homanp/superagent
Superagent
Superagent is a framework for AI assistant orchestration and agent security. It provides the tools to build intelligent assistants that integrate external APIs and maintain conversation memory to automate complex tasks.
The project focuses on AI agent security through adversarial testing, red teaming, and the detection of prompt injections and malicious tool calls. It includes automated vulnerability patching, which scans codebases and configurations for security flaws and generates pull requests with fixes.
The platform supports retrieval augmented generation by connecting language models to private documents and vector stores. Additional capabilities include sensitive data redaction for removing personally identifiable information and a provider-agnostic abstraction layer to switch between different large language model providers.
Integration is available via a REST API and language-specific SDKs.
Features
- AI Assistants - Creates intelligent AI assistants that integrate LLMs and external tools to automate complex tasks.
- AI Agent Security - Provides a comprehensive framework for securing AI agents against prompt injections and malicious tool calls.
- Agent Memory Management - Manages the storage and retrieval of context and interaction history for AI agents.
- Agent Input Defenses - Protects autonomous agents from prompt injections and malicious user instructions.
- Context Memory Management - Implements systems for maintaining application state and interaction history within LLM context windows.
- Conversation Memory Stores - Persists and retrieves interaction history to maintain context in agentic workflows.
- Input Filters - Intercepts incoming requests to scan for and block prompt injections and malicious instructions.
- Model Provider Configurations - Provides a standardized interface to configure and switch between multiple LLM providers.
- Model Red-Teaming - Provides automated adversarial testing and vulnerability scanning specifically designed to detect safety failures in AI models.
- Semantic Vector Search - Performs similarity searches against document stores using vector embeddings for context-aware generation.
- Adversarial Attacks - Simulates targeted attack scenarios against deployed agents to discover security vulnerabilities and exploit paths.
- AI Agent Vulnerability Scanners - Analyzes repositories specifically for attack vectors targeting AI agents.
- Agent Interaction Memory Stores - Maintains stateful conversation memory to preserve context across multiple user interactions.
- Retrieval Augmentation - Implements retrieval augmented generation to enable question-answering over private documents.
- AI Configuration Patching - Scans codebases and AI configurations for security flaws and automatically generates pull requests with fixes.
- Prompt Injection Filters - Detects and blocks prompt injections and unsafe tool calls to prevent agent misuse.
- Adversarial Red Teaming Toolkits - Simulates targeted adversarial attacks to identify security weaknesses in production agents.
- Model Provider Integrations - Provides unified interfaces for connecting and configuring multiple language model providers.
- Conversational Document Querying - Provides a natural language interface for querying indexed documents with citations.
- Third-Party Knowledge Connections - Connects AI agents to third-party APIs and vector stores for enriched knowledge retrieval.
- Model Provider Abstractions - Provides interfaces that normalize API interactions across multiple AI service providers for easy switching.
- Provider-Agnostic Model Interfaces - Standardizes inputs and outputs across multiple LLM providers through a provider-agnostic abstraction layer.
- RAG Implementations - Implements retrieval augmented generation by connecting language models to private documents and vector stores.
- Document Question Answering - Enables conversational interaction and question-answering over uploaded documents.
- Sensitive Data Redaction - Automatically redacts PII, PHI, and secrets from text to prevent data leaks.
- Repository Content Scanning - Scans repository content to identify vulnerabilities targeting AI agent configurations.
- Build Pipeline Scanning - Scans build pipelines for vulnerabilities in AI agents before deployment.
- CI/CD Pipeline Integrations - Integrates security scanning and automated patching directly into CI/CD pipelines.
- Security Scanning Integrations - Embeds vulnerability scanning into CI/CD pipelines to automatically generate security patches as pull requests.
- Vulnerability Fix PR Generators - Automatically generates pull requests with proposed security fixes in response to discovered vulnerabilities.
- External API Data Fetching - Fetches real-time data and triggers actions by calling third-party service endpoints during conversations.
- Security Finding Management - Implements a system to filter vulnerability reports and manage security findings by surfacing real exploit paths.
- Automated Triage - Automatically filters security reports to surface real exploit paths and suppress noise to reduce manual effort.
- Automatic Redaction - Automatically detects and removes sensitive information and personally identifiable information from data streams before transmission.
- Continuous Repository Scanners - Continuously scans repositories and configurations during CI/CD to identify vulnerabilities and generate automated fixes.
- Automated Code Fixers - Automatically corrects identified code quality and security issues in AI agents.
- Agent Frameworks - Platform for deploying agents to production environments.
- Autonomous Agent Frameworks - Simplifies the configuration and deployment of agents without coding.
- Personal AI Assistants - Automates personal workflows and task decomposition.
