--- title: "Awesome-LLMs-ICLR-24 vs langchain" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/azminewasi-awesome-llms-iclr-24-vs-langchain-ai-langchain" tools: ["azminewasi-awesome-llms-iclr-24", "langchain-ai-langchain"] --- # Awesome-LLMs-ICLR-24 vs langchain *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 langchain when pricing: LangChain itself is open-source and free to use. However, it might rely on paid services or premium models from external platforms like OpenAI.. [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. [langchain](https://docs.langchain.com/langchain/) has 142k stars, 24k forks, and 419 open issues, last pushed Jul 14, 2026. Figures are from public GitHub metadata via [Awesome-LLMs-ICLR-24's repository](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) and [langchain's repository](https://github.com/langchain-ai/langchain). | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [langchain](/tools/langchain-ai-langchain.md) | | --- | --- | --- | | Tagline | It is a comprehensive resource hub compiling all LLM papers accepted at the International Conference on Learning Representations (ICLR) in 2024. | The agent engineering platform. | | Stars | 72 | 141,713 | | Forks | 5 | 23,545 | | Open issues | 0 | 419 | | Language | - | Python | | Adopt for | - | LangChain is an open-source platform designed specifically for building agents and applications that leverage large language models (LLMs). It provides a standard framework to develop interoperable components and connect | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License, allowing free use for both personal and commercial purposes under its stipulated terms. | | Categories | AI Agents, LLM Frameworks, Vector Databases | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [langchain](/tools/langchain-ai-langchain.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 831d | 0d | | Open issues (now) | 0 | 419 | | Owner type | User | Organization | | Full report | [trust report](/tools/azminewasi-awesome-llms-iclr-24/trust.md) | [trust report](/tools/langchain-ai-langchain/trust.md) | ## Decision facts: langchain - **Pricing:** freemium - LangChain itself is open-source and free to use. However, it might rely on paid services or premium models from external platforms like OpenAI. - **Adopt for:** LangChain is an open-source platform designed specifically for building agents and applications that leverage large language models (LLMs). It provides a standard framework to develop interoperable components and connect - **License detail:** MIT License, allowing free use for both personal and commercial purposes under its stipulated terms. ## 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 Vector Databases. - Leaner open-issue backlog (0). ### Choose langchain if… - Pricing: LangChain itself is open-source and free to use. However, it might rely on paid services or premium models from external platforms like OpenAI.. - Tags unique to langchain: agents, ai-agents, anthropic, chatgpt. - * When aiming to build complex AI-powered agents or applications requiring high-level capabilities like planning, subagent interaction, and file system operations. ## 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 langchain - * When working on smaller, less complex projects where full-scale integration with sophisticated components is not necessary as LangChain's extensive features might introduce unnecessary complexity. - * If you are primarily focused on JavaScript or TypeScript development as the primary focus of LangChain is Python. Although there is a JS/TS equivalent (LangChain.js), it may not offer the same depth - * For projects requiring heavy customization at lower levels, where a more granular control over individual components is required rather than working with an integrated framework. ## Common questions ### What is the difference between Awesome-LLMs-ICLR-24 and langchain? 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.. langchain: The agent engineering platform.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLMs-ICLR-24 over langchain? Choose Awesome-LLMs-ICLR-24 over langchain 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 Vector Databases; Leaner open-issue backlog (0). ### When should I choose langchain over Awesome-LLMs-ICLR-24? Choose langchain over Awesome-LLMs-ICLR-24 when Pricing: LangChain itself is open-source and free to use. However, it might rely on paid services or premium models from external platforms like OpenAI.; Tags unique to langchain: agents, ai-agents, anthropic, chatgpt; * When aiming to build complex AI-powered agents or applications requiring high-level capabilities like planning, subagent interaction, and file system operations. ### 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 langchain? * When working on smaller, less complex projects where full-scale integration with sophisticated components is not necessary as LangChain's extensive features might introduce unnecessary complexity. * If you are primarily focused on JavaScript or TypeScript development as the primary focus of LangChain is Python. Although there is a JS/TS equivalent (LangChain.js), it may not offer the same depth * For projects requiring heavy customization at lower levels, where a more granular control over individual components is required rather than working with an integrated framework. ### Is Awesome-LLMs-ICLR-24 or langchain more popular on GitHub? langchain has more GitHub stars (141,713 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMs-ICLR-24 and langchain open source? Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, langchain: MIT). ### Where can I find alternatives to Awesome-LLMs-ICLR-24 or langchain? GraphCanon lists graph-backed alternatives at [Awesome-LLMs-ICLR-24 alternatives](/tools/azminewasi-awesome-llms-iclr-24/alternatives) and [langchain alternatives](/tools/langchain-ai-langchain/alternatives) ([Awesome-LLMs-ICLR-24 markdown twin](/tools/azminewasi-awesome-llms-iclr-24/alternatives.md), [langchain markdown twin](/tools/langchain-ai-langchain/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-langchain-ai-langchain.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 langchain? Awesome-LLMs-ICLR-24: Dormant. langchain: 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 langchain? 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); [langchain trust report](/tools/langchain-ai-langchain/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/_