GBrain vs Maximem Synap: Features, Pricing & Which Is Better (2026)

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

GBrain

Paid

A shared team brain that syncs memory, connected apps and scheduled skills to ChatGPT, Claude, Cursor and any AI your team uses.

Shared Team Chat: The whole team prompts in one shared chat where anyone can follow up and see how answers were worked out.
Plain-File Memory: Team knowledge lives as markdown notes in folders you can open, correct and copy, and every AI answers from the fix.
Connect Once Credentials: Connect Gmail, Calendar, Drive, Stripe and more once; AIs receive call results, never passwords.
Per-AI Permission Levels: Set each AI to Off, Read, Draft, Manage or Full per account, with every action on one record.
Scheduled Skills: One-click skills like Team Standup, Customer Comms Tracker and Inbox Cleanup run daily or once.
Multi-AI Sync: Sync the same memory to Claude, ChatGPT, Cursor, Claude Code, Codex and your terminal.
Tools Plan: A password manager for AI that puts your applications into every AI you use for individuals.

Team Standups: Summarize the team's work across GitHub, Gmail and Calendar and email it out each morning.
Customer Follow-Up: Track customers and prospects owed a reply and flag overdue ones daily.
Issue Triage: Turn customer bugs and requests from email and support chats into deduplicated tracker issues.
Meeting Prep: Build sourced briefs on candidates, guests or investors before you meet.
Inbox Cleanup: Archive mail you never read on a schedule.
Pricing Decisions: Combine call notes and billing data in a shared chat to evaluate a price change.

Maximem

Freemium

Maximem Synap is an agentic context management platform that gives AI agents persistent, scoped memory with sub-15ms retrieval.

Two-Call Memory API: Record messages asynchronously and fetch ranked, prompt-ready context before each reply — writes never block the agent.
Three-Layer Memory Model: Separate organizational, long-term per-user and short-term session memory with customer and client scoping for multi-tenant isolation.
Entity Resolution & Structured Extraction: Converts raw dialogue into structured facts and merges aliases of the same person or company automatically.
Anticipatory Retrieval: Pre-fetches context during the conversation for under 15ms P75 retrieval, fast enough for voice agents.
Agentic Compaction & Consolidation: Background cycles compact and consciously forget stale information to keep context lean and cut token costs.
Hybrid Vector, Graph & File Storage: Retrieval nets across semantic vectors, entity relationship graphs and raw documents, with FAST/ACCURATE/HYBRID modes.
Broad Framework Support: Python and TypeScript SDKs, REST and WebSocket APIs, a hosted MCP endpoint and native adapters for 23 frameworks including LangChain, CrewAI, OpenAI Agents and Claude Agent SDK.
Enterprise Controls: BYOK model providers, audit trails, webhooks, custom memory schemas, and VPC deployment, SSO/SAML and RBAC on enterprise plans.

Personalized Support Agents: Customer service bots remember prior issues and preferences across sessions instead of re-asking users.
Voice Agents: Real-time voice assistants pull user context in milliseconds without conversational pauses.
Multi-Tenant SaaS Assistants: B2B products keep each customer's memory isolated while sharing organization-wide policies and product facts.
Token Cost Reduction: Teams replace full-history prompt stuffing with compact ranked context to lower LLM spend.
Long-Running Copilots: Coding or productivity assistants recall what a user worked on days or weeks earlier.
Replacing Homegrown RAG Memory: Engineering teams avoid building and maintaining their own extraction, vector DB and ranking pipeline.