Your Agents Forget.
CogmemAi Remembers.
One persistent memory layer for OpenAi Agents SDK, LangChain, LangGraph, CrewAI, AutoGen, Claude Agent SDK, and anything else that speaks MCP or HTTP. Agents that remember users, decisions, and each other.
The Goldfish Problem in Agent Systems
Agents are getting smarter every quarter and still wake up empty. Context windows are working memory; they were never long-term memory.
Every Run Starts from Zero
The agent that spent an hour learning a user's preferences yesterday greets the same user as a stranger today. Nothing durable survives the end of a run.
Agents Cannot Learn from Each Other
Your research agent discovers a constraint; your planning agent violates it an hour later. Multi-agent systems without shared memory repeat each other's mistakes on a loop.
Memory Locked Inside One Framework
Framework-native memory lives and dies inside that framework. Move an agent from LangGraph to the OpenAi Agents SDK and its accumulated knowledge stays behind.
What CogmemAi Gives an Agent System
Cross-Framework Memory
One memory store, reachable from every framework via MCP or REST. Agents built on different stacks share the same knowledge, and migrating frameworks costs nothing in accumulated memory.
Per-User Memory for Consumer Agents
Companion, coaching, and support agents remember each end user across sessions: preferences, history, and context, with encryption at rest and clean per-user separation.
Shared Team Knowledge
Multi-agent systems write to and read from a common pool with author attribution. What one agent learns, every agent knows on the next recall.
Autonomous Capture
CogmemAi saves decisions, fixes, and discoveries even when the agent never calls save. The knowledge lands in memory without a single prompt-engineering hour.
Rules Your Agents Must Follow
Corrections synthesize into mandatory rules surfaced on every relevant recall. Tell one agent once; every agent behaves from then on.
Zero-Install Remote MCP
No npm, no local process: point any MCP client at the hosted endpoint and 30 memory tools appear. Or run the local server from npm when you prefer.
An Agent That Remembers, in One Config Block
from agents import Agent
from agents.mcp import MCPServerStdio
cogmemai = MCPServerStdio(
name="cogmemai",
command="npx",
args=["cogmemai-mcp"],
env={"COGMEMAI_API_KEY": "cm_your_key"}
)
agent = Agent(
name="support-agent",
instructions="Load context with get_project_context at task start. "
"Save discoveries with save_memory.",
mcp_servers=[cogmemai]
)Agent Questions, Answered
Which frameworks does CogmemAi work with?
Any framework that supports MCP servers (OpenAi Agents SDK, Claude Agent SDK, and others) connects natively. Everything else uses the REST API or the Python and JavaScript SDKs. If it can make an HTTP call, it can remember.
How is this different from a vector database?
A vector database is storage; CogmemAi is memory. Extraction decides what is worth keeping, reranking returns the right facts rather than the nearest ones, contradiction detection catches stale knowledge, and auto-skills turn corrections into standing behavior. You never manage embeddings, chunking, or index tuning.
Can multiple agents share one memory safely?
Yes. Shared pools carry author attribution, contradiction detection flags conflicts at recall, and per-user scoping keeps end-user memory cleanly separated from team knowledge.
What does it cost to start?
Nothing. The free tier includes 500 memories and needs no card. Paid plans start at $3.99/mo and scale to team and enterprise tiers, with on-premises deployment available by contract.
Give Your Ai a Memory Today
Free tier, no card, sixty-second setup.
Explore the rest of CogmemAi: Developers & Pricing · Edge On-Premises · Compare Ai Memory · Memory for Robots