windlass
Enables running and managing automated tasks with retry loops and machine-checkable success criteria via MCP tools.
README
Windlass
A looped agent orchestrator. Windlass runs Claude Code sessions against machine-checkable success criteria and retries until the criteria pass or a stop condition fires.
The core idea: an agent loop is gather, act, verify. The exit condition is named up front, before the agent runs, and it must be machine-checkable. A task succeeds when its HTTP check returns 200, its build command exits 0, or its test suite passes. It never succeeds because the agent says it's done. When a check fails, Windlass feeds the specific failure back into the next attempt's prompt, so each retry starts from evidence instead of a blank slate. Every attempt has a retry cap, a wall-clock timeout, and an audit trail on disk.
What it is
Two entry points over one engine:
- MCP server (
dist/index.js): exposes the orchestrator as tools any MCP client can call. - CLI (
windlass): run task files, check status, browse history, scaffold new definitions.
The engine spawns claude --print as a subprocess for each attempt, captures its output, and evaluates the declared criteria against the output and the real world (HTTP endpoints, files, build and test commands).
MCP tools
The server registers three tools (see src/mcp/server.ts):
| Tool | What it does |
|---|---|
run_task |
Run a task with success criteria and retry loops. Accepts an inline task definition or a path to a YAML/JSON file. A file containing a tasks array runs as a pipeline. |
check_task |
Fetch the status of a task execution by ID. |
list_tasks |
List running and recent executions from the audit log. |
Install
Requires Node.js 20+ and the Claude Code CLI (claude) on your PATH.
git clone https://github.com/blakestone-x/windlass.git
cd windlass
npm ci
npm run build
MCP configuration
Add the server to your MCP client config. For Claude Code, in .mcp.json:
{
"mcpServers": {
"windlass": {
"command": "node",
"args": ["/path/to/windlass/dist/index.js"]
}
}
}
CLI quickstart
# Scaffold a task definition
node dist/cli.js define test -o my-test.yaml
# Validate without executing
node dist/cli.js run my-test.yaml --dry-run
# Run it
node dist/cli.js run my-test.yaml --verbose
# Inspect
node dist/cli.js status
node dist/cli.js history -n 10
Task definitions
A task is a YAML or JSON file: a prompt for Claude Code plus the criteria that decide success. Example (adapted from src/tasks/build.yaml):
name: build-project
type: build
prompt: |
Build the project using the standard build command.
If there are compilation errors, read each error, fix the source, and rebuild.
model: sonnet
max_retries: 3
timeout_minutes: 10
escalation: stop
retry_backoff: exponential
retry_delay_seconds: 5
success_criteria:
- type: build_succeeds
command: "npm run build"
Success criteria
Six check types, defined in src/schema/task.ts and evaluated in src/evaluators/:
| Type | Passes when |
|---|---|
http_status |
URL returns the expected status, with optional body_contains / body_not_contains. |
grep_output |
A regex matches (or doesn't, with should_match: false) the session's stdout, stderr, or transcript. |
file_exists |
A file exists, optionally containing a given string. |
test_passes |
A test command succeeds and the parsed pass rate meets min_pass_rate. Understands Jest/Vitest, pytest, and cargo output. |
build_succeeds |
A build command exits 0. |
screenshot |
The URL is reachable. Visual review itself is a placeholder for now; the check logs the description for manual verification. |
Tasks can also declare pre_checks (gate before the agent runs) and post_checks (extra gate after success criteria pass). A post-check failure fails the task.
The loop and its stop conditions
For each task (src/engine/orchestrator.ts):
- Run pre-checks. If any fail, the task fails without spawning a session.
- Spawn a Claude Code session with the prompt.
- Evaluate every success criterion against the session output and the environment.
- All pass: run post-checks, then mark succeeded.
- Any fail: log the failure, wait out the backoff delay, and retry with a rebuilt prompt that includes the original task, the specific failed checks, and the tail of the last output.
Stop conditions are wired in, not implied:
max_retries: the loop runs at mostmax_retries + 1attempts, then fails with the configuredescalationlabel (notify,revert, orstop).timeout_minutes: a wall-clock budget for the whole task, checked before each attempt and passed down to the session subprocess, which gets SIGTERM then SIGKILL.- Retry backoff:
fixed,linear, orexponentialonretry_delay_seconds.
Pipelines
A file with a tasks array runs as a pipeline. Tasks declare depends_on; a topological scheduler (src/engine/scheduler.ts) rejects cycles, runs ready tasks in parallel up to max_concurrent, skips tasks whose dependencies failed, and stops early when fail_fast is set.
Audit log
Every execution, session, and failure analysis is written to SQLite at .windlass/audit.db (override the directory with WINDLASS_DATA_DIR). The status and history CLI commands and the check_task / list_tasks MCP tools read from it, so a crashed process loses no history.
Status
Extracted from a private orchestration stack built to automate engineering work at a national commercial field-service operation. This is its first public release. The core loop, evaluators, pipelines, and audit log are working; the screenshot criterion is a reachability check awaiting a real visual-review backend. Expect the API to move.
License
MIT. See LICENSE.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。