--- title: "chainlit vs databerry" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/chainlit-chainlit-vs-gmpetrov-databerry" tools: ["chainlit-chainlit", "gmpetrov-databerry"] --- # chainlit vs databerry *GraphCanon updated Jul 12, 2026* ## Verdict Pick chainlit when tags unique to chainlit: langchain, openai-chatgpt, python, ui; pick databerry when tags unique to databerry: ai, aichatbot, chatbot, chatbots. [chainlit](https://docs.chainlit.io) reports 12k GitHub stars, 1.7k forks, and 126 open issues, last pushed Jun 11, 2026. [databerry](https://chaindesk.ai) has 3.0k stars, 422 forks, and 166 open issues, last pushed Jun 17, 2024. Figures are from public GitHub metadata via [chainlit's repository](https://github.com/Chainlit/chainlit) and [databerry's repository](https://github.com/gmpetrov/databerry). | | [chainlit](/tools/chainlit-chainlit.md) | [databerry](/tools/gmpetrov-databerry.md) | | --- | --- | --- | | Tagline | Build Conversational AI in minutes ⚡️ | The no-code platform for building custom LLM Agents | | Stars | 12,293 | 2,960 | | Forks | 1,724 | 422 | | Open issues | 126 | 166 | | Language | Python | - | | Adopt for | Chainlit is a Python-based tool designed to streamline the development process of conversational AI applications, allowing developers to quickly build and interact with these apps. | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | AI Agents, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [chainlit](/tools/chainlit-chainlit.md) | [databerry](/tools/gmpetrov-databerry.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 29d | 753d | | Open issues (now) | 126 | 166 | | Owner type | Organization | User | | Security scan | No criticals | No lockfile | | Full report | [trust report](/tools/chainlit-chainlit/trust.md) | [trust report](/tools/gmpetrov-databerry/trust.md) | ## Decision facts: chainlit - **Adopt for:** Chainlit is a Python-based tool designed to streamline the development process of conversational AI applications, allowing developers to quickly build and interact with these apps. ## Choose when ### Choose chainlit if… - Tags unique to chainlit: langchain, openai-chatgpt, python, ui. - - When you want to develop conversational AI applications rapidly using familiar Python syntax. - More GitHub stars (12k vs 3.0k) - visibility, not fit. ### Choose databerry if… - Tags unique to databerry: ai, aichatbot, chatbot, chatbots. ## When NOT to use chainlit - - Avoid if your development team is not comfortable with Python as Chainlit relies heavily on its ecosystem for rapid conversational AI development. - - Not suitable if you require customization in low-level components, as it abstracts a lot of these away to provide quick builds. ## When NOT to use databerry - Last GitHub push was 755 days ago (dormant maintenance, Jun 17, 2024). Validate activity before betting a new project on databerry. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between chainlit and databerry? chainlit: Build Conversational AI in minutes ⚡️. databerry: The no-code platform for building custom LLM Agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose chainlit over databerry? Choose chainlit over databerry when Tags unique to chainlit: langchain, openai-chatgpt, python, ui; - When you want to develop conversational AI applications rapidly using familiar Python syntax; More GitHub stars (12k vs 3.0k) - visibility, not fit. ### When should I choose databerry over chainlit? Choose databerry over chainlit when Tags unique to databerry: ai, aichatbot, chatbot, chatbots. ### When should I avoid chainlit? - Avoid if your development team is not comfortable with Python as Chainlit relies heavily on its ecosystem for rapid conversational AI development. - Not suitable if you require customization in low-level components, as it abstracts a lot of these away to provide quick builds. ### When should I avoid databerry? Last GitHub push was 755 days ago (dormant maintenance, Jun 17, 2024). Validate activity before betting a new project on databerry. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is chainlit or databerry more popular on GitHub? chainlit has more GitHub stars (12,293 vs 2,960). Stars measure visibility, not whether either tool fits your constraints. ### Are chainlit and databerry open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to chainlit or databerry? GraphCanon lists graph-backed alternatives at [chainlit alternatives](/tools/chainlit-chainlit/alternatives) and [databerry alternatives](/tools/gmpetrov-databerry/alternatives) ([chainlit markdown twin](/tools/chainlit-chainlit/alternatives.md), [databerry markdown twin](/tools/gmpetrov-databerry/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/chainlit-chainlit-vs-gmpetrov-databerry.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, chainlit or databerry? chainlit: Active. databerry: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for chainlit and databerry? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [chainlit trust report](/tools/chainlit-chainlit/trust); [databerry trust report](/tools/gmpetrov-databerry/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=chainlit-chainlit`](/api/graphcanon/graph?tool=chainlit-chainlit) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_