcp: command not foundRe-run pip install neo-mcp and verify your PATH with which neo-mcp
Tools don't appear after registeringOpen a new session — MCP tools load at session start, not mid-session
Invalid API key (401)Re-check your key in the Neo dashboard
Trial or quota ended (403)Top up at the Neo dashboard
Task submitted but no files writtenDaemon failed to start — run neo-mcp doctor to diagnose
Status stuck on RUNNINGCall neo_task_status to check; run neo-mcp status to inspect the daemon

Source: python/README.md at commit 47a03c6

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v0.5.5LatestSep 16, 2026

Score

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  • Reached in the last 30 days25 pts

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  • Auth declared10 pts

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  • Tools listed15 pts

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