AI Skill Hub 强烈推荐:智能代理调试 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。
智能代理调试 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。
智能代理调试 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:npm 全局安装 npm install -g agent-inspect # 方式二:npx 直接运行(无需安装) npx agent-inspect --help # 方式三:项目依赖安装 npm install agent-inspect # 方式四:从源码运行 git clone https://github.com/rajudandigam/agent-inspect cd agent-inspect npm install npm start
# 命令行使用
agent-inspect --help
# 基本用法
agent-inspect [options] <input>
# Node.js 代码中使用
const agent_inspect = require('agent-inspect');
const result = await agent_inspect.run(options);
console.log(result);
# agent-inspect 配置说明 # 查看配置选项 agent-inspect --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 AGENT_INSPECT_CONFIG="/path/to/config.yml"
Local execution trees for TypeScript AI agents.
agent-inspect helps you understand what happened inside an AI agent run — locally. It turns manual steps, tool calls, LLM calls, structured logs, failures, durations, and run metadata into readable execution trees you can inspect from the terminal.
It is built for TypeScript/Node.js developers and teams shipping real agentic products — not just toy demos. Use it for local TypeScript agent debugging, eval iteration, and CI trace artifacts. It complements production observability platforms; it does not replace them.
The tool starts with manual traces and existing structured logs, and extends into optional framework callbacks and standards-aligned local export — without turning the core into a SaaS or a vendor pipeline.
No account. No cloud upload. No dashboard required.
Current npm release: 1.2.0.
npm install agent-inspect
pnpm add agent-inspect
Verify the CLI is available:
npx agent-inspect --help
For a clean npm/pnpm install checklist with ESM, CJS, and CLI checks, see Clean install smoke test.
Create demo.mjs:
import { inspectRun, step } from "agent-inspect";
const delay = (ms) => new Promise((resolve) => setTimeout(resolve, ms));
await inspectRun(
"support-agent",
async () => {
const plan = await step("plan", async () => {
await delay(40);
return { intent: "refund-policy", needsPolicy: true };
});
const policy = await step.tool("retrieve-policy", async () => {
await delay(60);
return { text: "Refunds are available within 30 days of purchase." };
});
return step.llm("generate-answer", async () => {
await delay(80);
return `Policy: ${policy.text} (intent: ${plan.intent})`;
});
},
{ traceDir: "./.agent-inspect" }
);
Run it, then inspect the trace:
node demo.mjs
npx agent-inspect list --dir ./.agent-inspect
npx agent-inspect view <run-id> --dir ./.agent-inspect
npx agent-inspect view <run-id> --dir ./.agent-inspect --summary
Full flow:
npm install agent-inspect
node demo.mjs
npx agent-inspect list --dir ./.agent-inspect
Simplified example output (actual CLI formatting may differ slightly):
support-agent
✔ plan
✔ tool:retrieve-policy
✔ llm:generate-answer
A runnable copy lives in examples/00-quickstart-demo.
Env-gated tracing (eval harnesses, CI): use maybeInspectRun and set AGENT_INSPECT=1 when you want a trace — otherwise no files are written.
import { maybeInspectRun } from "agent-inspect";
await maybeInspectRun("eval-case-42", async () => runAgent());
AGENT_INSPECT=1 node eval-runner.mjs
| Example | Shows |
|---|---|
| [examples/00-quickstart-demo](examples/00-quickstart-demo/README.md) | Fast install-and-try trace |
| [examples/01-basic](examples/01-basic) | inspectRun + step |
| [examples/02-nested-steps](examples/02-nested-steps) | Nested tree |
| [examples/03-parallel-steps](examples/03-parallel-steps) | Parallel siblings |
| [examples/04-error-handling](examples/04-error-handling) | Failed steps |
| [examples/05-observe-wrapper](examples/05-observe-wrapper) | observe() |
| [examples/06-log-to-tree](examples/06-log-to-tree) | logs / tail |
| [examples/08-langchain-adapter](examples/08-langchain-adapter/README.md) | LangChain callbacks |
| [examples/recipes/rag-pipeline](examples/recipes/rag-pipeline) | RAG-shaped flow |
| [examples/recipes/tool-failure-retry](examples/recipes/tool-failure-retry) | Tool failure + retry |
| [examples/recipes/multi-agent-handoff](examples/recipes/multi-agent-handoff) | Handoff |
| [examples/recipes/proactive-agent-logs](examples/recipes/proactive-agent-logs) | Structured logs |
| [examples/recipes/pino-json-logs](examples/recipes/pino-json-logs) | pino-shaped JSON |
| [examples/recipes/log4js-json-layout](examples/recipes/log4js-json-layout) | log4js embedded JSON |
| [examples/recipes/nestjs-json-logging](examples/recipes/nestjs-json-logging) | NestJS JSON logs |
| [examples/recipes/retry-fallback](examples/recipes/retry-fallback) | Fallback pattern |
| [examples/recipes/parallel-tools](examples/recipes/parallel-tools) | Parallel tools |
Recipes are deterministic and require no external services by default. Index: examples/README.md, examples/recipes/README.md.
| Command | Use it for |
|---|---|
list | Find recent runs |
view | Inspect one run as a tree |
clean | Safely remove old trace files |
logs | Turn existing structured logs into a local tree/timeline |
tail | Watch structured logs while the app runs |
export | Write Markdown / HTML / OpenInference-compatible JSON / OTLP JSON **locally** |
diff | Compare two local runs (read-only) |
Full flags and behavior: docs/CLI.md.
logs / tail at existing job or service logs to get a local execution view without shipping data upstream.It can complement LangSmith, Langfuse, Braintrust, Phoenix/OpenInference, OpenTelemetry, New Relic, Datadog, and similar platforms — but it does not replace their production or eval workflows.
For a detailed comparison, see Compare with other tools.
高质量的AI工作流调试工具
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。
总体来看,智能代理调试 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。
| 原始名称 | agent-inspect |
| 原始描述 | 开源AI工作流:Local execution trees for TypeScript AI agents. agent-inspect helps you underst。⭐95 · TypeScript |
| Topics | aiai-agentai-debuggingtypescript |
| GitHub | https://github.com/rajudandigam/agent-inspect |
| License | MIT |
| 语言 | TypeScript |
收录时间:2026-06-11 · 更新时间:2026-06-13 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端