--- title: "agentops vs oss-llmops-stack" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agentops-ai-agentops-vs-langfuse-oss-llmops-stack" tools: ["agentops-ai-agentops", "langfuse-oss-llmops-stack"] --- # agentops vs oss-llmops-stack *GraphCanon updated Aug 14, 2026* ## Verdict Pick agentops if agentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage; pick oss-llmops-stack if the OSS LLMOps Stack is designed for managing and unifying LLM APIs with LiteLLM, and providing detailed observability through Langfuse. [agentops](https://agentops.ai) reports 5.8k GitHub stars, 612 forks, and 176 open issues, last pushed Jun 25, 2026. [oss-llmops-stack](https://oss-llmops-stack.com) has 142 stars, 7 forks, and 1 open issues, last pushed Jul 28, 2026. Figures are from public GitHub metadata via [agentops's repository](https://github.com/AgentOps-AI/agentops) and [oss-llmops-stack's repository](https://github.com/langfuse/oss-llmops-stack). | | [agentops](/tools/agentops-ai-agentops.md) | [oss-llmops-stack](/tools/langfuse-oss-llmops-stack.md) | | --- | --- | --- | | Tagline | Python SDK for AI agent monitoring and LLM cost tracking | Modular open source LLMOps stack for LLM API unification, observability and prompt management | | Stars | 5,771 | 142 | | Forks | 612 | 7 | | Open issues | 176 | 1 | | Language | Python | - | | Adopt for | AgentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage. | The OSS LLMOps Stack is designed for managing and unifying LLM APIs with LiteLLM, and providing detailed observability through Langfuse. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents, Evaluation & Observability | Evaluation & Observability, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentops](/tools/agentops-ai-agentops.md) | [oss-llmops-stack](/tools/langfuse-oss-llmops-stack.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 49d | 0d | | Open issues (now) | 176 | 1 | | Full report | [trust report](/tools/agentops-ai-agentops/trust.md) | [trust report](/tools/langfuse-oss-llmops-stack/trust.md) | ## Decision facts: agentops - **Adopt for:** AgentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage. ## Decision facts: oss-llmops-stack - **Requirements:** Ensure your environment supports both LiteLLM and Langfuse functionalities for seamless operation of the OSS LLMOps Stack.; Consider server capacity to handle the additional load introduced by using this stack for API unification and observability services. - **Adopt for:** The OSS LLMOps Stack is designed for managing and unifying LLM APIs with LiteLLM, and providing detailed observability through Langfuse. ## Choose when ### Choose agentops if… - Tags unique to agentops: ai-agents, benchmarking, cost-tracking. - Also covers AI Agents. - Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK ### Choose oss-llmops-stack if… - Requirements: Ensure your environment supports both LiteLLM and Langfuse functionalities for seamless operation of the OSS LLMOps Stack.; Consider server capacity to handle the additional load introduced by using this stack for API unification and observability services.. - Tags unique to oss-llmops-stack: ai-gateway, llm-evaluation, open-source, prompt management. - Also covers Inference & Serving. - When you need to unify Multiple Large Language Model (LLM) APIs using LiteLLM's API mediation capabilities for efficient routing, cost control, and high-availability support. ## When NOT to use agentops - If specific integration support is needed for frameworks not listed including Autogen AG2 CamelAI - In case self-hosting of components is impractical due to resource constraints ## When NOT to use oss-llmops-stack - If your operational requirements are simple and you do not need comprehensive observability metrics or advanced LLM API unification capabilities provided by the stack. - In scenarios where you prefer a proprietary software solution over an open-source tool for security, support, or compliance reasons. ## Common questions ### What is the difference between agentops and oss-llmops-stack? agentops: Python SDK for AI agent monitoring and LLM cost tracking. oss-llmops-stack: Modular open source LLMOps stack for LLM API unification, observability and prompt management. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentops over oss-llmops-stack? Choose agentops over oss-llmops-stack when Tags unique to agentops: ai-agents, benchmarking, cost-tracking; Also covers AI Agents; Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK. ### When should I choose oss-llmops-stack over agentops? Choose oss-llmops-stack over agentops when Requirements: Ensure your environment supports both LiteLLM and Langfuse functionalities for seamless operation of the OSS LLMOps Stack.; Consider server capacity to handle the additional load introduced by using this stack for API unification and observability services.; Tags unique to oss-llmops-stack: ai-gateway, llm-evaluation, open-source, prompt management; Also covers Inference & Serving; When you need to unify Multiple Large Language Model (LLM) APIs using LiteLLM's API mediation capabilities for efficient routing, cost control, and high-availability support. ### When should I avoid agentops? If specific integration support is needed for frameworks not listed including Autogen AG2 CamelAI In case self-hosting of components is impractical due to resource constraints ### When should I avoid oss-llmops-stack? If your operational requirements are simple and you do not need comprehensive observability metrics or advanced LLM API unification capabilities provided by the stack. In scenarios where you prefer a proprietary software solution over an open-source tool for security, support, or compliance reasons. ### Is agentops or oss-llmops-stack more popular on GitHub? agentops has more GitHub stars (5,771 vs 142). Stars measure visibility, not whether either tool fits your constraints. ### Are agentops and oss-llmops-stack open source? Yes - both are open-source projects on GitHub (agentops: MIT, oss-llmops-stack: MIT). ### Where can I find alternatives to agentops or oss-llmops-stack? GraphCanon lists graph-backed alternatives at [agentops alternatives](/tools/agentops-ai-agentops/alternatives) and [oss-llmops-stack alternatives](/tools/langfuse-oss-llmops-stack/alternatives) ([agentops markdown twin](/tools/agentops-ai-agentops/alternatives.md), [oss-llmops-stack markdown twin](/tools/langfuse-oss-llmops-stack/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-langfuse-oss-llmops-stack.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentops or oss-llmops-stack? agentops: Steady. oss-llmops-stack: 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 oss-llmops-stack? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentops trust report](/tools/agentops-ai-agentops/trust); [oss-llmops-stack trust report](/tools/langfuse-oss-llmops-stack/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/_