--- title: "agentops vs langchain" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agentops-ai-agentops-vs-langchain-ai-langchain" tools: ["agentops-ai-agentops", "langchain-ai-langchain"] --- # agentops vs langchain *GraphCanon updated Jul 15, 2026* ## Verdict Pick agentops when tags unique to agentops: agent, agentops, agents-sdk, ai; 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.. [agentops](https://agentops.ai) reports 5.7k GitHub stars, 608 forks, and 172 open issues, last pushed Jun 25, 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 [agentops's repository](https://github.com/AgentOps-AI/agentops) and [langchain's repository](https://github.com/langchain-ai/langchain). | | [agentops](/tools/agentops-ai-agentops.md) | [langchain](/tools/langchain-ai-langchain.md) | | --- | --- | --- | | Tagline | Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and Ca | The agent engineering platform. | | Stars | 5,710 | 141,713 | | Forks | 608 | 23,545 | | Open issues | 172 | 419 | | Language | Python | 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, Inference & Serving, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentops](/tools/agentops-ai-agentops.md) | [langchain](/tools/langchain-ai-langchain.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 20d | 0d | | Open issues (now) | 172 | 419 | | Full report | [trust report](/tools/agentops-ai-agentops/trust.md) | [trust report](/tools/langchain-ai-langchain/trust.md) | ## Shared compatibility - **Python**: [agentops](/tools/agentops-ai-agentops.md) - Python runtime; [langchain](/tools/langchain-ai-langchain.md) - Python runtime ## 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 agentops if… - Tags unique to agentops: agent, agentops, agents-sdk, ai. - Also covers Inference & Serving. - Leaner open-issue backlog (172). ### 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, chatgpt, deepagents. - * 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 agentops - 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 agentops and langchain? agentops: Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and Ca. langchain: The agent engineering platform.. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentops over langchain? Choose agentops over langchain when Tags unique to agentops: agent, agentops, agents-sdk, ai; Also covers Inference & Serving; Leaner open-issue backlog (172). ### When should I choose langchain over agentops? Choose langchain over agentops 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, chatgpt, deepagents; * 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 agentops? 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 agentops or langchain more popular on GitHub? langchain has more GitHub stars (141,713 vs 5,710). Stars measure visibility, not whether either tool fits your constraints. ### Are agentops and langchain open source? Yes - both are open-source projects on GitHub (agentops: MIT, langchain: MIT). ### Where can I find alternatives to agentops or langchain? GraphCanon lists graph-backed alternatives at [agentops alternatives](/tools/agentops-ai-agentops/alternatives) and [langchain alternatives](/tools/langchain-ai-langchain/alternatives) ([agentops markdown twin](/tools/agentops-ai-agentops/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/agentops-ai-agentops-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, agentops or langchain? agentops: 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 agentops and langchain? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentops trust report](/tools/agentops-ai-agentops/trust); [langchain trust report](/tools/langchain-ai-langchain/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agentops-ai-agentops`](/api/graphcanon/graph?tool=agentops-ai-agentops) - 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/_