能力标签
harn
🔌
MCP工具

harn

基于 Rust · 让 AI 助手直接操作你的系统与工具
⭐ 6 Stars 💻 Rust 📄 Apache-2.0 🏷 AI 7.5分
7.5AI 综合评分
mcpacpagentsai-agentslanguagemcp-clientrust
✦ AI Skill Hub 推荐

经 AI Skill Hub 精选评估,harn 获评「推荐使用」。这款MCP工具在功能完整性、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。

📚 深度解析

harn 是一款基于 MCP(Model Context Protocol)标准协议的 AI 工具扩展。MCP 协议由 Anthropic 开发并开源,旨在建立 AI 模型与外部工具之间的标准化通信接口,目前已被 Claude Desktop、Claude Code、Cursor 等主流 AI 工具采纳。

通过安装 harn,你的 AI 助手将获得额外的工具调用能力,可以用自然语言直接操控该工具的功能,无需学习复杂的命令行语法。MCP 工具的核心价值在于"一次配置,永久增强"——配置完成后,每次与 AI 对话时都可以无缝调用这些工具。

在技术实现上,MCP 工具通过标准的 JSON-RPC 协议与 AI 客户端通信,工具的功能以"工具列表"的形式暴露给 AI 模型,AI 可以按需调用。harn 提供了结构化的工具调用接口,使 AI 模型能够精确地理解和使用每个功能点,显著降低 AI 在工具使用上的错误率。

与传统的 API 集成相比,MCP 工具的优势在于无需编写代码——用户只需在配置文件中添加几行 JSON,即可让 AI 获得全新能力。AI Skill Hub 将 harn 评为 AI 评分 7.5 分,属于同类工具中的优质选择。

📋 工具概览

harn 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。

GitHub Stars
⭐ 6
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
Apache-2.0
AI 综合评分
7.5 分
工具类型
MCP工具
Forks

📖 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」。

harn 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。

📌 核心特色
  • 通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
  • 提供结构化工具调用接口,显著降低 AI 集成复杂度
  • 支持 Claude Desktop 和 Claude Code 无缝接入,开箱即用
  • 可与其他 MCP 工具组合叠加,构建完整 AI 工作站
  • 轻量无侵入设计,不影响现有系统架构
🎯 主要使用场景
  • 在 Claude Desktop 对话中直接调用本地工具,实现 AI 与系统的深度联动
  • 通过自然语言驱动复杂的多步骤自动化任务,代替繁琐手动操作
  • 将多个 MCP 工具组合使用,构建个人专属 AI 工作站
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:通过 Claude Code CLI 一键安装
claude skill install https://github.com/burin-labs/harn

# 方式二:手动配置 claude_desktop_config.json
{
  "mcpServers": {
    "harn": {
      "command": "npx",
      "args": ["-y", "harn"]
    }
  }
}

# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
📋 安装步骤说明
  1. 确认已安装 Node.js(v18 或以上版本)
  2. 打开 Claude Desktop 或 Claude Code 的 MCP 配置文件
  3. 按「交给 Agent 安装 → Claude Desktop」标签中的 JSON 配置填入 mcpServers 字段
  4. 保存配置文件并重启 Claude 客户端
  5. 重启后,在对话中即可使用本工具
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
# 安装后在 Claude 对话中直接使用
# 示例:
用户: 请帮我用 harn 执行以下任务...
Claude: [自动调用 harn MCP 工具处理请求]

# 查看可用工具列表
# 在 Claude 中输入:"列出所有可用的 MCP 工具"
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
// claude_desktop_config.json 配置示例
{
  "mcpServers": {
    "harn": {
      "command": "npx",
      "args": ["-y", "harn"],
      "env": {
        // "API_KEY": "your-api-key-here"
      }
    }
  }
}

// 保存后重启 Claude Desktop 生效
📑 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。

Harn

CI

Harn is a programming language and runtime for orchestrating AI agents. It sits between product code and provider/runtime code: products declare workflows, policies, capabilities, and UI hooks, while Harn owns transcripts, context assembly, retries, tool routing, persistence, replay, and provider normalization.

Harn also emits portable opentrustgraph/v0.1 trust records for autonomy decisions, approval gates, and tier transitions. v0.1 adds three reserved metadata keys (effects_grant, effects_used, parent_record_id) so chain validators can prove that a child agent's effects_used stayed inside the parent's effects_grant. The public schema and fixtures live in opentrustgraph-spec/.

Core capabilities

- Typed workflow graphs via workflow_graph(...) and workflow_execute(...) with explicit nodes, edges, validation, policy attachment, map/join style stages, and resumable execution. - Planner-oriented action graphs via import "std/agents": action_graph(...), action_graph_batches(...), action_graph_flow(...), and action_graph_run(...) normalize planner schema variants into a shared executable schedule instead of leaving dependency repair and batch grouping to leaf pipelines. - Persona orchestration primitives via import "std/personas/prelude": verifier-then-actor gates, bounded loops, cheap-classifier escalation, circuit-broken parallel sweeps, audit receipt wrappers, and approval gates give durable personas reusable control flow without host-specific glue. - Transparent profile bulletin proposals via import "std/personas/bulletins": bulletin_propose builds typed harn.profile_bulletin.v1 envelopes with stable id, scope, evidence, source, privacy, and TTL fields; bulletin_emit always writes proposals to personas.bulletins.proposed, and bulletin_accept / bulletin_reject / bulletin_expire / bulletin_supersede emit harn.profile_bulletin_decision.v1 audit records so hosts (Burin local, Harn Cloud) can review persona context instead of accepting silent prompt mutation. - Delegated worker lifecycle builtins via spawn_agent(...), send_input(...), resume_agent(...), wait_agent(...), close_agent(...), and list_agents(), with child run lineage, persisted worker snapshots, and host-visible worker lifecycle events. Worker handles retain immutable original request metadata plus normalized provenance so parent orchestration can recover research questions, action items, workflow stages, and verification steps without positional rebinding. Agent loops also expose lifecycle tools for worker self-parking (agent_await_resumption) and opt-in parent-side subagent pause/resume control. - Per-worker execution scoping on spawn_agent(...): delegated workers inherit the current execution ceiling by default and can narrow it further with a policy dict or tools: ["name", ...] shorthand, with permission denials returned as structured tool results instead of opaque failures. - sub_agent_run(task, options?) for isolated child agent loops that preserve a clean parent transcript while returning a typed summary envelope or a background worker handle. - Explicit continuation policy for delegated workers: carry.transcript_mode (inherit, fork, reset, compact), artifact carryover, workflow resume control, and compact parent-facing worker_result artifacts. - Runtime schema helpers for structured LLM I/O: schema_check(...), schema_parse(...), schema_is(...), JSON Schema/OpenAPI conversion, and schema composition helpers, plus a lazy std/schema builder module for ergonomic schema authoring when imported. - Provider-neutral GraphQL connector helpers via import "std/graphql": request/envelope normalization, introspection and SDL fixture parsing, persisted-query metadata, cursor pagination helpers, auth headers, and generated-style operation wrapper source for GraphQL-first providers such as Linear. - Prompt fragment reuse via import "std/prompt_library": load TOML catalogs or front-matter .harn.prompt files, render cache-aware fragment payloads, and propose tenant-scoped k-means hotspots for repeated context prefixes. - Deterministic vision OCR via vision_ocr(...) and import "std/vision": image path / payload normalization, structured text output (blocks, lines, tokens), and event-log-backed OCR audit records for replayable agent/tool flows. - Manifest-backed extension ABI: packages can publish stable module entry points via [exports], declare custom tool and skill surfaces via [[package.tools]] and [[package.skills]], and ship provider/alias adapters declaratively via [llm] in harn.toml, without editing core runtime registration code. harn tool new <name> scaffolds a Harn-native tool package with manifest metadata, tests, docs, and CI, while harn package scaffold openapi turns an OpenAPI spec into a focused generated SDK package with a regeneration script and package checks. Local sibling packages can be added with harn add ../harn-openapi; Harn derives the alias from the dependency's harn.toml and live-links directory path dependencies into .harn/packages/ for fast multi-repo development. Registry-backed discovery is available through harn package search, harn package info, and harn add @burin/<name>@<version>, which resolve through the package index and then use the same git-backed install path as direct GitHub refs. Manifests can also pin direct git tags with tag = "v1.2.3" or resolve registry semver ranges with version = "^1.2". harn package list and harn package doctor expose locked exports, permissions, host requirements, and materialized-package integrity for host UI and CI policy checks. - Design-by-contract and project/runtime helpers: require ..., metadata/scanner runtime builtins, import "std/project" for freshness-aware metadata and scan state, and import "std/runtime" for generic runtime/process/interaction helpers inside Harn itself. - Isolated execution substrate via directory-scoped command builtins (exec_at, shell_at) plus the std/worktree module for git worktree creation, status, diff, shell execution, and cleanup. Worker execution profiles can pin delegated runs to a cwd, env overlay, or managed worktree so background execution is reproducible instead of ambient-cwd dependent. Subprocesses spawned under an active capability ceiling run inside a per-platform OS sandbox by default: Linux Landlock + seccomp, macOS sandbox-exec, or Windows AppContainer + Job Object, selected via CapabilityPolicy::sandbox_profile and documented in docs/src/sandboxing.md. Pipelines that spawn untrusted code opt into sandbox_profile: "os_hardened" to make the OS confinement required (the spawn fails as tool_rejected if the platform mechanism is missing) instead of best-effort. - Stronger preflight behavior via harn check: import graph resolution, literal template/render path validation, import symbol collision detection, and host capability contract validation all fail before runtime. harn check / harn run / the LSP share one recursive module graph that resolves every import (including std/* embeds) and rejects calls to names that are not builtins, local declarations, struct constructors, callable variables, or imported symbols, so stale or typo'd references surface before the VM starts. render(...) resolves relative to the module source tree (including inside imported modules) instead of the ambient process cwd. Literal delegated execution roots, exec_at(...) / shell_at(...) directories, and unknown host_call("capability.operation", ...) contracts are also checked before launch. - Runtime-local typed host mocking for tests via host_mock(...), host_mock_clear(), and host_mock_calls(), so .harn conformance and VM tests can exercise host-backed flows without requiring a live bridge host. import "std/testing" adds higher-level helpers such as mock_host_result(...), mock_host_error(...), and assert_host_called(...) for ordinary Harn tests. - Configurable LLM mock responses via llm_mock(...), llm_mock_calls(), and llm_mock_clear(): queue specific text, tool calls, or mixed responses for the mock provider. Supports FIFO queuing and glob-pattern matching against prompts. - Eval suite manifests and portable eval packs via eval_pack { ... }, eval_pack_manifest(...), eval_pack_run(...), eval_suite_manifest(...), eval_suite_run(...), persona_eval_ladder_run(...), `harn eval <manifest.json|harn.eval.toml>, and harn test package --evals`, so grouped replay, rubric, threshold, timeout-ladder, and package-shipped connector evals are first-class runtime data instead of external scripts. - Typed artifacts and resources as the real context boundary. Context selection is artifact-aware, budget-aware, and policy-driven rather than raw prompt concatenation. - Host-facing artifact helpers for workspace files, snapshots, editor selections, command/test/verification outputs, and diff/review decisions, so product code can pass structured state into Harn without rebuilding artifact taxonomy or provenance conventions. - Durable run records with persisted stage transcripts, artifacts, policy decisions, verification outcomes, delegated child lineage, and inspection/replay/eval entrypoints including recursive run-tree loading. - Provider-normalized LLM output with visible_text, private_reasoning, thinking_summary, tool_calls, blocks, provider, stop_reason, and transcript events. - Structured transcript lifecycle support: continue, fork, compact, summarize, render public-only output, or render full execution history. - Workflow meta-editing builtins such as workflow.inspect, clone/insert/ replace/rewire operations, per-node model/context/transcript policy edits, diff, validate, and commit-style validation. - Capability ceiling enforcement for workflows and sub-orchestration: internal plans may narrow capabilities but cannot exceed the host ceiling. - ACP pending user-message injects for agent execution: accept with a stable messageId, optionally replace or revoke while pending, steer after the current operation, or queue until the agent yields back to the human. - ACP pending reminder controls for operator UIs: inspect the bridge queue and revoke queued session/remind reminders before a checkpoint drains them. - Remote MCP over stdio and HTTP, including OAuth metadata discovery, stored bearer tokens for standalone CLI use, and automatic token reuse for HTTP MCP servers declared in harn.toml. - Runtime semantic cleanup for older surfaces: repeated catch e { ... } bindings work within the same enclosing block, and float division keeps IEEE NaN/Infinity behavior instead of raising runtime errors. - Formatter width handling wraps oversized comma-separated forms consistently across calls, list literals, dict literals, enum payloads, and struct-style construction instead of leaving long single-line output intact. - Tool lifecycle hooks via register_tool_hook(...): pre-execution deny/modify and post-execution result interception for agent tool calls, with glob-pattern matching on tool names. - Automatic transcript compaction in agent loops: microcompaction snips oversized tool outputs, auto-compaction triggers at configurable token thresholds, and compact_strategy supports default LLM summarization, truncate fallback, or custom Harn closure-based compaction. Host/user compaction instructions flow through the typed CompactionPolicy lane so /compact <instructions> style commands can reuse runtime audit/events without bespoke prompt wiring. The same pipeline is exposed directly as transcript_auto_compact(...). - Daemon agent mode (daemon: true): agents stay alive waiting for host-injected messages instead of terminating on text-only responses, with adaptive idle backoff, persisted snapshots, timer/file-watch wakes, and explicit bridge wake/resume signaling. - Per-agent capability policies with argument-level constraints: agent_loop accepts a policy dict to scope tool permissions, including tool_arg_constraints for pattern-matching on tool arguments. - Rule-based approval policies: approval_policy.rules expresses allow/ask/deny over tool names/kinds, side-effect levels, declared paths, command identity, URLs/domains/methods, MCP identity, agent/persona/mode, and repeat counts, with deny-by-default sensitive path guards and replayable policy-decision receipts in permission events and host approval prompts. - Dynamic per-agent permissions: agent_loop, sub_agent_run, and spawn_agent accept permissions with allow / deny tool rules, VM predicates over the tool args, and on_escalation callbacks that can grant a denied call once or for the session. Permission decisions emit PermissionGrant, PermissionDeny, and PermissionEscalation transcript events. - Generic call-site type checking is stricter: where-clause interface violations are errors, repeated generic parameters must bind to one concrete type, and container bindings like list<T> propagate their element type. - Workflow map stages can execute in parallel with "all", "first", or "quorum" join strategies plus max_concurrent throttling. - LSP completions surface inferred shape fields, struct members, and enum payload fields on dot access instead of defaulting to dict methods. - Adaptive context assembly with deduplication and microcompaction via select_artifacts_adaptive(...), plus estimate_tokens(...) and microcompact(...) utility builtins. - Model-aware token counting via tiktoken_count_tokens(...) and std/llm/budget, with exact tiktoken counts for known OpenAI models and labeled approximations for Claude/Gemini model families. - Host-aware static preflight: harn check can load host-specific capability schemas and alternate bundle roots from harn.toml or CLI flags so host adapters and bundled template layouts validate cleanly. - Mutation-session audit metadata for workflows, delegated workers, and bridge tool gates so hosts can group write-capable operations under one trust boundary without forcing one edit-application UX. - String method aliases for case normalization: .lower(), .upper(), .to_lower(), and .to_upper().

Install

One-line installer (recommended; no Rust toolchain required):

curl -fsSL https://harnlang.com/install.sh | sh

Detects OS/CPU, downloads the matching signed binary for the current GitHub release, verifies it against the release's SHA256SUMS manifest, and installs harn, harn-dap, and harn-lsp into the first writable directory among $HARN_INSTALL_DIR, $XDG_BIN_DIR, $HOME/bin, $HOME/.local/bin, or $HOME/.harn/bin. macOS binaries are notarized. To upgrade later: harn upgrade.

With Cargo:

cargo install harn-cli

From source:

git clone https://github.com/burin-labs/harn.git
cd harn
./scripts/dev_setup.sh
cargo install --path crates/harn-cli

Shell completions:

```bash mkdir -p ~/.local/share/bash-completion/completions harn completions bash > ~/.local/share/bash-completion/completions/harn

mkdir -p ~/.zfunc harn completions zsh > ~/.zfunc/_harn

Quick start

Run a bundled demo first. It needs no API keys or project setup:

harn demo                       # menu of bundled scenarios
harn demo merge-captain         # default scenario: persona-supervised PR triage
harn demo --list                # all scenarios with descriptions
harn demo provider-race --json  # machine-readable summary

Every demo runs in under 30 seconds against a checked-in LLM tape, so it finishes the same way on a laptop with zero credentials as it does in CI. Add --live to re-run against a configured provider.

Then scaffold a project of your own:

harn new my-project --template agent
cd my-project
harn quickstart --non-interactive
source .env
harn doctor --no-network
harn run main.harn
harn test tests/
harn portal

Remote MCP OAuth:

harn mcp redirect-uri
harn mcp login https://mcp.notion.com/mcp

harn mcp login prefers Harn's published CIMD client metadata document and falls back to dynamic client registration when the authorization server does not advertise CIMD support.

Simple LLM call:

let result = llm_call(
  "Explain quicksort in two sentences.",
  "You are a concise CS tutor."
)
log(result.visible_text)

Loop-until-done agent with tools:

tool read(path: string) -> string {
  description "Read a file"
  read_file(path)
}

tool search(pattern: string) -> string {
  description "Search project files"
  shell("rg " + pattern)
}

tool edit(path: string, content: string) -> string {
  description "Edit a file"
  write_file(path, content)
}

tool run(command: string) -> string {
  description "Run a command"
  shell(command)
}

let result = agent_loop(
  "Fix the failing test and verify the change.",
  "You are a senior engineer.",
  {
    loop_until_done: true,
    tools: read,
    max_iterations: 24
  }
)

log(result.status)
log(result.visible_text)

The tool keyword declares tools with typed parameters and optional descriptions. For programmatic tool registration, use tool_define(...), which also preserves extra config keys such as policy for capability enforcement.

Workflow runtime example

let graph = workflow_graph({
  name: "review_and_repair",
  entry: "plan",
  nodes: {
    plan: {
      kind: "stage",
      mode: "llm",
      task_label: "Planning task",
      model_policy: {model_tier: "small"},
      context_policy: {include_kinds: ["summary", "resource"], max_tokens: 1200}
    },
    implement: {
      kind: "stage",
      mode: "agent",
      tools: coding_tools(),
      model_policy: {model_tier: "mid"},
      retry_policy: {max_attempts: 2}
    },
    verify: {
      kind: "verify",
      verify: {
        command: "cargo test --workspace --quiet",
        expect_status: 0,
        assert_text: "test result: ok"
      }
    }
  },
  edges: [
    {from: "plan", to: "implement"},
    {from: "implement", to: "verify"},
    {from: "verify", to: "implement", branch: "failed"}
  ]
})

let artifacts = [
  artifact({
    kind: "resource",
    title: "Editor selection",
    text: read_file("src/lib.rs"),
    source: "workspace"
  })
]

let run = workflow_execute(
  "Refactor the parser error message and verify it.",
  graph,
  artifacts,
  {max_steps: 8}
)

log(run.status)
log(run.path)
log(run.run.stages)

verify nodes can either run an explicit command as shown above or use an agent/LLM mode when verification should stay provider-driven.

Release workflow

Maintainer release commands and gates live in Maintainer release workflow.

Host integration

Run Harn as an ACP backend:

harn serve acp agent.harn
harn serve acp --transport websocket --bind 127.0.0.1:8789 agent.harn
harn serve acp --api-key "$HARN_ACP_KEY" agent.harn
HARN_PROFILE_JSON=/tmp/acp.ndjson harn serve acp agent.harn

Inspect persisted run records:

harn portal
harn runs inspect .harn-runs/<run>.json
harn replay .harn-runs/<run>.json
harn eval .harn-runs/<run>.json

Queued human messages can be delivered to an in-flight agent with session/inject:

  • steer: inject after the current tool/operation boundary
  • queue: defer until the agent yields control
🎯 aiskill88 AI 点评 A 级 2026-06-04

该项目是一个开源的MCP工具,使用Rust语言开发,用于orchestrating AI agents。虽然星数较少,但项目的技术选型和设计思路值得关注。

📚 实用指南(长尾问题)
适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
  • 需要从图片、PDF 提取文字的文档自动化场景
最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • 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)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal
部署方案
  • Docker:harn 提供官方镜像,docker compose up 一键启动
  • CLI:直接 npm install -g / pip install,命令行调用
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
harn 中文教程harn 安装报错怎么办harn MCP 配置harn Docker 部署harn Agent 工作流harn 与同类工具对比harn 最佳实践harn 适合谁用

⚡ 核心功能

👥 适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
  • 需要从图片、PDF 提取文字的文档自动化场景
⭐ 最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • 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)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal

👥 适合人群

Claude Desktop / Claude Code 用户AI 工具开发者需要扩展 AI 能力的专业人士自动化工程师

🎯 使用场景

  • 在 Claude Desktop 对话中直接调用本地工具,实现 AI 与系统的深度联动
  • 通过自然语言驱动复杂的多步骤自动化任务,代替繁琐手动操作
  • 将多个 MCP 工具组合使用,构建个人专属 AI 工作站

⚖️ 优点与不足

✅ 优点
  • +Apache-2.0 协议,可免费商用
  • +标准化 MCP 协议,生态互联性强
  • +与 Claude 官方生态无缝对接
  • +即插即用,配置简单快捷
⚠️ 不足
  • 依赖 Claude 客户端,非 Claude 用户无法使用
  • MCP 协议仍在持续演进,接口可能变更
  • 需要一定的配置步骤
⚠️ 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

✅ 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。

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📰 相关 AI 新闻
🍿 AI 圈相关吃瓜
🗺️ 相关解决方案
🧩 你可能还需要
基于当前 Skill 的能力图谱,自动补全的工具组合

❓ 常见问题 FAQ

解答
💡 AI Skill Hub 点评

AI Skill Hub 点评:harn 的核心功能完整,质量良好。对于Claude Desktop / Claude Code 用户来说,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。

⬇️ 获取与下载
⬇ 下载源码 ZIP

✅ Apache-2.0 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 harn
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 harn
原始描述 开源MCP工具:A programming language for orchestrating AI agents.。⭐6 · Rust
Topics mcpacpagentsai-agentslanguagemcp-clientrust
GitHub https://github.com/burin-labs/harn
License Apache-2.0
语言 Rust
🔗 原始来源
🐙 GitHub 仓库  https://github.com/burin-labs/harn 🌐 官方网站  https://harnlang.com

收录时间:2026-06-04 · 更新时间:2026-06-04 · License:Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。