Agent-libOS 是 AI Skill Hub 本期精选Agent工作流之一。综合评分 7.5 分,整体质量较高。我们推荐使用将其纳入你的 AI 工具库,帮助提升工作效率。
Agent libOS提供了一个长期运行LLM代理的运行时基座,支持多种AI工作流。
Agent-libOS 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。
Agent libOS提供了一个长期运行LLM代理的运行时基座,支持多种AI工作流。
Agent-libOS 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。
# 方式一:pip 安装(推荐)
pip install agent-libos
# 方式二:虚拟环境安装(推荐生产环境)
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install agent-libos
# 方式三:从源码安装(获取最新功能)
git clone https://github.com/yingqi-z20/Agent-libOS
cd Agent-libOS
pip install -e .
# 验证安装
python -c "import agent_libos; print('安装成功')"
# 命令行使用
agent-libos --help
# 基本用法
agent-libos input_file -o output_file
# Python 代码中调用
import agent_libos
# 示例
result = agent_libos.process("input")
print(result)
# agent-libos 配置文件示例(config.yml) app: name: "agent-libos" debug: false log_level: "INFO" # 运行时指定配置文件 agent-libos --config config.yml # 或通过环境变量配置 export AGENT_LIBOS_API_KEY="your-key" export AGENT_LIBOS_OUTPUT_DIR="./output"
Agent libOS is an experimental agent-native libOS runtime written in Python. It models an agent as a long-running, schedulable, interruptible, capability-controlled AgentProcess, not as a single chat request or workflow thread.
The current contribution is the runtime authority boundary:
process identity + capability + primitive + audit
LLM-facing tools are ergonomic wrappers. They are not the security boundary. Protected effects happen only inside libOS primitives, where process identity, capabilities, human approval, policy, provider containment, events, and audit records are enforced.
This project is still in active development. agent_libos_design_doc.md is a historical design archive and may describe planned or superseded interfaces.
Install dependencies:
uv sync
Run tests:
uv run python -m unittest discover -s tests -v
Run the deterministic local demo:
uv run agent-libos demo
The demo does not call a real model. It exercises process spawn/fork, Object Memory, Deno/TypeScript JIT validation when Deno is available, checkpointing, capability denial before grant, human approval, filesystem write, final report object creation, and audit trace generation.
Run a small deterministic benchmark smoke:
uv run python experiments/run_benchmark.py --suite benchmarks/runtime_safety --runner agent_libos_full --limit 3 --output .benchmark_runs/m1-smoke
uv run python experiments/collect_metrics.py .benchmark_runs/m1-smoke
The benchmark defaults to mock/planned actions and does not spend model tokens. Real-model benchmark smoke is opt-in and must be scoped with `--llm real --limit 1 or a single --task`.
Send ordinary and interrupt messages:
uv run agent-libos --db .agent_libos.sqlite message <pid> "Please inspect the latest result"
uv run agent-libos --db .agent_libos.sqlite interrupt <pid> "Stop current work and read this first"
Run an interactive Codex CLI-style loop:
uv run agent-libos --db .agent_libos.sqlite run --interactive --pid <pid> --max-quanta 20
Manually control process cwd and lifecycle:
uv run agent-libos --db .agent_libos.sqlite cd <pid> src
uv run agent-libos --db .agent_libos.sqlite exec image.yaml "Review README.md" --pid <pid> --run
uv run agent-libos --db .agent_libos.sqlite exit <pid> --payload '{"done":true}'
Register and load the SWE-Agent style Skill:
uv run agent-libos --db .agent_libos.sqlite skills register skills/swe_agent.yaml
uv run agent-libos --db .agent_libos.sqlite skills load <pid> swe-agent:v0
Inspect or change runtime authority:
uv run agent-libos --db .agent_libos.sqlite capabilities list --subject <pid>
uv run agent-libos --db .agent_libos.sqlite capabilities explain <pid> filesystem:workspace:README.md read
uv run agent-libos --db .agent_libos.sqlite capabilities grant <pid> filesystem:workspace:README.md --rights read
Launch a coding agent against another workspace:
uv run python scripts/run_coding_agent.py --workspace /path/to/repo --goal "Implement the requested change"
On Windows PowerShell:
uv run python scripts\run_coding_agent.py --workspace ..\some-repo --goal "Summarize the current project"
See docs/cli.md for the full command reference.
Create a local .env file for real-model execution:
OPENAI_BASE_URL=https://example-openai-compatible-endpoint/v1
OPENAI_LANGUAGE_MODEL=your-model
OPENAI_API_KEY=...
The client uses the OpenAI Python SDK. It uses the Responses API for OpenAI-hosted models by default and falls back to Chat Completions for custom OpenAI-compatible base_url providers. Set OPENAI_API_MODE=responses or OPENAI_API_MODE=chat to force a mode.
Optional knobs include OPENAI_TIMEOUT, OPENAI_MAX_RETRIES, OPENAI_STORE, OPENAI_REASONING_EFFORT, OPENAI_VERBOSITY, and provider-specific OPENAI_ENABLE_THINKING.
Agent-libOS是一个开源的AI工作流库,提供了一个长期运行LLM代理的运行时基座,支持多种AI工作流。该库使用Python编写,易于使用和扩展。然而,库的文档和示例可能需要进一步完善。总体来说,Agent-libOS是一个值得关注的AI工作流库,值得进一步研究和开发。
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。
✅ Apache 2.0 — 宽松开源协议,可商用,需保留版权声明和 NOTICE 文件,含专利授权条款。
经综合评估,Agent-libOS 在Agent工作流赛道中表现稳健,质量良好。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。
| 原始名称 | Agent-libOS |
| 原始描述 | 开源AI工作流:Agent libOS provides a runtime substrate for long-running LLM agents with proces。⭐11 · Python |
| Topics | workflowpython |
| GitHub | https://github.com/yingqi-z20/Agent-libOS |
| License | Apache-2.0 |
| 语言 | Python |
收录时间:2026-06-06 · 更新时间:2026-06-06 · License:Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。
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