gitl

gitl

AI-powered git history reviewer for CLI and CI. gitl (git-log-lens) reads a repository's git history and turns it into a structured engineering artifact via LLM:

• gitl review <range> — AI review of a commit range / PR with machine-readable risk scoring (low|medium|high) for CI gating (--fail-on=high → non-zero exit code); streams tokens to the terminal in real time; on-disk LLM response cache; custom system-prompt templates; --staged reviews staged (uncommitted) changes before git commit.
• gitl changelog [<range>] — Keep a Changelog-style changelog, grouped by conventional commits (defaults to last tag → HEAD); deterministic by default, --ai optionally rewrites it with the model as readable release-note prose;
• gitl digest [--days=N] [--repos=a,b,c] — activity summary by author/topic/file, including multiple repositories in parallel; interactive TUI viewer (--tui).

A clean CLI binary plus a GitHub Action wrapper — no server, no database, no hosted key storage. BYOK (bring your own key) with multi-provider support: OpenAI-compatible API, Ollama (local/self-hosted), Azure OpenAI, native Anthropic (Claude), Google Gemini. No telemetry.

Status: v0.5.0 released — all three commands work on real repositories with all three output formats (md|text|json). The Action posts AI reviews as sticky PR comments and gates on risk score. Release binaries are cross-compiled, cosign-signed, and covered by SLSA L3 build provenance (see VERIFY.md).

Quick start

Requires Go 1.22+ and git in PATH.

# build
go build ./...

# AI review of a commit range — streams tokens to the terminal in real time
GITL_API_KEY=sk-... go run ./cmd/gitl review HEAD~5..HEAD

# no key = deterministic offline review (heuristic risk, no network call)
go run ./cmd/gitl review HEAD~5..HEAD

# review staged (not yet committed) changes before `git commit`
go run ./cmd/gitl review --staged

# review a GitHub PR by number — requires the `gh` CLI (installed + authenticated);
# resolves base/head via gh, fetches `pull/N/head` locally when needed, and reviews
# the merge-base diff (base...head), same as GitHub shows
go run ./cmd/gitl review pr/42

# machine-readable output for CI + risk gating
go run ./cmd/gitl review HEAD~5..HEAD --format=json
go run ./cmd/gitl review HEAD~5..HEAD --fail-on=high   # non-zero exit on high risk

# estimate cost without making an API call
go run ./cmd/gitl review HEAD~5..HEAD --dry-run

# custom system-prompt template (e.g. your team's review policy) — set via
# config only (prompt.system_template_file); there is no --system-template flag
# see Configuration → Custom templates below

# skip the on-disk LLM cache (always call the model)
go run ./cmd/gitl review HEAD~5..HEAD --no-cache

# disable streaming (non-interactive, buffered output)
go run ./cmd/gitl review HEAD~5..HEAD --no-stream

# changelog from last tag (or full history if no tags) — no LLM by default
go run ./cmd/gitl changelog
go run ./cmd/gitl changelog v1.2.0..HEAD --format=json

# AI changelog: the model rewrites the grouped result as release-note prose and
# reclassifies significant non-conventional commits out of "Other". Without an API
# key (or on a malformed model response) it falls back to the deterministic
# changelog with a warning — never fails. --dry-run/--max-cost-usd/--no-cache work
# the same as for review.
GITL_API_KEY=sk-... go run ./cmd/gitl changelog --ai

# activity summary for the last N days — no LLM
go run ./cmd/gitl digest --days=14

# multi-repo digest: runs in parallel; one unreachable repo does not fail the rest
go run ./cmd/gitl digest --repos=../service-a,../service-b --format=json

# interactive TUI viewer for digest (requires a TTY)
go run ./cmd/gitl digest --days=14 --tui

go run ./cmd/gitl version
go run ./cmd/gitl --help

# tests
go test ./...

Install:

# Go toolchain
go install github.com/akomyagin/gitl/cmd/gitl@latest

# Homebrew (macOS/Linux)
brew install akomyagin/tap/gitl

# npm — downloads the prebuilt binary for your platform from GitHub Releases
# and verifies its SHA256 checksum (no Go toolchain needed).
npx gitl-cli review HEAD~5..HEAD   # or: npm install -g gitl-cli

# Or download a signed release binary from GitHub Releases (see VERIFY.md)

Local multi-provider test (Ollama)

docker-compose.yml starts only the dev dependency — a local Ollama instance for testing the multi-provider LLM client (gitl itself is not containerized):

docker compose up ollama

Configuration

Two levels, merged by priority: flag > env > .gitl.yaml (repo) > ~/.config/gitl/config.yaml (personal). The repo-level .gitl.yaml is committed as a shared team policy (risk threshold, excluded paths, changelog categories). Without a key, gitl runs in deterministic offline mode.

In offline mode — or when a real model omits a valid risk block and gitl falls back to the heuristic — the risk header is annotated with *(heuristic)* (and "heuristic": true in --format=json), so a deterministic score is never mistaken for a model's own judgement.

Providers (llm.provider)

# OpenAI-compatible API (default)
llm:
  provider: "openai"
  api_key: ""            # or env GITL_API_KEY
  base_url: "https://api.openai.com/v1"
  model: "gpt-4o-mini"

# Ollama — local/self-hosted, no key, free
llm:
  provider: "ollama"
  base_url: "http://localhost:11434/v1"
  model: "llama3.1"

# Azure OpenAI — custom auth/endpoint format
llm:
  provider: "azure_openai"
  api_key: ""             # or env GITL_API_KEY
  model: "gpt-4o-mini"    # used only for cost estimation
  azure_openai:
    endpoint: "https://<resource>.openai.azure.com"
    deployment: "<deployment-name>"
    api_version: "2024-08-01-preview"

# Anthropic (native Claude Messages API)
llm:
  provider: "anthropic"
  api_key: ""            # or env GITL_API_KEY
  model: "claude-sonnet-4-6"
  # base_url optional; defaults to https://api.anthropic.com

# Google Gemini (Google AI Studio)
llm:
  provider: "gemini"
  api_key: ""            # or env GITL_API_KEY
  model: "gemini-2.5-flash"
  # base_url optional; defaults to https://generativelanguage.googleapis.com/v1beta

Streaming (output.stream)

When reviewing interactively (md or text format on a TTY), gitl streams tokens to the terminal as they arrive — no waiting for the full response. Streaming is on by default and switches off automatically in CI (non-TTY stdout) or with --format=json.

Streaming is currently implemented only for the OpenAI-compatible provider (openai / ollama / azure_openai). With the native anthropic or gemini provider, gitl transparently produces the same review as a single buffered response (no token-by-token output) regardless of output.stream / --no-stream.

output:
  stream: true   # default; set false to always buffer

Disable per-call: gitl review HEAD~5..HEAD --no-stream

LLM response cache (cache)

gitl review caches model responses on disk (SHA-256 of provider + model + prompt). Identical diffs reuse the cached result instantly, with no API call or cost.

cache:
  enabled: true    # default
  ttl_hours: 24    # entries older than this are ignored

Cache lives in ~/.cache/gitl/review/ (XDG-compliant). Disable per-call: gitl review HEAD~5..HEAD --no-cache

Custom templates (prompt.system_template_file / output.template_file)

Two independent, config-only overrides (there is no CLI flag for either):

• prompt.system_template_file — your own review system prompt, to steer the model's focus (security checklist, architecture constraints, team rules):

  prompt:
    system_template_file: "./review-policy.md"   # path relative to CWD
  

  The system-prompt template has access to {{ .Commits }}, {{ .Diff }}, {{ .Range }}, {{ .Staged }} (see internal/prompt/templates.go).

• output.template_file — your own md-format render template for the finished review artifact:

  output:
    template_file: "./review-output.tmpl"   # path relative to CWD
  

  The output template has the render template functions in internal/render/render.go (render.TemplateFuncs()).

Trust note: prompt.system_template_file/output.template_file can be set by a repo-level .gitl.yaml, not just your personal config — so running gitl review against a cloned repository you don't control can point it at a template inside that same repository. This is the intended mechanism for a team's shared review policy, not a bug: text/template here can't read arbitrary files or execute code, but treat an untrusted repo's .gitl.yaml with the same caution you'd give its .git/hooks or build scripts.

GitHub Action

gitl can be wired up as a GitHub Action: it AI-reviews a pull request's commits and posts a comment with the risk score, optionally blocking merge above a threshold. The Action builds gitl from source (go install at a pinned version). Also listed on the GitHub Marketplace if you'd rather add it from there.

Add .github/workflows/gitl-review.yml to your repository:

name: gitl review
on:
  pull_request:

permissions:
  contents: read          # for checkout
  pull-requests: write    # to post the review comment

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v7
        with:
          fetch-depth: 0    # required: without full history base..head won't resolve

      - uses: akomyagin/gitl@v0.5.0
        with:
          gitl-api-key: ${{ secrets.GITL_API_KEY }}   # BYOK, see below
          fail-on: high                               # optional: block merge on high risk

Security best practices:

• Key via secrets.* only. gitl-api-key comes from secrets.GITL_API_KEY (set under Settings → Secrets and variables → Actions), never hardcoded in YAML or committed. If the secret is not set, the Action runs in deterministic offline mode (no network, no cost).
• Minimal permissions:. Only pull-requests: write (posting the comment) and contents: read (checkout) are needed — do not grant broader rights.
• fetch-depth: 0 is required. GitHub provides base/head SHAs in the pull_request event, but a shallow clone won't resolve base.sha..head.sha.
• fail-on defaults to never. The Action only comments; it does not block merges unless you opt in explicitly (fail-on: high, etc.) — same "WARN by default, hard gate is explicit opt-in" principle as the CLI (--fail-on).
• Diff privacy. In CI, the diff is sent to whichever LLM provider is configured (default: OpenAI-compatible API). For private code, use a self-hosted/enterprise provider (Ollama, Azure OpenAI) — see Providers above.
• Secret masking. GitHub automatically masks secrets.* values in runner logs as ***, but that's not a reason to print the key in your own workflow steps.

Gitea Actions (experimental)

The same action.yml also runs on Gitea Actions — Gitea's runner executes GitHub-style composite actions, and gitl's action detects the platform at run time via the GITEA_ACTIONS=true variable that Gitea's act_runner injects into every job. The only platform-specific part — posting the sticky PR comment — then goes through Gitea's REST API (POST/PATCH /api/v1/repos/{owner}/{repo}/issues/...) with curl instead of the gh CLI, which only speaks GitHub's API. GitHub users are unaffected: without GITEA_ACTIONS the action behaves exactly as before.

Add .gitea/workflows/gitl-review.yml to your repository (full commented example: .gitea/workflows/gitl-review.yml in this repo):

name: gitl review
on:
  pull_request:

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: https://github.com/actions/checkout@v7
        with:
          fetch-depth: 0
      - uses: https://github.com/akomyagin/gitl@v0.5.0
        with:
          gitl-api-key: ${{ secrets.GITL_API_KEY }}   # BYOK; omit for offline mode

Requirements: Actions enabled, a recent act_runner (node24-capable), and a runner image providing bash, git, curl, jq and node. GITL_API_KEY goes into Gitea's Actions secrets, never into the YAML — same BYOK rules as on GitHub.

Verification status — read before relying on this. The curl-based REST calls (list comments, create, patch, sticky-detection) were run against a real Gitea instance (gitea/gitea in Docker) end-to-end — list-empty → POST-create → re-list-finds-it → PATCH-update → still exactly one comment. That part works as written. What's not yet verified is the surrounding act_runner CI context: whether GITEA_ACTIONS/GITHUB_API_URL/the PR event payload look exactly as assumed inside a live workflow run (this was cross-checked against Gitea/ act_runner/act-fork source, not run inside an actual job). Treat the CI-triggering path as experimental until someone confirms a green run end-to-end inside real Gitea Actions; bug reports from real instances are very welcome.

GitLab CI (experimental)

gitl also ships a GitLab CI/CD component — templates/gitl-review.yml — mirroring the GitHub Action: it installs gitl with go install at a pinned version, reviews the merge request's range ($CI_MERGE_REQUEST_DIFF_BASE_SHA..$CI_COMMIT_SHA), renders the comment through the shared platform-neutral ci/comment.sh, and creates/updates a sticky MR note through GitLab's REST API (same <!-- gitl-review --> marker as on GitHub/Gitea). The job only runs in merge request pipelines.

This repository lives on GitHub, so the component is not in a GitLab CI/CD Catalog yet — consume it via include:remote (inputs work with remote includes):

# .gitlab-ci.yml
include:
  - remote: "https://raw.githubusercontent.com/akomyagin/gitl/v0.5.0/templates/gitl-review.yml"
    inputs:
      fail_on: "never"      # default; set "high" to block risky MRs
      # max_cost_usd: "0.50"
      # gitl_version: "v0.5.0"

Setup — two CI/CD variables (Settings → CI/CD → Variables, both masked, never in YAML):

• GITL_API_KEY — the BYOK LLM key. Optional: without it gitl runs the deterministic offline review (no network, no cost). Defining the project variable is enough — it takes precedence over the component's empty gitl_api_key input default. If you use the input instead, pass a variable reference (gitl_api_key: $MY_LLM_KEY), never a literal key: input values are interpolated into the pipeline config.
• GITL_GITLAB_TOKEN — token for posting the MR note (project access token or PAT, api scope, Reporter role or higher; sent as PRIVATE-TOKEN). If unset, the job falls back to CI_JOB_TOKEN (JOB-TOKEN header) — but on most GitLab configurations CI_JOB_TOKEN is not authorized for the Notes API, so the fallback is expected to fail (with an explicit error message, not a silent skip). An explicit GITL_GITLAB_TOKEN is the reliable path.

A full commented self-test pipeline — also the closest thing to a complete usage example — is .gitlab-ci-selftest.yml (runnable as .gitlab-ci.yml in a GitLab mirror of this repo).

Verification status — read before relying on this. The GitLab REST calls (list MR notes + sticky-marker detection, POST create, PUT update) and the component YAML itself (spec:/inputs: interpolation, include:local with inputs, via the CI Lint API) were verified end-to-end against a real local GitLab CE instance (gitlab/gitlab-ce 19.2.0 in Docker) on a real merge request — list-empty → POST-create → re-list-finds-it → PUT-update → still exactly one note — using the exact curl/jq commands from the template. What's not yet verified is a live pipeline run: the values of CI_MERGE_REQUEST_DIFF_BASE_SHA/CI_COMMIT_SHA/CI_JOB_URL inside a real merge-request pipeline are written from GitLab docs, not observed, and the CI_JOB_TOKEN-fallback rejection is documented per GitLab's job-token allowlist docs, not reproduced. Treat the pipeline path as experimental until someone confirms a green end-to-end run; bug reports welcome.

Trust note. The component downloads ci/comment.sh from raw.githubusercontent.com at gitl_version and executes it — with no checksum/signature check, same trust boundary as the go install ...@${gitl_version} line right above it (same repo, same ref). If that matters for your threat model, pin gitl_version to a commit SHA rather than a tag (tags are movable). Publishing this component to the GitLab CI/CD Catalog would remove the fetch entirely — planned, not done yet.

Bitbucket Pipelines (experimental)

The Bitbucket integration ships as a Pipe — and pipes are Docker images by definition, so unlike the GitHub/Gitea action and the GitLab component (plain YAML wrappers) this one is a self-contained image: bitbucket-pipe/Dockerfile builds a static gitl binary and bakes in the shared ci/comment.sh renderer plus the entrypoint bitbucket-pipe/pipe.sh. The pipe resolves the PR range ($BITBUCKET_PR_DESTINATION_COMMIT..$BITBUCKET_COMMIT), runs gitl review --format=json, and creates/updates a sticky PR comment through the Bitbucket Cloud REST API (same <!-- gitl-review --> marker as on the other platforms). Variables reference: bitbucket-pipe/pipe.yml.

Image status. The image is not published to Docker Hub yet — the release workflow's docker-publish job skips the push until DOCKERHUB_USERNAME/DOCKERHUB_TOKEN secrets are provisioned (same graceful-skip pattern as npm). Until then, build it yourself from the repository root: docker build -f bitbucket-pipe/Dockerfile -t akomyagin/gitl-review-pipe:0.5.0 . and push it to a registry your pipeline can pull from.

# bitbucket-pipelines.yml
pipelines:
  pull-requests:
    '**':
      - step:
          name: gitl review
          clone:
            depth: full   # the default depth-50 clone may not contain the PR base commit
          script:
            - pipe: docker://akomyagin/gitl-review-pipe:0.5.0
              variables:
                GITL_API_KEY: $GITL_API_KEY                    # BYOK; omit for offline review
                GITL_BITBUCKET_TOKEN: $GITL_BITBUCKET_TOKEN    # posts the PR comment
                # FAIL_ON: "high"        # default "never" — comment only, no gate
                # MAX_COST_USD: "0.50"

Setup — two secured repository/workspace variables (Repository settings → Pipelines → Repository variables; always referenced as $VAR, never literal values in the YAML):

• GITL_API_KEY — the BYOK LLM key. Optional: without it gitl runs the deterministic offline review (no network, no cost).
• GITL_BITBUCKET_TOKEN — credential for posting the PR comment: a repository/project/workspace access token with the pullrequest:write scope, sent as Authorization: Bearer. Alternative: set GITL_BITBUCKET_USER + GITL_BITBUCKET_APP_PASSWORD (app password with pullrequest:write) for Basic auth instead. If neither is configured the pipe fails fast with an explicit message — before spending any LLM budget.

Supply-chain note (why this differs from the GitLab component). The pipe executes nothing fetched at run time: the gitl binary, ci/comment.sh and the entrypoint are all built into the versioned image from one source tree. The GitLab component has to download ci/comment.sh over the network with no integrity check (see its trust note above); the pipe closes that gap by construction.

Verification status — read before relying on this. The image build and the full in-container flow were verified locally: docker build from this repo, then docker run against a real test git repository with emulated BITBUCKET_* variables — offline review → correct sticky comment.md → comment create (POST), sticky update (PUT, still exactly one comment) and --fail-on exit-code propagation, exercised end-to-end against a local mock of the Bitbucket comments API; the fail-fast paths (missing credential/PR variables) and the fallback notice on a bad range were also exercised in the container. What's not yet verified: anything touching real Bitbucket infrastructure — the REST calls against api.bitbucket.org (shapes taken from Atlassian's API docs), the exact predefined variables inside a live PR pipeline (BITBUCKET_PR_DESTINATION_COMMIT etc. are documented assumptions, not observed values), and how Pipelines mounts the clone into pipe containers. Treat the live pipeline path as experimental until someone confirms a green run on a real Bitbucket workspace; bug reports welcome.

MCP server

gitl mcp runs gitl as a Model Context Protocol stdio server — a separate, additional channel from the CLI/CI usage above, for using gitl interactively inside an agent session (Claude Desktop, Cursor, Windsurf, etc.) instead of shelling out. It exposes two tools:

• gitl_review — same review engine as gitl review: range/pr/staged (exactly one), optional per-call provider/model/base_url overrides. Always returns the structured JSON artifact (no md/text rendering, no streaming — a tool result is atomic). risk.level is returned as data; there is no --fail-on in MCP mode since there is no process exit code to gate.
• gitl_digest — same as gitl digest: days (default 7), optional repos. Without an explicit repos argument the tool only digests the server's working directory (plus digest.repos from .gitl.yaml, if configured) — it never walks arbitrary paths on its own initiative. An explicit repos argument is honored as-is (the calling agent already has filesystem access through its own tools; this isn't an access-control boundary, just a "don't surprise the user" default).

Add to your MCP client config (Claude Desktop, Cursor, etc.):

{
  "mcpServers": {
    "gitl": {
      "command": "gitl",
      "args": ["mcp"]
    }
  }
}

Config is loaded once at startup the same way as the plain commands (.gitl.yaml + personal config + GITL_* env, from the directory gitl mcp is launched in). Without a key, tool calls run in the same deterministic offline mode as the CLI. stdout is reserved for the MCP protocol — nothing human-readable is ever written there; warnings go to stderr.

License

MIT.