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

A side-by-side comparison of Kit for AI and Toolport — 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.

Free

Free open-source local MCP gateway. Set up each server once and share it across Claude, Cursor, VS Code, Codex, Windsurf.

Universal MCP gateway: Set up any MCP server once and every agent (Claude, Cursor, VS Code, Windsurf, Codex, Antigravity) shares it with hot toggles and no restarts.
Lazy tool discovery: Exposes a handful of meta-tools instead of dumping hundreds of tool definitions, cutting tool-definition tokens 74–91% at the same task success on a frontier model.
Tool integrity checks: Fingerprints every tool and flags rug-pulls (a definition changing after approval) and tool poisoning (hidden instructions in descriptions), on by default and entirely local.
Keychain secrets: API keys live in your OS keychain and are injected at runtime — never in a config file, never in the cloud.
Per-tool governance: Toggle any tool on or off with one switch to hide destructive tools from every agent fleet-wide.
Live observability: Per-server latency, error rates, and a full audit trail of every tool call built into the app.
Cross-platform local runtime: Runs on Windows, macOS, and Linux with no account and no cloud dependency, released under the MIT license on GitHub.
Toolport for Teams: Shared governed set of MCP servers for a whole team (free for up to 5 people) while each person's API keys stay on their own machine.

Individual AI power users running Claude, Cursor, and Codex who want a single place to configure MCP servers instead of pasting the same setup into each agent.
Engineers hitting context-window limits from bloated tool-definition tokens who need lazy discovery to keep long agent sessions cheap and sharp.
Security-minded developers who need API keys stored in the OS keychain and cryptographic integrity checks against tool poisoning and definition rug-pulls.
Small teams (up to 5 people) who want one governed catalog of MCP servers while keeping each engineer's credentials on their own machine.
Agent-tooling authors who want a local audit trail of latency, error rates, and every tool invocation across servers for debugging.
Ops leads applying per-tool governance to hide destructive actions (writes, deletes) from every agent with a single fleet-wide toggle.