--- title: "agentops vs logfire" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agentops-ai-agentops-vs-pydantic-logfire" tools: ["agentops-ai-agentops", "pydantic-logfire"] --- # agentops vs logfire *GraphCanon updated Sep 20, 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 logfire if logfire provides specific tools for monitoring and evaluating AI systems in production environments, with strong emphasis on log management and traceability. [agentops](https://agentops.ai) reports 5.8k GitHub stars, 625 forks, and 184 open issues, last pushed Jun 25, 2026. [logfire](https://pydantic.dev/logfire/) has 4.5k stars, 284 forks, and 191 open issues, last pushed Sep 10, 2026. Figures are from public GitHub metadata via [agentops's repository](https://github.com/AgentOps-AI/agentops) and [logfire's repository](https://github.com/pydantic/logfire). | | [agentops](/tools/agentops-ai-agentops.md) | [logfire](/tools/pydantic-logfire.md) | | --- | --- | --- | | Tagline | Python SDK for AI agent monitoring and LLM cost tracking | AI observability platform for production LLM and agent systems | | Stars | 5,830 | 4,468 | | Forks | 625 | 284 | | Open issues | 184 | 191 | | Language | Python | Python | | Adopt for | AgentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage. | Logfire provides specific tools for monitoring and evaluating AI systems in production environments, with strong emphasis on log management and traceability. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents, Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentops](/tools/agentops-ai-agentops.md) | [logfire](/tools/pydantic-logfire.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 86d | 0d | | Open issues (now) | 184 | 191 | | Stars delta | +59 (30d) | +52 (30d) | | Open issues delta | +8 (30d) | -68 (30d) | | Full report | [trust report](/tools/agentops-ai-agentops/trust.md) | [trust report](/tools/pydantic-logfire/trust.md) | ## Shared compatibility - **Python**: [agentops](/tools/agentops-ai-agentops.md) - Python runtime; [logfire](/tools/pydantic-logfire.md) - Python runtime ## 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: logfire - **Adopt for:** Logfire provides specific tools for monitoring and evaluating AI systems in production environments, with strong emphasis on log management and traceability. ## 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 logfire if… - Tags unique to logfire: agent-observability, ai, ai-observability, evals. - Use Logfire when your project requires comprehensive observability tailored specifically for large language models (LLM) and agent-based systems. - More recently updated (last pushed Sep 10, 2026). ## 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 logfire - Avoid using Logfire if your application does not involve LLMs or agent systems, as its features are finely tuned for these specific technologies. - Do not use if you prefer tools with broader application across different technology stacks rather than a specialized toolkit focused on Python and related frameworks. ## Common questions ### What is the difference between agentops and logfire? agentops: Python SDK for AI agent monitoring and LLM cost tracking. logfire: AI observability platform for production LLM and agent systems. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentops over logfire? Choose agentops over logfire 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 logfire over agentops? Choose logfire over agentops when Tags unique to logfire: agent-observability, ai, ai-observability, evals; Use Logfire when your project requires comprehensive observability tailored specifically for large language models (LLM) and agent-based systems; More recently updated (last pushed Sep 10, 2026). ### 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 logfire? Avoid using Logfire if your application does not involve LLMs or agent systems, as its features are finely tuned for these specific technologies. Do not use if you prefer tools with broader application across different technology stacks rather than a specialized toolkit focused on Python and related frameworks. ### Is agentops or logfire more popular on GitHub? agentops has more GitHub stars (5,830 vs 4,468). Stars measure visibility, not whether either tool fits your constraints. ### Are agentops and logfire open source? Yes - both are open-source projects on GitHub (agentops: MIT, logfire: MIT). ### Where can I find alternatives to agentops or logfire? GraphCanon lists graph-backed alternatives at [agentops alternatives](/tools/agentops-ai-agentops/alternatives) and [logfire alternatives](/tools/pydantic-logfire/alternatives) ([agentops markdown twin](/tools/agentops-ai-agentops/alternatives.md), [logfire markdown twin](/tools/pydantic-logfire/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-pydantic-logfire.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentops or logfire? agentops: Steady. logfire: 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 logfire? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentops trust report](/tools/agentops-ai-agentops/trust); [logfire trust report](/tools/pydantic-logfire/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/_