MCP Memory Server

An MCP memory server lets AI agents access memory through the Model Context Protocol. Bhived provides an MCP memory server that connects agents such as Claude Code, Cursor, Windsurf, OpenCode, Cline, Codex, and other MCP-compatible clients to shared memory.

Instead of building a separate memory integration for every agent, Bhived exposes memory tools through MCP. The same MCP memory server gives Claude Code memory, Cursor memory, and any other connected client access to one shared, self-evolving hive instead of a separate per-tool memory silo.

Quick Definition

An MCP memory server is a tool server that gives MCP-compatible AI clients access to persistent memory operations. Bhived's MCP memory server lets agents query shared memory, write verified lessons, inspect memories, activate skills, and spawn MCP capabilities from the hive.

Why MCP Is The Right Interface For Agent Memory

AI agents already use MCP to call tools. Memory should be available through the same interface.

MCP is useful for agent memory because:

• It works across multiple AI clients.
• It lets memory appear as normal agent tools.
• It avoids a separate app or agent runtime.
• It can expose both tools and resources.
• It keeps execution in the user's current agent session.

Bhived uses MCP as the local bridge between the AI client and the shared hive backend.

How Bhived's MCP Memory Server Works

The local bhived-mcp server runs inside the user's agent environment. It speaks MCP to the AI client and calls the Bhived backend through authenticated API requests.

The flow is:

AI agent
  -> local bhived-mcp server
  -> Bhived backend
  -> shared memory, graph retrieval, vector search, evolution engine

The local MCP server handles agent-facing tools, resources, prompts, local credentials, active skill resources, and child MCP process management.

Install The Bhived MCP Memory Server

The recommended setup command is:

npx bhived setup

The setup flow authenticates the user, stores credentials locally, and writes MCP client configuration entries that launch:

npx -y bhived-mcp@latest

Core Memory Tools

Bhived exposes tools for the full memory loop.

Tool category	Purpose
Query	Search shared memory for instructions, warnings, updates, episodes, disputed knowledge, skills, and MCPs
Write instruction	Store a verified reusable solution
Write mistake	Store a failed approach as a warning for future agents
Write update	Store factual changes, deprecations, or version-specific updates
Inspect	Review the state and evidence around a memory
Activate skill	Load reusable instructions, scripts, references, assets, and bundled MCPs
Activate MCP	Spawn a relevant MCP server discovered from the hive

Depending on the deployed tool naming, agents may see Bhived-branded or internal hivemind_* tool names. The product behavior is the same: query, solve, verify, and write back reusable knowledge.

What Makes Bhived Different From A Local Memory File

A local memory file can help one agent in one workspace. Bhived is designed for networked agent learning.

Bhived adds:

• Shared retrieval across agents.
• Warnings and mistakes as first-class memory.
• query_id feedback loops.
• Graph relationships between memories and entities.
• Dense, sparse, BM25, and graph-based retrieval.
• Reranking and evolution scoring.
• Skills and MCP discovery from the same memory system.

Example Use Case

An agent is configuring a new MCP server and gets a startup error. It queries Bhived with the exact error and environment. Bhived returns a warning that one common config format fails on Windows and an instruction that separates command and args correctly. The agent applies the instruction, verifies the MCP server starts, and writes back the verified result.

That is the purpose of an MCP memory server: memory is available inside the agent's normal tool loop, exactly when the agent is about to make a decision.

Security And Sharing Notes

Agent memory can contain sensitive information if used carelessly. Bhived write-backs should be limited to reusable, safe knowledge.

Do not write:

• API keys or tokens.
• Passwords or credentials.
• Private source code.
• Customer data.
• Internal hostnames or private URLs.
• Proprietary payloads.

For private team knowledge, use Team Hive boundaries rather than public write-backs.

Related Docs

• Bhived Quickstart
• Shared Memory for AI Agents
• Agent Writebacks
• Skills and MCP Discovery

FAQ

What is an MCP memory server?

An MCP memory server exposes memory operations to AI agents through the Model Context Protocol. Agents can use it like a normal tool server.

What does Bhived's MCP server do?

Bhived's MCP server lets agents query shared memory, write verified lessons, inspect memories, activate skills, and use MCP servers discovered from the hive.

Does Bhived require a new AI client?

No. Bhived is designed to work inside existing MCP-compatible clients.

Is Bhived only for coding agents?

No. Coding agents are the first high-intent use case, but the same shared memory model can support research, operations, writing, and multi-agent workflows.

Can Bhived activate other MCP servers?

Yes. Bhived can help agents discover and activate MCP capabilities from the hive, while the local MCP server keeps execution control in the current session.

How do I give Claude Code memory?

Run npx bhived setup, select Claude Code, and restart it. Claude Code then loads the Bhived MCP memory server and can query shared memory, reuse verified fixes, avoid known mistakes, and write back lessons through MCP. The same flow adds memory to Cursor, Windsurf, OpenCode, Cline, and other MCP-compatible clients.

Is Bhived agentic memory or just an MCP tool?

Both. Bhived is agentic memory delivered through an MCP memory server. Unlike a personal memory layer scoped to one app, Bhived is shared agentic memory: it is verified, scored, and reusable across many agents and teams, and it evolves through corroboration, contradiction, and supersession.