> ## Documentation Index > Fetch the complete documentation index at: https://docs.auditynow.com/llms.txt > Use this file to discover all available pages before exploring further. # Working with Nucleus > Search memories, capture notes, surface insights, from outside the app. ## What Nucleus is Nucleus is Audity's persistent memory layer. It remembers facts about your clients (memories), ingests text from outside sources like meeting transcripts (captures), and produces proactive observations from background jobs (insights). The agent API exposes the read and write surface of Nucleus so you can query and update it from Claude, Cursor, n8n, or any other agent, the same way Audity's internal AI does inside the web app. ## What's exposed to the agent | Capability | Endpoint | Notes | | -------------------------------- | ---------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | | List memories | `GET /api/nucleus/memories?type=&projectId=` | Three types: `client`, `pattern`, `preference` | | Create a memory | `POST /api/nucleus/memories` | Source defaults to `explicit` when created via API | | Update a memory | `PATCH /api/nucleus/memories` | Body-passed `memoryId` + `subject` and/or `content`. Mirrors the contacts pattern. | | Delete a memory | `DELETE /api/nucleus/memories/{id}` (idempotent 204) or `DELETE /api/nucleus/memories` with `{memoryId}` in body | Both soft-delete (`is_archived = true`). Prefer the path form, it's idempotent. | | List captures | `GET /api/nucleus/captures?channel=&status=&projectId=` | 8 channels: `transcript`, `voice_note`, `text_note`, `email`, `calendar`, `zoom`, `crm_sync`, `file_drop` | | Get a capture + items | `GET /api/nucleus/captures/{id}` | Returns `{ capture, items }` (extracted action items, decisions, key insights) | | Submit a text capture | `POST /api/nucleus/capture/note` | Triggers extraction pipeline. Rate-limited to 30/hour. | | Reprocess a capture | `POST /api/nucleus/captures/{id}` | Resets status to `pending` and re-runs extraction. Useful after a failure. | | Delete a capture | `DELETE /api/nucleus/captures/{id}` | Soft delete. Idempotent. | | List contacts | `GET /api/nucleus/contacts?search=` | Lightweight CRM. Search matches name + company, case-insensitive. | | Create / update / delete contact | `POST` / `PATCH` / `DELETE /api/nucleus/contacts` | All take the contact in the JSON body, not the URL path. | | Read insights | `GET /api/nucleus/insights?type=&unreadOnly=` | Generated by background jobs | | Get prompt suggestions | `GET /api/nucleus/suggestions?projectId=` | 3 contextual prompts | The full request and response shapes are in the [API Reference](/api-reference/openapi.json). ## What's not exposed (and why) * **Nucleus chat** (`/api/nucleus/chat`). Exposing it would turn Audity into a chatbot middleman, your agent IS the chatbot. Use the underlying memory and capture endpoints directly. * **Live co-pilot** (`/api/nucleus/live`). Real-time, requires a UI. * **Backfill and admin operations**. Internal-only. * **Slash commands**. Internal Nucleus chat affordances. ## Pattern: "What does Audity remember about this client?" ```text theme={null} Search my Audity Nucleus for everything related to Acme Corp's stakeholder concerns. Cross-reference with any patterns I've seen across other healthcare clients. ``` What runs: 1. `GET /api/nucleus/memories?type=client` filtered to `projectId` for Acme 2. `GET /api/nucleus/memories?type=pattern` for cross-client patterns 3. The agent synthesizes the join This is the killer use case: your accumulated client knowledge becomes searchable from any AI assistant, not just inside Audity. Every memory carries `confidence` (0–1) and `sourceType` (`explicit`, `extracted`, `detected`). Tell the agent to cite both, detected patterns under 0.8 confidence are hypotheses, not facts. ## Pattern: "Capture this for me" After a client call, paste a transcript: ```text theme={null} Here's the transcript from my call with Initech this morning. Drop it into Nucleus as a capture for project {projectId}. Once it's processed, summarize the action items and decisions. ``` What runs: 1. `POST /api/nucleus/capture/note` with the transcript text and `projectId`. Returns immediately with the capture in `pending` status. 2. The capture goes into the extraction pipeline (Inngest job, runs async). Action items, decisions, key insights, and contact mentions are extracted into structured items. 3. After \~15–60 seconds: `GET /api/nucleus/captures/{id}` returns `{ capture, items }`. The agent reads the items and summarizes. The agent should poll, not block. If the capture is still in `processing` after a minute, something failed, call `POST /api/nucleus/captures/{id}` to trigger a reprocess. ## Pattern: "What insights are sitting in my queue?" ```text theme={null} Pull my unread Audity insights. Group by type. Tell me which look most time-sensitive. ``` `GET /api/nucleus/insights?unreadOnly=true` returns the typed insight set. In v1, four insight types are actively generated by background jobs: * `overdue_followup`, leads or clients you said you'd follow up with, where the date has passed * `pattern_detected`, cross-client pattern Nucleus noticed in your portfolio * `similar_lead`, a new lead matches a profile of a past project * `stale_client`, a client who's been quiet long enough to risk going cold The OpenAPI spec also lists `pre_meeting`, `referral_opportunity`, `portfolio_insight`, and `content_suggestion` as valid types. These are reserved in the schema but not yet produced by any background job. Don't promise them to your customers as if they're live; they aren't. ## Pattern: "Daily Nucleus brief" A common n8n / scheduled workflow: ```text theme={null} Every weekday at 8am: 1. Get unread Audity insights 2. Get Audity captures from the last 24 hours 3. Summarize as a 5-bullet brief and post to Slack ``` This pairs cleanly with [n8n's MCP Client node](/guides/n8n), every Audity tool the daily brief needs (`list_insights`, `list_captures`) is exposed. ## Memory hygiene Memories accumulate. Periodically have the agent prune: ```text theme={null} List my Audity memories of type "pattern". Find any that look duplicate or contradictory. Don't delete anything yet, propose deletes I can approve. ``` What runs: 1. `GET /api/nucleus/memories?type=pattern` 2. The agent dedupes / contradicts client-side 3. The agent proposes a deletion list, **wait for approval** 4. After approval: `DELETE /api/nucleus/memories/{id}` for each approved ID `DELETE /api/nucleus/memories/{id}` is idempotent and soft (sets `is_archived = true`); the row stays on disk for audit purposes. To **edit** a memory in place, use `PATCH /api/nucleus/memories` with the target ID in the body: ```json theme={null} { "memoryId": "", "content": "", "subject": "" } ``` At least one of `subject` / `content` must be present. PATCH returns `{ success: true }` on success. There's no separate "version history", the row is updated in place, so reserve PATCH for content corrections rather than running edit logs. ## Confidence and sourceType Every memory has: * `confidence` (0.00–1.00), how trustworthy the memory is * `sourceType` * `explicit`, you (or your agent) asked Nucleus to remember it * `extracted`, pulled from a conversation by the extraction pipeline * `detected`, AI-identified pattern When the agent surfaces a memory, ask it to include both numbers. A high-confidence `explicit` memory is a fact. A low-confidence `detected` pattern is a hypothesis worth sanity-checking against fresh data. ## What's next * [Lead conversion playbook →](/guides/lead-conversion) * [Connect Claude →](/guides/claude) * [Authentication deep dive →](/authentication)