Many teams build custom memory systems for their agents. pref0 replaces the preference-learning portion with a purpose-built API that handles extraction, scoring, and compounding.
| pref0 | Custom Memory | |
|---|---|---|
| Development time | Minutes — 2 API endpoints | Weeks to months of engineering |
| Preference extraction | Automatic via LLM analysis | Manual implementation required |
| Confidence scoring | Built-in, compounds automatically | Must be designed and implemented |
| Correction detection | Automatic — corrections score higher | Must be built from scratch |
| Maintenance | Managed service | Ongoing engineering effort |
| Customization | Predefined preference categories | Fully custom to your needs |
A custom memory system that handles preference extraction, confidence scoring, and cross-session compounding takes significant engineering effort. pref0 provides all of this out of the box with 2 endpoints.
pref0 uses specialized LLM prompts tuned for preference extraction and correction detection. Building equivalent quality in a custom system requires extensive prompt engineering and iteration.
Custom systems offer unlimited flexibility. pref0 is opinionated about preference structure and confidence scoring. If you need a very custom data model, a custom system may be necessary.
Yes. Start sending conversations to pref0's /track endpoint. pref0 will build preference profiles from scratch. You can run both systems in parallel during migration.
No. pref0 specifically handles preference learning. If you need fact storage, conversation history, or other types of memory, you'll still need those parts of your custom system.
pref0 extracts key-value preferences with confidence scores. If you need a fundamentally different data model, a custom system is the right choice.
Your users are already teaching your agent what they want. pref0 makes sure the lesson sticks.