Every time your AI agent starts a conversation without memory, you stuff thousands of tokens of raw history into the prompt. DeepRaven replaces that with a 400-token profile that knows everything. 30× cheaper. Infinitely smarter.
Free during beta · No credit card required
tokens per call when you stuff prior conversations as raw context — at $3/M tokens, that adds up fast
the average number of times a customer repeats the same information to agents who don't remember them
drop in conversion rate when AI agents lack context from previous interactions with the same customer
Token economics
The status quo costs 37× more than it needs to. Every call.
The difference
An agent without memory makes every conversation feel like the first one. An agent with DeepRaven feels like it knows you.
Why DeepRaven
Not just a memory store. A purpose-built intelligence layer for sales agents.
Profiles survive session resets, agent swaps, and channel changes. What your agent learned last month is still there today — without replaying the history.
Replace raw conversation dumps with a compact 400-token profile. At 1,000 calls/day, that's over $15,000/year saved in LLM input costs alone.
Just POST the conversation. DeepRaven's LLM extracts pain points, objections, buying triggers, and personal details automatically — no structured input required.
When a customer changes their budget or reveals a new objection, the profile updates — it doesn't duplicate. One customer, one evolving truth.
One endpoint to ingest. One to fetch. Plug DeepRaven into any LLM agent, CRM, or sales tool in under an hour.
Not a generic key-value store. The profile is structured around how sales actually works: budget, objections, triggers, rapport, channel preference. Open source (Apache 2.0) — self-host or use the cloud.
How it works
DeepRaven fits into your existing stack without changing how your agents are built.
After each interaction, POST the raw messages to DeepRaven. No tagging, no labelling, no structured format required — just the transcript.
DeepRaven runs the conversation through an extraction model that intelligently updates the customer profile — merging new facts with what was already known, never duplicating.
Fetch the profile before any interaction. Your agent walks in knowing the customer's budget, their last objection, their daughter's name, and how they prefer to communicate.
Open source
DeepRaven is fully open source. Use our free cloud or self-host in minutes — no licence fees, no vendor lock-in, ever.
Create an account and start building in under 60 seconds. We handle the infra, scaling, and updates.
Get started free →Clone the repo, run Docker Compose, and you're done. Apache 2.0 — use it commercially, modify it, fork it.
git clone https://github.com/alpha-digital-minds/deepraven
docker compose up
How we compare
Both are memory layers for AI agents. Here's how they differ for sales use cases.
| Feature | DeepRaven | mem0 |
|---|---|---|
| Persistence model | Structured profile, evolves forever | Episodic memory fragments |
| Token efficiency | ~400-token compact profile per call | Varies — retrieval adds overhead |
| Sales-optimized schema | Yes — budget, objections, triggers, rapport | Generic key-value memory |
| Profile evolution | Merges + updates existing profile | Appends new memory fragments |
| Self-hosting | Yes, Apache 2.0, Docker Compose | Yes, open source |
| Pricing | Free cloud + unlimited self-host | Free tier with paid plans |
DeepRaven is live, free during beta, and takes under an hour to integrate. Stop burning tokens on history you could just remember.