Strands Sglang
C+ 79 completed
Ai Ml
unknown / python · small
55
Files
6,676
LOC
1
Frameworks
4
Languages
Pipeline State
completedRun ID
#303032Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
SkippedDecision
skip_scaffold_dupNovelty
49.77Framework unique
—Isolation
—Last stage change
2026-04-16 18:15:42Deduplication group #48287
Member of a group with 1 similar repo(s) — canonical #6293 view group →
Top concepts (6)
RepositoryProject DescriptiontestingTestingAuthenticationTesting
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🧪 Code Distillation
Browse all specs →Sample distilled functions (click for full spec)
get_processorRetrieves and returns a shared, cached multimodal processor instance. It accepts a string argument which can be either a local file path or a HuggingFace model identifier. The function outputs an object conforming to the ProcessorMixin interface. No explicit side-effects are mentioned, but it intern
get_tokenizerRetrieves and returns a pre-trained tokenizer instance. It accepts a string argument which can be either a local file path or a HuggingFace model identifier. The function outputs a fully initialized tokenizer object, and its notable side-effect is that it utilizes the AutoTokenizer class from the tr
get_client_from_slime_argsRetrieves and returns a shared, cached SGLangClient instance by constructing a base URL from the provided arguments object. It utilizes the arguments object to calculate the maximum number of connections required based on server concurrency and GPU allocation. The function accepts optional parameter
AI Prompt
Create a Python library that integrates the SGLang model provider with the Strands Agents SDK. The main goal is to enable agent-based reinforcement learning training by providing token-in/token-out rollouts. Specifically, the implementation must support token IDs along with their corresponding logprobs and masks to prevent retokenization drift. I'm looking for a solution that helps make the Strands Agents SDK training-ready by exposing these end-to-end, token-level rollouts.
python sglang reinforcement-learning agent-sdk rl-training token-out pytorch
Generated by gemma4:latest
Catalog Information
The strands-sglang project provides a SGLang model to the Strands Agents SDK, enabling token-in/token-out support for agent-based reinforcement learning training.
Description
Strands-sglang is a model provider designed for use with the Strands Agents SDK. It supports token-in and token-out functionality, which is essential for agent-based reinforcement learning training. This project leverages the capabilities of the SGLang model to facilitate efficient and effective training processes.
الوصف
هذا المشروع يوفّر نموذج SGLang للمكتبة Strands Agents SDK، ويوفر دعمًا لوصف الإدخال والخروج بالتوقيعات، مما يساعد على تدريب العمليات التعلمية المستندة إلى الوكلاء.
Novelty
7/10Tags
agent-based-reinforcement-learning rl-training model-provider sglang-model strands-agents-sdk
Technologies
huggingface openai pytorch
Claude Models
claude-opus-4.6 claude-opus-4.5
Quality Score
C+
78.6/100
Structure
89
Code Quality
75
Documentation
64
Testing
85
Practices
72
Security
90
Dependencies
90
Strengths
- CI/CD pipeline configured (github_actions)
- Good test coverage (125% test-to-source ratio)
- Code linting configured (ruff (possible))
- Consistent naming conventions (snake_case)
- Good security practices \u2014 no major issues detected
- Properly licensed project
Weaknesses
- Potential hardcoded secrets in 1 files
Recommendations
- Move hardcoded secrets to environment variables or a secrets manager
Security & Health
4.6h
Tech Debt (B)
High
DORA Rating
A
OWASP (100%)
Open data scored by Repobility · https://repobility.com
PASS
Quality Gate
A
Risk (2)
Apache-2.0
License
11.5%
Duplication
Languages
Frameworks
pytest
Symbols
method44
variable22
class21
constant16
function13
property13
Concepts (6)
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