System Log Analyzer

[ms-shashank]

Current file: app/src/lib/tools/log-analyzer.ts
Current model: deepseek-r1-0528
Current approach: Single prompt with raw logs pasted in. Asks for severity assessment, error summary, root cause, timeline, patterns, fixes, and prevention. No programmatic log parsing, no pattern detection, no timestamp analysis.

Problems with current approach:

• Error counts are estimated by the LLM, not computed. Frequently inaccurate for large log files.
• Timeline analysis is guesswork since timestamps are not programmatically parsed.
• Cannot detect patterns in high-volume logs (thousands of lines exceed context window).
• No structured error classification (logs are treated as unstructured text).
• Correlation detection between different error types is unreliable.

Upgrade plan:

Step	Agent	Action
1	Log Parser	Programmatic: Parse log lines using regex patterns for common log formats (syslog, JSON, Apache, nginx, application logs). Extract: timestamp, level (ERROR/WARN/INFO/DEBUG), source, message. Handle multi-line stack traces.
2	Statistical Analyzer	Programmatic: Count occurrences of each unique error. Compute error rate over time. Detect escalation patterns (increasing frequency). Identify time clusters (bursts of errors within short windows). Compute time-to-first-error and time-between-errors.
3	Correlation Detector	Programmatic: Group errors by time proximity. Identify cascading patterns (Error A consistently followed by Error B within N seconds). Detect co-occurring errors.
4	Root Cause Agent	Receive the parsed structure, computed statistics, and detected correlations. Perform deep analysis to identify root causes, recommend fixes, and suggest monitoring changes.
5	Report Assembler	Programmatic: Merge computed statistics (tables, timelines) with LLM analysis into a structured markdown report.

• You are free to enhance the agents stacks in the above plan layout, the above one is just for reference. You can enhance more if needed.

Model suggestions to start with:

• Step 4: Try deepseek-r1-0528 (current model, strong at reasoning). Also try kimi-k2.6 or glm-5 for complex infrastructure analysis.
• Since Steps 1-3 and 5 are fully programmatic, model choice only affects the root cause analysis step. Focus engineering effort on the log parser and statistical analyzer.

Model Selection Guidance

• You are free to pick any model from the Oxlo catalog based on your own testing and evaluation.
• The Models suggestions above, not mandates. Try them first, and if they do not meet the accuracy target, experiment with alternatives.

Compare against: Claude Sonnet 4.6 Thinking and ChatGPT 5.3.

Acceptance criteria:

• Error counts must be 100% accurate (programmatic counting).
• Timestamp parsing supports at least: ISO 8601, syslog format, Unix epoch, and common application log formats.
• Escalation detection correctly identifies increasing error frequency trends.
• Cascading failure patterns detected when errors occur within a configurable time window.
• Handles log files up to 10,000 lines (via chunked parsing, not context window).
• Root cause quality matches or exceeds Claude Sonnet 4.6 Thinking and ChatGPT 5.3 Thinking on test log files.
• Overall accuracy at 80%+.