canonical: https://jentic.com/apis/memchats.com/memchats

# Memchats API

The Memchats API keeps a conversation memory for each person in a user's life and lets Alma, its counsellor, answer questions about that person. Contacts carry a relationship type such as friend, client, cofounder, or dating, and the API returns each contact's memory, a pre-call summary, a relationship health score, detected behaviour patterns, and a compatibility snapshot between two contacts. Alma answers across four areas (legal, relaciones, conocer, negocio), and a dating toolkit analyses profile screenshots, drafts messages, and critiques the user's own bio. Requests authenticate with an mc_live_ API key sent as a bearer token.

## For AI agents

Create Memchats contacts, read each contact's conversation memory, pre-call summary, and health score, search past messages, and ask Alma for advice about a person in the legal, relaciones, conocer, or negocio area.

## Scope

Does not send messages through WhatsApp, Telegram, or dating apps, manage sales pipelines, or book meetings. Use for personal relationship memory and Alma's advice about contacts only.

## Capabilities

- Create a contact with a name and relationship_type such as client, cofounder, family, or dating, plus an optional context_note
- Read a contact's conversation memory, a pre-call summary before a meeting, and a relationship health score
- Ask Alma about a contact, letting her pick the area or choosing legal, relaciones, conocer, or negocio explicitly
- Search message history by full-text query, optionally narrowed to one contact_id
- Import a person from dating-profile or chat screenshots and track the import until it finishes
- Analyse dating-app screenshots into a profile and draft the next message in the user's own writing voice
- Export a contact's full memory as downloadable JSON

## Use cases

### Pre-meeting briefings from an agent through Jentic

Before a call, a personal assistant agent pulls what Memchats remembers about the person on the calendar. Through Jentic it finds the contact, reads the pre-call summary and the health score, and asks Alma a focused question such as how to raise a delayed payment with a client. The mc_live_ key stays in your own Jentic One instance, and every Alma question draws from the plan's weekly LLM query quota.

Example prompt: Call GET `/api/v1/contacts` to find the contact named 'Laura Gómez', call GET `/api/v1/contacts/{id}/pre-call` and GET `/api/v1/contacts/{id}/health-score` for that id, then summarise both for the user

### Advice about a specific person

Users ask Alma for help with one relationship at a time. The ask endpoint takes a contact_id and a question and routes to one of four areas (legal, relaciones, conocer, negocio); the respond endpoint lets Alma choose the area herself from who the contact is and what the exchange is about. Previous answers for a contact can be listed later, and feedback on a suggested reply records what the user actually did.

Example prompt: Call POST `/api/v1/advisors/negocio/ask` with contact_id set to the cofounder's id and question 'How should I propose changing our equity split?', then return Alma's answer

### Dating conversation assistant

For dating contacts, the API analyses app screenshots into a profile, crops photos into a gallery, and drafts the next message with an intent and optional character limit. It can also translate stored texts and critique the user's own bio, rewriting it three times in their voice, so an assistant helps with both sides of a match.

Example prompt: Call GET `/api/v1/dating` to list dating conversations, pick the first one, then call POST `/api/v1/contacts/{id}/suggest-message` with intent 'propose_date' and return the draft

### Personal memory search and export

Months of messages become searchable. The search endpoint runs a full-text query across conversations with a q parameter and optional contact_id, and the export endpoint returns everything Memchats keeps about a contact as downloadable JSON, so a user can find a past promise or take their data elsewhere.

Example prompt: Call GET `/api/v1/search` with q 'invoice' and the client's contact_id, list the matching messages, then call GET `/api/v1/contacts/{id}/export` for a JSON backup

## Key endpoints

| Method | Path | Description |
| --- | --- | --- |
| POST | `/api/v1/contacts` | Create a contact |
| GET | `/api/v1/contacts` | List contacts |
| GET | `/api/v1/contacts/{id}/memory` | Get contact memory |
| GET | `/api/v1/contacts/{id}/pre-call` | Get pre-call summary |
| POST | `/api/v1/advisors/{slug}/ask` | Ask Alma about a contact in one area |
| GET | `/api/v1/search` | Full-text search in messages |
| POST | `/api/v1/contacts/{id}/suggest-message` | Draft a dating message |
| POST | `/api/v1/imports/people` | Import a person from screenshots |

## Key resources

- **Contacts** — People with a relationship type, context note, memory, health score, and relationship history
- **Conversations** — Message threads per contact with full-text search and read state
- **Alma** — The counsellor answering about one contact across legal, relaciones, conocer, and negocio
- **Dating profiles** — Profiles and photo galleries built from dating-app screenshots, with message drafting
- **People imports** — Screenshot-based imports that create a contact and sweep its history

## AI readiness

This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.

- **Score:** 59 / 100
- **Maturity:** Foundational
- **Dimensions:**
  - Foundational Compliance: 50 / 100
  - Developer Experience & Jentic Compatibility: 62 / 100
  - AI-Readiness & Agent Experience: 48 / 100
  - Agent Usability: 90 / 100
  - Security: 60 / 100
  - AI Discoverability: 61 / 100
- **View full report:** https://jentic.com/apis/memchats.com/memchats/scorecard
- **How the score is calculated:** https://docs.jentic.com/reference/api-readiness-framework/overview/
- **More about the dimensions:** https://docs.jentic.com/reference/api-readiness-framework/specification/#dimensional-model-overview

### Score it yourself

Every API in the directory is allowlisted, so you can re-score it with no key required.

- **Score your own API:** https://jentic.com/scorecard.md
- **Scoring CLI agent skill:** https://github.com/jentic/jentic-api-scorecard/blob/main/skills/jentic-api-scorecard/SKILL.md

```sh
npx @jentic/api-scorecard-cli score <openapi-url>
```

## Why Jentic

- **Setup:** Wiring Memchats by hand means generating an mc_live_ key, sending it as a bearer token, tracking asynchronous screenshot imports until they finish, and budgeting calls that draw on the weekly LLM quota. Through Jentic you install once, import Memchats from the API Directory, store the key once, and your agent calls it.
- **Permission scoping:** Memchats puts the contact id in the URL path (`/api/v1/contacts/{id}/...`), so a rule can pin your agent to one contact and the operations you pick. You choose the operations it may call, so destructive ones like deleting a contact or a dating photo are not included unless you add them.
- **Credential handling:** Your Memchats API key is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.
- **Discovery method:** Agents search Jentic by intent such as 'summarise a contact before a call' or 'search my messages', and Jentic returns the matching Memchats operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

## Related APIs

- **Attio API** — Flexible CRM with people, companies, lists, and notes for teams
- **Memara API** — Long-term memory store for AI agents with scoped spaces and semantic search
- **Clay API** — People and company search with enrichment routines

## FAQ

### What authentication does the Memchats API use?

Per its OpenAPI spec, the Memchats API uses HTTP bearer auth with an API key that starts with mc_live_, generated in the app under API keys settings and sent in the Authorization header. Account endpoints such as signup, the token exchange at `/api/v1/auth/token`, and the sign-in provider routes do not require authentication. Through Jentic the key is stored encrypted by your own Jentic One instance and injected at call time.

### Can I get a summary of a contact before a call with the Memchats API?

Yes. GET `/api/v1/contacts/{id}/pre-call` returns a pre-call summary for the contact, GET `/api/v1/contacts/{id}/memory` returns the full contact memory, and GET `/api/v1/contacts/{id}/health-score` returns a relationship health score, so an agent can brief the user in one pass.

### What are the rate limits for the Memchats API?

The OpenAPI spec does not state numeric rate limits. It does note that Alma questions and conversation replies each consume one LLM query from the plan's weekly quota, and plans differ in how many analyses per week they allow. Check https://memchats.com/developers for current limits.

### Is there a Memchats MCP server?

You don't need an MCP server to give your agent Memchats. Jentic connects it directly from the API Directory: import it, store your API key once, and your agent calls the contact, memory, and Alma operations it needs on demand without loading another server's tool definitions into its context.

### How do I ask Alma about a contact through Jentic?

Search Jentic for 'ask Alma about a contact', load the POST `/api/v1/advisors/{slug}/ask` operation, and execute it with an area slug such as negocio, the contact_id, and your question. GET `/api/v1/advisors` lists the available areas first. To run it on your own infrastructure, install Jentic One from its GitHub repo.

### Can I limit what my agent is allowed to do with the Memchats API?

Yes. For example, write a rule that allows only the pre-call, memory, and health-score reads under `/api/v1/contacts/{id}`/ for one client contact, so the agent can brief you on that person but cannot delete contacts, send messages, or ask Alma questions that use your weekly quota. Every call it makes is logged.