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Coming SoonReady to save your context→
Persistent memory for every AI agent.
Memron turns raw conversations into structured, encrypted memory. Stop token waste by ~90%, eliminate hallucinations, and seamlessly share context across Claude, Cursor, and any MCP agent.
›01//CONTEXT BACKBONE
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[ 01 // CONTEXT BACKBONE & AGENT WIRE ]
One memory for every agent
that touches the work.
Claude saves the decision and the next step. Memron resolves what changed. Cursor and Codex continue with the context they need.
AGENTS & WORKSPACES
MODELS, PROTOCOLS & VECTOR ENGINES
›02//ADAPTIVE WORKFLOW
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Claude CodeMCP · memron connected
DEVELOPER
How did we resolve the AuthProvider session race condition in last sprint's PKCE refactor?
●memron_search_context{ "query": "AuthProvider session race condition PKCE", "scope": "decisions" }
RECALLED // SPRINT 14 (CURSOR SESSION)CONFIDENCE 99.4%
DECISION:Wrap token refresh in BroadcastChannel mutex lock. Do not re-challenge PKCE verifier on concurrent browser tabs.
CLAUDE CODE
Found the architectural consensus from Tuesday: We use a BroadcastChannel mutex in AuthProvider.tsx to synchronize token rotation across tabs without re-issuing PKCE verifiers. Applying that exact pattern now.
✔1,480 entities synced · Zero drift verified across Cursor & Claude
02 · ADAPTIVE WORKFLOW MEMORY
It starts inside the
agent you already use.
Connect once via MCP, authorize your workspace, and point your agent at any task. From there it stores decisions, recalls past bugs, and syncs context across independent sessions. Agents self-serve over MCP; humans use the dashboard or npx @memron/cli.
RUNS INSIDE YOUR AGENTClaudeChatGPTVS CodeWindsurfCline
ClaudeCursor
ChatGPTVS CodeWindsurfClineGemini
Copilot
›03//5-LAYER ARCHITECTURE
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[ARCHITECTURE // 05 COORDINATED LAYERS]
One memory system. Five coordinated subsystems.
Watch raw agent activity move through Memron's protocol, knowledge, retrieval, analysis, and encrypted storage engine.
›END//5-LAYER ARCHITECTURE
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How It Works
AI + MCP + Graph Memory
Memron combines the power of Model Context Protocol with encrypted temporal graph memory to create truly persistent AI context - 26% higher response quality with 90% fewer tokens.
Conversations
→
Extraction Phase
MCP Server
Summary
Context Memory
Pattern
Pref
→
Sync Phase
Graph Memory
Knowledge Graph
Temporal Vector
Compressed Memory
Research & Core ArchitectureBuilding the Future
Building the Future
of AI Memory
We're actively researching and integrating cutting-edge technologies — Reinforcement Learning from Memory (RLM), Meta's V-JEPA for multimodal understanding, temporal knowledge graphs, pointer-based context compression, and zero-knowledge AES-256-GCM encryption.
Native MCP architecture ensures zero-config integration for Claude, Cursor, and any autonomous AI agent.
Explore our architecture</> One Protocol • Every Agent • Infinite Memory
Cursor
ClaudeVS Code
WindsurfOpenAI
GeminiCline
GitHub CopilotQwenWarp
Roo CodeCursorClaudeVS CodeWindsurfOpenAIGeminiClineGitHub CopilotQwenWarpRoo CodeConnect any AI agent via MCP and unleash infinite memory