glabflow 0.1.0a2

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

labflow

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 labflow

async def main():
    async with labflow.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 labflow
from labflow.graphql import Query, DataLoader

async def main():
    async with labflow.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

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

Mode	Users/sec	vs python-gitlab	vs Async Wrapper	Purpose
labflow DEFAULT (GIL off)	1207/s	100-200x faster	3.36x	MAXIMUM SPEED
labflow 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)
labflow 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

labflow provides two modes for different needs:

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

   async with labflow.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 labflow.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 labflow

Or with pip: pip install labflow

We recommend uv for Python project and dependency management.

Quick Start — REST API (Bulk Operations)

import asyncio
import labflow

async def main():
    async with labflow.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 labflow
from labflow import fanout

async def get_mr_count(gl: labflow.Client, user: labflow.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 labflow.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!

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

See REST API Guide for complete endpoint list.

Error Handling

import labflow

async with labflow.Client("https://gitlab.example.com", token) as gl:
    try:
        user = await gl.users.get(999999)
    except labflow.NotFoundError:
        print("User not found")
    except labflow.RateLimitError:
        print("Rate limited — reduce concurrency")

See Error Handling Documentation for details.

Requirements

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