为什么 Lovable 会忘记我的项目上下文？

Lovable forgets your project context because each generation works from a sliding window of recent chat plus a single project knowledge blob, with documented drift somewhere around the 15–20 component mark. The product, domain, and architectural decisions you discussed early in the chat are not retrieved later. The fix is to attach a persistent project memory Lovable can pull from over REST.

Lovable is a vibe-coding app builder that generates React apps from natural-language prompts. Three design choices push project context out of the model's working memory:

1. Generations rely on recent chat plus visible code. The model composes each component from the prompt, the conversation tail, and whatever files are in scope. Decisions you made in week one about user roles, pricing tiers, or naming live in chat history that no longer fits in the active window.

2. Component drift after ~15–20 components. As the project grows, the visible code window shrinks relative to the codebase, and conventions that were obvious early in the project blur. Domain terms get re-translated, navigation gets re-organized, and earlier features get rebuilt.

3. Project knowledge is one text blob. Lovable's project knowledge area accepts standing instructions, and it applies to every generation, but it is global text — not retrieval. Detailed product context (personas, flows, edge cases) competes with everything else for that single blob's attention. The official Lovable docs at docs.lovable.dev cover project knowledge and its limits.

The result: Lovable is brilliant in early sessions and steadily blanker as the project grows.

Drift is not just cosmetic. It eats real building time:

• Re-explanation. "Trial users cannot invite teammates. Paid users can invite up to five. Admins can invite anyone." This rule, set in week one, has to be retyped every other generation.
• Re-invented features. The dashboard you shipped last sprint quietly gets re-proposed under a different name.
• Terminology drift. "Workspace" becomes "team" becomes "organization" across three views, because the project's canonical vocabulary lives in old chat the model can no longer see.

The fix is not "write longer prompts" — it is to keep product, domain, and decision memory outside the chat.

Lovable offers three ways to fight context loss. None of them is project memory in the structured sense.

Project knowledge lets you paste standing rules — product description, conventions, do/don't lists — that apply to every generation. Useful, and limited by being a single text field that the model reads as one chunk per generation.

Pinned files keep specific source files in context so the model can reference them reliably. Excellent for canonical components, and quickly capped by how many files you can keep visible at once.

Re-prompting and corrections let you steer back to the right context after drift. This works and it is exactly the manual work persistent memory is supposed to remove.

The deeper issue is that project context for a real product spans much more than one chat. You have PRDs, customer interviews, support tickets, design files, and prior decisions. A single project knowledge blob cannot hold them, and pinned files do not scale beyond a handful.

Project context needs to live above the builder.

MemoryLake is a cross-model memory layer Lovable reads from over REST. Instead of stuffing every fact into project knowledge, you store the project's real context — PRDs, decisions, vocabulary, rules — as Memories and let Lovable pull the relevant slice per generation.

• Per-project, retrieval-driven memory. Personas, flows, vocabulary, and architectural decisions live as structured Memories in the project. The retrieval engine returns only what matters for the component being generated, instead of relying on one global text blob.
• Decisions survive component drift. "Trial users cannot invite teammates" is not lost when chat scrolls. It is stored once and surfaced whenever a relevant generation is in progress.
• 10,000× the retrieval reach of raw prompting. MemoryLake reads from billions of tokens of project memory and feeds Lovable only what is relevant per turn, so you stop competing with chat scrollback for context space.

MemoryLake scored 94.03% on the LoCoMo long-context benchmark, the top published result as of 2026, with millisecond retrieval and AES-256 end-to-end encryption.

1. Create a project and load your context. Sign in to MemoryLake, open Project Management, click Create Project, and name it something like "Lovable — Acme SaaS app". Upload your PRD, persona docs, brand guide, and prior chat exports through the Document Drive. Add domain vocabulary and core rules — "trial users cannot invite teammates" — as Memories in the Memories tab.
2. Generate an MCP Server endpoint. Open the MCP Servers tab inside your project, click Add MCP Server, name it "Lovable project memory", and click Generate. MemoryLake returns an API key ID, secret, and endpoint URL. Copy the Bearer token immediately — it is shown only once.
3. Connect Lovable via REST. Lovable does not yet speak MCP natively, so use the REST API. Either paste your context into Lovable's project knowledge area and refresh it from MemoryLake whenever it changes, or run a setup script that calls MemoryLake's REST endpoint with your Bearer token and updates project knowledge on every release. Every new generation now opens with the project context already loaded.