经 AI Skill Hub 精选评估,ActPlane Agent工作流 获评「推荐使用」。这款Agent工作流在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 7.5 分,适合有一定技术背景的用户使用。
ActPlane Agent工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
ActPlane Agent工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
# 克隆仓库 git clone https://github.com/eunomia-bpf/ActPlane cd ActPlane # 查看安装说明 cat README.md # 按 README 完成环境依赖安装后即可使用
# 查看帮助 actplane --help # 基本运行 actplane [options] <input> # 详细使用说明请查阅文档 # https://github.com/eunomia-bpf/ActPlane
# actplane 配置说明 # 查看配置选项 actplane --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量(覆盖配置文件) export ACTPLANE_CONFIG="/path/to/config.yml"
Runtime observability and enforcement for AI agent harnesses: declare information-flow policies in a compact DSL, ActPlane enforces them at the kernel level.
ActPlane sits below the tool layer, so a rule holds information-flow constraints across every process, file access, and network connection the agent touches, no matter what tool, subprocess, or script it uses to get there.
Each rule sets its own mode: notify (observe and notify agent), block (stop the action before it commits), or kill (terminate the process). In every mode the rule match's reason is fed back to the agent as a reminder, so it can self-correct instead of just hitting a wall. Agents can write and validate their own rules (actplane check).
Prompt constraints and model guardrails are probabilistic. ActPlane is deterministic.
What you can express:
codex may run git push or write outside /src": fine-grained sandboxing rules follow process lineage, no bypass via bash scripts or python.specs/*, also update the server, SDK, and docs": ActPlane never blocks the edit, it notifies the agent that downstream outputs are now stale.make check & npm tests before committing": causal ordering, not just per-operation checks.cargo install actplane is all most users need. To hack on ActPlane:
git clone --recurse-submodules https://github.com/eunomia-bpf/ActPlane
cd ActPlane/collector && cargo build --release # uses the prebuilt eBPF object
Editing the kernel eBPF (bpf/*.bpf.c) requires the BPF toolchain (clang/llvm, libelf, zlib) and the libbpf/bpftool submodules. Rebuild and refresh the committed object with:
ACTPLANE_REBUILD_BPF=1 cargo build -p ebpf-ifc-engine # regenerates bpf/prebuilt/process.bpf.o
Run the tests:
make test # bpf C unit tests + collector Rust unit tests
sudo bash script/e2e_examples.sh # live E1–E12 enforcement
Install with one command. The eBPF program ships prebuilt (CO-RE, architecture independent), so there is no clang/llvm/libbpf to install — just a Rust toolchain:
cargo install actplane
Write a policy and run an agent (or any command) under the harness:
actplane init # write a starter actplane.yaml
actplane check # validate rules (no privileges)
sudo -E actplane run claude -p "review this repo"
When a rule matches, ActPlane kills the action and tells the agent why:
🚫 KILLED: process 'git' (pid 4213, ppid 4210) — /usr/bin/git
effect: kill
reason: no git under the agent; use the review workflow
The agent receives this reason through its hook integration, understands the constraint, and takes a different path to complete the task.
Requirements: Linux kernel 5.8+ with BTF (/sys/kernel/btf/vmlinux). run and watch load the eBPF engine, so they need root (or CAP_BPF + CAP_SYS_ADMIN); ActPlane drops the target command back to your user. With BPF-LSM enabled, rules can block before the action commits; otherwise they notify (report) or kill.
ActPlane feeds rule-match reasons back to agents via their hook systems.
Claude Code (.claude/settings.local.json):
{
"hooks": {
"PostToolUse": [{ "matcher": "*", "hooks": [{ "type": "command", "command": "/path/to/actplane feedback-hook" }] }],
"PostToolUseFailure": [{ "matcher": "*", "hooks": [{ "type": "command", "command": "/path/to/actplane feedback-hook" }] }]
}
}
Codex (.codex/hooks.json):
{
"hooks": {
"PostToolUse": [{ "matcher": ".*", "hooks": [{ "type": "command", "command": "/path/to/actplane feedback-hook" }] }]
}
}
The adapter forwards new rule matches as hook context. The kernel remains the sole authority for observation and enforcement. See script/CLAUDE.snippet.md for the agent instruction snippet.
高质量的AI工作流管理工具
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
AI Skill Hub 点评:ActPlane Agent工作流 的核心功能完整,质量良好。对于自动化工程师和运维人员来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。
| 原始名称 | ActPlane |
| 原始描述 | 开源AI工作流:eBPF-Based Information Flow Policy Engine for AI Agent Harnesses。⭐17 · C |
| Topics | AIeBPF信息流政策 |
| GitHub | https://github.com/eunomia-bpf/ActPlane |
| License | MIT |
| 语言 | C |
收录时间:2026-05-30 · 更新时间:2026-05-31 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
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