Your agents forget everything.
Ours don't.
d33pmemory is a cognitive memory engine for AI agents. It doesn't just store what users say — it understands it. Every conversation becomes structured knowledge with confidence tracking, evidence chains, and automatic contradiction resolution.
{
"user_message": "Book dinner for me and Emma",
"source": "slack-bot"
}fact: gluten-free diet 0.95 stated
rel: companion: Emma 0.88 inferred
event: dinner booking 0.92 stated
pref: Italian cuisine 0.74 inferredQuick start
Two integration paths
Install the plugin. Everything is automatic — no code changes needed. Hooks handle ingest and recall.
Or via npm: npm install @d33pmemory/openclaw-plugin
Set agentId: "" in config — it auto-uses your gateway's agent name.
View on npm →Add skill.md to your agent. Use heartbeat batching + on-demand recall.
View skill.md →Sign up and create an API key for your account
Create a named agent via POST /v1/agents (e.g. 'personal-assistant')
Ingest: call POST /v1/ingest with agent_id after your agent responds
Recall: call POST /v1/recall with agent_id and a natural language query
The problem
Generic memory tools weren't built for agents.
Vector stores, conversation dumps, and DIY parsers all fall apart at scale. Here's why.
Vector stores
Conversation history
DIY solutions
d33pmemory was built from scratch for one thing: making AI agents actually know their users.
How it works
Every conversation becomes structured knowledge
Multi-layer cognitive model
Episodic, semantic, and procedural memory layers — mirroring how humans organize knowledge.
Confidence tracking
Every memory scored 0.0–1.0. Stated vs inferred is always clear. Evolves as evidence accumulates.
Auto consolidation
Corroborated memories strengthen. Stale ones decay. Contradictions resolve automatically.
Semantic recall
Search by meaning, not keywords. Describe what you need in natural language.
Context compilation
142 tokens of distilled intelligence replacing 15,000 tokens of conversation history.
Evidence chains
Every fact has provenance — which interaction, when, and whether stated or inferred.
Why d33pmemory
Beyond vector search
Standard RAG
d33pmemory
Memory structure
Five types of knowledge
Our extraction engine categorizes every piece of knowledge, each with confidence and provenance.
{
"type": "fact",
"content": "User is gluten-free",
"confidence": 0.95,
"source": "stated",
"scope": "shared",
"contributed_by": "slack-bot"
}Use cases
Agents that actually know people.
Personal AI assistant
Remembers preferences, habits, relationships. Gets smarter every conversation. Never asks twice what it already knows.
Customer support bots
10 agents, 1 shared brain. New agent joins and already knows your top issues, common questions, and product quirks.
Code agents
Tracks your stack, preferences, past decisions. Stops asking the same architectural questions every session.
Agents & Teams
One API key. As many agents as you need.
Create named agents and organize them into teams. Each agent builds its own private memory. Agents in the same team share a collective pool — new agents inherit everything instantly.
Your account (1 API key)
├── agent: personal-assistant → private
├── agent: slack-bot → private
└── team: customer-support
├── agent: support-1 → private + shared
├── agent: support-2 → private + shared
└── shared pool → all team agents
Pricing
Simple, transparent pricing
Start free. Scale when your agents need more memory.
Pro
Popular- Unlimited agents & teams
- 100,000 memories
- 20,000 ingests/mo
- Shared team memory
"We replaced 200 lines of RAG pipeline code with two API calls. Our agent went from forgetting users mid-conversation to remembering preferences from weeks ago."
AKAI EngineerBuilding with d33pmemory since beta
AI agents that remember.
Give your agents persistent memory in under 5 minutes. Free to start.