agentic-radar vs Langfuse
Side-by-side comparison of two AI agent tools
Short answer
- agentic-radar has had no commit in 10 months; Langfuse is actively maintained (2,013 commits in the last 90 days).
- Langfuse is growing faster: +1,807 GitHub stars in the last 30 days vs +19 for agentic-radar.
- Pick agentic-radar for: a security scanner for your LLM agentic workflows. Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management.
From GitHub data refreshed daily.
agentic-radaropen-source
A security scanner for your LLM agentic workflows
Langfuseopen-source
Open-source LLM engineering platform for observability, evaluation, prompt and dataset management
Metrics
| agentic-radar | Langfuse | |
|---|---|---|
| Stars | 1.1k | 35.3k |
| Star velocity /mo | 19.26315789473684 | 1.8k |
| Commits (90d) | 0 | 2.0k |
| Releases (6m) | 0 | 10 |
| Downloads (30d, npm + PyPI) | 5.3K | 22.4M |
| Overall score | 0.20717140935755865 | 0.8971312686464765 |
Pros
- +Specialized focus on LLM agentic workflow security vulnerabilities that traditional scanners miss
- +Includes built-in visualization tools for clear security assessment reporting and analysis
- +Integrates with popular frameworks like CrewAI and provides easy PyPI installation
- +Open source with MIT license allowing full customization and transparency, plus active community support
- +Comprehensive feature set combining observability, prompt management, evaluations, and datasets in one platform
- +Extensive integrations with major LLM frameworks and tools including OpenTelemetry, LangChain, and OpenAI SDK
Cons
- -Appears to be a relatively new tool with limited documentation visibility from the provided materials
- -May require specialized knowledge of agentic systems to effectively interpret and act on scan results
- -May require significant setup and configuration for self-hosted deployments
- -Could be overwhelming for simple use cases that only need basic LLM monitoring
- -Self-hosting requires technical expertise and infrastructure resources
Use Cases
- •Security assessment of autonomous AI agent systems before production deployment
- •Compliance auditing for organizations using LLM-powered workflows in regulated industries
- •Continuous security monitoring of agentic systems to detect emerging vulnerabilities
- •Production LLM application monitoring to track performance, costs, and identify issues in real-time
- •Prompt engineering and management for teams collaborating on optimizing model prompts and tracking versions
- •LLM evaluation and testing to measure model performance across different datasets and use cases
FAQ
- Which is more popular, agentic-radar or Langfuse?
- Langfuse has more GitHub stars (35,329 vs 1,057).
- Which is more actively developed, agentic-radar or Langfuse?
- Langfuse had more commits in the last 90 days (2,013 vs 0).
- Should I use agentic-radar or Langfuse?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.