
AgentPool
io.github.sidduHERE由 GitHub 账号 sidduHERE 发布v0.1.13更新于 Oct 24, 2026
See each coding-agent subscription's live limits and offload work to one with headroom.
概览
本地 MCP 服务器兼命令行工具,显示各编程代理订阅的实时用量上限,并让助手把任务交给仍有余额的提供方执行。
- 可以做什么
- 查看已配置的编程代理提供方、可用模型以及带置信度和重置时间的用量/容量摘要,并读取委派偏好与入门资源。在指定提供方和仓库上显式创建工作任务会话,可选只读或 worktree 隔离,然后观察、轮询、发送消息、中断、收集结果和终止会话。读取产物清单并分页查看会话记录。可按需启用统计、会话、租约和 worktree 工具集。
- 适用场景
- 适合同时付费订阅多个编程代理(Codex、Claude Code、Cursor、Copilot、Devin、Droid)的用户,让助手在分派任务前先看清哪个订阅还有余量。也适合在无 shell 访问的 MCP 原生客户端中运行并监督委派的工作会话。
- 运行要求
- 需在本机安装 Python 3.11 及以上,并通过 PyPI 的 agentpool-cli 包以 mcp 子命令启动。默认运行时需要 PATH 中有 tmux;termctrl、Git、CodexBar 和 ccusage 为可选。实时运行时支持 macOS 或 Linux(Windows 仅限 WSL)。各提供方 CLI 需已安装并完成登录;服务器本身未声明任何凭据。
安装
在 SourceWeft 中
- 打开 控制台中的 AgentPool,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
README
AgentPool
You pay for several coding-agent subscriptions — Codex, Claude Code, Cursor, Copilot, Devin, Droid — but you work in one at a time. The rest sit idle until your active provider hits its 5-hour or weekly limit, and then you stall.
AgentPool is a local Python CLI and MCP server that reads the live usage limits of every coding-agent subscription you have and lets you — or your primary agent — offload work to whichever one still has headroom. Use the capacity you already pay for, and keep moving instead of hard-stopping at a cap.
It is a control plane, not an auto-router. AgentPool exposes live provider, model, session, artifact, lease, and best-effort usage/capacity state, and runs the work you offload as explicit worker sessions. You or your agent still choose the provider and model — AgentPool makes the limits visible so that choice is informed, never automatic.
The v0.1 alpha posture is conservative:
- tmux is the default runtime; Terminal Control is optional when configured.
- Provider selection is explicit;
provider=autois rejected. - Usage/capacity summaries are confidence-tagged and keyed by provider id.
- CodexBar and ccusage are optional usage helpers when installed or configured.
- AgentPool does not store provider credentials, scrape browser usage pages, rank models, merge code, or push code.
Requirements
- Python 3.11 or newer.
- tmux on
PATH. - Optional:
termctrlonPATHfor the Terminal Control runtime. - Git for worktree isolation and diff collection.
- macOS or Linux for live runtimes. Windows is not a v0.1 target except through WSL-like shells.
Install
AgentPool publishes to PyPI as agentpool-cli; the installed command is
agentpool.
Then:
The agentpool-cli package installs on macOS, Linux, and Windows, but live
terminal runtimes are supported on macOS or Linux (Windows via WSL).
Optional Terminal Control config:
Install from source:
Or run from a development checkout:
A GitHub release install (wheel pinned to a tag, no PyPI required) is also supported:
See docs/install.md for first-run, upgrade, and MCP setup notes.
Quickstart
For AI agents, start by loading the bundled version-matched skill:
That last command is the one you will run most: it shows every configured
subscription's remaining limit, reset time, and a usable flag, so you can see
at a glance which provider to offload the next task to.
Start an explicitly selected read-only worker:
spawn defaults --initial-prompt-mode to provider_default. For Codex CLI
this resolves to arg, which passes the first task as the Codex prompt argument
instead of relying on a paste-and-submit startup cycle. Providers that expose
reasoning controls also accept process-local overrides such as
--reasoning-effort high; Codex also accepts --service-tier priority.
AgentPool does not edit your provider config.
For AgentPool-created edit isolation, choose worktrees explicitly:
Worktree isolation is not forced by default. Users often have their own
worktree setup and cleanup rules, so AgentPool only creates a worktree when
requested through --isolation worktree or policy configuration.
Usage And Capacity
usage-summary returns a providers object keyed by provider id. It is not
ordered and it is not a recommendation list. Each row includes usable,
unusable_reason, quota windows, confidence, age/staleness, and reset timing when
the provider exposes it. The older CLI capacity-summary command is retained
as a human convenience alias; MCP also exposes get_capacity_summary as a
compatibility alias for get_usage_summary.
The default buffer is policy.min_remaining_percent = 10. If any reported
quota window is below that buffer, the provider row is marked unusable for the
summary. Staleness is reported as age information only; it does not by itself
make a provider unusable. If you want cached summary reads to refresh
automatically after a threshold, set policy.usage_auto_refresh_after_seconds
in ~/.agentpool/config.yaml.
AgentPool still does not pick an alternative provider for you.
MCP usage refreshes are intentionally bounded and may return partial=true;
use the CLI commands above when a shell-capable agent needs a complete live
refresh. Use --no-interactive or AGENTPOOL_NO_INTERACTIVE_USAGE=1 when a
shell script must avoid provider TUI fallback probes.
Provider Matrix
Compatibility note: the PRD calls Factory's coding product factory-droid, but
AgentPool exposes it as droid-cli because the installed command is droid.
Do not add a duplicate factory-droid inventory row unless a distinct harness
appears.
Privacy Posture
AgentPool is local-first, but usage probes can still be sensitive because they read existing CLI auth state and may call provider APIs on explicit refresh.
AgentPool does not:
- store provider credentials;
- read browser cookies by default;
- scrape browser dashboards;
- trigger login flows;
- silently accept paid overage.
AgentPool does store:
- SQLite session, event, usage snapshot, artifact, and lease metadata;
- transcript and artifact files under
~/.agentpool/artifactsby default; - generated runtime settings that are not credentials.
Run:
See SECURITY.md and docs/usage-detection.md.
MCP
Start the MCP server:
Example host config:
Verified install helpers (deeplink or one-liner shell command):
Raw config generators:
Use --absolute-command if the MCP host does not inherit your shell PATH.
Verified per-host steps live in docs/mcp-clients.md.
Team templates: .cursor/mcp.json.example,
.mcp.json.example, and docs/examples/README.md.
MCP Registry metadata: server.json. It advertises the
agentpool-cli PyPI package and should be bumped with each release. Release
checklist: docs/release.md.
Provider setup guides:
Cursor,
Cursor Agent CLI,
Codex,
Claude Code,
Copilot,
Droid, and
Devin.
MCP-connected agents should read these once on connect:
agentpool://onboardingagentpool://skill.mdagentpool://preferences.md
Then use tools for live operations. The user-owned preferences file also shows
up through agentpool preferences and get_delegation_preferences(). It may
say to use your native subagent system instead of AgentPool for some tasks. The
default MCP toolset is deliberately small: inventory, usage snapshot, usage
summary, provider models, preferences, spawn, observe, poll, send, interrupt,
collect, artifact manifest, transcript paging, and terminate. Add opt-in
toolsets with agentpool mcp --toolsets default,stats,sessions,leases,worktrees.
Shell-capable agents can use agentpool skills get agentpool instead of MCP
resources to load the same core usage guidance from the installed CLI.
Coding agents with shell access should prefer the CLI path. It is more
token-efficient because large worker output stays in artifact files and
observe/collect return compact manifests by default. MCP remains first-class
for MCP-native hosts and no-shell environments. See
docs/agent-cli-and-mcp.md.
Development Checks
Development and CI checks are documented in CONTRIBUTING.md.
来源:README.md,提交 de87ff2
工具
0版本历史
1- v0.1.13最新Sep 16, 2026
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