--- title: "Awesome-LLMs-ICLR-24 vs anything-llm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/azminewasi-awesome-llms-iclr-24-vs-mintplex-labs-anything-llm" tools: ["azminewasi-awesome-llms-iclr-24", "mintplex-labs-anything-llm"] --- # Awesome-LLMs-ICLR-24 vs anything-llm *GraphCanon updated Jul 15, 2026* ## Verdict Pick Awesome-LLMs-ICLR-24 when tags unique to Awesome-LLMs-ICLR-24: large-language-model, large-language-models, large-language-models-and-translation-sy, large-language-models-for-graph-learning; pick anything-llm when tags unique to anything-llm: agent-computer, agent-harness, agentic-ai, local-ai. [Awesome-LLMs-ICLR-24](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) reports 72 GitHub stars, 5 forks, and 0 open issues, last pushed Apr 4, 2024. [anything-llm](https://anythingllm.com) has 63k stars, 6.9k forks, and 320 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [Awesome-LLMs-ICLR-24's repository](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) and [anything-llm's repository](https://github.com/Mintplex-Labs/anything-llm). | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [anything-llm](/tools/mintplex-labs-anything-llm.md) | | --- | --- | --- | | Tagline | It is a comprehensive resource hub compiling all LLM papers accepted at the International Conference on Learning Representations (ICLR) in 2024. | Self-hosted agent experience with deployment scripts for multiple environments | | Stars | 72 | 63,100 | | Forks | 5 | 6,907 | | Open issues | 0 | 320 | | Language | - | JavaScript | | Adopt for | - | Self-hosted AI agent experience with robust deployment scripts across multiple environments. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents, LLM Frameworks, Vector Databases | AI Agents, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [anything-llm](/tools/mintplex-labs-anything-llm.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 831d | 0d | | Open issues (now) | 0 | 320 | | Owner type | User | Organization | | Full report | [trust report](/tools/azminewasi-awesome-llms-iclr-24/trust.md) | [trust report](/tools/mintplex-labs-anything-llm/trust.md) | ## Decision facts: anything-llm - **Adopt for:** Self-hosted AI agent experience with robust deployment scripts across multiple environments. ## Choose when ### Choose Awesome-LLMs-ICLR-24 if… - Tags unique to Awesome-LLMs-ICLR-24: large-language-model, large-language-models, large-language-models-and-translation-sy, large-language-models-for-graph-learning. - Also covers LLM Frameworks, Vector Databases. - Leaner open-issue backlog (0). ### Choose anything-llm if… - 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 Awesome-LLMs-ICLR-24 - Last GitHub push was 832 days ago (dormant maintenance, Apr 4, 2024). Validate activity before betting a new project on Awesome-LLMs-ICLR-24. - 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. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## 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 Awesome-LLMs-ICLR-24 and anything-llm? Awesome-LLMs-ICLR-24: It is a comprehensive resource hub compiling all LLM papers accepted at the International Conference on Learning Representations (ICLR) in 2024.. 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 Awesome-LLMs-ICLR-24 over anything-llm? Choose Awesome-LLMs-ICLR-24 over anything-llm when Tags unique to Awesome-LLMs-ICLR-24: large-language-model, large-language-models, large-language-models-and-translation-sy, large-language-models-for-graph-learning; Also covers LLM Frameworks, Vector Databases; Leaner open-issue backlog (0). ### When should I choose anything-llm over Awesome-LLMs-ICLR-24? Choose anything-llm over Awesome-LLMs-ICLR-24 when 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 Awesome-LLMs-ICLR-24? Last GitHub push was 832 days ago (dormant maintenance, Apr 4, 2024). Validate activity before betting a new project on Awesome-LLMs-ICLR-24. 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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### 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 Awesome-LLMs-ICLR-24 or anything-llm more popular on GitHub? anything-llm has more GitHub stars (63,100 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMs-ICLR-24 and anything-llm open source? Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, anything-llm: MIT). ### Where can I find alternatives to Awesome-LLMs-ICLR-24 or anything-llm? GraphCanon lists graph-backed alternatives at [Awesome-LLMs-ICLR-24 alternatives](/tools/azminewasi-awesome-llms-iclr-24/alternatives) and [anything-llm alternatives](/tools/mintplex-labs-anything-llm/alternatives) ([Awesome-LLMs-ICLR-24 markdown twin](/tools/azminewasi-awesome-llms-iclr-24/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/azminewasi-awesome-llms-iclr-24-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, Awesome-LLMs-ICLR-24 or anything-llm? Awesome-LLMs-ICLR-24: 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 Awesome-LLMs-ICLR-24 and anything-llm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLMs-ICLR-24 trust report](/tools/azminewasi-awesome-llms-iclr-24/trust); [anything-llm trust report](/tools/mintplex-labs-anything-llm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=azminewasi-awesome-llms-iclr-24`](/api/graphcanon/graph?tool=azminewasi-awesome-llms-iclr-24) - 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/_