CleanSlop vs Contextberg: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of CleanSlop and Contextberg — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
CleanSlop
CleanSlop
Scans vibe-coded apps for launch-blocking issues like leaked keys and data exposure, and reports what to fix first, right from your coding agent.
Key features
- Agent-Native Scanning: Tell Claude Code, Codex, Cursor or other agents to scan with CleanSlop and fix findings where you build.
- System-Level Code Mapping: Maps routes, background jobs, data and external services so each finding follows a real call path.
- Prioritized Reports: Findings grouped as needs attention, worth a look and minor, worst first.
- Evidence for Every Finding: Shows where each issue lives in your code and why it was flagged.
- Plain-English Fixes: Explains what is wrong, why it matters and what to do about it.
- Copy for LLM: Hands any report to your AI tool so it can apply the fixes.
- GitHub App Integration: Scan only the repositories you select, read-only access.
- Expert Triage Calls: Book a 15-minute call with a CleanSlop engineer to walk through findings.
Best for
- A solo founder checks a Next.js and Supabase app built with an AI coding assistant before launching.
- A developer discovers an exposed AI API key bundled into browser code and moves it to a server route.
- A side-project builder verifies that one user's data cannot be read by another through database policies.
- A Claude Code user runs a scan from the terminal and has the agent fix the top findings.
- A team lead gets a short prioritized list instead of hundreds of linter warnings.
- An indie hacker books an expert triage call to confirm an ambiguous finding.
Contextberg
Contextberg
Surfaces your active work as persistent agent memory and serves it to agents via the Model Context Protocol (MCP).
Key features
- Work-to-Memory Conversion: Extracts contextual signals from a user's workspace (files, tabs, app state and activity) and converts them into structured memory artifacts usable by agents.
- MCP Serving: Exposes collected memory via the Model Context Protocol (MCP) so any MCP-compatible agent or tool can query and consume context in a standard way.
- Long-Term Persistence: Stores and indexes historical context across sessions to provide agents with continuity and long-term state for multi-step or recurring tasks.
- Interoperability with Agent Tooling: Designed to plug into developer workflows and agent infrastructures, enabling multiple agents and platforms to reuse the same context artifacts.
- Context Enrichment: Organizes and surfaces relevant snippets of work history so agents receive concise, actionable context rather than raw logs or bulk files.
- Serve work history and artifacts as agent-readable memory via the Model Context Protocol (MCP).
- Index and persist long-term context so agents can access historical state across sessions.
- Provide a standardized memory endpoint for agent frameworks and tooling to query context.
- Integrate with developer workflows and tooling to capture relevant context from work artifacts.
- Reduce context-switching by making workspace context available to multiple agents and tools.
Best for
- Persistent Coding Assistants: Provide code-focused agents with project history, design decisions, and prior edits so suggestions and refactorings consider long-term context.
- Customer Support Augmentation: Supply support agents with the user’s prior interactions, documents, and troubleshooting steps to enable faster, context-aware responses.
- Personal Productivity Agents: Let personal assistants recall past tasks, notes, and project context to manage follow-ups, scheduling, and multi-session workflows.
- Team Knowledge Access: Serve a shared, queryable memory layer to team agents so newcomers and tools can access project context and rationale without manual handoffs.
- Agent Handoffs and Orchestration: Allow multiple specialized agents to request the same memory artifacts via MCP when coordinating complex, multi-step automations.
- Enable agents to complete multi-step tasks using a user's historical project context.
- Provide persistent memory for coding assistants so they retain project-specific state between sessions.
- Bridge work artifacts (files, commits, notes) into a standardized memory layer for orchestration.
- Improve agent decision-making by serving relevant long-term context during automated workflows.