⚡ AgentTrace：生产级 Agent 可观测性与自愈引擎

生产环境在线演示：

一套面向自主 AI Agent 流水线的端到端可观测性 SDK 与仪表盘。它能够监控多步骤工具调用、可视化延迟瓶颈，并在运行时自动修复格式错误的 LLM 工具参数，而不会导致工作流崩溃。

LLM 在多步骤运行过程中经常会臆造工具参数：

传入字符串而非浮点数（例如，用 "1200 INR" 代替 1200.0）
编造键名（例如，用 "user_identifier" 代替 "user_id"）
遗漏 Schema 所要求的必填字段

通常，这些问题会导致运行时立即崩溃。AgentTrace 会捕获这些失败，并在运行时自动修复。

💡 架构与技术栈

**装饰器 SDK：**Python、Pydantic（在工具运行前验证 Schema）
**自愈层：**通过 Groq 进行快速推理，在失败时修复载荷
**采集器后端：**采用 SQLite 持久化（traces.db）的 FastAPI
**实时仪表盘：**Next.js、Tailwind CSS，并配备 Payload Diff Inspector

# In project root
python -m uvicorn main:app --reload --port 8000

⚡ AgentTrace: Production-Grade Agent Observability & Self-Healing Engine

Live Production Demo:

An end-to-end observability SDK and dashboard for autonomous AI agent pipelines. It monitors multi-step tool calls, visualizes latency bottlenecks, and automatically repairs malformed LLM tool arguments at runtime without crashing workflows.

LLMs frequently hallucinate tool arguments during multi-step runs:

Passing strings instead of floats (e.g. "1200 INR" instead of 1200.0)
Inventing key names (e.g. "user_identifier" instead of "user_id")
Omitting required schema fields

Normally, these cause immediate runtime crashes. AgentTrace catches these failures and auto-repairs them at runtime.

💡 Architecture & Tech Stack

Decorator SDK: Python, Pydantic (Validates schema before tool run)
Self-Healing Layer: Fast inference via Groq to repair payloads on failure
Collector Backend: FastAPI with SQLite persistence (traces.db)
Live Dashboard: Next.js, Tailwind CSS with Payload Diff Inspector

# In project root
python -m uvicorn main:app --reload --port 8000

Show HN：AgentTrace——面向 AI Agent 的可观测性与运行时自愈引擎

中文

⚡ AgentTrace：生产级 Agent 可观测性与自愈引擎

💡 架构与技术栈

English / 原文

⚡ AgentTrace: Production-Grade Agent Observability & Self-Healing Engine

💡 Architecture & Tech Stack