Know what you spend, see what you send. Lightweight local proxy that logs every LLM call - costs, tokens, full prompts and responses.

llm.log

Lightweight LLM observability — costs, tokens, and full request/response capture.
Three commands to start. Local proxy, single binary, zero config.

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

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llm.log TUI dashboard
_{TUI — llm-log dash}

_{Web UI — llm-log ui}

## What is llm.log? A lightweight local proxy for LLM observability. It sits between your apps and LLM APIs, captures every request and response, tracks token usage, and calculates costs — all stored locally in SQLite, without changing a single line of code. Not as heavy as enterprise platforms (Langfuse, Helicone, etc.) that need Docker, databases, and cloud accounts. Not as limited as log parsers (ccusage, tokscale) that only show token counts. llm.log gives you **full prompt/response capture with cost tracking** in a single binary you can install in seconds. - **Zero code changes** — works via `HTTPS_PROXY`, picked up by most apps automatically - **Full request/response capture** — every prompt and response stored, searchable, exportable - **Real costs** — auto-updated pricing for 800+ models, cache token breakdowns - **All major providers** — OpenAI, Anthropic, OpenRouter, Groq, DeepSeek, Mistral, and [more](#providers-and-formats) - **All API formats** — Chat Completions, Responses API, Anthropic Messages - **Claude Code aware** — on a subscription? see what you'd pay without it. On API keys? see your actual spend - **Web UI + TUI** — browser dashboard with analytics, or terminal dashboard with interactive heatmap and mouse support - **Minimal overhead** — async logging, never blocks your requests - **Single binary** — pure Go, no CGO, no dependencies, no Docker ## Install ```bash # 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](https://github.com/lanesket/llm.log/releases) page. ## Quick Start ```bash 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 ```bash llm-log ui # opens http://localhost:9923 ``` | Page | What it shows | |------|---------------| | **Dashboard** | Real-time metrics, contribution heatmap, area charts, 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 ```bash llm-log dashboard # or: llm-log dash ``` | Tab | What it shows | |-----|---------------| | **Overview** | Total spend, request count, cache hit rate, interactive contribution heatmap with per-day breakdown, 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 · `tab` switch · `hjkl`/arrows navigate · `p` period · `s` source · `f` provider · `m` model/provider · `enter` detail · `c/p/r` copy · `e` export · `?` help · `q` quit **Mouse:** click tabs, heatmap cells, request rows · scroll wheel to navigate lists ## CLI ```bash 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. ```bash 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). ```bash 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](docs/extending.md). ### 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 Source: https://github.com/lanesket/llm.log

@llmdotlog

llm.log