Centralized AI Agent Memory

One platform memory layer
for all your agents.

Centralized memory for AI agents is a single platform layer — connectors, graph, citations, and MCP write-back — instead of separate vector stores and MCP servers per agent product.

Platform teams should not maintain separate vector stores, MCP servers, and session memories per agent. Gyri centralizes ingestion, graph, citations, and write-back in one workspace.

Related: Build vs buy RAG · RAG vs knowledge graph

The problem

Why typical alternatives
fall short.

01

N siloed agent stores

Mem0, Zep, and custom RAG stacks per product line multiply connector maintenance and permission drift.

02

No operational federation

Dev-focused memory layers index conversations — not live CRM deals, email threads, and Slack decisions.

03

Write-back is an afterthought

Centralized read memory without CRM and insight write-back means agents still cannot close the loop.

Why Gyri

Four capabilities
most lack.

Federated GTM stack

One query across your stack

CRM, email, Slack, and docs federated into a workspace graph — not doc-only search or single-app plugins.

Cited AI answers

Synthesis you can audit

Claim-level citations with links to source records — trace any answer back to source. Verify before customer-facing or legal use.

Context that compounds

Memory that persists

Typed insights in a knowledge graph accumulate across agents and quarters — not session resets.

MCP write-back

Agents that close the loop

Claude, Cursor, and internal agents search, synthesize, and write insights back to CRM and workflows.

Compare

Gyri vs typical
alternatives.

CapabilityGyriDev memory (Mem0/Zep)DIY RAG + MCP
Single ingestion + graph layer⚠️
Operational source federation⚠️
Typed insight persistence⚠️
Native MCP endpoint⚠️
CRM / workflow write-back

Named comparisons: Build vs buy enterprise RAG · RAG vs knowledge graph

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Use cases

Built for
GTM operators.

Platform consolidation

Replace three agent memory projects with one federated graph and one MCP surface.

Permission consistency

Workspace-scoped auth applies uniformly to humans, Claude, and Cursor — not per-agent hacks.

Faster time to value

Native OAuth connectors (Gmail, Slack, Drive, Pipedrive, Insightly) are live in minutes; agents query federated workspace data without a quarter-long build.

Gyri think verbs

Agents that remember
what they conclude.

Gyri think verbs turn federated search into a research lifecycle — recall prior insights, ground in live CRM, reflect drafts, promote cited conclusions that compound across Claude, Cursor, and stored agents.

think verb=recall

Semantic orient — load prior cited conclusions before you re-derive from scratch.

think verb=ground

FTS-only orient on live federated CRM, email, and Slack — zero embed cost.

think verb=consider

Classify novelty against the workspace corpus before you commit a thesis.

think verb=reflect

Capture drafts, tensions, and questions as typed thoughts on the graph.

think verb=traverse

Multihop GraphQL in one think call — deal → contact → email → ticket.

think verb=fetch

Hydrate full rows by ref when search or traverse surfaced the right node.

Thought verbs deep dive · Claude + Gyri · Cursor + Gyri

Centralize agent memory without rebuilding RAG.

Free trial — federated connectors, cited synthesis, and MCP write-back on one platform.

Frequently asked questions

What is centralized AI agent memory?

Centralized agent memory consolidates ingestion, entity graph, cited synthesis, and MCP tools in one workspace — so platform teams do not maintain separate memory stacks per agent.

Can Gyri replace Mem0 or custom RAG?

For GTM operational intelligence across CRM, email, Slack, and docs, Gyri replaces DIY RAG plus MCP with a managed federated graph and write-back agents.

Does Gyri expose MCP?

Yes. Gyri provides a native MCP server for Claude, Cursor, and custom agents with workspace-scoped permissions and audit trails.