llm.log

Track tokens, costs, and every prompt across all your LLM APIs.
Runs locally. Web + TUI dashboard, single binary, zero config.

Install · Quick Start · Dashboard · CLI · Export · Data Management · How it Works

       

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_{Web UI — llm-log ui}

_{TUI — llm-log dash}

What is llm.log?

A local proxy that sits between your apps and LLM APIs. It intercepts requests, extracts token usage, calculates costs, and stores everything in SQLite — without changing a single line of code.

• Zero code changes — works via HTTPS_PROXY, picked up by most apps automatically
• All major providers — OpenAI, Anthropic, OpenRouter, Groq, DeepSeek, Mistral, and more
• All API formats — Chat Completions, Responses API, Anthropic Messages
• Real costs — auto-updated pricing for 780+ models, cache token breakdowns
• Claude Code aware — on a subscription? see what you'd pay without it. On API keys? see your actual spend
• Web UI — browser-based dashboard with real-time charts, analytics, and detailed request inspection
• TUI dashboard — terminal-based overview, charts, cost breakdown, request inspector
• Minimal overhead — logging is async and never blocks your requests
• Single binary — pure Go, no CGO, no dependencies

Install

# macOS / Linux (Homebrew)
brew install lanesket/tap/llm-log

# or with Go
go install github.com/lanesket/llm.log/cmd/llm-log@latest

# or from source
git clone https://github.com/lanesket/llm.log.git && cd llm.log && make build

Pre-built binaries for macOS and Linux on the Releases page.

Quick Start

llm-log setup   # one-time: generate CA cert, trust it, configure shell
llm-log start   # start the proxy
llm-log dash    # open the dashboard

After setup, open a new terminal (or run source ~/.zshrc) — then every LLM API call is logged automatically.

Already running apps need a restart to pick up the proxy. On macOS, new apps from Dock pick it up automatically.

Note: llm.log intercepts requests that go directly from your machine to LLM APIs. Tools that route through their own servers (Cursor Pro, VS Code Copilot with built-in subscription) won't be logged. If the tool supports your own API key, requests go directly to the provider and llm.log captures them.

Web UI

llm-log ui   # opens http://localhost:9923

Page	What it shows
Dashboard	Real-time metrics (animated), area charts (requests/cost/tokens), provider breakdown, top models
Requests	Paginated table with sorting, filters, search. Click a row to see full detail with copyable values
Analytics	Tabbed sections — Cost (over time, cumulative, by provider, distribution, top expensive), Tokens (over time, avg/model, cache hit rate), Performance (latency, heatmap)

TUI Dashboard

llm-log dashboard   # or: llm-log dash

Tab	What it shows
Overview	Total spend, request count, cache hit rate, sparkline, top models
Chart	Cumulative cost, requests, tokens, cache hit rate over time
Cost	Breakdown by provider/model with percentages, latency, bars
Requests	Browse requests, inspect full prompt/response JSON

Keys: 1-4 tabs · p period · s source · f provider · m model/provider · j/k navigate · enter detail · c/p/r copy · e export · ? help · q quit

CLI

llm-log status                    # daemon status + today's summary
llm-log logs                      # recent requests
llm-log logs --id 42              # full detail with prompt/response
llm-log logs -s cc:sub            # filter by source
llm-log stats                     # usage stats by provider
llm-log stats -b model -p week   # by model, last week
llm-log stats --json              # JSON output

Export

Export logged data to CSV, JSON, or JSONL for analysis in Excel, Jupyter, pandas, etc.

llm-log export                            # CSV to stdout (last month)
llm-log export -f json -o data.json       # JSON to file
llm-log export -f jsonl -p week           # JSONL, last week
llm-log export --from 2025-03-01 --to 2025-03-15  # date range
llm-log export -s cc:sub --provider anthropic      # filtered
llm-log export --with-bodies -p today     # include request/response bodies

Flag	Description	Default
-f, --format	Output format: csv, json, jsonl	csv
-p, --period	Period: today, week, month, all	month
--from	Start date (YYYY-MM-DD)	—
--to	End date (YYYY-MM-DD)	—
-s, --source	Filter by source	all
--provider	Filter by provider	all
-o, --output	Output file (default: stdout)	stdout
--with-bodies	Include request/response bodies	false

--from/--to override --period when both are provided.

In the dashboard, press e to quick-export the current filtered view to llm-log-export-{timestamp}.csv in the current directory.

Data Management

Request/response bodies can grow large over time. Use prune to delete old bodies while keeping all metadata (tokens, costs, timestamps).

llm-log prune --older-than 30d              # delete bodies older than 30 days
llm-log prune --older-than 30d --dry-run    # preview without deleting
llm-log prune --older-than 6m --force       # skip confirmation (for cron)

Supported durations: 7d, 30d, 6m, 1y.

How it Works

Your app ──HTTPS_PROXY──▸ llm.log (127.0.0.1:9922)
                              │
                              ├─ LLM provider? ──▸ MITM ──▸ parse usage ──▸ SQLite
                              └─ Other?         ──▸ tunnel through (no interception)

1. llm-log setup generates a CA certificate and adds it to your system trust store
2. llm-log start launches a daemon and sets HTTPS_PROXY + CA env vars for all major tools
3. The proxy MITMs only known LLM domains — everything else tunnels untouched
4. Streaming responses are tee'd — client gets data in real-time, proxy parses accumulated result
5. Costs are calculated from auto-updated pricing data (780+ models)

Providers and formats

Provider	Domain	API formats
OpenAI	api.openai.com	Chat Completions, Responses API
Anthropic	api.anthropic.com	Anthropic Messages
OpenRouter	openrouter.ai	All three
Groq	api.groq.com	Chat Completions
Together AI	api.together.xyz	Chat Completions
Fireworks	api.fireworks.ai	Chat Completions
DeepSeek	api.deepseek.com	Chat Completions (custom cache tokens)
Mistral	api.mistral.ai	Chat Completions
Perplexity	api.perplexity.ai	Sonar, Chat Completions
xAI	api.x.ai	Chat Completions

Providers and wire formats are extensible — see Extending llm.log.

Proxy activation

Mechanism	Scope	Platform
~/.llm.log/env	New terminal sessions	macOS, Linux
launchctl setenv	GUI apps from Dock	macOS
systemctl --user	GUI apps from menu	Linux (systemd)

CA trust is configured for Node.js, Python, curl, Go, and Ruby via NODE_EXTRA_CA_CERTS, SSL_CERT_FILE, REQUESTS_CA_BUNDLE, and CURL_CA_BUNDLE.

Data storage

Everything in ~/.llm.log/ — SQLite (WAL mode), CA cert, cached pricing, env file, PID. Request/response bodies are gzip-compressed in a separate table.

License

MIT