8 Best Superagent Alternatives in 2026 (Open Source)

Superagent — Superagent protects your AI applications against prompt injections, data leaks, and harmful outputs. Embed safety directly into your app and prove compliance to your customers.. YC-backed AI safety SDK that pivoted from general agent building to focused safety tooling — provides guard, redact, and scan capabilities with open-weight models for self-hosting, filling the gap between building agents and securing them

These 8 open-source tools do the same job. They are ordered by how closely they match Superagent, with live GitHub data so you can see which projects are actively maintained.

1. 1. LLM Guard

   The Security Toolkit for LLM Interactions

   Best for: Enterprise teams deploying LLMs in production needing security guardrails; Organizations with strict data leakage prevention requirements; Applications handling sensitive user data through LLM interfaces

   ⭐ 3.2kSuperagent vs LLM Guard →Full profile →

2. 2. Guardrails

   NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.

   What sets it apart: Only framework offering 5-layer programmable guardrails (input/dialog/retrieval/execution/output) with a dedicated Colang scripting language, backed by NVIDIA

   Best for: Enterprise LLM apps needing safety and compliance guardrails; Chatbots requiring strict topic control; RAG pipelines needing retrieval rail filtering

   ⭐ 7.2kSuperagent vs Guardrails →Full profile →

3. 3. Guardrails AI

   Adding guardrails to large language models.

   What sets it apart: Largest ecosystem of pre-built LLM validators (700+ in Hub) with automatic re-prompting — vs Instructor (structured output only) or NeMo Guardrails (conversational focus)

   Best for: Adding safety guardrails to LLM outputs in production; Enforcing structured output from any LLM; Teams needing PII detection, toxicity filtering, or format validation

   ⭐ 7.5kSuperagent vs Guardrails AI →Full profile →

4. 4. LangKit

   🔍 LangKit: An open-source toolkit for monitoring Large Language Models (LLMs). 📚 Extracts signals from prompts & responses, ensuring safety & security. 🛡️ Features include text quality, relevance m

   What sets it apart: Open-source text metrics toolkit for LLM monitoring with built-in security detection (jailbreaks, prompt injection), quality scoring, and whylogs integration

   Best for: llm-output-monitoring; detecting-prompt-injection; text-quality-observability

   ⭐ 997Superagent vs LangKit →Full profile →

5. 5. agentic-radar

   A security scanner for your LLM agentic workflows

   What sets it apart: The first dedicated security scanner specifically designed for agentic AI workflows, combining static analysis with runtime adversarial testing and automatic prompt hardening — no other tool maps agent vulnerabilities to OWASP AI security frameworks

   Best for: Security teams auditing agentic AI systems before production deployment; DevOps teams integrating AI security scanning into CI/CD pipelines

   ⭐ 1.1kSuperagent vs agentic-radar →Full profile →

6. 6. Promptfoo

   Test your prompts, agents, and RAGs. Red teaming/pentesting/vulnerability scanning for AI. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and

   What sets it apart: Unlike LangSmith (production observability) or Langfuse (logging), promptfoo is the only open-source tool combining eval + red teaming + CI/CD code scanning — now backed by OpenAI while remaining fully MIT-licensed

   Best for: Teams hardening LLM apps against prompt injection and jailbreaks with automated red teaming; Engineering teams adding LLM eval regression tests to CI/CD pipelines

   ⭐ 25.6kSuperagent vs Promptfoo →Full profile →

7. 7. UpTrain

   UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured checks (covering language, code, embedding use-cases), perform ro

   What sets it apart: vs generic eval tools: 20+ preconfigured evaluations with customizable prompts, few-shot examples, and scenario descriptions — all running locally for data privacy with root cause analysis on failures

   Best for: RAG system evaluation and quality assurance; LLM application testing before production deployment; Safety and security testing for prompt injection vulnerabilities

   ⭐ 2.4kSuperagent vs UpTrain →Full profile →

8. 8. langwatch

   The platform for LLM evaluations and AI agent testing

   What sets it apart: Unified platform combining agent simulation, evaluation, observability, and prompt optimization with OpenTelemetry-native design — vs separate tools for tracing (Langfuse), eval (DeepEval), and prompt management

   Best for: Teams wanting eval + observability + prompt management in one tool; Agent simulation testing before production deployment; Organizations needing OpenTelemetry-native LLM observability

   ⭐ 4.9kSuperagent vs langwatch →Full profile →