Cleanlist AI vs Contextberg: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Cleanlist AI and Contextberg — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Cleanlist AI
Cleanlist
AI-powered B2B prospecting and enrichment that turns plain-English prompts into verified, CRM-ready lead lists via a 15-provider waterfall.
Key features
- Conversational List Builder: Describe any target list in plain English and the Co-Pilot returns a fully enriched, CRM-ready set of leads.
- 15-Provider Waterfall Enrichment: Providers compete in real time on every contact to deliver 98% email accuracy and 85% direct-dial find rates.
- Company-First ICP Search: Filter by industry, size, geography, tech stack, hiring signals, and funding stage, then pull every relevant decision-maker.
- LinkedIn & Sales Navigator Extension: Open any profile, click Cleanlist, and get a verified email and direct dial in two seconds.
- CSV Enrichment: Drop 500 rows and each is run through the full waterfall, returning verified emails, direct dials, titles, and firmographics.
- Native CRM Sync: One-click, two-way sync to HubSpot, Salesforce, Pipedrive, Outreach, Salesloft, and lemlist — no Zapier required.
- AI Columns and Smart Agents: AI-generated columns add ICP scoring, company summaries, and competitor notes with the reasoning behind each answer.
Best for
- Outbound Prospecting: Sales reps build targeted lead lists on demand and push them straight to a sequencer.
- RevOps CRM Cleanup: Ops teams re-enrich stale HubSpot or Salesforce records to restore email deliverability and phone connect rates.
- Inbound Speed-to-Lead: Trigger enrichment on new form fills so reps have full context within minutes of a signup.
- ABM Account Expansion: Discover every relevant decision-maker inside a target account list without juggling multiple databases.
- Sales Stack Consolidation: Replace ZoomInfo/Apollo plus a separate email verifier and phone tool with one credit-based platform.
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.