superagent-ai/superagent
Superagent
Superagent is an AI safety platform that protects applications from prompt injections, data leaks, and harmful outputs through built-in guardrails. It functions as a prompt injection detection system, data redaction tool, and red team testing tool, automatically removing personally identifiable information and protected health data from AI inputs and outputs while scanning image uploads with vision AI to detect visual prompt injection attacks before processing.
The platform routes every prompt through a sequential pipeline of safety checks including injection detection, data redaction, and content filtering, with safety capabilities loaded as interchangeable plugins that can be composed into custom guardrail configurations. It intercepts all prompts at a network proxy layer before they reach the language model for inspection and filtering, and can filter and redact sensitive data from language model responses in real-time as they stream back to the client. The system also simulates adversarial scenarios against production AI agents to evaluate their security and robustness, and analyzes code repositories to identify and report AI agent-targeted attacks and security vulnerabilities.
Beyond its security core, the platform enables building conversational AI agents that answer questions, generate content, and automate workflows using large language models, with the ability to pull information from third-party APIs and vector stores to enrich responses. It supports querying documents through retrieval-augmented generation, maintains conversation context across turns, and provides a unified interface over multiple vector database backends for document storage and semantic search. All capabilities are exposed through both a REST API and client SDKs for Python, TypeScript, and Swift.
Features
- AI Application Security - Protects AI applications from prompt injections, data leaks, and harmful outputs with built-in safety guards.
- AI Safety Guardrails - Protects applications from prompt injections, data leaks, and harmful outputs with built-in guardrails.
- AI Agents and Assistants - Builds conversational AI agents that answer questions, generate content, and automate workflows using LLMs.
- AI Red Teaming - Simulates adversarial scenarios against production AI agents to evaluate their security and robustness.
- Sensitive Data Redaction - Automatically removes personally identifiable information and protected health data from AI inputs and outputs.
- Prompt Injection Detectors - Detects and blocks prompt injection attacks, jailbreaks, and malicious instructions before they reach the language model.
- Safety Guard Blocks - Blocks malicious prompts including injections, jailbreaks, and data exfiltration attempts with detailed reasoning.
- Middleware-Style Guardrail Pipelines - Routes every prompt through a sequential pipeline of safety checks including injection detection and data redaction.
- Data Redaction Tools - Automatically redacts personally identifiable information and protected health data from AI inputs and outputs.
- Adversarial Red Teaming Toolkits - Simulates adversarial scenarios against production AI agents to evaluate security and robustness.
- Guardrail Plugin Architectures - Loads safety capabilities as interchangeable plugins that compose into custom guardrail configurations.
- Prompt Interception & Modification - Intercepts all prompts at a network proxy layer before they reach the language model for inspection.
- Conversation Memory Managers - Retains conversation context across turns so the assistant can reference earlier exchanges.
- Conversational Session Management - Maintains conversation state across turns using session identifiers that link to stored context.
- Third-Party Knowledge Connections - Pulls information from third-party APIs and vector stores to enrich assistant responses.
- Streaming Content Filters - Filters and redacts sensitive data from language model responses in real-time as they stream back to the client.
- Agent Response Streams - Sends assistant replies to the client incrementally as they are generated for real-time interaction.
- Document Question Answering - Answers questions over uploaded documents by combining vector search with language model generation.
- AI Agent Vulnerability Scanners - Analyzes code repositories to identify and report AI agent-targeted attacks and security vulnerabilities.
- Vector Stores - Converts documents into vector embeddings and stores them in supported vector databases for semantic search.
- Vector Database Abstractions - Provides a unified interface over multiple vector database backends for document storage and semantic search.
- Agent Threat Scanners - Analyzes code repositories to identify and report AI agent-targeted attacks and security vulnerabilities.
- REST API Integrations - Connects applications to AI assistant capabilities through a standard HTTP API for programmatic control.
- SDK Integrations - Builds AI assistants into applications using Python, TypeScript, or Swift client libraries.
- SDK Interfaces - Exposes all assistant capabilities through both a REST API and client SDKs for Python, TypeScript, and Swift.
- Visual Input Scanners - Scans image uploads with vision AI to detect and block visual prompt injection attacks before processing.
- Visual Prompt Injection Detectors - Scans image uploads with vision AI to detect and block visual prompt injection attacks before processing.
- Third-Party API Integrations - Integrates third-party services and data sources into assistant workflows through API connectivity.
