Screencap vs Superagent: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Screencap and Superagent — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Screencap
Proteus Computer Use
Local-first macOS screen recorder that captures, labels, and indexes team workflows so knowledge stays searchable and private.
Key features
- Local-First Capture: Recordings live in ~/.screencap on your Mac and never leave unless you explicitly share them.
- On-Device Task Segmentation: An on-device model breaks long recordings into labeled tasks like payroll runs, expense approvals, or CRM data entry.
- Full-Text Search of Workflows: Every spoken word and on-screen moment is indexed so any past workflow can be surfaced months later by search.
- Privacy-Enforced Recording: Password managers and banking apps are cut before a frame is written; email and chat are masked in real time.
- MCP Context Snapshots: While recording, Screencap queries connected MCP servers to capture the exact Gusto/Attio/Linear/Notion record on screen inside the video.
- Deliberate Sharing With PII Scrubbing: Every shared copy is scrubbed of names, secrets, and PII, and only the recordings you pick ever leave the machine.
- Source-Available Codebase: The full capture engine, encryption, agent, and anonymizer are public on GitHub under PolyForm Noncommercial 1.0.0.
Best for
- Team Onboarding: Assemble ordered collections of real workflow recordings so new teammates learn exactly how work is actually done.
- Institutional Knowledge Capture: Preserve the tacit steps behind payroll runs, reconciliations, and quarterly reports as searchable video.
- Ops Documentation: Replace stale wikis by pointing teammates at labeled task recordings that stay current with the real system.
- Compliance-Sensitive Recording: Capture back-office work in banking, finance, and HR without leaking passwords, account balances, or PII.
- Computer-Use Dataset Contribution: Optionally donate reviewed, scrubbed recordings to a public dataset for training open computer-use models.
Superagent
Superagent Technologies, Inc.
Open-source AI security platform that provides runtime protection for agents, prevents data leaks, and offers hosted compliance trust centers.
Key features
- Runtime Protection: Real-time interception and inspection of agent prompts and tool calls to detect and stop data exfiltration, malicious inputs, or unsafe behavior before actions are executed.
- Prompt Inspection & Validation: Analyze and enforce policies on prompts and inputs, validate tool-call schemas and parameters, and block or modify calls that violate rules or expose sensitive data.
- Tool Call Firewall & Sandboxing: Validate, allow, or deny external tool invocations and run tools in isolated sandboxes (e.g., Vibekit) to contain risk from third-party models and tools.
- Data Redaction & Sensitive Data Protection: Automatic redaction/masking of PII and secret material in transit and in logs, preventing sensitive data from being stored or leaked to external services.
- Hosted Trust Center & Compliance Artifacts: Generate and host audit trails, dashboards, and compliance proofs that demonstrate runtime protections to enterprise buyers and security teams.
- Multi-language SDKs & High-performance Proxies: SDKs for TypeScript and Python plus proxy implementations in Node and Rust for flexible integration and production-grade performance.
- Observability & Auditing: Detailed telemetry, logging, and audit trails of agent decisions and tool usage to support incident investigation, forensics, and regulatory reviews.
- Deployment Tools & Integrations: CLI, Docker configurations, and docs for straightforward deployment into CI/CD pipelines, staging environments, and production agent stacks.
- Prompt inspection and runtime monitoring of agent interactions
- Tool-call validation and enforcement to block malicious or sensitive operations
- Real-time blocking of threats and prevention of data leaks
- Multiple proxy implementations: Node.js and Rust (high-performance)
- SDKs for TypeScript and Python for programmatic control (agent/tool creation and invocation)
- Command-line interface and Docker configurations for deployment
- Sensitive-data redaction and observability baked into sandboxes (vibekit)
- Hosted trust center for compliance evidence and buyer assurance
- Support for multiple LLM providers (OpenAI, Anthropic, etc.)
- Models and additional resources published on Hugging Face and GitHub
Best for
- Preventing data exfiltration from production agents by inspecting prompts and blocking tool calls that attempt to leak secrets or PII.
- Proving enterprise compliance during vendor security reviews by providing hosted trust center dashboards and audit artifacts that show runtime protections.
- Running third-party LLMs and coding agents in isolated sandboxes to safely evaluate capabilities without exposing sensitive corpora or credentials.
- Instrumenting copilots and agent-based workflows to validate tool-call schemas, enforce business policies, and prevent unauthorized actions programmatically.
- Redacting sensitive customer or internal data before logging or sending requests to external APIs to reduce breach and compliance risk.
- Providing a developer-facing security layer (SDKs + proxies) to integrate policy enforcement and observability into existing agent deployments.
- Protecting conversational agents and copilots from exfiltration and malicious tool calls
- Adding runtime enforcement and validation around third-party tool integrations
- Running coding agents in isolated sandboxes with redaction and observability
- Demonstrating vendor and deployment compliance to enterprise buyers via a trust center
- Embedding SDK-driven agent management (create agents, add tools, invoke agents) in applications
- Self-hosting or containerized deployment using Docker and provided proxies