pumaDB vs Toolport: Features, Pricing & Which Is Better (2026)

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

pumaDB

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

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.