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Memory for AI agents

Give your agents
memory that lasts

Persistent, queryable memory for AI agents — retain, recall, reflect. Your agent does the work; CogniMemo owns durable storage and fast typed recall.

Start free View docs

$ npm i @cognimemo/client

Trusted by 2k+ developers

api.cognimemo.com / recallLIVE

const { results } = await cm.recall({
  bank: "user@acme.com",
  query: "deployment preferences",
  types: ["preference", "procedure"],
});

// → [{ type: "preference", text: "Prefers us-east-1", score: 0.94 }]

Backed by

Built for agent frameworks and AI tools

Memory in action

Three operations that cover the full memory lifecycle — plus integrations, self-hosting, and developer tools.

Retain

Capture what your agent learns

Store memories as text, files, or URLs. Embeddings, entities, and links are computed on ingest — so the next session starts with context, not from zero.

Your agent finishes a session and needs to remember user preferences for the next one.
Rather than stuffing everything into the context window, you call retain with typed memory blocks.
CogniMemo computes embeddings, entities, and links on ingest — ready for recall in the next turn.
Each bank is strictly isolated — keys never leak across projects or users.

See retain docs

api.cognimemo.com / retainLIVE

await cm.retain({
  bank: "user@acme.com",
  text: "Prefers concise PR summaries",
  type: "preference",
});

“Our agents are finally context-aware across sessions.”

Recall

Retrieve the right context at runtime

Hybrid search fuses semantic, keyword, graph, and temporal signals — then reranks results. Filter by memory type or search everything with a single call.

Your agent loop needs relevant context injected before every LLM call.
Recall fuses semantic, keyword, graph, and temporal signals — then reranks results.
Filter by memory type — procedure, preference, reasoning — or search everything at once.
Sub-150ms latency keeps production agent loops fast.

Try the playground

api.cognimemo.com / recallLIVE

const { results } = await cm.recall({
  bank: "user@acme.com",
  query: "deployment preferences",
  types: ["preference", "procedure"],
});

“MCP integration took five minutes in Cursor.”

Reflect

Synthesize insight from accumulated memory

Reflect generates disposition-aware answers from everything your agent has learned. Memories decay through lifecycle states so stale knowledge fades while frequently-used facts stay strong.

Your agent has hundreds of stored memories but needs a single synthesized answer.
You call reflect with a natural-language query instead of manually ranking recall results.
Lifecycle states — New, Active, Expiring, Forgotten — keep stale knowledge from polluting answers.
Corrections automatically reset relevant memories when users fix mistakes.

Memory model

api.cognimemo.com / reflectLIVE

const answer = await cm.reflect({
  bank: "user@acme.com",
  query: "What does this user prefer?",
});

“Finally, an AI memory that actually evolves.”

Integrations

Works with the tools you already use

Native MCP for AI IDEs, SDKs for custom agents, and a gateway API for any HTTP client.

Claude

OpenAI

Windsurf

LangChain

CrewAI

LiteLLM

See integrations

Self-Hosting

Run the engine on your infrastructure

One container, no external services required. Same retain / recall / reflect API — your keys, your Postgres, your embedding model.

Self-hosting guide

Single container

Engine + Postgres + pgvector + ONNX embeddings bundled

BYOK encryption

AES-256-GCM at rest with your 32-byte master key

SDK-compatible

Point the client at your engine — identical API surface

Developers

Ship memory in minutes

SDKs, API, MCP server, and a live playground — pick your integration path.

TypeScript SDK

@cognimemo/client — the official TypeScript / JavaScript client.

npm i @cognimemo/client

Python SDK

cognimemo-client — async-first with context manager support.

pip install cognimemo-client

API Reference

Interactive reference for retain, recall, reflect, and gateway endpoints.

api.cognimemo.com

MCP Server

Connect Claude, Cursor, and Windsurf with no SDK — OAuth-based MCP tools.

https://api.cognimemo.com/mcp

Pricing

Plans that scale with your agents

Start free, upgrade when you need more quota. Pay-as-you-go available on every plan.

Free

$0/mo

Experiment and prototype with core memory operations.

Memories1,000
Recalls5,000
Reflects100
RPM60

Start free

Pro

$29/mo

Production agents with higher quotas and room to grow.

Memories100,000
Recalls500,000
Reflects10,000
RPM600

Get Pro

Ultra

$49.99/mo

High-volume agents with generous monthly limits.

Memories500,000
Recalls2,500,000
Reflects50,000
RPM1,200

Get Ultra

Scale

$99.99/mo

Unlimited memory operations for demanding production workloads.

MemoriesUnlimited
RecallsUnlimited
ReflectsUnlimited
RPM6,000

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FAQ

Everything you need to know

Common questions about how CogniMemo works and how to get started.

CogniMemo is a persistent memory service for AI agents. It provides retain (store), recall (retrieve), and reflect (synthesize) operations — durable, typed storage that your agent reads and writes.

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Start building with memory

Get from zero to a working memory in five minutes. Sign up, create a project, and make your first retain call.

Retain, recall, and reflect in three API calls
TypeScript and Python SDKs with async support
MCP server for Claude, Cursor, and Windsurf
Self-host the full engine on your infrastructure
Encryption at rest with project-level policies

Open playground

Persistent, queryable memory for AI agents — retain, recall, reflect.

Give your agentsmemory that lasts