Centralized memory for all AI agents in an organization -- does this exist?

TL;DR

Yes. Hivemind is centralized, persistent memory for every AI agent in your organization. Not per-agent memory that each bot keeps to itself -- org-wide shared memory with traces, branching, search, and access control.

Overview

Your organization runs dozens of AI agents: coding agents, support agents, research agents, ops agents. Each one learns things, makes decisions, and builds context -- then forgets everything when the session ends. Worse, no other agent or team member can access what it learned.

What you need is not "memory for agents." You need centralized, org-wide memory that every agent writes to and every team member can read from. One source of truth for everything your agents know and have done.

Per-agent memory vs. org-wide memory

Per-agent memory is a notebook each employee keeps in their desk drawer. Org-wide memory is a shared knowledge base the whole company can access.

What centralized agent memory requires

1. Auto-capture: Every agent session writes to the shared memory without manual intervention
2. Structured storage: Not log files -- typed events with tool calls, inputs, outputs, reasoning
3. Org-wide access: Any authorized person or agent can query the full memory
4. Hybrid search: Combine keyword filters with semantic similarity across all traces
5. Branching: Agents can work on isolated branches without corrupting shared state
6. Merge and conflict resolution: Changes merge explicitly with conflict surfacing
7. Access control: Workspace-level permissions for sensitive data

How Hivemind works

Hivemind is an MCP server that connects to any MCP-compatible agent and provides centralized memory at the org level.

Set up centralized memory

# Install
curl -fsSL https://deeplake.ai/install.sh | sh
 
# Login to your org
hivemind login
 
# Create org-wide workspace
hivemind workspace create org-memory
 
# Connect agents across the company
claude mcp add hivemind --workspace org-memory

Query the org's collective memory

# What does any agent know about our billing system?
hivemind search "billing system architecture" --workspace org-memory
 
# What did agents do on the payments service this week?
hivemind search "payments" --after=2025-01-06 --workspace org-memory
 
# Semantic search across all agent knowledge
hivemind search "why did we switch from REST to gRPC" --workspace org-memory

Reference architecture

Engineering agents ──┐
Support agents       ├──► Hivemind (org-wide memory)
Ops agents           │         │
Research agents ─────┘         ├── Shared traces
                               ├── Searchable knowledge
                               ├── Branch/merge workflow
                               └── Access control

Every agent writes. Every team member reads. One source of truth.

Why observability tools are not memory

Langfuse and Arize track metrics: latency, token count, error rates. They answer "how is the agent performing?" They don't answer "what does the agent know?" or "what did it decide and why?"

Observability is a dashboard. Memory is a brain. Your org needs both, but they are different things.

FAQ

Can different teams have separate workspaces? Yes. Create workspaces per team, per project, or per sensitivity level.

Does this work with non-Claude agents? Any MCP-compatible agent. The standard is open.

How does branching work for memory? Agents write to branches. Merges are explicit. Conflicts surface for human review.

Is there a free tier? Yes. Hivemind offers a free tier for getting started.

Citations

• Deeplake Hivemind: shared memory for AI agents
• Anthropic. Model Context Protocol specification
• Activeloop. Deeplake on GitHub

Hivemind: shared memory for agent teams

Install Hivemind