--- title: "LaVague vs anything-llm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lavague-ai-lavague-vs-mintplex-labs-anything-llm" tools: ["lavague-ai-lavague", "mintplex-labs-anything-llm"] --- # LaVague vs anything-llm *GraphCanon updated Aug 18, 2026* ## Verdict Pick LaVague if large Action Model (LaVague) framework supports developing AI web agents using customizable Large Language Models (LLMs), offering tracking of token usage for cost estimation purposes; pick anything-llm if self-hosted AI agent experience with robust deployment scripts across multiple environments. [LaVague](https://docs.lavague.ai/en/latest/) reports 6.4k GitHub stars, 573 forks, and 104 open issues, last pushed Jan 21, 2025. [anything-llm](https://anythingllm.com) has 65k stars, 7.1k forks, and 319 open issues, last pushed Aug 13, 2026. Figures are from public GitHub metadata via [LaVague's repository](https://github.com/lavague-ai/LaVague) and [anything-llm's repository](https://github.com/Mintplex-Labs/anything-llm). | | [LaVague](/tools/lavague-ai-lavague.md) | [anything-llm](/tools/mintplex-labs-anything-llm.md) | | --- | --- | --- | | Tagline | Large Action Model framework to develop AI Web Agents | Self-hosted agent experience with deployment scripts for multiple environments | | Stars | 6,386 | 64,716 | | Forks | 573 | 7,132 | | Open issues | 104 | 319 | | Language | Python | JavaScript | | Adopt for | Large Action Model (LaVague) framework supports developing AI web agents using customizable Large Language Models (LLMs), offering tracking of token usage for cost estimation purposes. | Self-hosted AI agent experience with robust deployment scripts across multiple environments. | | Persona | - | - | | Runtime | - | - | | License | LaVague's license (Apache-2.0) allows free use in both open and closed-source applications, provided that copyright notices are preserved. | MIT | | Categories | AI Agents | AI Agents, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LaVague](/tools/lavague-ai-lavague.md) | [anything-llm](/tools/mintplex-labs-anything-llm.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 574d | 1d | | Open issues (now) | 104 | 319 | | Stars delta | +6 (30d) | +1.4k (30d) | | Open issues delta | 0 (30d) | +2 (30d) | | Full report | [trust report](/tools/lavague-ai-lavague/trust.md) | [trust report](/tools/mintplex-labs-anything-llm/trust.md) | ## Decision facts: LaVague - **Pricing:** freemium - Free to use with customizable LLMs; actual costs depend on the specific LLM used and operational complexity. - **Requirements:** Min 2 GB RAM; Requires Python for development. - **Adopt for:** Large Action Model (LaVague) framework supports developing AI web agents using customizable Large Language Models (LLMs), offering tracking of token usage for cost estimation purposes. - **License detail:** LaVague's license (Apache-2.0) allows free use in both open and closed-source applications, provided that copyright notices are preserved. ## Decision facts: anything-llm - **Adopt for:** Self-hosted AI agent experience with robust deployment scripts across multiple environments. ## Choose when ### Choose LaVague if… - LaVague is primarily Python; anything-llm is JavaScript. - License: LaVague is Apache-2.0, anything-llm is MIT. - Pricing: Free to use with customizable LLMs; actual costs depend on the specific LLM used and operational complexity.. - Requirements: Min 2 GB RAM; Requires Python for development.. - Tags unique to LaVague: ai, browser, large-action-model, oss. - When you need a flexible framework that allows customization of LLMs, particularly if your primary model is OpenAI's `gpt4-o` or similar models. ### Choose anything-llm if… - anything-llm is primarily JavaScript; LaVague is Python. - License: anything-llm is MIT, LaVague is Apache-2.0. - Tags unique to anything-llm: agent-computer, agent-harness, agentic-ai, local-ai. - Also covers Inference & Serving. - When you need flexibility in deploying your AI agents on various cloud platforms like AWS, GCP, Digital Ocean, and more. ## When NOT to use LaVague - Avoid using LaVague if you are working on projects that do not involve AI web agent development, as the framework is specifically designed around this use-case. - Do not opt for LaVague if you require a non-Python environment for your development since the framework is Python-based. ## 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. ## Common questions ### What is the difference between LaVague and anything-llm? LaVague: Large Action Model framework to develop AI Web Agents. anything-llm: Self-hosted agent experience with deployment scripts for multiple environments. See the comparison table for live GitHub stats and shared categories. ### When should I choose LaVague over anything-llm? Choose LaVague over anything-llm when LaVague is primarily Python; anything-llm is JavaScript; License: LaVague is Apache-2.0, anything-llm is MIT; Pricing: Free to use with customizable LLMs; actual costs depend on the specific LLM used and operational complexity.; Requirements: Min 2 GB RAM; Requires Python for development.; Tags unique to LaVague: ai, browser, large-action-model, oss; When you need a flexible framework that allows customization of LLMs, particularly if your primary model is OpenAI's `gpt4-o` or similar models. ### When should I choose anything-llm over LaVague? Choose anything-llm over LaVague when anything-llm is primarily JavaScript; LaVague is Python; License: anything-llm is MIT, LaVague is Apache-2.0; Tags unique to anything-llm: agent-computer, agent-harness, agentic-ai, local-ai; Also covers Inference & Serving; When you need flexibility in deploying your AI agents on various cloud platforms like AWS, GCP, Digital Ocean, and more. ### When should I avoid LaVague? Avoid using LaVague if you are working on projects that do not involve AI web agent development, as the framework is specifically designed around this use-case. Do not opt for LaVague if you require a non-Python environment for your development since the framework is Python-based. ### 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. ### Is LaVague or anything-llm more popular on GitHub? anything-llm has more GitHub stars (64,716 vs 6,386). Stars measure visibility, not whether either tool fits your constraints. ### Are LaVague and anything-llm open source? Yes - both are open-source projects on GitHub (LaVague: Apache-2.0, anything-llm: MIT). ### Where can I find alternatives to LaVague or anything-llm? GraphCanon lists graph-backed alternatives at [LaVague alternatives](/tools/lavague-ai-lavague/alternatives) and [anything-llm alternatives](/tools/mintplex-labs-anything-llm/alternatives) ([LaVague markdown twin](/tools/lavague-ai-lavague/alternatives.md), [anything-llm markdown twin](/tools/mintplex-labs-anything-llm/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/lavague-ai-lavague-vs-mintplex-labs-anything-llm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LaVague or anything-llm? LaVague: Dormant. anything-llm: 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 LaVague and anything-llm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LaVague trust report](/tools/lavague-ai-lavague/trust); [anything-llm trust report](/tools/mintplex-labs-anything-llm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=lavague-ai-lavague`](/api/graphcanon/graph?tool=lavague-ai-lavague) - 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/_