> ## Documentation Index > Fetch the complete documentation index at: https://docs.nomiq.site/llms.txt > Use this file to discover all available pages before exploring further. # Memory API: Iterate and Refine Existing Brand Generations > Use Nomiq Memory Nodes to reference prior generations and refine brand assets iteratively, preserving style seeds without restarting from a blank prompt. Every successful generation job in Nomiq produces a **Memory Node** — a persisted context object that captures the prompt, the derived style seed, and the structural decisions the AI Engine made during that run. By passing a Memory Node's ID into a subsequent generation call, you instruct the engine to load the prior stylistic context and treat your new prompt as a refinement instruction rather than a fresh creative brief. This is the primary mechanism for iterative brand work: adjusting colors while keeping the logo form, tightening voice while preserving visual identity, or generating multiple client-facing variations from a single approved root. ## How Memory IDs Are Returned Memory Nodes are created automatically at the end of every successful generation job. You do not call a separate endpoint to create them. The `memory_id` for a completed job is returned inside the `generation.success` webhook payload under `data.memory_id`: ```json theme={null} { "event_id": "evt_5k6l7m8n", "type": "generation.success", "created_at": 1677649428, "data": { "job_id": "job_9x8y7z6w", "project_id": "proj_1A2b3C4d5E", "memory_id": "mem_X1y2Z3w4", "assets": { "logo_svg": "...", "primary_color": "#4338ca", "secondary_color": "#faf7f0", "font_family": "Inter" } } } ``` Store the `memory_id` value (`mem_X1y2Z3w4` in this example) in your database alongside the job record. You will pass it back to the Generation API in any subsequent refinement calls. *** ## Iterating with a Memory ID To refine an existing generation, include the `memory_id` parameter in the body of a new `POST /v1/brands/generate` request. The new `prompt` should describe only the change you want — you do not need to repeat the entire original brief. ### The `memory_id` Parameter on Generate The unique identifier of an existing Memory Node (prefix `mem_`) from a previous successful generation. When provided, the AI Engine loads the referenced stylistic context and applies the new `prompt` as an incremental refinement. The resulting job produces a new Memory Node, allowing you to chain refinements in sequence. If omitted, the generation starts from scratch using only the provided `prompt`. ### Example: Iterative Refinement The following example shows a two-step workflow. The first call creates the initial brand identity for a coffee shop. The second call references the resulting Memory Node to adjust only the typography, while preserving the indigo color palette and logo form established in step one. **Step 1 — Initial generation:** ```bash cURL theme={null} curl -X POST "https://api.nomiq.com/v1/brands/generate" \ -H "Authorization: Bearer sk_live_xxx" \ -H "Content-Type: application/json" \ -d '{ "project_id": "proj_1A2b3C4d5E", "prompt": "A modern coffee shop in Brooklyn that specializes in cold brew. Minimalistic cold brew glass logo, deep indigo and warm cream palette, clean geometric sans-serif typography.", "webhook_url": "https://yourapp.com/api/nomiq/webhook" }' ``` ```javascript Node.js theme={null} // Step 1: dispatch the initial generation const initialResponse = await fetch("https://api.nomiq.com/v1/brands/generate", { method: "POST", headers: { "Authorization": "Bearer sk_live_xxx", "Content-Type": "application/json", }, body: JSON.stringify({ project_id: "proj_1A2b3C4d5E", prompt: "A modern coffee shop in Brooklyn that specializes in cold brew. " + "Minimalistic cold brew glass logo, deep indigo and warm cream palette, " + "clean geometric sans-serif typography.", webhook_url: "https://yourapp.com/api/nomiq/webhook", }), }); const initialJob = await initialResponse.json(); // Save initialJob.id — you will receive memory_id via webhook ``` ```python Python theme={null} import requests # Step 1: dispatch the initial generation initial_response = requests.post( "https://api.nomiq.com/v1/brands/generate", headers={ "Authorization": "Bearer sk_live_xxx", "Content-Type": "application/json", }, json={ "project_id": "proj_1A2b3C4d5E", "prompt": ( "A modern coffee shop in Brooklyn that specializes in cold brew. " "Minimalistic cold brew glass logo, deep indigo and warm cream palette, " "clean geometric sans-serif typography." ), "webhook_url": "https://yourapp.com/api/nomiq/webhook", }, ) initial_job = initial_response.json() # Save initial_job["id"] — you will receive memory_id via webhook ``` After the `generation.success` webhook fires, you receive `memory_id: "mem_X1y2Z3w4"`. Now use that ID to refine the typography in a second call: **Step 2 — Refine typography using the Memory Node:** ```bash cURL theme={null} curl -X POST "https://api.nomiq.com/v1/brands/generate" \ -H "Authorization: Bearer sk_live_xxx" \ -H "Content-Type: application/json" \ -d '{ "project_id": "proj_1A2b3C4d5E", "prompt": "Change the typography to a humanist serif. Keep the logo and color palette exactly as they are.", "memory_id": "mem_X1y2Z3w4", "webhook_url": "https://yourapp.com/api/nomiq/webhook" }' ``` ```javascript Node.js theme={null} // Step 2: refine typography — preserve logo and palette via memory_id const refinedResponse = await fetch("https://api.nomiq.com/v1/brands/generate", { method: "POST", headers: { "Authorization": "Bearer sk_live_xxx", "Content-Type": "application/json", }, body: JSON.stringify({ project_id: "proj_1A2b3C4d5E", prompt: "Change the typography to a humanist serif. " + "Keep the logo and color palette exactly as they are.", memory_id: "mem_X1y2Z3w4", // ID from the generation.success webhook webhook_url: "https://yourapp.com/api/nomiq/webhook", }), }); const refinedJob = await refinedResponse.json(); // refinedJob.status === "queued" // A new memory_id will be returned in the next generation.success webhook ``` ```python Python theme={null} import requests # Step 2: refine typography — preserve logo and palette via memory_id refined_response = requests.post( "https://api.nomiq.com/v1/brands/generate", headers={ "Authorization": "Bearer sk_live_xxx", "Content-Type": "application/json", }, json={ "project_id": "proj_1A2b3C4d5E", "prompt": ( "Change the typography to a humanist serif. " "Keep the logo and color palette exactly as they are." ), "memory_id": "mem_X1y2Z3w4", # ID from the generation.success webhook "webhook_url": "https://yourapp.com/api/nomiq/webhook", }, ) refined_job = refined_response.json() # refined_job["status"] == "queued" # A new memory_id will be returned in the next generation.success webhook ``` Each generation call that includes a `memory_id` produces its own new Memory Node. This means you can chain refinements in a linear sequence — or branch multiple times from the same parent node to produce parallel variations. *** ## Use Cases Memory is most valuable when you need to make targeted adjustments without disrupting everything the AI Engine established in a prior run. Common patterns include: | Goal | Prompt Strategy | | ---------------------------------------- | -------------------------------------------------------------------------------------- | | Refine colors while keeping the logo | `"Shift the primary color from indigo to forest green. Preserve the logo form."` | | Adjust brand voice while keeping visuals | `"Rewrite the tagline and font pairing to feel more playful. Keep all visual assets."` | | Generate light and dark mode variants | `"Produce a dark-mode version of the palette. Logo and typography stay the same."` | | Create size-specific logo variants | `"Generate a compact wordmark variant suitable for 32×32 px favicons."` | Memory is especially powerful in **client revision workflows**. When a client approves a logo but requests color changes, branch a new generation from the approved Memory Node rather than re-running from scratch. This preserves billable approval milestones as discrete nodes in the graph and gives you a clean audit trail of every revision. Because Nomiq's Memory graph is acyclic, you can also dispatch multiple parallel branches from the same approved node — for example, three color palette variations — and present them simultaneously to the client for a single round of feedback. *** ## Listing Memory Nodes for a Project `GET https://api.nomiq.com/v1/projects/{project_id}/memory` Returns a chronologically ordered list of all Memory Nodes belonging to a project, most recent first. Use this endpoint to inspect the full revision history of a project or to look up a `memory_id` you did not store at webhook delivery time. The unique identifier of the project whose Memory Nodes you want to list. Maximum number of Memory Nodes to return. Defaults to `10`, maximum `100`. A Memory Node ID cursor for pagination. Returns the page of results after this node. ```bash cURL theme={null} curl -X GET "https://api.nomiq.com/v1/projects/proj_1A2b3C4d5E/memory?limit=5" \ -H "Authorization: Bearer sk_live_xxx" ``` ```javascript Node.js theme={null} const response = await fetch( "https://api.nomiq.com/v1/projects/proj_1A2b3C4d5E/memory?limit=5", { headers: { Authorization: "Bearer sk_live_xxx" }, } ); const history = await response.json(); history.data.forEach((node) => { console.log(node.id, node.prompt); }); ``` ```python Python theme={null} import requests response = requests.get( "https://api.nomiq.com/v1/projects/proj_1A2b3C4d5E/memory", headers={"Authorization": "Bearer sk_live_xxx"}, params={"limit": 5}, ) history = response.json() for node in history["data"]: print(node["id"], node["prompt"]) ``` ```json theme={null} { "object": "list", "data": [ { "id": "mem_L9m8N7o6", "object": "memory_node", "prompt": "Change the typography to a humanist serif. Keep the logo and color palette exactly as they are.", "parent_id": "mem_X1y2Z3w4", "created_at": 1677649500 }, { "id": "mem_X1y2Z3w4", "object": "memory_node", "prompt": "A modern coffee shop in Brooklyn that specializes in cold brew. Minimalistic cold brew glass logo, deep indigo and warm cream palette, clean geometric sans-serif typography.", "parent_id": null, "created_at": 1677649420 } ], "has_more": false, "next_cursor": null } ``` *** ## Next Steps Dispatch brand generation jobs and pass `memory_id` to iterate on existing outputs. Learn how to write effective refinement prompts that work well with Memory Nodes.