log-essence
Auto-detects Apache web server log formats and provides token-efficient summaries with secret redaction for LLM analysis.
Analyzes Docker container logs and Docker Compose service logs, providing tools for container log retrieval, log analysis, and live monitoring.
Auto-detects Kubernetes log formats and enables analysis of Kubernetes pod logs for template extraction and semantic clustering.
Auto-detects Nginx web server log formats and enables semantic clustering and error chain analysis for LLM consumption.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@log-essenceAnalyze recent journalctl logs for database errors"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
https://github.com/user-attachments/assets/97d66d89-1745-4d0f-ac10-38744f98b636
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
Related MCP server: Log MCP Server
Installation
# Using uv (recommended)
uvx log-essence
# Using pip
pip install log-essenceCLI Usage
# Analyze a log file
log-essence /var/log/app.log
# Analyze with glob pattern
log-essence "/var/log/*.log"
# Read from stdin — pipe any command's output
docker logs my-app 2>&1 | log-essence -
journalctl --since "1h ago" --no-pager | log-essence -
# 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
# JSON output for programmatic use
log-essence /var/log/app.log -o json
# Watch mode for live log monitoring
log-essence /var/log/app.log --watch --interval 5
# Run as MCP server
log-essence --serveCLI Options
Option | Description |
| Maximum tokens in output (default: 8000) |
| Number of semantic clusters (default: 10) |
| Filter by severity (ERROR, WARNING, INFO, DEBUG) |
| Only logs since TIME (1h, 30m, 2d, 2025-01-01) |
| Redaction: strict, moderate (default), minimal, disabled |
| Disable redaction |
| Output format: markdown (default) or json |
| Watch log file for changes (live updates) |
| Update interval for watch mode (default: 3.0) |
| Path to config file |
| Use named configuration profile |
| Run as MCP server |
| Show version number |
Web UI
A browser-based interface for quick log analysis without command-line setup.
# Using uvx (recommended - no installation needed)
uvx --with log-essence[ui] log-essence ui
# Or specify a custom port
uvx --with log-essence[ui] log-essence ui --port 8080
# Using pip (if you prefer to install)
pip install log-essence[ui]
log-essence uiFeatures
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 achieved
Download: Export analysis as markdown file
UI Options
Option | Description |
| Port to run on (default: 8501) |
| 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
You ask Claude about logs in natural language
Claude calls log-essence with the appropriate tool and parameters
log-essence analyzes the logs, redacts secrets, and returns a compressed summary
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
discover_log_sources
Find log sources for a code project — project-scoped, so it returns the project's own log
files (vendored/VCS/build dirs pruned), the containers and Compose file belonging to that
project's Docker Compose stack, and — since most code projects log to stdout rather than
files — the run commands (from package.json scripts) you can pipe in, plus any standard
agent-instruction files (CLAUDE.md, AGENTS.md, GEMINI.md, …) that document how to run
and where logs go. It does not surface machine-wide system logs or other projects'
containers. Each result is annotated with the tool or command to use. Call this first when
you don't know what's available.
discover_log_sources() # scan the current directory
discover_log_sources(path="/repo") # scan a specific projectget_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_docker_logs
Analyze logs from Docker Compose services.
get_docker_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
)get_journald_logs
Analyze systemd journal logs.
get_journald_logs(
unit="nginx.service",
since="1h",
priority="err"
)list_containers
List running Docker containers.
list_containers()list_docker_services
List Docker Compose services in a project.
list_docker_services(path="/path/to/project")Per-cluster retrieval
Every analysis tool (get_logs, get_docker_logs, get_container_logs,
get_journald_logs) ends its summary with an analysis_id. Pass it back to
get_raw_logs to pull the full, redacted lines on demand:
get_raw_logs(analysis_id)— all lines (paginate withstart_line/max_lines).get_raw_logs(analysis_id, cluster_id=N)— only the lines behind "Cluster N" from the summary, so an agent can expand exactly the cluster under investigation without pulling the whole log back.
Retrieved lines carry the same redaction as the summary (redacted unless that
analysis was run with redact=False).
Secret Redaction
Logs are automatically redacted before analysis to prevent leaking sensitive data to external LLMs.
Redaction Modes
Mode | Description |
| Default. Emails, IPs, credit cards, SSNs, phones, API keys |
| All moderate patterns + high-entropy strings in key=value |
| Only obvious secrets (bearer tokens, API keys) |
| No redaction (use only for internal logs) |
Output Format
Redacted values use the format [TYPE:length?:hash4]:
# Input
user@acme.com logged in from 192.168.1.50
Error processing payment for user@acme.com 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 Severity
### 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
This server cannot be installed
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