CrawlForge MCP vs pumaDB: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of CrawlForge MCP and pumaDB — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
CrawlForge MCP
CrawlForge
MCP server giving AI agents 31 web scraping, search and extraction tools that return clean markdown or structured JSON.
Key features
- 31 MCP-Native Tools: Fetch, scrape, extract, search and research tools exposed through one MCP server that plugs into Claude, Cursor and LangChain.
- Clean Markdown and Structured JSON: Converts any URL into AI-ready markdown or schema-defined JSON in a single call.
- Site Mapping and Deep Crawling: map_site and crawl_deep discover and follow internal links across entire websites.
- Batch Scraping: Process hundreds of URLs in parallel with batch_scrape and retrieve paginated results.
- Browser Sessions and Actions: Keep a page alive across calls and scrape pages that need clicks, scrolls or form fills.
- Search and Research: search_web, reddit_search and deep research tools return ranked results and multi-source findings.
- Change Tracking: track_changes monitors pages with baselines, schedules and alerts.
- Credit-Based Metering: Each tool costs 1-5+ credits per call, so usage and cost stay transparent.
Best for
- Agent Web Access: Giving a coding or research agent live web data through one MCP server.
- RAG Ingestion: Converting documentation sites into clean markdown for retrieval pipelines.
- Competitor Monitoring: Tracking pricing or content changes on competitor pages with alerts.
- SEO Analysis: Checking where a domain ranks for keywords and generating llms.txt files.
- Lead and Market Research: Extracting structured data from product, directory and social pages at scale.
- Document Processing: Pulling text out of PDFs and other documents found online.
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pumaDB
pumaDB
Durable JSON memory API for agents that stores and serves agent memory via hosted MCP or REST without requiring database setup.
Key features
- Hosted MCP Endpoint: Provides a managed MCP interface so agents can connect to a memory control plane without self-hosting infrastructure or managing databases.
- REST API Compatibility: Offers a standard REST API for inserting, querying, and retrieving JSON memory rows from existing services and agent frameworks.
- Durable JSON Row Storage: Persists structured JSON rows as durable memory entries, enabling stateful behavior across agent sessions and long-lived context retention.
- Memory Review and Inspection: Includes capabilities to review stored memories so developers and auditors can inspect agent state and historical interactions.
- No-Database Setup: Eliminates the need to provision, configure, or maintain a dedicated database project — simplifying prototyping and production deployment.
- Lightweight Integration: Designed for quick integration with agent systems and assistants, reducing engineering overhead to add persistent memory.
- Hosted MCP and REST endpoints for integrations
- Store arbitrary JSON rows as durable memory
- Durable agent memory without a separate database project
- Review and retrieve persisted agent memory
- Simple API surface to connect agents to persistent storage
Best for
- Persistent Conversational Context: Store user preferences and past conversation turns as JSON so chatbots can recall history across sessions.
- Stateful Autonomous Agents: Provide long-term memory for agents that require recall of decisions, tasks, and learned information over time.
- Rapid Prototyping without DB Work: Enable developers to build and test memory-enabled agents without provisioning or maintaining database infrastructure.
- Audit and Debugging of Agent Behavior: Review stored memory entries to trace agent reasoning, reproduce issues, and validate decision contexts.
- Cross-Service Memory Sharing: Use REST or MCP interfaces to share agent memory between microservices, chat platforms, and orchestration layers.
- User Profile Management for Assistants: Persist structured user data (preferences, settings, history) as JSON to personalize assistant responses.
- Maintain long-term memory for conversational agents
- Persist agent state and interaction history as JSON
- Enable stateless agents to access shared durable memory across sessions
- Prototype agents quickly without managing database infrastructure