Open Assistant is a community-driven open source AI conversation project initiated by LAION, which collects multilingual conversation data and trains open source chat models through crowdsourcing.

Open Assistant

Core parameters and statistics

Open Assistant (OA) is a community-driven open source AI dialogue project initiated by LAION. The project itself was officially announced as completed in October 2023. Its core value lies not in operating a continuously online chat service, but in producing one of the most influential crowdsourced data sets in the field of open source conversational AI - oasst2, as well as a complete infrastructure for conversational data collection, annotation and model training.

Projects	Public Information
Official positioning	"OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically"
Project status	Completed (announced on 2023-10-25)
Final dataset	oasst2 (released by HuggingFace)
Open Source License	Apache-2.0
GitHub Stars	37.4k
GitHub Forks	3.3k
Contributors	304 people
Released versions	127 (latest v0.0.4-alpha2, 2023-11-26)
Main languages	Python (71.5%), TypeScript (27.1%)
Languages covered	English, Chinese, German, French, Spanish, Japanese and dozens of other languages
Support Platform	Web

Project Life Cycle: OA will be initiated from the end of 2022 to completion in October 2023, with a core life cycle of approximately 12 months. This tight window produced over 37k GitHub Stars and 127 releases, reflecting the open source community's ability to collaborate efficiently under focused goals. After the project is over, the OA team clearly recommends users to use the oasst2 dataset and subsequent community-derived projects.

Dataset size: oasst2 is one of the largest open source multilingual dialogue datasets currently, covering tree dialogue structures in dozens of languages. Each conversation contains prompts, multiple rounds of replies and human preference annotations, which can be directly used for supervised fine-tuning (SFT) and reward model (RM) training. The data set design follows the three-stage process of the InstructGPT paper (SFT → Preference Sampling → RLHF), with the intention of replicating the data pipeline of ChatGPT.

Differences in positioning from similar projects: OA does not completely benchmark commercial chat products, but focuses on the disciplined production of data - it does not provide "a better chat experience", but "high-quality open data that can be used to train chat models". This positioning determines that its users are not ordinary consumers, but AI researchers and model trainers.

User and market recognition of Open Assistant

The market recognition of OA is iconic in the global open source AI community, which is reflected in three levels: community participation, academic impact and derivative projects.

GitHub community popularity: 37.4k Stars, 3.3k Forks, 304 contributors, 127 releases - these numbers are at the top level among AI open source projects in 2023. In particular, the 304 direct code contributors (excluding volunteers who only participated in data annotation) reflect that the project has crossed the pure research prototype stage and formed a sustainable external contribution pipeline. However, it should be noted that the growth of Star after the end of the project mainly comes from the access traffic of "archive mark" and is not a signal of active development.

HuggingFace dataset influence: The downloads of the oasst2 dataset on HuggingFace continue to grow and are widely used as the training basis for multi-language dialogue models. Many subsequent open source projects (such as OpenAssistantGPT, OASST2-derived fine-tuning models) directly rely on this dataset, forming an ecosystem starting from OA data.

Academic Citations and Industry References: OA's data collection methodology and platform architecture have been cited in multiple ACL and EMNLP papers, especially in the fields of low-cost conversation data collection and multi-language alignment. Its "crowdsourcing + quality grading" annotation model was adopted by subsequent projects (such as Dolly and ShareGPT's data collection strategies).

Industry implementation bottleneck: The nature of the OA project determines that it does not have the market recognition indicators of commercial products. There are no public enterprise customer numbers, revenue figures, or SLA commitments. In professional AI teams, OA is regarded as a "research resource" rather than a "production tool" - there is an order of magnitude gap between model performance and closed-source products of the same period, and direct use in customer scenarios requires a lot of fine-tuning and security alignment.

Cost Advantages of Open Assistant

The cost advantage of OA is not reflected in being "cheaper than competing products", but in producing verifiable, high-quality conversation data in a zero-budget model - this is its fundamental difference from all commercial AI projects.

C-side/individual: completely free, zero threshold: OA's web platform, data sets and model weights are free and open to all users, and can be downloaded and used without registration. Individual users can directly participate in data annotation (completed) or download completed data sets for research on the OA platform.

Developer/API: No commercial API, models need to be self-deployed: OA does not provide commercial API services. Users need to download the model weights from HuggingFace and deploy them on their own GPU. Taking the Pythia base model of OA 1.0 (approximately 6.9B parameters) as an example, a single card A100-40G can run inference, but the throughput and latency cannot meet production-level requirements. If higher performance inference is required, it is recommended to refer to subsequent community-derived models or use alternatives with better model optimization.

Enterprise/Private: Open Source License, Zero License Cost: All model weights of OA are released under the Apache-2.0 license, and enterprises can freely use, modify, and distribute within the scope of the license without paying licensing fees. But please note: OA does not provide any enterprise-level support, security updates or compliance certifications, and enterprises need to evaluate their own data privacy and security compliance responsibilities when selecting.

Hidden Costs: The hidden costs of using OA models and data are mainly reflected in three aspects: First, the model performance is much lower than commercial products, and a large amount of engineering investment (prompt engineering, fine-tuning, alignment) is required on complex tasks to reach usable levels; second, the quality of OA data sets relies on volunteer contributions, and there are problems with label consistency fluctuations and uneven language coverage, requiring additional filtering for downstream use; third, the project has ended, and there will be no official updates and security patches.

Main functions of Open Assistant

The functional system of OA is designed around the goal of "open source GPT data pipeline". It is not a chat product for end users, but a data production infrastructure for AI researchers and model trainers.

• Crowdsourced dialogue data collection platform: The core product of OA is a web application. Volunteers can create dialogue prompts (prompts), write responses (responses), prioritize multiple responses (ranking), and annotate quality labels (labeling) on ​​the platform. The entire process mimics the data collection pipeline described in the InstructGPT paper. Applicable tasks: Quickly accumulate multi-language and multi-scenario dialogue data, and lower the threshold for dialogue model training.

• Tree dialogue structure (Thread/Tree): OA's data model is organized in a tree structure - an initial prompt can derive multiple reply branches, and each branch can continue to deepen the dialogue for multiple rounds. This structure is richer than a simple "question and answer pair" format and supports training models to understand conversation context and branching choices. Applicable value: Provide training data for dialogue models that is closer to real interaction scenarios.

• Quality Grading and Preference Annotation: Volunteers not only write responses, but also grade and sort multiple responses under the same prompt. These preference data are directly used to train the reward model (Reward Model) and are a key input to the RLHF process. Applicable value: Inject human preferences into the model to make the model output more consistent with user expectations.

• Multi-language coverage: The OA platform supports conversation data collection in dozens of languages. The platform front-end, annotation interface, and guides are all available in multiple languages, lowering the barrier to entry for non-English-speaking contributors. The resulting oasst2 dataset is one of the largest publicly available conversation datasets in language coverage other than English. Applicable value: It makes up for the gap in the business model in low-resource language conversation data.

• Open source model weight release: OA trained and released multiple versions of model weights based on collected data, including SFT models based on LLaMA and Pythia (oasst-sft) and preference optimization models based on RLHF (oasst-rlhf). All rights reserved on HuggingFace under the Apache-2.0 license. Applicable value: Provides the open source community with a complete reproducible reference implementation of "from data to model".

Model and version evolution of Open Assistant

The version evolution of OA can be summarized as "compact, transparent, and ending". The project released 127 versions during an active period of about 12 months, culminating with the oasst2 dataset and model.

Prototype and Early Release (2022-12 to 2023-03)

• Proto version (~2022-12): Initial proof of concept, the core team builds a basic data collection process to verify the feasibility of the crowdsourcing model. The code uses Python (FastAPI) backend + TypeScript (Next.js) frontend.
• v0.0.1-alpha (~2023-02): The first Alpha release, the platform supports basic data collection and annotation functions, and is open to limited test user participation.

Public beta and scale expansion (2023-03 to 2023-06)

• v0.0.2-alpha (~2023-05): Platform functions have been greatly improved, supporting Discord Bot integration, multi-language interface, and data export. Community contributors quickly grew to 100+ people. At this stage, the first batch of large-scale data collection was completed, with the data volume exceeding 100,000 conversations.
• Multi-language expansion (2023-06): Centralized support for data collection in non-English languages, and platform interface translation covers dozens of languages. Volunteers can participate in annotation in their native language.

Final version and project completion (2023-06 to 2023-11)

• v0.0.3-alpha (~2023-08): The model training pipeline is officially released, SFT and RLHF training on LLaMA and Pythia basic models are completed, and oasst-sft and oasst-rlhf weights are generated.
• v0.0.4-alpha2 (2023-11-26, final version): The last version officially released by the project, which integrates all fixes and optimizations before the completion of the project.
• Project Completion Announcement (2023-10-25): LAION officially announced the completion of the Open Assistant project. The main deliverable is the oasst2 data set. It is recommended that the community turn to subsequent derivative projects and the subsequent use of oasst2.

Version inspiration

The version of OA is not about "how many functions", but about "process completeness" - the early version verifies the feasibility of data collection, the mid-term version expands the scale and language coverage, and the final version delivers a complete data set and model. This version rhythm with data output as the milestone has reference value for open source data projects.

Technical advantages of Open Assistant

The technical advantage of OA lies not in the innovative architecture of the underlying model (the model is fine-tuned based on LLaMA/Pythia), but in the design quality of the crowdsourcing data pipeline and the scalability of the multi-language annotation project.

End-to-end design of crowdsourcing data pipeline: OA's entire platform realizes full-link automation from registration → task allocation → conversation creation → reply writing → hierarchical annotation → quality review → data set export. This pipeline design refers to the data collection methodology of InstructGPT, but it is not a simple "online questionnaire", but an engineered system that includes quality monitoring, consistency verification and anti-abuse mechanisms. The direct technical effect is: without a full-time operations team, the project accumulated hundreds of thousands of quality-screened multilingual conversation data within 12 months.

Engineering implementation of tree conversation structure: OA's data model uses a tree structure instead of flat question and answer pairs, which increases the complexity of back-end database design (PostgreSQL + tree query), front-end interaction (tree expansion/collapse) and model training (tree-based serialization). But this design makes the data have a higher training value than simple question-and-answer pairs—the model can learn the potential directions of conversation branches and the diversity of user intentions.

Scalable architecture for multi-language annotation: OA's platform uses the i18n framework to achieve complete front-end multilingualization, and non-English contributors can participate in their native language. The back-end data model supports language tags and multi-language mixed annotations. This is an important architectural decision for the open source community - most commercial data platforms have limited investment in language coverage other than English, while OA produces representative multilingual data sets at a controllable cost through crowdsourcing + multilingual interfaces.

Crowdsourcing mechanism for quality control: OA does not simply "collect content", but embeds multiple layers of quality control in the annotation process: each annotation task is completed by multiple independent volunteers, annotations with low consistency will trigger review, and malicious or spam contributions will be reported and deleted by the community. This mechanism draws on Wikipedia’s community governance experience, but OA’s data quality consistency is still constrained by differences in volunteers’ professional levels—a common problem in all crowdsourcing projects.

Data boundaries and structural limitations: OA data only covers conversation scenarios (prompt + response + ranking), and does not cover structured data (tables, database records), document-level processing (PDF, long text summaries) or image/multimodal content. For scenarios where AI data needs to be used outside of conversations, OA's single-scenario data coverage is not enough and needs to be combined with other data sets (such as ShareGPT, Dolly, Alpaca).

Recall and discoverability pain points: The effective annotation density of the OA data set in multi-lingual mixed queries and professional terminology-intensive fields is significantly lower than that in the general conversation field. For example, in conversational scenarios that require domain knowledge, such as medicine, law, or engineering, the quality of crowdsourced annotators is difficult to guarantee. It is recommended that downstream models trained using OA data be used in conjunction with Domain-Specific Fine-tuning, rather than relying directly on OA data to cover long-tail expertise.

Security Compliance: OA datasets are publicly released under the Apache-2.0 license and contain no automated cleaning processes for personally identifiable information (PII), but no RBAC (role-based access control) or document-level permission isolation. The dataset has no geographical access restrictions and can be downloaded by users around the world. All data is used to train open source models and will not be used for secondary training of commercial closed source models. The OA project itself does not involve GDPR or SOC2 compliance certification.

How to use Open Assistant

The usage of OA is divided into three different paths: data users, model users and platform reproducers:

How to use	Suitable for people	Features	Cost
Download oasst2 data set	AI researchers, model trainers	HuggingFace direct download for SFT/RM training	Completely free
Download model weights	Developers, self-deployment users	HuggingFace obtains weights and needs to manage the inference context by yourself	Free + GPU fee
Web Chat Demo	Review User	Try the conversation at chat.open-assistant.io (Archived)	Free
Research platform construction	Team/organization	Use Docker to start the complete back-end stack (for development only), technical capabilities are required	Infrastructure costs
Contribute data (closed)	Volunteer	Project completed, new data contributions stopped	—

Quick experience path: Visit the HuggingFace dataset page OpenAssistant/oasst2 to directly download the dataset (Parquet format) without registration. Load using the HuggingFace Datasets library:

from datasets import load_dataset

dataset = load_dataset("OpenAssistant/oasst2", split="train")
# The data set contains fields such as message_id, parent_id, role, text, lang, rank, etc.

Model inference example: Get the OpenAssistant/oasst-sft model on HuggingFace and use the Transformers library to load inference. It should be noted that the OA model is based on the earlier LLaMA/Pythia architecture, and the inference speed and quality are different from the current mainstream models. It is recommended to use it with an understanding of its limitations.

Self-hosted development with context: The original project provides Docker Compose configuration, which can start the complete stack (front-end + back-end + database) locally. The Docker command is docker compose --profile ci up --build --attach-dependencies, and access http://localhost:3000 after startup. However, the OA project has ended, the code is no longer maintained, and production bound deployment is not recommended.

Product Pricing for Open Assistant

OA is a purely open source community project and does not have a commercial pricing system. All assets (data, code, model weights) are open under the Apache-2.0 license, with no registration restrictions, no paywalls, and no Freemium tiering.

C-side/Individual: Zero cost to use. Datasets and model weights are available for direct download, and the web chat interface (archived) is free. Individual contributors do not need to pay during the annotation period, but they no longer receive direct financial returns.

Developers/API: No official API service. Developers need to obtain the model from HuggingFace and deploy it in their own environment. Inference cost depends on hardware configuration - an A100-40G can run OA 1.0 models, but with limited throughput. If you need production-level inference services, it is recommended to use subsequent community-optimized versions or business models.

Enterprise/Institution: Free for use under the Apache-2.0 License. Enterprises can directly use OA data sets for model training within the scope of the license. But OA does not provide SLAs, technical support, security patches, or GDPR compliance commitments. Enterprises should conduct a legal review before using it to confirm the compatibility of the Apache-2.0 license with the business scenario.

Application scenarios of Open Assistant

The value release of OA is concentrated in scenarios that "require open dialogue data" rather than "require the best dialogue experience".

• 开源对话模型的训练数据源：oasst2 数据集被广泛用作对话模型的多语言训练数据，特别是非英语语言的对话场景。 oasst2 is probably the largest public conversation training dataset for low-resource languages ​​(e.g., German, French, Spanish). 落地提示：oasst2 的标注一致性因语言而异——英语数据的一致性最高，小众语言的标注质量差异较大，使用前建议按语言分组做质量过滤。

• Education and reference for the RLHF process: The OA project fully implements InstructGPT's three-stage pipeline (SFT → Preference Sampling → RLHF), and its code base, data set and model weights can be used as a complete reference for RLHF teaching and practice. Implementation Tips: The technical implementation reference value of OA is higher than the product usage value - it is recommended that AI learners pay more attention to the design of its data annotation process rather than directly using its model.

• Multilingual NLP Research and Benchmarking: oasst2’s multilingual coverage makes it a benchmark data source for evaluating conversational models’ cross-language capabilities. Researchers can use the oasst2 test set to evaluate the performance of their models on multilingual dialogue. Implementation Tip: The labeling time of the data set (2023) is earlier than the rapid development period of most large models. When used as a benchmark, the time window of the data needs to be noted.

• Reference Architecture for Data Privacy Protection: OA open source self-hosted data model can provide a reference for "how to build a data collection process that does not rely on closed source APIs" for organizations and institutions that require data sovereignty. Implementation Tips: The architecture of OA is early and unmaintained. It is more efficient to refer to its design concept rather than code implementation.

Applicable groups of Open Assistant

The positioning of OA determines that its core users are AI researchers and model trainers, rather than ordinary users seeking the best chat experience.

• AI researchers and NLP students: Understand the entire process of "from crowdsourcing data to conversation models" through OA's data sets and code libraries. Unfit boundary: If the research focus is on the latest architecture after 2025 (MoE, sparse attention, long context), the technology stack of OA is too old and has limited reference value.

• Open Source Model Trainer: Use the oasst2 data set as multi-language dialogue training data, or perform instruction fine-tuning and preference alignment based on the OA model. Misfit Boundary: If the training goal is the best benchmark performance, the performance ceiling of the OA model is low, and it is recommended to only use its data set instead of the model weights.

• Low Resource Language NLP Developer: OA's multi-language data coverage is an important source of non-English conversation data, especially suitable for teams that need conversation data in German, French, Spanish, Japanese and other languages. Not suitable for boundaries: The data coverage quality of OA for Chinese conversations is not as good as that of English and German. It is recommended to use Chinese native data sets (such as BELLE, Firefly) for Chinese tasks.

• AI Education and Training Institution: The engineering implementation of OA (front-end and back-end, data model, training pipeline) is a complete teaching case for "data engineering" and "RLHF practice" in the AI ​​course. Not suitable for boundaries: OA's code base is based on the technology stack of 2023, and some dependencies and APIs are outdated. Please pay attention to the version compatibility instructions when teaching.

Summary and Outlook

Open Assistant is an important node project in the field of open source AI dialogue - it used community power to deliver the largest multilingual dialogue data set and complete data pipeline in 2022-2023, when ChatGPT triggered the conversational AI craze. The project completed the project from scratch to data to model in about 12 months of activity, proving the hypothesis that "non-commercial organizations can also produce usable conversation training data."

Core Contribution: The oasst2 dataset is OA’s most enduring asset and is still widely used as training data for multilingual dialogue models. The project's data collection and annotation methodology influenced multiple subsequent crowdsourced AI data projects (Dolly, ShareGPT's data process design).

Current limitations: The project has officially ended in October 2023, and the code is no longer maintained. Model performance lags 2-3 generations behind the current mainstream level. The quality consistency of data sets varies by language and annotator level. The entire OA technology stack (Python 3.9, Next.js 13, early versions of PyTorch) is partially outdated, and direct reuse requires adaptation.

Comparison with similar projects:

Dimensions	Open Assistant	Dolly 2.0	ShareGPT	Alpaca
Data source	Crowdsourced volunteers	Internal to Databricks	User uploads	Self-Instruct
Data License	Apache-2.0	CC BY-SA 3.0	Non-Commercial License	CC BY-NC 4.0
Multi-language coverage	Dozens of languages	English only	User uploaded languages	English only
Data structure	Tree conversation	Question and answer pairs	Dialogue	Command-reply
Project Status	Completed	Active Maintenance	Active Operations	Archived
RLHF data	Contains preference annotations	None	None	None
Model License	Apache-2.0	CC BY-SA 3.0	Non-Commercial	Non-Commercial

Follow-up observation points: Ecological differentiation after the end of OA - data enhancement, model fine-tuning and deployment practices based on oasst2 will continue to evolve in the community; whether community-derived projects (such as OpenAssistantGPT) can make more engineered products based on OA data; as LLM data requirements evolve, whether similar crowdsourcing data patterns will reappear in the new technology cycle.

Procurement and Adoption Risk Assessment: OA is not suitable as a drop-in replacement for production-grade AI assistants—model performance, secure alignment, and update support are not sufficient for production requirements. It is recommended to position OA as a "research and learning resource" rather than a "deployment target". For enterprises that require high-precision dialogue models, it is recommended to use the OA data set as one of the training data sources and fine-tune with the current mainstream base models (such as LLaMA 3, DeepSeek, Qwen) instead of directly using the model weights released by OA. Academic researchers can use the oasst2 dataset (Apache-2.0 license) with confidence, but should indicate the time window and annotation method of the data at the time of publication. Multilingual data from OA is an important supplementary data source for dialogue system development in low-resource languages, but is recommended to be mixed with the latest data to close domain and time domain gaps.

Related tools: DeepSeek, ChatGPT

Version Info

• OA 2.0 ：There is no official precise date yet; based on the improved basic model, enhanced multi-language support. （~2025-12）
• OA 1.0 ：No official precise date yet; first public version, fine-tuned based on Pythia and LLaMA models. （~2024-06）