
AgentPool
io.github.sidduHERE由 GitHub 帳號 sidduHERE 發布v0.1.13更新於 Oct 24, 2026
See each coding-agent subscription's live limits and offload work to one with headroom.
概覽
本機 MCP 伺服器兼命令列工具,顯示各程式代理訂閱的即時用量上限,讓助理把工作交給仍有餘量的供應商執行。
- 可以做什麼
- 檢視已設定的程式代理供應商、可用模型,以及附信心標記與重置時間的用量/容量摘要,並讀取委派偏好與入門資源。在指定供應商與儲存庫上明確建立工作階段,可選唯讀或 worktree 隔離,然後觀察、輪詢、傳送訊息、中斷、收集結果與終止工作階段。讀取產物清單並分頁查看工作階段紀錄。可視需要啟用統計、工作階段、租約與 worktree 工具集。
- 適用情境
- 適合同時付費訂閱多個程式代理(Codex、Claude Code、Cursor、Copilot、Devin、Droid)的使用者,讓助理在分派工作前先看清哪個訂閱還有餘量。也適合在沒有 shell 存取權的 MCP 原生用戶端中執行並監督委派的工作階段。
- 執行需求
- 需在本機安裝 Python 3.11 以上,並透過 PyPI 的 agentpool-cli 套件以 mcp 子命令啟動。預設執行環境需要 PATH 中有 tmux;termctrl、Git、CodexBar 與 ccusage 為選用。即時執行環境支援 macOS 或 Linux(Windows 僅限 WSL)。各供應商 CLI 需已安裝並完成登入;伺服器本身未宣告任何憑證。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 AgentPool,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
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.
來源:README.md,提交 de87ff2
工具
0版本歷史
1- v0.1.13最新Sep 16, 2026
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