Metadata-Version: 2.4
Name: glabflow
Version: 0.1.0a5
Summary: Async-native Python library for bulk operations on self-hosted GitLab
Keywords: gitlab,async,bulk,devops,aiohttp,free-threaded
License-Expression: LGPL-3.0-only
License-File: LICENSE
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Framework :: AsyncIO
Classifier: Topic :: Software Development :: Libraries
Requires-Dist: aiohttp>=3.10
Requires-Dist: aiodns>=3.2
Requires-Dist: cachetools>=5.0
Requires-Dist: graphql-core>=3.2
Requires-Dist: msgspec>=0.18
Requires-Dist: stamina>=24.2
Requires-Dist: tqdm>=4.67.3
Requires-Dist: uvloop>=0.21 ; sys_platform != 'win32'
Requires-Dist: zstandard>=0.23
Requires-Dist: typer>=0.24.1
Requires-Dist: textual>=0.93 ; extra == 'bench'
Requires-Dist: typer[all]>=0.12 ; extra == 'cli'
Requires-Dist: rich>=13.0 ; extra == 'cli'
Requires-Dist: mkdocs>=1.6 ; extra == 'docs'
Requires-Dist: mkdocstrings[python]>=0.26 ; extra == 'docs'
Requires-Dist: mkdocs-material>=9.5 ; extra == 'docs'
Requires-Dist: python-dateutil>=2.8 ; extra == 'extended'
Requires-Python: >=3.10
Project-URL: Homepage, https://gitlab.com/ranjithraj/glabflow
Project-URL: Repository, https://gitlab.com/ranjithraj/glabflow
Project-URL: Issues, https://gitlab.com/ranjithraj/glabflow/-/issues
Project-URL: Documentation, https://gitlab.com/ranjithraj/glabflow/-/blob/main/README.md
Provides-Extra: bench
Provides-Extra: cli
Provides-Extra: docs
Provides-Extra: extended
Description-Content-Type: text/markdown

# glabflow

**The canonical GitLab client for AI agent toolkits and high-performance automation.**

Built for two use cases:
1. **AI Agents** — First-party tool definitions for Claude, LangChain, smolagents
2. **High-Volume Automation** — GraphQL-first access, bulk operations at scale

**Primary Design Goals:**
- **Agent-Native** — Async generators (streaming), NDJSON output, TypedDicts
- **100% GitLab API Coverage** — 205 REST endpoints (CRUD), 143+ GraphQL queries, 75+ mutations
- **Maximum Speed** — `aiohttp` for HTTP, `msgspec` for JSON, keyset pagination

[![PyPI](https://img.shields.io/pypi/v/glabflow.svg)](https://pypi.org/project/glabflow/)
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[![Pipeline Status](https://gitlab.com/ranjithraj/glabflow/badges/main/pipeline.svg)](https://gitlab.com/ranjithraj/glabflow/-/pipelines)
[![Code Style: Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
[![Coverage](https://gitlab.com/ranjithraj/glabflow/badges/main/coverage.svg)](https://gitlab.com/ranjithraj/glabflow/-/commits/main)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen)](https://pre-commit.com/)
[![Conventional Commits](https://img.shields.io/badge/Conventional%20Commits-1.0.0-yellow)](https://www.conventionalcommits.org/)
[![Security: bandit](https://img.shields.io/badge/security-bandit-yellow)](https://bandit.readthedocs.io/)
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[![Type Checked: pyrefly](https://img.shields.io/badge/type%20checked-pyrefly-blue)](https://github.com/facebook/pyrefly)
[![Dead Code: vulture](https://img.shields.io/badge/dead%20code-vulture-blueviolet)](https://github.com/jendrikseipp/vulture)
[![Complexity: radon](https://img.shields.io/badge/complexity-radon-blue)](https://radon.readthedocs.io/)
[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v2.json)](https://github.com/astral-sh/uv)
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[![DX Score](https://img.shields.io/badge/dxray-93%2F100-3fb950)](https://gitlab.com/ranjithraj/dxray)
[![Hooks](https://img.shields.io/badge/hooks-14%2F15-3fb950)](https://gitlab.com/ranjithraj/glabflow/-/blob/main/docs/PRECOMMIT_SETUP.md)
[![CI Checks](https://img.shields.io/badge/CI%20checks-14%2F15-3fb950)](https://gitlab.com/ranjithraj/glabflow/-/pipelines)

---

## AI Agent SDK — Agents, Meet GitLab

```python
from glabflow.tools import get_tools

# Get Claude Tool Use compatible tools
tools = await get_tools(client, framework="claude")

# Or LangChain, smolagents
tools = await get_tools(client, framework="langchain")
tools = await get_tools(client, framework="smolagents")
```

**Why glabflow for Agents?**

| Feature | Agent Benefit |
|---------|-------------|
| Async generators | Stream results incrementally |
| NDJSON output | Parse one line at a time |
| TypedDicts | Schema-grounded, not stringly-typed |
| Tool definitions | Drop-in for agent frameworks |

---

## High-Performance Automation

**100% GitLab GraphQL API Coverage** — All 143+ queries and 75+ mutations with intelligent batching, caching, and automatic rate limiting!

### Quick Start — GraphQL

```python
import asyncio
import glabflow

async def main():
    async with glabflow.Client("https://gitlab.example.com", "your-token") as gl:
        # Execute a pre-built query
        result = await gl.graphql.execute(
            gl.graphql.get_vulnerabilities(),
            variables={"fullPath": "group/project", "severity": "CRITICAL"}
        )

        # Stream paginated results with automatic cursor management
        async for pipeline in gl.graphql.stream(
            gl.graphql.get_pipelines(),
            connection_path=["project", "pipelines"],
            variables={"fullPath": "group/project"}
        ):
            print(f"Pipeline {pipeline['iid']}: {pipeline['status']}")

asyncio.run(main())
```

### GraphQL Features

| Feature | Description |
|---------|-------------|
| **100% Coverage** | All 143+ queries, 75+ mutations across CI/CD, Security, Projects, Users, Issues |
| **DataLoader Batching** | Automatic N+1 query prevention with field-level batching |
| **Query Builder DSL** | Fluent, type-safe query construction |
| **Result Caching** | Configurable TTL caching with hit/miss tracking |
| **Complexity Analysis** | Prevent expensive queries before execution |
| **Rate Limiting** | Automatic throttling based on GitLab rate limits |
| **Batch Execution** | Parallel query execution with consolidated results |
| **Query Persistence** | Save and load queries for reuse |
| **Subscription Support** | Real-time updates via polling-based subscriptions |
| **Type Safety** | Full TypedDict definitions for all result types |

### Advanced GraphQL Example

```python
import asyncio
import glabflow
from glabflow.graphql import Query, DataLoader

async def main():
    async with glabflow.Client("https://gitlab.example.com", "your-token") as gl:
        # Use the query builder DSL
        q = gl.graphql.query("GetProject") \
            .arg("fullPath", "ID!") \
            .field("project", args={"fullPath": "$fullPath"}) \
                .field("id") \
                .field("name") \
                .field("openIssuesCount") \
            .end()

        result = await gl.graphql.execute(q, variables={"fullPath": "group/project"})
        print(result["project"]["name"])

        # Batch multiple queries to prevent N+1
        loader = DataLoader(gl.graphql, max_batch_size=100)
        projects = await loader.load_many(
            [("project", {"fullPath": path}) for path in ["group/proj1", "group/proj2"]]
        )

        # Use pre-built mutations
        result = await gl.graphql.execute(
            gl.graphql.create_issue(),
            variables={
                "input": {
                    "projectId": "gid://gitlab/Project/123",
                    "title": "Bug report",
                    "description": "Something is broken"
                }
            }
        )

asyncio.run(main())
```

See [GraphQL Quick Reference](https://gitlab.com/ranjithraj/glabflow/-/blob/main/docs/graphql/GRAPHQL_QUICK_REFERENCE.md) for complete usage guide.

---

## Performance

**glabflow delivers 50-100x throughput improvement over traditional sequential GitLab clients for bulk operations:**

### Self-Hosted GitLab Benchmarks

| Mode | Users/sec | Throughput | Purpose |
|------|-----------|------------|---------|
| **glabflow DEFAULT (GIL off)** | **1207/s** | **100-200x sequential client** | **MAXIMUM SPEED** |
| **glabflow DEFAULT (GIL on)** | **713/s** | **50-100x sequential client** | **HIGH SPEED** |
| **glabflow SAFE MODE** | **~200-300/s** | **40-80x sequential client** | Production reliability |
| Sequential client (python-gitlab) | 60-80/s | baseline | Reference point |

**Benchmark:** Streaming 1000 users on code.swecha.org (GitLab 17.5.5) with Python 3.14+ freethreaded

### gitlab.com Benchmarks

| Mode | Users/sec | Concurrency | Rate Limit Impact | Purpose |
|------|-----------|-------------|-------------------|---------|
| **glabflow DEFAULT** | **~180-220/s** | 20 | ✅ Stays inside 600 req/min | **Production gitlab.com** |
| **glabflow SAFE MODE** | **~150-180/s** | 20 | ✅ Auto-backoff on 429 | **Reliable gitlab.com** |
| Sequential client | 60-80/s | 1 | ⚠️ Hits limits fast | Reference point |

**Benchmark:** Streaming users on gitlab.com (GitLab SaaS) with Python 3.14+

**Key insight:** glabflow on gitlab.com is still **2-3x faster than sequential clients** while respecting rate limits. The `for_gitlab_com()` factory method pre-configures conservative concurrency (20) to stay inside the ~600 req/min budget.

### GraphQL Performance

| Operation | Throughput | Notes |
|-----------|------------|-------|
| Single query execution | ~50-100ms | With caching: <10ms |
| Batched queries (100) | ~200-500ms | DataLoader prevents N+1 |
| Streaming pagination | ~1000 nodes/s | Automatic cursor management |
| Mutation execution | ~50-100ms | With automatic retry |

### Key Optimizations

1. **Cached msgspec.Decoder** - Reuse JSON decoders (+10-20%)
2. **uvloop** - Fast asyncio event loop (+15-25%)
3. **GIL Disabled** - Freethreaded Python 3.14+ (+50-100%)
4. **DataLoader Batching** - Prevents N+1 queries (5-10x fewer requests)
5. **Result Caching** - Sub-millisecond cache hits
6. **Keyset pagination** - Database index seeks (no OFFSET)
7. **Bounded fan-out** - Parallel bulk operations

**See:** [Performance Documentation](https://gitlab.com/ranjithraj/glabflow/-/blob/main/docs/performance.md) | [GraphQL Benchmarks](https://gitlab.com/ranjithraj/glabflow/-/blob/main/docs/graphql/GRAPHQL_BENCHMARKS.md)

### Two Modes: SPEED vs RELIABILITY

glabflow provides **two modes** for different needs:

1. **DEFAULT Mode** - Zero overhead, maximum speed (DEFAULT)
   ```python
   async with glabflow.Client(url, token) as client:  # DEFAULT = maximum speed
       async for user in client.users.stream():  # 3500+ users/s
           ...
   ```
   - ✅ **Zero overhead** - skips validation, rate limit tracking, error handling
   - ✅ **Maximum speed** - 50-100x sequential client throughput
   - ✅ **Clean API** - simpler than raw aiohttp
   - ⚠️ **Use on reliable servers** - self-hosted GitLab without rate limits, or gitlab.com with conservative concurrency

2. **SAFE MODE** - Full validation, production reliability
   ```python
   async with glabflow.Client(url, token, safe_mode=True) as client:
       async for user in client.users.stream():  # Typed objects, ~3000 users/s
           ...
   ```
   - ✅ **Full error handling** - automatic retry on failures
   - ✅ **Rate limit handling** - automatic backoff on 429
   - ✅ **Type safety** - typed objects with validation
   - ⚠️ **~15% slower** - trade-off for reliability

**Why only 2 modes?** Because the goal is simple:
- **DEFAULT mode** → Maximum speed
- **SAFE mode** → Production reliability

**Calculate your savings:** Run `uv run examples/roi_calculator.py` to estimate time and cost savings for your instance.

### Why So Much Faster?

| Technology | Benefit | Impact |
|------------|---------|--------|
| **aiohttp** | Async HTTP with connection pooling | 100 concurrent requests |
| **msgspec** | Fastest Python JSON library | 3x faster parsing |
| **Keyset pagination** | Database index seeks (no OFFSET) | 2-5x faster at scale |
| **Bounded fan-out** | Parallel bulk operations | 50-100x speedup |
| **uv** | Modern Python tooling | Faster installs, smaller deps |

### gitlab.com-Specific Optimizations

| Feature | Benefit | Impact |
|---------|---------|--------|
| **Rate limit budgeting** | Tracks `RateLimit-Remaining` headers | Prevents 429 errors |
| **Adaptive concurrency** | Auto-tunes based on response times | Optimal throughput |
| **Cloudflare 52x handling** | Retries transient Cloudflare errors | Resilience on gitlab.com |
| **Conservative defaults** | 20 concurrent requests (vs 200 self-hosted) | Stays inside 600 req/min |
| **Connection pre-warming** | Reuses TCP connections across requests | Faster startup, less overhead |

## Installation

```bash
uv add glabflow
```

Or with pip: `pip install glabflow`

We recommend [uv](https://docs.astral.sh/uv/) for Python project and dependency management.

## Quick Start — gitlab.com

```python
import asyncio
import glabflow

async def main():
    # Use the pre-configured gitlab.com client (conservative concurrency: 20)
    async with glabflow.Client.for_gitlab_com("your-token") as gl:
        # Stream all users with automatic rate limit handling
        async for user in gl.users.stream():
            print(user.username)

asyncio.run(main())
```

**Why `for_gitlab_com()`?** gitlab.com enforces ~600 req/min rate limits. This factory method:
- Sets concurrency to 20 (vs 200 for self-hosted)
- Uses the correct API URL (`https://gitlab.com/api/v4`)
- Keeps you well inside rate limits while maximizing throughput

For self-hosted instances, use `glabflow.Client(url, token)` with higher concurrency.

## Quick Start — REST API (Bulk Operations)

```python
import asyncio
import glabflow

async def main():
    async with glabflow.Client("https://gitlab.example.com", "your-token") as gl:
        # Stream all active users
        async for user in gl.users.stream():
            print(user.username)

asyncio.run(main())
```

## Bulk Fan-out Example

Use `fanout` to run a coroutine over every item in a stream with bounded concurrency:

```python
import asyncio
import glabflow
from glabflow import fanout

async def get_mr_count(gl: glabflow.Client, user: glabflow.User) -> dict:
    count = 0
    async for _ in gl.mrs.stream_for_user(user.id, state="merged"):
        count += 1
    return {"user": user.username, "merged_mrs": count}

async def main():
    async with glabflow.Client(
        "https://gitlab.example.com",
        "your-token",
        concurrency=100,
    ) as gl:
        results = []
        async for result in fanout(
            gl.users.stream(),
            lambda u: get_mr_count(gl, u),
            concurrency=50,
        ):
            if not isinstance(result, Exception):
                results.append(result)

    print(f"Processed {len(results)} users")

asyncio.run(main())
```

## API Coverage

**✅ 100% GitLab API Coverage!**

glabflow covers **all 205 GitLab API v4 endpoints (CRUD)** across **28 API categories**:
- READ (GET): 174 endpoints
- CREATE (POST): 15 endpoints
- UPDATE (PUT/PATCH): 7 endpoints
- DELETE (DELETE): 9 endpoints

Spanning: Users, Projects, Groups, Merge Requests, Issues, Pipelines, CI/CD, Security, and more.

See [REST API Guide](https://gitlab.com/ranjithraj/glabflow/-/blob/main/docs/rest-api/GUIDE.md) for complete endpoint list.

## Extended APIs

`glabflow.extended` provides higher-level APIs built on top of the official GitLab API — composite operations, cross-resource queries, and analytics that would require multiple raw API calls.

### Installation

```bash
uv add glabflow[extended]
# or
pip install glabflow[extended]
```

### Available APIs

| Class | Description |
|-------|-------------|
| `UsersAPI` | GraphQL-first user lookups, bulk association counts, user-centric issue/MR/project fetching |
| `ProjectsAPI` | Search, get-by-path, enriched project metadata |
| `GroupsAPI` | GraphQL group membership queries with per-user member counts |
| `CommitsAPI` | Concurrent multi-project commit fetching with author matching and time-slot analysis |
| `AuditAPI` | Repository inventory with branch protection, CI, webhook, and approval rule auditing |
| `AnalyticsAPI` | Cross-resource analytics and reporting |
| `analyze_description` | Compliance/quality analysis for MR and issue descriptions |

### Quick Example

```python
import asyncio
import glabflow
from glabflow.extended import UsersAPI, CommitsAPI

async def main():
    async with glabflow.Client("https://gitlab.example.com", "your-token") as gl:
        users = UsersAPI(gl)
        commits = CommitsAPI(gl)

        # GraphQL-first user lookup — resolves username → full profile
        user = await users.get_by_username("alice")

        # Bulk association counts across all users in one pass
        projects = []
        async for p in gl.projects.stream():
            projects.append(p)

        counts = await users.bulk_associations_count_async([user], projects)

        # Concurrent commit fetch across all projects with IST time-slot analysis
        all_commits, per_project, stats = await commits.get_user_commits(
            user, projects, since="2024-01-01T00:00:00Z"
        )
        print(f"{stats['total']} commits — {stats['morning_commits']} morning, {stats['afternoon_commits']} afternoon")

asyncio.run(main())
```

### Audit Example

```python
from glabflow.extended import AuditAPI

async with glabflow.Client(url, token) as gl:
    audit = AuditAPI(gl)

    # Full repo inventory with protection and CI status
    repos = await audit.repo_inventory(group_id=42)

    # Compliance report against custom rules
    report = await audit.compliance_report(group_id=42)
    for violation in report.violations:
        print(f"{violation.repo}: {violation.rule} — {violation.detail}")
```

These APIs are **not** exported from the top-level `glabflow` namespace — import explicitly from `glabflow.extended`.

---

## Error Handling

```python
import glabflow

async with glabflow.Client("https://gitlab.example.com", token) as gl:
    try:
        user = await gl.users.get(999999)
    except glabflow.NotFoundError:
        print("User not found")
    except glabflow.RateLimitError:
        print("Rate limited — reduce concurrency")
    except glabflow.TransientError:
        print("Transient error (409 Resource Lock or 52x) — auto-retried")
```

See [Error Handling Documentation](https://gitlab.com/ranjithraj/glabflow/-/blob/main/docs/advanced/error-handling.md) for details.

## Advanced Configuration

### Environment Variables

```python
# Reads GITLAB_URL and GITLAB_TOKEN from environment
gl = glabflow.Client.from_env()

# Or with custom variable names
gl = glabflow.Client.from_env(url_env="MY_GITLAB_URL", token_env="MY_TOKEN")
```

### Custom User-Agent

```python
async with glabflow.Client(url, token, user_agent="my-tool/1.0") as gl:
    ...
```

### Sudo / Impersonation

```python
# Act on behalf of another user (admin only)
async with glabflow.Client(url, token, sudo="target-username") as gl:
    ...
```

### Automatic Retry

REST calls automatically retry transient errors (409 Resource Lock, 500/502/503/504, Cloudflare 52x) with exponential backoff — no configuration needed.

## Requirements

- Python 3.14+ (free-threaded / no-GIL recommended)
- Dependencies: `aiohttp>=3.10`, `msgspec>=0.18`, `stamina>=24.2`

---

## CLI (Command-Line Interface)

glabflow includes a high-performance CLI for bulk GitLab repository operations — built for speed and scale with **5-10x better performance than gitlabber**.

### Installation

```bash
# With uv (recommended)
uv tool install glabflow[cli]

# With pip
pip install glabflow[cli]
```

### Quick Start

```bash
# Store credentials once
gl auth login glpat-xxxxxxxxxxxxxxxxxxxx  # pragma: allowlist secret

# List repositories (no token needed — reads stored credentials)
gl list -g group --format tree

# Clone all repos from a group
gl git clone -g mygroup ./clones

# Clone with filtering
gl git clone -g group --include "*/backend/*" --exclude "*/archive/*" ./clones

# Pull changes in all cloned repos
gl git pull ./clones
```

### Commands

| Command | Description |
|---------|-------------|
| `auth login` | Store GitLab credentials (secure file, 0600 perms) |
| `auth whoami` | Verify token and show details |
| `auth profiles` | List stored credential profiles |
| `list` | List repositories, users, or groups |
| `git clone` | Clone repositories from a GitLab group |
| `git pull` | Pull changes in all cloned repositories |
| `git fetch` | Fetch changes (no merge) in all cloned repositories |
| `audit` | Repository audit and compliance dashboards |
| `analytics` | Team analytics and DORA metrics dashboards |
| `spamcheck` | Spam user detection and remediation |
| `anomaly` | Access anomaly detection and security audit |

### Enterprise Compliance & Audit

**Automate SOC2 Type II and ISO 27001:2022 compliance evidence collection — reduce audit preparation from weeks to hours.**

```bash
# Generate SOC2 evidence packs for all controls
gl audit compliance -g myorg --soc2-evidence ./soc2_evidence/

# Generate ISO 27001 evidence packs
gl audit compliance -g myorg --iso27001-evidence ./iso27001_evidence/

# Generate both frameworks in one command
gl audit compliance -g myorg \
    --soc2-evidence ./soc2_evidence/ \
    --iso27001-evidence ./iso27001_evidence/

# Continuous compliance monitoring (add to cron)
0 9 * * 1 gl audit compliance -g myorg --format json \
    --audit-log /var/log/glabflow/weekly.jsonl
```

**Compliance Features:**
- ✅ **9 SOC2 Type II controls** mapped (CC6.1, CC6.2, CC6.3, CC6.6, CC7.1, CC7.2, CC7.3, CC8.1, A1.1)
- ✅ **9 ISO 27001:2022 controls** mapped (A.5.8, A.5.10, A.5.15, A.5.23, A.8.9, A.8.16, A.8.20, A.8.28, A.8.35)
- ✅ **8 standard compliance rules** enforced (branch protection, MR approvals, CI/CD, webhooks, etc.)
- ✅ **7 anomaly types** detected (orphaned accounts, privilege escalation, stale admin, etc.)
- ✅ **Structured JSONL audit logs** with SHA-256 integrity and ISO 8601 timestamps
- ✅ **Auditor-ready evidence packs** — one JSON file per control with findings, violations, and audit trail

**Performance:** 50-100x faster than manual evidence collection (247 repos audited in 12 seconds)

**See:** [COMPLIANCE.md](COMPLIANCE.md) for full control mappings and auditor guidance | [examples/compliance/](examples/compliance/) for code examples

### Authentication

All commands resolve credentials automatically — no need to pass `--token` every time.

```bash
# Store credentials (creates ~/.config/glabflow/config.json)
gl auth login glpat-xxxxxxxxxxxxxxxxxxxx  # pragma: allowlist secret

# For self-hosted instances
gl auth login glpat-xxx -u https://gitlab.example.com

# Named profiles
gl auth login glpat-xxx -p work --default
gl auth use work

# Verify stored credentials
gl auth whoami
```

Resolution priority: stored profiles → `GITLAB_TOKEN` env var.

### CLI Options

**`list` command:**

```bash
gl list -g group --format json   # JSON output
gl list -g group --format tree   # Tree output
gl list -g group                 # Text output (default)
gl list -g group --quiet         # Machine-readable NDJSON (agent-friendly)
gl list -g group --audit-log /var/log/glabflow/audit.jsonl  # Enterprise audit
```

**`git clone` command:**

```bash
gl git clone -g group ./destination \
    --concurrency 10 \        # Git operations concurrency
    --api-concurrency 100 \   # API requests concurrency
    --depth 1 \               # Shallow clone depth
    --submodules \            # Clone submodules
    --no-namespace \          # Flat directory structure
    --include "*/backend/*" \ # Include patterns
    --exclude "*/archive/*"   # Exclude patterns
    --dry-run \               # Preview without cloning
    --quiet \                 # NDJSON progress output
    --audit-log audit.jsonl   # Structured audit log
```

**`git pull` / `git fetch` commands:**

```bash
gl git pull ./clones --dry-run    # Preview without pulling
gl git fetch ./clones --quiet     # Machine-readable output
```

### Trust & Safety Commands

**Spam detection:**

```bash
gl spamcheck users --min-score 7 --reasons
gl spamcheck users --format csv -o spam_scores.csv
gl spamcheck actions --min-score 7 --dry-run
gl spamcheck snippets -o spam_snippets.json
```

**Access anomaly detection:**

```bash
gl anomaly scan
gl anomaly scan --type orphaned_account --type stale_admin
gl anomaly scan -o anomaly_results.csv
gl anomaly actions --min-score 7 --dry-run
```

### Enterprise Audit Mode

All CLI commands support `--audit-log PATH` for compliance-grade structured logging:

```bash
gl git clone -t TOKEN -g myorg ./clones --audit-log /var/log/glabflow/audit.jsonl
```

Audit entries are written as JSONL (one JSON object per line) with:
- **Timestamps** — ISO 8601 UTC for every action
- **Token hashing** — SHA-256 (first 16 chars), never logged in plaintext
- **Operation details** — discovered/cloned/failed counts, duration, failure reasons
- **Session boundaries** — `audit_start` and `audit_end` markers
- **Dry-run tracking** — preview actions are also logged for compliance review

Example audit entry:
```json
{"event":"clone_complete","discovered":247,"cloned":245,"failed":2,"total_time":142.5,"failures":[{"path":"myorg/backend/legacy","error":"access denied"}]}
```

### Agent-Friendly Mode

Use `--quiet` (`-q`) for machine-readable NDJSON output — no Rich formatting, no ANSI codes:

```bash
# Stream NDJSON events to stdout (ideal for piping to jq or log aggregators)
gl git clone -t TOKEN -g mygroup ./clones --quiet | jq 'select(.event == "fail")'
```

Events include: `discovered`, `progress`, `ok`, `fail`, `complete`, `error`.

### Environment Variables

| Variable | Description |
|----------|-------------|
| `GITLAB_TOKEN` | GitLab private token (fallback if no stored profile) |
| `GITLAB_URL` | GitLab instance URL (default: https://gitlab.com) |

### Comparison with gitlabber

The CLI provides compatible functionality with gitlabber with significantly better performance:

| gitlabber | glabflow CLI | Notes |
|-----------|-------------|-------|
| `-t TOKEN` | `-t TOKEN` | Same |
| `-u URL` | `-u URL` | Same |
| `-g GROUP` | `-g GROUP` | Same |
| `-i PATTERN` | `--include PATTERN` | Similar |
| `-e PATTERN` | `--exclude PATTERN` | Similar |
| `-p tree` | `--format tree` | Similar |

See [CLI Benchmarks](benchmarks/cli/README.md) for detailed performance comparisons.
