--- title: "anything-llm vs private-gpt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mintplex-labs-anything-llm-vs-zylon-ai-private-gpt" tools: ["mintplex-labs-anything-llm", "zylon-ai-private-gpt"] --- # anything-llm vs private-gpt *GraphCanon updated Jul 11, 2026* ## Verdict Pick anything-llm if self-hosted AI agent experience with robust deployment scripts across multiple environments; pick private-gpt if privateGPT provides a comprehensive API layer to build private, on-premise AI applications leveraging local OpenAI-compatible inference servers. It offers features such as RAG, skills, tools, text-to-SQL functionalities,. [anything-llm](https://anythingllm.com) reports 63k GitHub stars, 6.9k forks, and 320 open issues, last pushed Jul 11, 2026. [private-gpt](https://www.zylon.ai/private-gpt) has 57k stars, 7.6k forks, and 5 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [anything-llm's repository](https://github.com/Mintplex-Labs/anything-llm) and [private-gpt's repository](https://github.com/zylon-ai/private-gpt). | | [anything-llm](/tools/mintplex-labs-anything-llm.md) | [private-gpt](/tools/zylon-ai-private-gpt.md) | | --- | --- | --- | | Tagline | Self-hosted agent experience with deployment scripts for multiple environments | Complete API layer for private AI applications on local models | | Stars | 63,100 | 57,329 | | Forks | 6,907 | 7,598 | | Open issues | 320 | 5 | | Language | JavaScript | Python | | Adopt for | Self-hosted AI agent experience with robust deployment scripts across multiple environments. | PrivateGPT provides a comprehensive API layer to build private, on-premise AI applications leveraging local OpenAI-compatible inference servers. It offers features such as RAG, skills, tools, text-to-SQL functionalities, | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | AI Agents, Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [anything-llm](/tools/mintplex-labs-anything-llm.md) | [private-gpt](/tools/zylon-ai-private-gpt.md) | | --- | --- | --- | | Open issues (now) | 320 | 5 | | Full report | [trust report](/tools/mintplex-labs-anything-llm/trust.md) | [trust report](/tools/zylon-ai-private-gpt/trust.md) | ## Decision facts: anything-llm - **Adopt for:** Self-hosted AI agent experience with robust deployment scripts across multiple environments. ## Decision facts: private-gpt - **Requirements:** Min 8 GB RAM; Requires Docker - **Adopt for:** PrivateGPT provides a comprehensive API layer to build private, on-premise AI applications leveraging local OpenAI-compatible inference servers. It offers features such as RAG, skills, tools, text-to-SQL functionalities, ## Choose when ### Choose anything-llm if… - anything-llm is primarily JavaScript; private-gpt is Python. - License: anything-llm is MIT, private-gpt is Apache-2.0. - Tags unique to anything-llm: agent-computer, agent-harness, agentic-ai, llm. - Also covers AI Agents. - When you need flexibility in deploying your AI agents on various cloud platforms like AWS, GCP, Digital Ocean, and more. ### Choose private-gpt if… - private-gpt is primarily Python; anything-llm is JavaScript. - License: private-gpt is Apache-2.0, anything-llm is MIT. - Requirements: Min 8 GB RAM; Requires Docker. - Tags unique to private-gpt: ai, ai-tools, local-models, mcp. - private-gpt ships Docker support for self-hosted deployment. - - You need to deploy and operationalize your own locally-run models without relying on cloud APIs. ## When NOT to use anything-llm - Avoid if you require an agent without additional setup or prefer SaaS solutions over self-managed deployments. - Not suitable for users who are looking for no-code alternatives as setting up AnythingLLM might necessitate some coding knowledge despite offering multiple scripts and methods. ## When NOT to use private-gpt - - You prefer simplicity and ease-of-use over full control; PrivateGPT requires more setup than using direct cloud-based AI services. - - Your project does not involve running models locally but strictly relies on public cloud resources for inference server operations. - - You do not have the technical capability to run an OpenAI-compatible inference server or manage local infrastructure effectively. ## Common questions ### What is the difference between anything-llm and private-gpt? anything-llm: Self-hosted agent experience with deployment scripts for multiple environments. private-gpt: Complete API layer for private AI applications on local models. See the comparison table for live GitHub stats and shared categories. ### When should I choose anything-llm over private-gpt? Choose anything-llm over private-gpt when anything-llm is primarily JavaScript; private-gpt is Python; License: anything-llm is MIT, private-gpt is Apache-2.0; Tags unique to anything-llm: agent-computer, agent-harness, agentic-ai, llm; Also covers AI Agents; When you need flexibility in deploying your AI agents on various cloud platforms like AWS, GCP, Digital Ocean, and more. ### When should I choose private-gpt over anything-llm? Choose private-gpt over anything-llm when private-gpt is primarily Python; anything-llm is JavaScript; License: private-gpt is Apache-2.0, anything-llm is MIT; Requirements: Min 8 GB RAM; Requires Docker; Tags unique to private-gpt: ai, ai-tools, local-models, mcp; private-gpt ships Docker support for self-hosted deployment; - You need to deploy and operationalize your own locally-run models without relying on cloud APIs. ### When should I avoid anything-llm? Avoid if you require an agent without additional setup or prefer SaaS solutions over self-managed deployments. Not suitable for users who are looking for no-code alternatives as setting up AnythingLLM might necessitate some coding knowledge despite offering multiple scripts and methods. ### When should I avoid private-gpt? - You prefer simplicity and ease-of-use over full control; PrivateGPT requires more setup than using direct cloud-based AI services. - Your project does not involve running models locally but strictly relies on public cloud resources for inference server operations. - You do not have the technical capability to run an OpenAI-compatible inference server or manage local infrastructure effectively. ### Is anything-llm or private-gpt more popular on GitHub? anything-llm has more GitHub stars (63,100 vs 57,329). Stars measure visibility, not whether either tool fits your constraints. ### Are anything-llm and private-gpt open source? Yes - both are open-source projects on GitHub (anything-llm: MIT, private-gpt: Apache-2.0). ### Where can I find alternatives to anything-llm or private-gpt? GraphCanon lists graph-backed alternatives at [anything-llm alternatives](/tools/mintplex-labs-anything-llm/alternatives) and [private-gpt alternatives](/tools/zylon-ai-private-gpt/alternatives) ([anything-llm markdown twin](/tools/mintplex-labs-anything-llm/alternatives.md), [private-gpt markdown twin](/tools/zylon-ai-private-gpt/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/mintplex-labs-anything-llm-vs-zylon-ai-private-gpt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, anything-llm or private-gpt? anything-llm: Very active. private-gpt: Very active. 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 anything-llm and private-gpt? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [anything-llm trust report](/tools/mintplex-labs-anything-llm/trust); [private-gpt trust report](/tools/zylon-ai-private-gpt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mintplex-labs-anything-llm`](/api/graphcanon/graph?tool=mintplex-labs-anything-llm) - 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/_