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This article introduces the LogTriage project, demonstrating how to build an intelligent log classification system using Xiaomi's MiMo reasoning model, converting massive alerts into precise root cause analysis and freeing on-call engineers from log noise.
Section 01
This article introduces the LogTriage project, demonstrating how to build an intelligent log classification system using Xiaomi's MiMo reasoning model, converting massive alerts into precise root cause analysis and freeing on-call engineers from log noise.
Section 02
Section 03
In modern microservices architectures, a deployment anomaly can cause an instant surge in log volume, generating hundreds of ERROR-level alerts. Traditional log aggregation tools (like ELK, Datadog) can collect and display logs, but they often struggle with massive alerts—they can't distinguish between "critical failures requiring immediate handling" and "ignorable temporary noise".
On-call engineers face the dilemma: too many alerts, but too few actionable ones. An ERROR log like "connection reset" might just be an automatic retry success after a network blip, yet it triggers the same alert level. This "boy who cried wolf" effect causes real failures to be buried in noise.
Section 04
The core insight of the LogTriage project is: log classification is essentially a reasoning task, not a simple pattern matching one. To judge the severity of a log like "ERROR connection reset, retry attempt 1 succeeded", context understanding is needed—it's a network blip that has automatically recovered, so it should be marked as the transient_network category and warn level instead of error.
This kind of understanding requires real reasoning ability, which is exactly the strength of large language models (especially reasoning models). LogTriage chooses Xiaomi's MiMo reasoning model as the underlying engine, leveraging its strong context understanding and logical reasoning capabilities to achieve classification accuracy that traditional rule engines can hardly match.
Section 05
The most unique design of LogTriage is its "locked" nine-category classification system. Unlike open classification, these nine categories are fixed at the code level and cannot be expanded arbitrarily:
Section 06
| Category | Meaning | Typical Scenarios |
|---|---|---|
| transient_network | Transient Network Issue | Connection reset, DNS jitter, retry success |
| upstream_outage | Upstream Outage | External dependency unavailable, cannot be fixed locally |
| config_error | Configuration Error | Environment variable error, YAML format issue, missing key |
| resource_exhaustion | Resource Exhaustion | OOM, disk full, file descriptor exhaustion, thread starvation |
| code_bug | Code Bug | Null pointer, type error, logical error |
| deployment_drift | Deployment Drift | Version mismatch, outdated binary, code/Schema inconsistency |
| data_quality | Data Quality | Input format error, Schema violation, encoding issue |
| auth_authz | Authentication & Authorization | Token expiration, permission denied, mTLS failure |
| noise | Noise | Known harmless, automatically downgraded to debug level |
This locked design has the following benefits: classification results are predictable, auditable, and statable. There will be no infinite growth of the "unknown" category—every log must fall into one of these nine categories.
Section 07
LogTriage's processing flow is very complete, and the output is a structured TriageReport containing the following fields:
Section 08
Operational Severity Level ≠ Log Level: This is the core difference between LogTriage and traditional log systems. The model is explicitly instructed to override the original log level when necessary, such as downgrading a network error that has automatically recovered from ERROR to WARN.
Evidence Quotation: The model will accurately quote key fragments from the log as the basis for classification. This interpretability allows on-call engineers to quickly verify or question the classification result.
Reasoning Trace: A complete record of the reasoning process, facilitating post-audit and model improvement.
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