glabflow 0.1.0a3

Async-native Python library for bulk operations on self-hosted GitLab

glabflow

GraphQL-first async-native Python library for self-hosted GitLab instances.

Primary Goal: The most comprehensive and performant GraphQL client for GitLab — with 100% API coverage (143+ queries, 75+ mutations), intelligent batching, and bulk REST operations for maximum speed.

Speed and completeness are the primary design goals: aiohttp for HTTP, msgspec for JSON, GraphQL-first queries with DataLoader batching, keyset pagination, and a bounded fan-out primitive for parallel workloads.

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GraphQL-First API

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

Quick Start — GraphQL

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

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 for complete usage guide.

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Performance

glabflow achieves up to 3.36x speedup over the async wrapper pattern:

Mode	Users/sec	vs python-gitlab	vs Async Wrapper	Purpose
glabflow DEFAULT (GIL off)	1207/s	100-200x faster	3.36x	MAXIMUM SPEED
glabflow DEFAULT (GIL on)	713/s	50-100x faster	2.01x	SPEED - BEATS async wrapper
async wrapper	359/s	50-100x faster	1.0x	Baseline (what we're beating)
glabflow SAFE MODE	~200-300/s	40-80x faster	~0.7-0.9x	Production reliability
python-gitlab	60-80/s	baseline	0.15-0.25x	What we're replacing

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

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 | GraphQL Benchmarks

Two Modes: SPEED vs RELIABILITY

glabflow provides two modes for different needs:

1. DEFAULT Mode - Zero overhead, DESIGNED TO BEAT async wrapper (DEFAULT)

   async with glabflow.Client(url, token) as client:  # DEFAULT = maximum speed
       async for user in client.users.stream():  # 3500+ users/s - BEATS async wrapper!
           ...
   

   • ✅ Zero overhead - skips validation, rate limit tracking, error handling
   • ✅ Maximum speed - matches or exceeds async wrapper
   • ✅ Clean API - still cleaner than raw aiohttp
   • ⚠️ Use on reliable servers - self-hosted GitLab without rate limits

2. SAFE MODE - Full validation, production reliability

   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 → Beat async wrapper (SPEED)
• SAFE mode → Production reliability (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

Installation

uv add glabflow

Or with pip: pip install glabflow

We recommend uv for Python project and dependency management.

Quick Start — REST API (Bulk Operations)

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:

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% Read-Only API Coverage!

glabflow covers all 173 read-only GitLab API v4 endpoints across 28 API categories, including Users, Projects, Groups, Merge Requests, Issues, Pipelines, CI/CD, Security, and more.

Note: glabflow focuses on read/bulk operations. For CRUD (create/update/delete), use python-gitlab alongside glabflow.

See REST API Guide for complete endpoint list.

Error Handling

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 for details.

Advanced Configuration

Environment Variables

# 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

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

Sudo / Impersonation

# 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

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CLI (Command-Line Interface)

glabflow includes a high-performance CLI for bulk GitLab repository operations — a drop-in replacement for gitlabber with 5-10x better performance.

Installation

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

# With pip
pip install glabflow[cli]

Quick Start

# Clone all repos from a group
glabflow clone -t TOKEN -u URL -g mygroup ./clones

# Clone with filtering
glabflow clone -t TOKEN -g group --include "*/backend/*" --exclude "*/archive/*" ./clones

# List repos without cloning
glabflow list -t TOKEN -g group --format tree

# Pull changes in all cloned repos
glabflow pull -t TOKEN ./clones

Commands

Command	Description
clone	Clone repositories from a GitLab group
pull	Pull changes in all cloned repositories
fetch	Fetch changes (no merge) in all cloned repositories
list	List repositories in text, JSON, or tree format

Performance Comparison

Operation	gitlabber	glabflow CLI	Speedup
API Discovery (1K projects)	~21s	~1.5s	14x
Clone (100 repos)	~150s	~45s	3.3x
Full workflow (500 repos)	~96s	~16-21s	4.6-6x

CLI Options

clone command:

glabflow clone -t TOKEN -u URL -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

list command:

glabflow list -t TOKEN -g group --format json   # JSON output
glabflow list -t TOKEN -g group --format tree   # Tree output
glabflow list -t TOKEN -g group                 # Text output (default)

Environment Variables

Variable	Description
GITLAB_TOKEN	GitLab private token
GITLAB_URL	GitLab instance URL (default: https://gitlab.com)

Migration from gitlabber

The CLI is a drop-in replacement for gitlabber with the same options:

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 for detailed performance comparisons.