ReadyAgents

Name: ReadyAgents
Author: readyagentsdev

by readyagentsdevversion 2.0.12i★ –iRuns on your computer

Registry nameio.github.readyagentsdev/readyagents

Install

Overview

Description from the maker (original English)

Local YAML/JSON agent workflow CLI with approvals, resume, and optional stdio or loopback MCP HTTP.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "readyagents": {
      "command": "uvx",
      "args": [
        "readyagentsdev",
        "mcp",
        "serve"
      ]
    }
  }
}

Run this in your terminal

claude mcp add readyagents -- uvx readyagentsdev mcp serve

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "readyagents": {
      "command": "uvx",
      "args": [
        "readyagentsdev",
        "mcp",
        "serve"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "readyagents": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "readyagentsdev",
        "mcp",
        "serve"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account readyagentsdev, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32380 servers in this category
Published to the registry more recently than 52% of the other 32380 servers in this category
Basic checks equal to or better than 100% of the other 32380 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is ReadyAgents safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (2.0.12) reached the registry 36 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Dechonet MCP

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

ReadyAgents Core

ReadyAgents runs YAML agent workflows locally. Every node is checkpointed, so a run pauses for human approval, resumes from where it stopped, and replays offline as a regression test. Your keys, your machine, no daemon.

Site: readyagents.dev. Repo: github.com/readyagentsdev/readyagents-core.

Tried it? Open an I-ran-this issue. We are not launching. We are listening.

This repository is the free core. You keep the provider account and the bill. Install with pip install readyagentsdev, or from this clone.

60-second start

Requires Python 3.11–3.14 on Linux, macOS, or Windows. Current version is 2.0.12. Install with pip install readyagentsdev, or from this clone. The 2 versions the core contract only; extras carry their own maturity tiers.

pip install readyagentsdev

readyagents new my-flow readyagents run my-flow/workflow.yaml readyagents runs list

The wheel ships the example workflows too — no clone needed:

readyagents new f --from-example calc_pipeline

readyagents run f/workflow.yaml

readyagents new --list-examples shows all of them.

Or from a clone:

git clone https://github.com/readyagentsdev/readyagents-core.git

cd readyagents-core python -m venv .venv source .venv/bin/activate # Windows: .venv\Scripts\activate pip install -e . readyagents run examples/calc_pipeline.yaml readyagents runs list readyagents doctor

readyagents run examples/calc_pipeline.json is the same graph.

HITL next: docs/first-ten-minutes.md.

What it does

Define agent workflows as YAML or JSON (nodes + edges)
Run agent, tool, condition, transform, approval, parallel, include, foreach, and decide nodes. Agent nodes may declare a tools: allowlist for a bounded tool-use loop. type: decide is experimental; see docs/decisions.md.
Persist after every node and resume a paused or failed run from the last successful node
Inspect past runs: readyagents runs list / show / replay / fork / diff / freeze / report (local HTML)
Scaffold a starter: readyagents new my-flow (basic, approval, research, pipeline, review, foreach, agent-tools, gated, decide)
Builtin tools with zero extra servers: now, calc, json_get, list_dir, read_file, write_file
MCP client and server (readyagents mcp serve, readyagents mcp probe) with official tasks and MRTR approvals
Policy, spend ledger, budgets and caps: per-node token/cost, --estimate / --max-spend caps, model fallback, JSON logs

Beyond the core, 40+ opt-in extras ship in the same install — teams, studio, browser, knowledge, distillation, registry, environments and more. Each is listed with its maturity tier and its limits in docs/extras.md. None of them is required, and none of them changes how the core behaves.

The agent firewall (taint, tool policy, MCP pinning — security model, policy) is defence in depth, not a solution to prompt injection.

Architecture

flowchart LR

YAML[Workflow YAML/JSON] --> Engine subgraph Core["ReadyAgents Core"] Engine[Workflow engine] Tools[Builtin tools] LLM[BYOK LLM providers] MCP[MCP client / server] Packs[Pack loader] end Engine --> Tools Engine --> LLM Engine --> MCP Packs --> Engine Packs --> Tools LLM --> OpenAI[OpenAI] LLM --> Anthropic[Anthropic] LLM --> Compat[OpenAI-compatible]

CLI

Command	Purpose
`readyagents init`	Write `.env` from `.env.example` if missing
`readyagents new [name] [--template basic\	approval\	research\	pipeline\	review\	foreach\	agent-tools\	gated\	decide] [--from-example NAME] [--list-examples]`	Scaffold workflow + README + `.env.example` + local JSON Schema, or copy a shipped example
`readyagents import SOURCE PATH [--out DIR] [--explain SOURCE]`	Import n8n / LangGraph / CrewAI / trigger-action exports (structural translation only)
`readyagents validate PATH`	Schema-validate a workflow (source-located errors on failure)
`readyagents schema`	Print/write/check the generated workflow JSON Schema
`readyagents eval PATH`	Score a keyless fixture suite (exit 0/1)
`readyagents simulate PATH [...]`	Declaration-driven cases scored with eval; distinct failures frozen
`readyagents optimize PATH --eval SUITE`	Reflective prompt optimization against your eval suite
`readyagents prompts list` / `show` / `history` / `diff` / `rollback`	Versioned prompts beside the workflow; rollback restores exactly
`readyagents env status` / `history` / `diff` / `deploy`	Declared environments and pinned releases
`readyagents registry scan` / `annotate` / `check` / `list` / `show` / `stats` / `card` / `export`	Local agent inventory from declared roots (Annex VIII export is a draft)
`readyagents distill plan` / `dataset` / `train` / `evaluate` / `promote`	Local one-node adapters from consented runs (pack trains; holdout required)
`readyagents promote PATH --from SRC --to DST`	Copy a source pin onto a target after eval/fixture/bench/health/approval gates
`readyagents rollback --env NAME`	Restore the previous release atomically; never auto-forwards
`readyagents feedback export` / `stats`	Consent-gated correction datasets (production data)
`readyagents run PATH [--input KEY=VALUE] [--dry-run] [--approve NODE] [--reject NODE] [--decision-file FILE] [--actor NAME] [--pack PATH] [--policy PATH] [--estimate] [--max-spend USD] [--max-tokens N] [--label KEY=VALUE] [--sovereign] [--stream] [--env NAME]`	Execute (or `--estimate` without running). `--env` runs a pinned release. `--stream` is opt-in
`readyagents batch PATH --input-file FILE [--concurrency N] [--max-spend USD] [--out FILE]`	Foreground: one workflow, many JSONL/CSV rows (opt-in)
`readyagents attest RUN_ID`	Data-residency attestation (technical evidence, not legal compliance)
`readyagents bundle --out DIR`	Offline wheel set for `pip install --no-index --find-links`
`readyagents resume RUN_ID [--approve NODE] [--reject NODE] [--decision-file FILE] [--policy PATH]`	Resume a paused or failed run
`readyagents wake`	Evaluate wait conditions (lazy; starts no timer or daemon)
`readyagents event`	Inject a signed event (unsigned events are refused)
`readyagents policy check PATH`	Validate a firewall policy file (fail closed)
`readyagents policy explain PATH [--policy PATH]`	Show which tools each node may call and why
`readyagents evidence RUN_ID [--out DIR]`	Local evidence pack
`readyagents audit verify [--file PATH]`	Walk the hash-chained audit trail
`readyagents spend [--since DATE] [--by day\	workflow\	model\	actor\	label]`	TokenOps: aggregate the local spend ledger (informational vs the provider invoice)
`readyagents graph PATH`	Deterministic Mermaid routing (executes nothing)
`readyagents decide RUN_ID [--file FILE \	--node ID --decision approve] [--token-file JWT] [--actor NAME] [--reason TEXT]`	Inject an approval; `--actor` stays the default, `--token-file` identifies
`readyagents approvals list [--role ROLE] [--actor NAME] [--expiring-within 1h]`	Queue of paused gates the caller may see
`readyagents delegate --from A --to B --until TS [--scope ROLE]`	Time-bounded, single-hop, revocable approval delegation
`readyagents delegations list \	revoke ID`	List or revoke local delegations
`readyagents identity verify --token-file JWT`	Verify an assertion against local trust anchors
`readyagents identity whoami`	Workload fingerprint (never the private key)
`readyagents runs list`	List persisted runs
`readyagents runs show RUN_ID`	Node timeline + stored state (`inspect` is an alias)
`readyagents runs report RUN_ID`	Local HTML summary of a run
`readyagents runs replay RUN_ID`	New run from stored inputs
`readyagents runs delete RUN_ID --yes`	Delete one local run record
`readyagents runs gc --yes`	Prune succeeded/failed/cancelled runs (paused kept; in-window records refused unless `--override-retention`)
`readyagents runs fork` / `diff` / `freeze` / `migrate`	Time machine: fork a run, diff two, freeze a cassette, migrate JSON→SQLite
`readyagents connectors list` / `show` / `test`	Small governed catalog (`rest`, `sql`, `object_storage`, `message`, `ingest`) — not 500 SaaS
`readyagents sign` / `verify` / `lock` / `sbom` / `trust`	Supply-chain: signatures prove origin, not safety
`readyagents approvals serve`	Foreground localhost approval page
`readyagents studio [--port 8790] [--open] [--read-only]`	Foreground localhost canvas and run inspector
`readyagents models list` / `show` / `route --explain`	Dry model catalog and routing explain (no provider call)
`readyagents mcp serve`	Stdio MCP server (builtin tools); `--json` prints protocol versions
`readyagents mcp probe URL`	Read-only `server/discover` diagnostic (never calls a tool)
`readyagents a2a serve PATH`	Foreground A2A door for one workflow (loopback by default)
`readyagents a2a card PATH`	Deterministic Agent Card (no network)
`readyagents a2a probe URL`	Read-only remote card diagnostic (no secret values)
`readyagents memory list` / `show` / `search` / `forget` / `export`	Local scoped memory (offline except optional embeddings)
`readyagents knowledge ...`	Ingest, sync, cite, and forget knowledge documents (foreground only)
`readyagents table ...`	Inspect intermediate tables: head, schema, stats
`readyagents triggers ...`	Inspect declared triggers, dry-run mappings, list events (starts no listener)
`readyagents skills ...`	Install, list, export, and remove Agent Skills (open format; no marketplace)
`readyagents agents-md`	Emit project context: how to run, validate, and test workflows here
`readyagents package ...`	Build, install, and catalog workflow packages (review-before-install)
`readyagents health`	Cluster failures by fingerprint over the run store (no daemon, no telemetry)
`readyagents bench ...`	Offline-by-default benchmark suite with labelled engine vs live timing

Examples (no keys unless noted)

File	What it shows
`examples/calc_pipeline.yaml`	Builtin tools, transform, condition
`examples/calc_pipeline.json`	Same graph as `calc_pipeline.yaml`
`examples/approval_gate.yaml`	Human-in-the-loop pause / resume
`examples/ollama_local.yaml`	Keyless loopback OpenAI-compat path (no live model)
`examples/quorum_gate.yaml`	Two-approver gate (keyless)
`examples/expiring_gate.yaml`	Lazy deadline, `on_expire: reject` (keyless)
`examples/multi_gate.yaml`	Two sequential approval gates
`examples/fanout_gate.yaml`	Parallel branches + approval
`examples/include_demo.yaml`	Sub-workflow `include` (`new --from-example`)
`examples/composed_gate.yaml`	Include + parallel + approval (`new --from-example`)
`examples/research_brief.yaml`	Agent node (needs a key)
`examples/support_triage.yaml`	Classify then branch (needs a key)

| `examples/code_r

Only the beginning is shown; read the rest in the repo

ReadyAgents

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Dechonet MCP

README from the maker

ReadyAgents Core

60-second start

What it does

Architecture

CLI

Examples (no keys unless noted)