log-essence 0.1.0b1

Intelligent log analysis for LLMs - extract patterns, redact secrets, compress 80-95%

log-essence

Extract the essence of your logs for LLM analysis.

Analyzes log files using template extraction (Drain3) and semantic clustering (FastEmbed) to produce token-efficient summaries for LLM consumption. Includes automatic secret/PII redaction for safe external analysis.

Watch demo

Raw logs → Token-efficient summary with automatic secret redaction

Features

• Auto-detection: JSON, syslog, Apache, nginx, Docker, Kubernetes log formats
• Template extraction: Drain3 algorithm identifies log patterns and groups similar messages
• Semantic clustering: Groups related patterns using FastEmbed embeddings
• Token budget: Respects LLM context limits with intelligent summarization
• Secret redaction: Correlation-preserving redaction of emails, IPs, API keys, credit cards
• Error chain analysis: Traces root causes through related log entries
• Time filtering: Filter logs by duration (1h, 30m, 2d) or datetime
• Multi-source: Files, directories, glob patterns, Docker containers, journald
• Web UI: Paste-and-copy interface with real-time processing metrics

Installation

# Using uv (recommended)
uvx log-essence

# Using pip
pip install log-essence

CLI Usage

# Analyze a log file
log-essence /var/log/app.log

# Analyze with glob pattern
log-essence "/var/log/*.log"

# Filter by severity
log-essence /var/log/app.log --severity ERROR WARNING

# Filter by time
log-essence /var/log/app.log --since 1h

# Strict redaction mode
log-essence /var/log/app.log --redact strict

# Disable redaction (for internal logs only)
log-essence /var/log/app.log --no-redact

# Run as MCP server
log-essence --serve

CLI Options

Option	Description
--token-budget N	Maximum tokens in output (default: 8000)
--clusters N	Number of semantic clusters (default: 10)
--severity LEVEL...	Filter by severity (ERROR, WARNING, INFO, DEBUG)
--since TIME	Only logs since TIME (1h, 30m, 2d, 2025-01-01)
--redact MODE	Redaction: strict, moderate (default), minimal, disabled
--no-redact	Disable redaction
--serve	Run as MCP server

Web UI

A browser-based interface for quick log analysis without command-line setup.

# Install with UI dependencies
pip install log-essence[ui]

# Launch the web UI
log-essence ui

# Or specify a custom port
log-essence ui --port 8080

Features

• Browser-based analysis: Paste logs, get LLM-ready output with real-time metrics
• Configurable settings: Token budget, cluster count, redaction mode, severity filter
• Processing metrics: Real-time stats displayed after analysis
  • Time: Processing duration
  • Redactions: Number of secrets/PII items redacted
  • Tokens: Original → Output token count
  • Savings: Compression percentage (typically 80-95%)
• Download: Export analysis as markdown file

UI Options

Option	Description
--port N	Port to run on (default: 8501)
--no-browser	Don't auto-open browser

MCP Server Usage

log-essence can run as an MCP (Model Context Protocol) server, allowing Claude Desktop, Claude Code, Cursor, and other MCP-compatible clients to directly analyze logs from your system. This enables natural language interactions like:

"Check the logs from the last hour for any database errors" "What's causing the slow response times in my API?" "Analyze the docker logs and find the root cause of the crash"

Setup

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "log-essence": {
      "command": "uvx",
      "args": ["log-essence", "--serve"]
    }
  }
}

Restart Claude Desktop. You'll see log-essence listed in the MCP servers (🔌 icon).

How It Works

1. You ask Claude about logs in natural language
2. Claude calls log-essence with the appropriate tool and parameters
3. log-essence analyzes the logs, redacts secrets, and returns a compressed summary
4. Claude interprets the results and provides actionable insights

The logs never leave your machine unredacted - log-essence strips sensitive data before Claude sees it, and the analysis runs locally using Drain3 and FastEmbed.

Available Tools

get_logs

Analyze and consolidate log files.

get_logs(
    path="/var/log/app.log",
    token_budget=8000,
    num_clusters=10,
    severity_filter=["ERROR", "WARNING"],
    since="1h",
    redact=True  # or "strict", "minimal", False
)

get_container_logs

Analyze Docker container logs.

get_container_logs(
    container="my-app",
    since="1h",
    token_budget=8000
)

get_compose_logs

Analyze logs from all services in a docker-compose project.

get_compose_logs(
    path="/path/to/project",
    services=["api", "worker"],
    since="30m"
)

get_error_chain

Trace error root causes through related log entries.

get_error_chain(
    path="/var/log/app.log",
    error_pattern="database",
    time_window=60
)

search_logs

Semantic search through log entries.

search_logs(
    path="/var/log/app.log",
    query="connection timeout",
    top_k=10
)

Secret Redaction

Logs are automatically redacted before analysis to prevent leaking sensitive data to external LLMs.

Redaction Modes

Mode	Description
moderate	Default. Emails, IPs, credit cards, SSNs, phones, API keys
strict	All moderate patterns + high-entropy strings in key=value
minimal	Only obvious secrets (bearer tokens, API keys)
disabled	No redaction (use only for internal logs)

Output Format

Redacted values use the format [TYPE:length?:hash4]:

# Input
[e-mail] logged in from 192.168.1.50
Error processing payment for [e-mail] card 4111111111111111

# Output (same entity → same hash for correlation)
[EMAIL:a7f2] logged in from [IPV4:3bc1]
Error processing payment for [EMAIL:a7f2] card [CC:16:d4e8]

Detected Patterns

PII:

• Email addresses
• IPv4 and IPv6 addresses
• Credit card numbers (Luhn-validated)
• Social Security Numbers (xxx-xx-xxxx)
• Phone numbers

Secrets:

• AWS access keys and secret keys
• GitHub tokens (ghp_, ghs_)
• Stripe API keys (sk_live_, sk_test_)
• JWT tokens
• Bearer tokens
• Private key headers
• Connection strings (postgres://, mongodb://, redis://)

Example Output

# Log Analysis Summary

**Format detected:** docker
**Total lines:** 15,432
**Unique patterns:** 47
**Semantic clusters:** 10

---

## Log Patterns by Frequency

### Cluster 1: Database Operations

**Occurrences:** 5,234 | **Patterns:** 8

- `Query executed in <*>ms` (2,341x)
- `Connection pool size: <*>` (1,892x)
- `Transaction committed` (1,001x)

**Example:**

2025-01-01T10:00:00Z INFO Query executed in 45ms

### Cluster 2: HTTP Requests

**Occurrences:** 4,123 | **Patterns:** 5

- `[IPV4:3bc1] - GET /api/<*> <*>` (3,456x)
- `Response time: <*>ms` (667x)

Development

# Clone and install
git clone https://github.com/petebytes/log-essence
cd log-essence
uv sync --all-groups

# Run tests
uv run pytest

# Lint
uv run ruff check src/ tests/

# Format
uv run ruff format src/ tests/

License

MIT