anything-llm vs private-gpt

Common questions

What is the difference between anything-llm and private-gpt?

anything-llm: Self-hosted AI agent experience. 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; Pricing: Free to use under the MIT License, but users may incur costs based on their chosen deployment method and infrastructure.; Requirements: Min 4 GB RAM; Requires Docker; Requires a local environment setup or a cloud service account for deployment.; Tags unique to anything-llm: agent-computer, agent-harness, agent-orchestration, agentic-ai; Also covers AI Agents, Developer Tools; When you need a local-first AI agent experience that you can fully control and customize.

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?

If you require a cloud-based solution with minimal setup and maintenance, as anything-llm requires self-hosting and local management. When you need a platform that does not offer extensive deployment flexibility, as anything-llm provides multiple deployment options which might be overwhelming for users seeking simplicity.

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 (66,167 vs 57,497). 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 and private-gpt alternatives (anything-llm markdown twin, private-gpt markdown twin), 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 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; private-gpt trust report.