chainlit
Chainlit is a Python framework for rapidly building conversational AI applications with a web UI, designed to work with LLMs like OpenAI and integrations such as LangChain. As of May 2025, it transitioned to community maintenance after the original team stepped back, with responsibility now held by designated maintainers.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | Chainlit/chainlit |
| Owner | Chainlit |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 12.3k |
| Forks | 1.7k |
| Open issues | 134 |
| Latest release | 2.11.1 (2026-04-22) |
| Last updated | 2026-06-11 |
| Source | https://github.com/Chainlit/chainlit |
What chainlit is
A Python-based framework providing decorators and async message handling for LLM-powered chat applications, with built-in UI generation and support for tool integration and conversation state management. Architecture separates backend (Python) from frontend, requiring Node and pnpm for development builds.
Get the chainlit source
Clone the repository and explore it locally.
git clone https://github.com/Chainlit/chainlit.gitcd chainlit# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires Python environment and basic async/await patterns; developers must understand LLM API fundamentals and rate limits.
- Frontend rebuild (Node/pnpm) needed for customization; development workflow involves both backend Python and frontend toolchain.
- State management relies on application code; no built-in persistence layer—production deployments must handle session/conversation storage.
- Integration points (OpenAI, Anthropic, LangChain) are external dependencies; verify API stability and cost structure before committing.
- Testing and CI are basic; no apparent mention of load testing, error recovery, or production-hardening patterns in README.
When to avoid it — and what to weigh
- Mature Commercial Product Requiring SLA — Community-maintained as of May 2025 with no upstream vendor warranty. Not suitable if you need contractual support, guaranteed response times, or vendor indemnification.
- Multi-Tenant SaaS at Scale — Designed for internal or single-tenant applications; no built-in multi-tenancy, role-based access control, or audit logging apparent from documentation.
- Strict Compliance & Security Boundaries — No evidence of SOC 2, HIPAA, or other compliance certifications. Unknown security posture regarding data handling, encryption, and vulnerability response process.
- Mobile-First or Native App Requirements — Web-only UI framework; no native mobile app or mobile-optimized interface documented. Not suitable if primary users are mobile.
License & commercial use
Apache License 2.0 (Apache-2.0) is a permissive, OSI-approved open-source license allowing commercial use, modification, and distribution with minimal restrictions. Requires attribution and includes liability disclaimer.
Apache 2.0 permits commercial use without royalty or license fees. However, given community-maintenance status and lack of vendor warranty (as stated in README), assess risk tolerance for production use. Ensure your usage complies with any upstream dependencies' licenses (LangChain, LlamaIndex, etc.).
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
No security audits, certifications, or vulnerability disclosure process mentioned. Data handling (conversation logs, user inputs) is not clearly documented; ensure API keys and sensitive inputs are not logged or exposed in UI. Community maintenance means no guaranteed security response SLA. Assess before handling regulated or sensitive data.
Alternatives to consider
LangChain + Streamlit
Streamlit is more mature, widely used, and has stronger commercial backing; steeper learning curve but more flexible for complex UIs and non-conversational apps.
Gradio
Lighter-weight UI framework, ideal for simple demos and model interfaces; less opinionated about LLM integration, broader use cases beyond chat.
Rasa + Custom Web UI
Industry-standard conversational AI platform with NLU pipelines and dialogue management; higher complexity but more control over conversation logic and training data.
Build on chainlit with DEV.co software developers
Start with Chainlit's quickstart guide and explore the cookbook for production-ready examples. Assess community maintenance and security posture for your use case.
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chainlit FAQ
Can I use Chainlit in production?
What LLM providers are supported?
Do I need to know React or web development?
Is there a way to persist conversations?
Work with a software development agency
Need help beyond evaluating chainlit? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and ai frameworks integrations — and maintain them long-term.
Build Your AI Chat App Today
Start with Chainlit's quickstart guide and explore the cookbook for production-ready examples. Assess community maintenance and security posture for your use case.