dunetrace
Active·★ 45·NOASSERTION·Updated 2026-05-21
★ Trending★ Multi-Agent★ Observability
Real-time monitoring of your production agents. No raw content transmitted.
Dunetrace monitors every run of AI agents in real-time, detecting structural failures like tool loops, retry storms, and context bloat within 15 seconds of completion. It fires alerts via Slack or webhook, provides plain-English explanations, and offers one-click fixes through Langfuse or GitHub PRs.
#agent-observability#ai#ai-agents#ai-tools#analytics#autogen#crewai#langchain
01
Features
01Real-time monitoring with 15-second alert latency
0217 automatic structural detectors (no configuration needed)
03Diagnose with plain-English explanations and LLM-powered root-cause analysis (via Langfuse)
04One-click fix application (prompt version or GitHub PR)
05Policies for mid-run guardrails (cost cap, tool call limit)
02
Compatibility
Python
SDK (Python)
Verified via docs
Node.js
SDK (Node.js)
Verified via docs
LangChain
LangChain/LangGraph
Verified via docs
Langfuse
Langfuse Deep Analysis
Verified via docs
MCP
MCP Server
Verified via docs
03
Quick start
1
$ git clone https://github.com/dunetrace/dunetrace
2
$ cd dunetrace
3
$ cp .env.example .env
4
$ docker compose build
5
$ docker compose up -d
04
Use cases
↳Monitor production AI agents for silent failures like tool loops and cost spikes
↳Automatically detect and alert on agent behavioral anomalies
↳Quickly diagnose and fix agent issues with one-click actions
05
Alternatives
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Comments
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- JJesse KimMay 18, 2026
Real-time production agent monitoring without raw content transmission is smart for privacy
- SShawn JacksonApr 19, 2026
Used for production agent oversight, catches behavioral anomalies without logging sensitive data
- MMarlowe HarrisMar 12, 2026
The monitoring architecture gives visibility without the data exposure risk
- KKai BrownMar 10, 2026
Good for organizations that need agent monitoring with data residency requirements