Agentic Project Management (APM): a spec-driven multi-agent framework for Claude Code, Codex CLI and Cursor
A framework for managing complex projects with spec-driven multi-agent workflows.
At a glance
- What is it?
- APM is an npm-installed CLI that drops Planner, Manager and Worker commands into your AI assistant's workspace and keeps project state in files outside any agent's context. It suits long builds you are willing to babysit step by step, and it is the wrong tool if you want unattended automation.
- Who is it for?
- Adopt APM if you are already driving one AI assistant through a multi-week build and want the Spec, Plan and Rules written down before code starts, with a human checkpoint on every agent message. Do not adopt it if you want agents to run unattended, or if the project fits comfortably in a single chat.
- Can I use it commercially?
- Check first. The repository uses a licence we do not classify automatically, so read its LICENSE file before any commercial use.
- Is it still maintained?
- Yes. The repository last received commits 115 days ago.
- What is it written in?
- Mainly JavaScript, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on October 1, 2026, and from our analysis. They are not legal advice.
Editorial analysis
The failure APM is built around: context that degrades before the project ends
The README states the problem plainly: as conversations grow, AI context degrades, the assistant loses track of requirements, produces bad code, and hallucinates details. Anyone who has pushed a single chat past a few thousand lines of work has seen this. The usual escape is to restart the agent with a fresh summary, which throws away everything it had learned about its corner of the codebase.
APM's answer is structural rather than prompt-level. Work is split across three agent types, each with its own context window and only the information it needs. A Planner runs structured project discovery and produces three documents: a Spec (what to build), a Plan (how the work is organized), and Rules (how work is performed). A Manager coordinates execution, assigns Tasks to Workers, reviews completed work and maintains project state, operating on execution summaries rather than raw code. Workers execute Tasks inside defined domains such as frontend, backend or API.
The audience is narrow and worth stating: people who build with AI agents and own what they ship, per the README. If you are not already running one of the supported assistants on a real project, APM has nothing to attach to. It is not a hosted service and it is not a chat interface. It is a set of commands and templates that live inside your existing assistant's workspace.
Where the state actually lives, and why the human is in the loop
Project state lives in structured files outside any agent's context. That single design decision explains most of APM's behaviour. Because the state is not trapped inside a conversation, an agent that reaches its limits can be replaced: you run a Handoff command and a fresh instance of the same agent picks up the working knowledge rather than starting cold. Completed sessions can be archived and their context carried forward, which is what `apm archive` manages.
It also explains the workflow shape. You mediate every exchange between agents by running commands in the appropriate conversation. The README is explicit that this keeps every step visible and auditable, and that each agent tells you exactly what to run, in which conversation, and what to do next. One command delivers a task to a Worker. Another brings the Worker's report back to the Manager.
That is a deliberate cost, not an oversight. The README frames message delivery as a built-in checkpoint: you see every task assignment before it reaches a Worker and every result before the Manager acts on it. If your instinct on reading that is annoyance, APM is probably not for you. The framework gives up throughput in exchange for review points, and it does not appear to offer a mode that skips them.
The Worker model differs from subagent approaches in one specific way the README calls out: the agents doing implementation work are not restarted fresh on each task. They accumulate working context across assignments and build familiarity with their domain as the project progresses. When context fills, a structured Handoff transfers that working knowledge to a fresh instance instead of discarding it.
Install and first run: npm, apm init, then one slash command
The README gives a two-step install. The CLI is published on npm as `agentic-pm` and is installed globally. The package.json declares `"engines": { "node": ">=18" }`, so check your Node version first if you are on an older runtime.
npm install -g agentic-pmFrom your project directory, run `apm init`. The README says the CLI prompts you to select your AI assistant, then installs commands, guides, skills, and project artifact templates into your workspace. The supported assistants listed in the README are Claude Code, Codex CLI, Cursor, GitHub Copilot, Antigravity, and OpenCode.
apm initAfter initialization, open your AI assistant and run the Planner initiation command. The README shows it bare, and also shows it with context attached as an argument.
/apm-1-initiate-plannerThe README's own example of passing context is worth quoting because it sets expectations about how much steering the Planner needs: `/apm-1-initiate-planner I want you to build Claude Opus 5. Make no mistakes.` The Planner asks structured questions while reading your codebase. You correct it, steer it, add context, and sign off on its understanding. That conversation produces the three planning documents.
Once you approve them, the README directs you to open a new conversation and run `/apm-2-initiate-manager` to begin coordinated execution. From there each agent directs you through the workflow. The CLI carries additional commands beyond init: `apm custom` installs from custom repositories, `apm update` updates to the latest compatible version, `apm archive` archives the current session or manages archives, `apm add` and `apm remove` manage assistants, and `apm status` shows installation state.
The limitation that matters most: you are the message bus
Every exchange between agents passes through you. The README presents this as transparency, and it is, but it is also a hard ceiling on how much work APM can move without you sitting in front of it. A project with many small tasks means many command runs, each one a place you have to be present. Compare that to a single-agent setup where you describe a goal and walk away. APM trades that away on purpose, and the trade only pays off if the review is worth more than the interruption.
The second limitation is that the framework does not remove the underlying context limit, it redistributes it. Workers still fill their windows; the README says so directly, describing the Handoff as the response to a Worker that fills its context. You are managing the same resource, just at a finer granularity and with a recovery path that does not lose the domain knowledge.
Third, APM assumes your assistant has a filesystem-oriented workflow that can host slash commands and read templates from the workspace. The README lists six assistants, and the CLI asks you to pick one during `apm init`. If you are working in a chat-only interface without that kind of workspace integration, the installed commands have nowhere to run.
Finally, the README does not document rollback. There is `apm archive` for archiving the current session and managing archives, but nothing in the README describes undoing a bad Task assignment or reverting a Worker's changes. If you need that, you are relying on your own version control.
How APM differs from a general subagent setup
The obvious alternative is a subagent-based approach in the same assistant: define specialized agents, hand each one a task, collect the result. The README draws the distinction itself. In a subagent approach the implementation agents are restarted fresh on each task. In APM they accumulate working context across assignments.
That difference has a practical consequence. A fresh subagent has to be re-briefed every time, and the briefing is where requirements drift. An APM Worker that has been on the backend for two weeks carries its accumulated familiarity into the next assignment, and when it finally runs out of room, the Handoff is a structured transfer rather than a summary you write by hand.
The second alternative is simply not using a framework: one long conversation with a single assistant. That works fine for small projects, and the README concedes the point by scoping APM to ambitious software projects. The point where a single chat breaks down is the point where APM starts to make sense.
A third option is a hosted agent platform that orchestrates multiple agents for you. APM is the opposite shape: a local CLI that installs files into your workspace, with state in your repository and no server in the middle. If you need the coordination to happen without a human relaying messages, APM is not the tool that gets you there.
Licence, customization and what upgrades cost
The README badge and package.json both declare MPL-2.0, and the LICENSE file is present at the repository root. The GitHub API reports the licence as NOASSERTION, which usually means the detector could not match the file to a known template. That discrepancy is worth resolving against the actual LICENSE text before you build on APM, particularly if you plan to modify and redistribute it. This is a description of what the repository states, not legal advice.
MPL-2.0 is file-level copyleft. If your team forks APM and modifies its files, the licence terms attach to those modified files rather than to your whole codebase, which is a meaningfully different position from a strong copyleft licence. Again, read the LICENSE file rather than taking that summary as authoritative.
Customization is a first-class path in the README, aimed at teams that want to modify the workflow. Two routes are described: fork the repo for upstream sync, or use "Use this template" for a clean start. You then adjust templates, build, release, and install with `apm custom -r owner/repo`. A customization skill is included under `skills/apm-customization/` to guide AI agents through that process.
The upgrade surface is the CLI itself. `apm update` moves you to the latest compatible version, and the repository keeps a VERSIONING.md alongside CHANGELOG.md, which suggests the project takes compatibility seriously enough to write it down. The repository layout includes `src/`, `build/`, `templates/` and `skills/`, so a fork that changes templates is editing the same tree the CLI installs from.
On maintenance: the last push was on 2026-06-08, and the most recent release, v1.0.2, carries the same date. Earlier releases v1.0.1 and v1.0.0 landed on 2026-04-23 and 2026-04-09. The release cadence in that window was roughly monthly, but the repository has not been pushed to since June, so treat the current state as stable rather than moving.
Editorial conclusion
Adopt APM if you are already driving one AI assistant through a multi-week build and want the Spec, Plan and Rules written down before code starts, with a human checkpoint on every agent message. Do not adopt it if you want agents to run unattended, or if the project fits comfortably in a single chat. Before committing, verify two things yourself: that the terms in the repository's LICENSE file cover your intended use, since the GitHub API reports NOASSERTION while package.json declares MPL-2.0, and that your assistant is one of Claude Code, Codex CLI, Cursor, GitHub Copilot, Antigravity or OpenCode, because apm init only installs commands for the assistant you select at the prompt.
Frequently asked questions
What is agentic project management in the context of APM?
In this project, it means managing a software project with a coordinated team of AI agents rather than one long chat. APM splits the work across a Planner that produces Spec, Plan and Rules documents, a Manager that coordinates execution, and Workers that execute Tasks in defined domains.
How do I install Agentic Project Management?
Install the CLI globally from npm with `npm install -g agentic-pm`, then run `apm init` from your project directory and select your AI assistant when prompted. The CLI installs commands, guides, skills and artifact templates into your workspace.
Which AI assistants does APM support?
The README lists Claude Code, Codex CLI, Cursor, GitHub Copilot, Antigravity and OpenCode. You choose one during `apm init`, and `apm add` and `apm remove` manage assistants afterwards.
What happens when a Worker runs out of context in APM?
You run a Handoff command, and a fresh instance of that same agent picks up the working knowledge instead of starting from nothing. The README describes this as a structured transfer of working knowledge, and says the same applies to the Manager.
Can I modify the APM workflow for my team?
Yes. The README describes forking the repo for upstream sync or using "Use this template" for a clean start, then adjusting templates, building, releasing, and installing with `apm custom -r owner/repo`. A customization skill is included under `skills/apm-customization/`.
Official sources
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