--- title: "koog vs langchain" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jetbrains-koog-vs-langchain-ai-langchain" tools: ["jetbrains-koog", "langchain-ai-langchain"] --- # koog vs langchain *GraphCanon updated Jul 15, 2026* ## Verdict Pick koog when koog is primarily Kotlin; langchain is Python; pick langchain when langchain is primarily Python; koog is Kotlin. [koog](https://docs.koog.ai) reports 4.4k GitHub stars, 447 forks, and 162 open issues, last pushed Jul 6, 2026. [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 [koog's repository](https://github.com/JetBrains/koog) and [langchain's repository](https://github.com/langchain-ai/langchain). | | [koog](/tools/jetbrains-koog.md) | [langchain](/tools/langchain-ai-langchain.md) | | --- | --- | --- | | Tagline | Koog is a JVM (Java and Kotlin) framework for building predictable, fault-tolerant and enterprise-ready AI agents across all platforms – from backend services to Android and iOS, JVM, and even in-brow | The agent engineering platform. | | Stars | 4,447 | 141,713 | | Forks | 447 | 23,545 | | Open issues | 162 | 419 | | Language | Kotlin | 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 | Apache-2.0 | MIT License, allowing free use for both personal and commercial purposes under its stipulated terms. | | Categories | AI Agents, Inference & Serving, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [koog](/tools/jetbrains-koog.md) | [langchain](/tools/langchain-ai-langchain.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 9d | 0d | | Open issues (now) | 162 | 419 | | Full report | [trust report](/tools/jetbrains-koog/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 koog if… - koog is primarily Kotlin; langchain is Python. - License: koog is Apache-2.0, langchain is MIT. - Tags unique to koog: agentframework, agentic-ai, ai, ai-agents-framework. - Also covers Inference & Serving. ### Choose langchain if… - langchain is primarily Python; koog is Kotlin. - License: langchain is MIT, koog is Apache-2.0. - 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: ai-agents, chatgpt, deepagents, enterprise. - * 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 koog - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## 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 koog and langchain? koog: Koog is a JVM (Java and Kotlin) framework for building predictable, fault-tolerant and enterprise-ready AI agents across all platforms – from backend services to Android and iOS, JVM, and even in-brow. langchain: The agent engineering platform.. See the comparison table for live GitHub stats and shared categories. ### When should I choose koog over langchain? Choose koog over langchain when koog is primarily Kotlin; langchain is Python; License: koog is Apache-2.0, langchain is MIT; Tags unique to koog: agentframework, agentic-ai, ai, ai-agents-framework; Also covers Inference & Serving. ### When should I choose langchain over koog? Choose langchain over koog when langchain is primarily Python; koog is Kotlin; License: langchain is MIT, koog is Apache-2.0; 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: ai-agents, chatgpt, deepagents, enterprise; * 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 koog? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### 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 koog or langchain more popular on GitHub? langchain has more GitHub stars (141,713 vs 4,447). Stars measure visibility, not whether either tool fits your constraints. ### Are koog and langchain open source? Yes - both are open-source projects on GitHub (koog: Apache-2.0, langchain: MIT). ### Where can I find alternatives to koog or langchain? GraphCanon lists graph-backed alternatives at [koog alternatives](/tools/jetbrains-koog/alternatives) and [langchain alternatives](/tools/langchain-ai-langchain/alternatives) ([koog markdown twin](/tools/jetbrains-koog/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/jetbrains-koog-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, koog or langchain? koog: Active. 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 koog and langchain? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [koog trust report](/tools/jetbrains-koog/trust); [langchain trust report](/tools/langchain-ai-langchain/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jetbrains-koog`](/api/graphcanon/graph?tool=jetbrains-koog) - 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/_