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Wire

Permissioned context containers any MCP client can read and write, with per-user isolation and an API

freemiumCredit-based, with 1,000 starter credits on signup for every account. Pay as you go is $0/month: keep the starter credits, top up with credit packs from $5 to $500 whenever you need them, and get unlimited containers, unlimited team members and MCP access from any compatible client. Wire Pro starts at $20/month and auto-renews monthly with cancellation at any time, buying a fixed monthly credit allocation plus 10% bonus credits on every pack — published steps are 20,000 credits for $20, 50,000 for $50, 100,000 for $100, 200,000 for $200 and 500,000 for $500, and you can switch between amounts at any time. Enterprise is custom, adding isolated always-warm compute, SSO via SAML or OIDC, SCIM provisioning, single-tenant isolation, BYOC or pinned-region deployment, audit log export, a custom DPA and dedicated support with SLAs. No feature is gated by plan — unlimited containers, unlimited members and full MCP access are on every tier.View full pricing →

Visit Wire

https://usewire.io

About Wire

Wire is shared context storage for AI agents, summarized by its own line: code has GitHub, everything else has Wire. You create a container, put documents, data and notes into it, and connect Claude, Codex or any other MCP client with a single link, so every agent you work with reads and writes to the same place instead of each one starting from an empty context window. Content gets in three ways — direct file upload of PDFs, CSVs, DOCX and Markdown; agents writing entries themselves through wire_write; or a REST API and connectors pushing on a schedule. Each container is an isolated, permissioned environment with its own database, MCP server and API endpoint, so you can spin one up per person, team, project or customer and scope each connected agent to only what it needs. The use cases the vendor targets are more interesting than personal note storage: agent product builders who want every end user to get their own container without building that layer themselves, AI agencies shipping client agents without rebuilding context infrastructure per engagement, and public distribution — publishing a container so any agent can learn about your product through MCP. The recurring technical argument is retrieval over reload: agents that pull the slice they need from a container spend fewer tokens and reach past their context window, rather than re-ingesting a corpus every session.

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Key Features

One link connects any MCP client to a shared context container
Per-container database, MCP server and API with scoped permissions
Agents write entries directly via wire_write, not just file upload
Container-per-user model for agent products and agencies
No feature gates — unlimited containers and members on every plan

Tags

mcpai-agentscontextknowledge-baseapi
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