Kit for AI vs pumaDB: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Kit for AI and pumaDB — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Kit for AI

Freemium

MCP-native memory + knowledge platform: turn any file, URL, or YouTube video into grounded, searchable context for any LLM agent.

MCP Memory Tools: remember, recall, and search exposed as native MCP tools any agent can call mid-conversation to persist users, preferences, and decisions.
Document Conversion: Converts PDF, Word, Excel, PowerPoint, CSV, HTML, and images (OCR) to clean Markdown ready for LLM ingestion.
URL → Markdown: Extracts main content from JS-heavy, gated, and region-specific web pages into clean Markdown with tables preserved.
YouTube Transcripts as Docs: Paste a YouTube link and the transcript becomes a searchable, citable document in a knowledge base.
Hybrid Semantic Search: Combines vector embeddings with full-text search, fused via RRF and reranked for precise cited retrieval.
Knowledge Bases with Citations: Group documents into KBs with grounded chat, cited answers, feedback corrections, and a visual doc graph.
Token-efficient Retrieval: Pulls only the passages an agent needs, cutting token usage by up to 90% versus dumping whole documents.
Private by Default: Files encrypted at rest, API keys hashed, spaces isolate projects, and data is never used for training.

Give any MCP agent persistent memory: Attach Kit to Claude, Cursor, or a custom agent and let it remember users, preferences, and decisions across sessions.
RAG pipelines without the stack: Ingest company docs, chunk and embed automatically, and query via one API instead of stitching a vector DB and reranker.
AI support bots with citations: Ground a support agent on product docs so answers cite the exact passage they came from.
Chat with YouTube content: Turn lectures, talks, and tutorials into searchable knowledge for research or content workflows.
Invoice and form extraction: Use JSON extraction to pull typed fields from documents into a user-defined schema.
Clean scraping replacement: Convert URLs to Markdown for training data, fine-tuning datasets, or agent context.

Paid

Durable JSON memory API for agents that stores and serves agent memory via hosted MCP or REST without requiring database setup.

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

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