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智能工作流
⚙️
Agent工作流

智能工作流

基于 Rust · 无代码搭建完整 AI 自动化流程
英文名:homebrew-pandafilter
⭐ 97 Stars 🍴 10 Forks 💻 Rust 📄 MIT 🏷 AI 8.0分
8.0AI 综合评分
ai-codingrustagentic-coding
✦ AI Skill Hub 推荐

AI Skill Hub 强烈推荐:智能工作流 是一款优质的Agent工作流。AI 综合评分 8.0 分,在同类工具中表现稳健。如果你正在寻找可靠的Agent工作流解决方案,这是一个值得深入了解的选择。

📚 深度解析

智能工作流 是一套完整的 AI Agent 自动化工作流方案。随着 AI 能力的不断提升,基于 Agent 的自动化工作流正在成为提升个人和团队效率的核心方式。区别于传统的 RPA 自动化(模拟鼠标键盘操作),AI Agent 工作流通过理解任务意图、动态规划执行路径,能够处理更复杂的非结构化任务。

智能工作流 工作流的设计遵循"最小配置,最大复用"原则:核心逻辑已经封装好,用户只需配置自己的 API Key 和业务参数即可快速上手。工作流内置错误处理和重试机制,在网络波动或 API 限速等情况下仍能稳定运行,适合作为生产环境的自动化基础设施。

在实际部署时,建议先在测试环境中运行 3-5 次,验证各个环节的输出结果符合预期,再部署到生产环境。AI Skill Hub 评分 8.0 分,是同类 Agent 工作流中的精选推荐。

📋 工具概览

智能工作流 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。

GitHub Stars
⭐ 97
开发语言
Rust
支持平台
Windows / macOS / Linux
维护状态
轻量级项目,按需更新
开源协议
MIT
AI 综合评分
8.0 分
工具类型
Agent工作流
Forks
10

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

智能工作流 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。

📌 核心特色
  • 可视化 Agent 工作流编排,无需编写复杂代码
  • 支持多步骤自动化任务链,实现全流程无人值守
  • 与外部 API、数据库和第三方服务无缝集成
  • 内置错误处理与自动重试机制,保障稳定运行
  • 提供可复用的自动化模板,快速在同类场景部署
🎯 主要使用场景
  • 自动化日常重复性工作,将精力集中于创造性任务
  • 构建数据采集 → 处理 → 输出的完整自动化管线
  • 实现跨平台、跨系统的数据流转和业务协同
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 方式一:cargo install(推荐)
cargo install homebrew-pandafilter

# 方式二:从源码编译
git clone https://github.com/AssafWoo/homebrew-pandafilter
cd homebrew-pandafilter
cargo build --release
# 二进制在 ./target/release/homebrew-pandafilter
📋 安装步骤说明
  1. 访问 GitHub 仓库获取工作流文件
  2. 在对应平台(Dify / Flowise / Make 等)中找到「导入工作流」功能
  3. 上传工作流文件
  4. 按照提示配置必要的环境变量和 API Key
  5. 运行测试确认流程正常后投入使用
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 查看帮助
homebrew-pandafilter --help

# 基本运行
homebrew-pandafilter [options] <input>

# 详细使用说明请查阅文档
# https://github.com/AssafWoo/homebrew-pandafilter
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
# homebrew-pandafilter 配置说明
# 查看配置选项
homebrew-pandafilter --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 HOMEBREW_PANDAFILTER_CONFIG="/path/to/config.yml"
📑 README 深度解析 真实文档 完整度 52/100 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

简介

<p align="center"> <img src="assets/logo.png" alt="PandaFilter" width="160" /> </p>

PandaFilter

<p align="center"><strong>The context intelligence layer for AI coding agents.</strong></p>

<p align="center">The layer between your tools and your AI. PandaFilter understands what's noise and what matters — compressing, routing, and preserving the right context so your agent thinks faster, costs less, and never loses its place.</p>

<p align="center"> <a href="https://github.com/AssafWoo/PandaFilter/stargazers"> <img src="https://img.shields.io/github/stars/AssafWoo/PandaFilter?style=for-the-badge&logo=github&logoColor=white&label=Star%20the%20panda%20%F0%9F%90%BC%E2%AD%90&labelColor=4b4b4b&color=7c3aed" alt="Star PandaFilter on GitHub"> </a> </p>

<p align="center"> <a href="https://discord.com/invite/FFQC3bxYQ"><img src="https://img.shields.io/badge/Discord-Join-5865F2?style=for-the-badge&logo=discord&logoColor=white" alt="Discord"></a> &nbsp; <a href="LICENSE"><img src="https://img.shields.io/badge/License-MIT-yellow.svg?style=for-the-badge" alt="License: MIT"></a> &nbsp; <a href="https://github.com/AssafWoo/PandaFilter/releases/latest"><img src="https://img.shields.io/github/v/release/AssafWoo/PandaFilter?style=for-the-badge" alt="Latest Release"></a> </p>

<p align="center"> <img src="https://img.shields.io/badge/Claude_Code-supported-8b5cf6?style=flat-square&logo=anthropic&logoColor=white" alt="Claude Code"> &nbsp; <img src="https://img.shields.io/badge/Cursor-supported-0ea5e9?style=flat-square" alt="Cursor"> &nbsp; <img src="https://img.shields.io/badge/Windsurf-supported-06b6d4?style=flat-square" alt="Windsurf"> &nbsp; <img src="https://img.shields.io/badge/Gemini_CLI-supported-4285F4?style=flat-square&logo=google&logoColor=white" alt="Gemini CLI"> &nbsp; <img src="https://img.shields.io/badge/Codex-supported-10b981?style=flat-square&logo=openai&logoColor=white" alt="Codex"> &nbsp; <img src="https://img.shields.io/badge/Cline-supported-f59e0b?style=flat-square" alt="Cline"> &nbsp; <img src="https://img.shields.io/badge/VS_Code_Copilot-supported-007ACC?style=flat-square&logo=visualstudiocode&logoColor=white" alt="VS Code Copilot"> &nbsp; <img src="https://img.shields.io/badge/OpenClaw-supported-ef4444?style=flat-square" alt="OpenClaw"> </p>

---

What's new in v1.3.0

FeatureBeforeAfter (PandaFilter v1.3.0)
Bash outputFull outputCompressed by type (error-focus, dedup, stats, etc.)
File re-readsFull file every timeDelta diff or structural digest
Context compaction60–70% conversation lostSession digest preserved and restored
Filtering strategyFixed pipeline alwaysAdaptive router — right expert per content type
Quality visibilityToken savings onlyMulti-signal quality score in panda gain
Agent support7 agents8 agents — OpenClaw added

---

Install

brew tap AssafWoo/pandafilter
brew install pandafilter

Linux / any platform:

curl -fsSL https://raw.githubusercontent.com/AssafWoo/homebrew-pandafilter/main/install.sh | bash
First run: PandaFilter downloads the BERT model (~90 MB, all-MiniLM-L6-v2) from HuggingFace and caches it at ~/.cache/huggingface/. Subsequent runs are instant.

Then wire it in — one command installs for every AI agent you have:

panda init --agent all

Auto-detects Claude Code, Cursor, Gemini CLI, Codex, Windsurf, Cline, OpenClaw, and VS Code Copilot. Skips anything that isn't installed. Or target one specifically:

panda init                          # Claude Code (default)
panda init --agent cursor           # Cursor
panda init --agent gemini           # Gemini CLI
panda init --agent codex            # Codex (CLI + VS Code extension)
panda init --agent windsurf         # Windsurf
panda init --agent cline            # Cline
panda init --agent openclaw         # OpenClaw
panda init --agent copilot          # VS Code Copilot

---

or: cargo uninstall panda

rm -rf ~/.local/share/panda # analytics + sessions rm -rf ~/.cache/huggingface/hub/models--sentence-transformers--all-MiniLM-L6-v2 ```

</details>

---

now active automatically — no config change needed.

[focus] enabled = false # disabled by default — enable with panda focus --enable after testing min_files = 25 # skip for repos smaller than this min_lines = 2000 # skip for repos with fewer source lines

[commands.git] patterns = [ { regex = "^(Counting|Compressing|Receiving|Resolving) objects:.*", action = "Remove" }, ]

[commands.cargo] patterns = [ { regex = "^\\s+Compiling \\S+ v[\\d.]+", action = "Collapse" }, { regex = "^\\s+Downloaded \\S+ v[\\d.]+", action = "Remove" }, ]


Pattern actions: `Remove`, `Collapse`, `ReplaceWith = "text"`, `TruncateLinesAt = N`, `HeadLines = N`, `TailLines = N`, `MatchOutput = "msg"`, `OnEmpty = "msg"`.

Pricing uses `cost_per_million_tokens` from `panda.toml` if set, otherwise `ANTHROPIC_MODEL` env var (Opus 4.6: $15, Sonnet 4.6: $3, Haiku 4.5: $0.80), otherwise $3.00.

</details>

<details>
<summary><strong>User-defined filters</strong></summary>

Place `filters.toml` at `.panda/filters.toml` (project-local) or `~/.config/panda/filters.toml` (global). Project-local overrides global for the same key. Runs before any built-in handler.
toml [commands.myapp] patterns = [ { regex = "^DEBUG:", action = "Remove" }, { regex = "^\\S+\\.ts\\(", action = "TruncateLinesAt", max_chars = 120 }, ] on_empty = "(no relevant output)"

[commands.myapp.match_output] pattern = "Server started" message = "ok — server ready" unless_pattern = "error"


</details>

<details>
<summary><strong>Session intelligence</strong></summary>

State tracked via `PANDA_SESSION_ID=$PPID`, stored at `~/.local/share/panda/sessions/<id>.json`.

- **Result cache** — post-pipeline bytes frozen per input hash; returned identically on repeat calls to prevent prompt cache busts.
- **Semantic delta** — repeated commands emit only new/changed lines: `[Δ from turn N: +M new, K repeated — ~T tokens saved]`.
- **Cross-turn dedup** — identical outputs (cosine > 0.92) collapse to `[same output as turn 4 (3m ago) — 1.2k tokens saved]`.
- **Elastic context** — pipeline pressure scales with session size. At >80% pressure: `[⚠ context near full — run panda compress --scan-session]`.
- **Intent-aware query** — reads the agent's last message from the live session JSONL and uses it as the BERT query.
- **File delta re-reads** *(v1.3.0)* — re-reading a changed file sends a unified diff instead of the full content. Unchanged re-reads send a structural digest (function/class signatures). Both save 60–95% of re-read tokens automatically.
- **Compaction digest** *(v1.3.0, Claude Code only)* — before Claude auto-compacts, PandaFilter serializes edited files, error signatures, and top commands to `~/.local/share/panda/compacts/`. On the next session start, the digest is injected into context so the agent resumes oriented.

</details>

<details>
<summary><strong>Supported agents (7)</strong></summary>

All agents share the same binary and filtering pipeline. `panda init --agent all` installs for everything detected on your machine in one shot.

| Agent | Install | Config |
|-------|---------|--------|
| Claude Code | `panda init` | `~/.claude/settings.json` |
| Cursor | `panda init --agent cursor` | `~/.cursor/hooks.json` |
| Gemini CLI | `panda init --agent gemini` | `~/.gemini/settings.json` |
| Codex (CLI + VS Code) | `panda init --agent codex` | `~/.codex/hooks.json` |
| Windsurf | `panda init --agent windsurf` | `~/.codeium/windsurf/hooks.json` |
| Cline | `panda init --agent cline` | `.clinerules` (project dir) |
| VS Code Copilot | `panda init --agent copilot` | `.github/hooks/` (project dir) |

**Hook-based agents** (Claude Code, Cursor, Gemini, Codex, Windsurf) intercept every command before and after execution via the agent's native hook system.

**Rules-based agents** (Cline, Copilot) inject `panda run <cmd>` directives into the agent's context file, relying on the model to follow them.

**PreToolUse:** known handler → rewrites to `panda run <cmd>`; unknown → no-op; already wrapped → no double-wrap; compound commands → each segment rewritten independently.

**PostToolUse:** Bash → full pipeline; Read → BERT + session dedup; Glob → grouped by directory; Grep → compact paths.

**UserPromptSubmit:** Context Focusing module → queries file graph → injects guidance (recommended + excluded files).

**Hook integrity:** `panda init` writes SHA-256 baselines (chmod 0o444). PandaFilter verifies at every invocation and exits 1 with a warning if tampered. `panda verify` checks all installed agents.

</details>

<details>
<summary><strong>Crate overview</strong></summary>
ccr/ CLI binary (panda) — handlers, hooks, session state, commands ccr-core/ Core library (no I/O) — pipeline, BERT summarizer, config, analytics ccr-sdk/ Conversation compression — tiered compressor, deduplicator, Ollama ccr-eval/ Evaluation suite — fixtures against Claude API config/ Embedded default filter patterns

</details>

<details>
<summary><strong>Uninstall</strong></summary>
bash panda init --uninstall # Claude Code panda init --agent cursor --uninstall # Cursor panda init --agent gemini --uninstall # Gemini CLI panda init --agent codex --uninstall # Codex panda init --agent windsurf --uninstall # Windsurf panda init --agent cline --uninstall # Cline panda init --agent copilot --uninstall # VS Code Copilot

brew uninstall pandafilter && brew untap AssafWoo/pandafilter # Homebrew

FAQ

Does PandaFilter change what the agent can see? It removes noise — build progress, passing test lines, module download logs. Errors, file paths, and results are always kept.

What if I don't want a specific command filtered? Add a rule to .panda/filters.toml to customize or override any handler. See the User-defined filters section. You can also use panda proxy <cmd> to run a command raw with no filtering.

What about commands PandaFilter doesn't know? Output passes through unchanged. PandaFilter never silently drops output from unknown commands.

How do I verify it's working? Run panda gain after a session. To see exactly what the agent received from a specific command: panda run git log --oneline -20.

Does PandaFilter send any data outside my machine? No. All processing is fully local. BERT runs on-device.

What is Context Focusing? An opt-in feature that tells the agent which files are relevant for the current prompt, preventing it from reading unrelated files. Enable with panda focus --enable after running panda doctor to confirm the index is ready.

---

🎯 aiskill88 AI 点评 A 级 2026-05-30

高质量的AI工作流智能层

📚 实用指南(长尾问题)
适合谁
  • 构建多智能体协作系统的 Agent 开发者
最佳实践
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
部署方案
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
homebrew-pandafilter 中文教程homebrew-pandafilter 安装报错怎么办homebrew-pandafilter Agent 工作流homebrew-pandafilter 与同类工具对比homebrew-pandafilter 最佳实践homebrew-pandafilter 适合谁用

⚡ 核心功能

👥 适合谁
  • 构建多智能体协作系统的 Agent 开发者
⭐ 最佳实践
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
⚠️ 常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)

👥 适合人群

自动化工程师和运维人员项目经理和业务分析师希望减少重复性工作的专业人士数字化转型团队

🎯 使用场景

  • 自动化日常重复性工作,将精力集中于创造性任务
  • 构建数据采集 → 处理 → 输出的完整自动化管线
  • 实现跨平台、跨系统的数据流转和业务协同

⚖️ 优点与不足

✅ 优点
  • +MIT 协议,可免费商用
  • +大幅减少重复性人工操作
  • +可视化流程,清晰直观
  • +可扩展性强,支持复杂场景
⚠️ 不足
  • 初始配置和调试需投入一定时间
  • 强依赖外部服务的稳定性
  • 复杂场景需具备一定技术基础
⚠️ 使用须知

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。

📄 License 说明

✅ 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。

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❓ 常见问题 FAQ

一个开源的AI工作流智能层
💡 AI Skill Hub 点评

总体来看,智能工作流 是一款质量优秀的Agent工作流,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。

⬇️ 获取与下载
⬇ 下载源码 ZIP

✅ MIT 协议 · 可免费商用 · 直接从 aiskill88 服务器下载,无需跳转 GitHub

📚 深入学习 智能工作流
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 homebrew-pandafilter
原始描述 开源AI工作流:The context intelligence layer for AI coding agents. Compressing noise, routing 。⭐97 · Rust
Topics ai-codingrustagentic-coding
GitHub https://github.com/AssafWoo/homebrew-pandafilter
License MIT
语言 Rust
🔗 原始来源
🐙 GitHub 仓库  https://github.com/AssafWoo/homebrew-pandafilter 🌐 官方网站  https://assafwoo.github.io/homebrew-pandafilter/

收录时间:2026-05-30 · 更新时间:2026-05-30 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。