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AI SDK middleware for memory injection and prompt management","maintainers":[{"name":"vserifsaglam","email":"vahit.serif119@gmail.com"}],"readme":"# @kognitivedev/vercel-ai-provider\n\nVercel AI SDK provider wrapper that integrates the Kognitive memory layer into your AI applications. It injects memory context, emits live turn traces, and logs finalized conversation snapshots back to the backend.\n\nFor new runs, the provider can also emit incremental live trace milestones to the backend while a generation or stream is still in progress.\n\n## Installation\n\n```bash\nnpm install @kognitivedev/vercel-ai-provider\n```\n\n### Peer Dependencies\n\nThis package requires the Vercel AI SDK:\n\n```bash\nnpm install ai\n```\n\n## Quick Start\n\n```typescript\nimport { createCognitiveLayer } from \"@kognitivedev/vercel-ai-provider\";\nimport { openai } from \"@ai-sdk/openai\";\nimport { generateText } from \"ai\";\n\n// 1. Create the cognitive layer\nconst cl = createCognitiveLayer({\n provider: openai,\n clConfig: {\n appId: \"my-app\",\n projectId: \"project-123\",\n baseUrl: \"http://localhost:3001\",\n apiKey: process.env.COGNITIVE_API_KEY!,\n }\n});\n\n// 2. Use it with Vercel AI SDK\nconst { text } = await generateText({\n model: cl(\"gpt-4o\", {\n userId: \"user-123\",\n sessionId: \"session-abc\",\n }),\n prompt: \"What's my favorite color?\"\n});\n```\n\nFor tracing, treat `projectId` and `sessionId` as required. Regular chat integrations now follow:\n\n- `1 session -> 1 agent_run -> many turn traces`\n- each completed turn writes its own `conversation_snapshot`\n- each turn emits its own live `trace-events`\n\n## Configuration\n\n### `CognitiveLayerConfig`\n\n| Option | Type | Required | Default | Description |\n|--------|------|----------|---------|-------------|\n| `appId` | `string` | Yes | - | Unique identifier for your application |\n| `defaultAgentId` | `string` | - | `\"default\"` | Default agent ID when not specified per-request |\n| `baseUrl` | `string` | - | `\"http://localhost:3001\"` | Kognitive backend API URL |\n| `apiKey` | `string` | - | - | API key for authentication (if required) |\n| `processDelayMs` | `number` | - | `500` | Delay before triggering memory processing (set to 0 to disable) |\n| `logLevel` | `LogLevel` | - | `'info'` | Controls console logging verbosity |\n| `providerFactory` | `(baseURL: string, apiKey: string) => (modelId: string) => LanguageModel` | - | - | Factory for creating a provider that routes through a gateway URL |\n\n### `LogLevel`\n\nControls verbosity of console output from the provider.\n\n| Level | Description |\n|-------|-------------|\n| `'none'` | No logging |\n| `'error'` | Only errors |\n| `'warn'` | Errors and warnings |\n| `'info'` | Errors, warnings, and info messages (default) |\n| `'debug'` | All messages including detailed snapshot data |\n\n```typescript\nconst cl = createCognitiveLayer({\n provider: openai,\n clConfig: {\n appId: \"my-app\",\n logLevel: \"debug\" // see full snapshot data in console\n }\n});\n```\n\n## API Reference\n\n### `createCognitiveLayer(config)`\n\nCreates a cognitive layer that adds memory capabilities to any Vercel AI SDK provider.\n\n**Parameters:**\n\n```typescript\ncreateCognitiveLayer({\n provider: any, // Vercel AI SDK provider (e.g., openai, anthropic)\n clConfig: CognitiveLayerConfig\n}): CognitiveLayer\n```\n\n**Returns:** `CognitiveLayer` - A callable model wrapper with `.streamText()` and `.generateText()` methods attached.\n\n---\n\n### `CognitiveLayer`\n\nThe return type of `createCognitiveLayer`. It is callable as a model wrapper and also exposes prompt-driven helpers.\n\n```typescript\ntype CognitiveLayer = CLModelWrapper & {\n streamText: (options: CLStreamTextOptions) => Promise>;\n generateText: (options: CLGenerateTextOptions) => ReturnType;\n resolvePrompt: (slug: string) => Promise;\n logConversation: (payload: LogConversationPayload) => Promise;\n triggerProcessing: (userId: string, projectId: string, sessionId: string) => void;\n clearSessionCache: (sessionKey?: string) => void;\n};\n```\n\n---\n\n### `CLModelWrapper`\n\nThe callable signature of `CognitiveLayer`.\n\n```typescript\ntype CLModelWrapper = (\n modelId: string,\n settings?: {\n userId?: string;\n agentId?: string;\n sessionId?: string;\n },\n providerOptions?: Record\n) => LanguageModel;\n```\n\n**Parameters:**\n\n| Parameter | Type | Required | Description |\n|-----------|------|----------|-------------|\n| `modelId` | `string` | Yes | Model identifier (e.g., `\"gpt-4o\"`, `\"claude-3-opus\"`) |\n| `settings.userId` | `string` | - | User identifier (required for memory features) |\n| `settings.agentId` | `string` | - | Override default agent ID |\n| `settings.sessionId` | `string` | - | Session identifier (required for logging) |\n| `providerOptions` | `Record` | - | Provider-specific options passed directly to the underlying provider |\n\n---\n\n### `PromptConfig`\n\nUsed with `cl.streamText()` and `cl.generateText()` to reference a managed prompt.\n\n```typescript\ninterface PromptConfig {\n slug: string;\n variables?: Record;\n}\n```\n\n## Usage Examples\n\n## Live Trace Events\n\nWhen `userId`, `projectId`, and `sessionId` are available, the provider now uses three complementary backend paths:\n\n- `POST /api/cognitive/agent-run` to ensure or update the logical run\n- `POST /api/cognitive/trace-events` for append-only live milestones such as trace start, assistant progress, and tool completion\n- `POST /api/cognitive/log` for the finalized conversation snapshot after the turn completes\n\nFor chat apps, the provider reuses one session-scoped run for the lifetime of the session and emits one new trace per turn.\n\nThis is additive:\n\n- existing memory logging behavior stays the same\n- old traces without live events remain valid\n- dashboards normalize both legacy traces and event-backed traces into one read model\n\n### With OpenAI\n\n```typescript\nimport { createCognitiveLayer } from \"@kognitivedev/vercel-ai-provider\";\nimport { openai } from \"@ai-sdk/openai\";\nimport { generateText } from \"ai\";\n\nconst cl = createCognitiveLayer({\n provider: openai,\n clConfig: {\n appId: \"my-app\",\n baseUrl: \"https://api.kognitive.dev\"\n }\n});\n\nconst { text } = await generateText({\n model: cl(\"gpt-4o\", {\n userId: \"user-123\",\n sessionId: \"session-abc\"\n }),\n prompt: \"Remember that my favorite color is blue\"\n});\n```\n\n### With OpenRouter (Provider Options)\n\nPass provider-specific options as the third parameter:\n\n```typescript\nimport { createCognitiveLayer } from \"@kognitivedev/vercel-ai-provider\";\nimport { createOpenRouter } from \"@openrouter/ai-sdk-provider\";\nimport { generateText } from \"ai\";\n\nconst openrouter = createOpenRouter({\n apiKey: process.env.OPENROUTER_API_KEY\n});\n\nconst cl = createCognitiveLayer({\n provider: openrouter.chat,\n clConfig: {\n appId: \"my-app\",\n baseUrl: \"https://api.kognitive.dev\"\n }\n});\n\n// Pass provider-specific options as the third parameter\nconst { text } = await generateText({\n model: cl(\"moonshotai/kimi-k2-0905\", {\n userId: \"user-123\",\n sessionId: \"session-abc\"\n }, {\n provider: {\n only: [\"openai\"]\n }\n }),\n prompt: \"What's the weather like?\"\n});\n```\n\n### With Anthropic\n\n```typescript\nimport { createCognitiveLayer } from \"@kognitivedev/vercel-ai-provider\";\nimport { anthropic } from \"@ai-sdk/anthropic\";\nimport { streamText } from \"ai\";\n\nconst cl = createCognitiveLayer({\n provider: anthropic,\n clConfig: {\n appId: \"my-app\",\n defaultAgentId: \"claude-assistant\"\n }\n});\n\nconst result = await streamText({\n model: cl(\"claude-3-5-sonnet-latest\", {\n userId: \"user-456\",\n sessionId: \"chat-xyz\"\n }),\n prompt: \"What did I tell you about my favorite color?\"\n});\n\nfor await (const chunk of result.textStream) {\n process.stdout.write(chunk);\n}\n```\n\n### With System Prompts\n\nThe provider automatically injects memory context into your system prompts:\n\n```typescript\nconst { text } = await generateText({\n model: cl(\"gpt-4o\", {\n userId: \"user-123\",\n sessionId: \"session-abc\"\n }),\n system: \"You are a helpful assistant.\",\n prompt: \"What do you know about me?\"\n});\n\n// Memory context is automatically appended to system prompt\n```\n\n### Prompt Management\n\nUse `cl.streamText()` and `cl.generateText()` to resolve prompts by slug from the backend. Prompts are fetched from the `/api/cognitive/prompt` endpoint and cached for 60 seconds.\n\nPrompt templates use [Handlebars](https://handlebarsjs.com/) syntax, giving you variable interpolation and conditional blocks. This means prompts can be fully managed from the dashboard without any string concatenation in application code.\n\n#### Template Syntax\n\n**Variable interpolation** — insert a value by name:\n\n```handlebars\nHello {{userName}}, welcome to {{companyName}}.\n```\n\n**Conditional blocks** — include content only when a variable is truthy:\n\n```handlebars\n{{#if hasImages}}\nThe user has attached images. Please analyze them.\n{{/if}}\n```\n\n**If / else** — provide a fallback when the condition is falsy:\n\n```handlebars\n{{#if isPremium}}\nYou have access to all features.\n{{else}}\nUpgrade to premium for full access.\n{{/if}}\n```\n\n**Unless** — inverse of `if`, includes the block when the variable is falsy:\n\n```handlebars\n{{#unless hasHistory}}\nThis is a new user. Introduce yourself.\n{{/unless}}\n```\n\n**Nesting** — conditionals can be nested arbitrarily:\n\n```handlebars\n{{#if hasAttachments}}\n Attachments provided.\n {{#if hasImages}}\n Includes images for visual analysis.\n {{/if}}\n{{/if}}\n```\n\n#### Variable Types\n\nVariables can be `string` or `boolean`:\n\n| Type | Truthy | Falsy |\n|------|--------|-------|\n| `string` | Any non-empty string | `\"\"` (empty string) |\n| `boolean` | `true` | `false` |\n\nUndefined variables render as empty string in interpolation and are treated as falsy in conditionals.\n\n> **Note:** Content is not HTML-escaped — prompt text passes through as-is, which is the correct behavior for LLM prompts.\n\n#### Usage with `cl.streamText()` / `cl.generateText()`\n\nPass variables in the `prompt.variables` field:\n\n```typescript\n// Stream with a managed prompt\nconst result = await cl.streamText({\n model: cl(\"gpt-4o\", {\n userId: \"user-123\",\n sessionId: \"session-abc\"\n }),\n prompt: {\n slug: \"fitness-coach\",\n variables: {\n userName: \"Sarah\",\n fitnessGoal: \"Build muscle\",\n hasImages: true,\n hasAttachments: false\n }\n },\n messages: [{ role: \"user\", content: \"Check my squat form\" }]\n});\n\nfor await (const chunk of result.textStream) {\n process.stdout.write(chunk);\n}\n```\n\n```typescript\n// Generate with a managed prompt (string-only variables work too)\nconst { text } = await cl.generateText({\n model: cl(\"gpt-4o\", {\n userId: \"user-123\",\n sessionId: \"session-abc\"\n }),\n prompt: {\n slug: \"summarizer\",\n variables: {\n language: \"English\"\n }\n },\n messages: [{ role: \"user\", content: \"Summarize this document...\" }]\n});\n```\n\n#### Standalone `renderTemplate()`\n\nThe template engine is also exported directly for use outside of prompt management:\n\n```typescript\nimport { renderTemplate } from \"@kognitivedev/vercel-ai-provider\";\n\nconst result = renderTemplate(\n \"Hello {{name}}! {{#if isVip}}Welcome back, VIP.{{/if}}\",\n { name: \"Alice\", isVip: true }\n);\n// → \"Hello Alice! Welcome back, VIP.\"\n```\n\n#### Full Example Template\n\nA prompt template stored in the dashboard might look like:\n\n```handlebars\nYou are a fitness coach AI assistant.\n\nUser: {{userName}}\nGoal: {{fitnessGoal}}\n\n{{#if hasImages}}\nThe user has attached images for form analysis. Please review them carefully.\n{{/if}}\n{{#if hasAttachments}}\nAdditional documents have been provided for context.\n{{/if}}\n{{#unless hasHistory}}\nThis is a new user with no prior conversation history. Introduce yourself.\n{{/unless}}\n\nPlease provide personalized advice.\n```\n\n### Gateway Routing\n\nWhen a prompt has a `gatewaySlug` configured on the backend, requests are automatically routed through the gateway endpoint at `{baseUrl}/api/cognitive/gateway/{gatewaySlug}`.\n\nTo enable gateway routing, provide a `providerFactory` in your config:\n\n```typescript\nimport { createOpenAI } from \"@ai-sdk/openai\";\n\nconst cl = createCognitiveLayer({\n provider: openai,\n clConfig: {\n appId: \"my-app\",\n baseUrl: \"https://api.kognitive.dev\",\n providerFactory: (baseURL, apiKey) => {\n const gatewayProvider = createOpenAI({ baseURL, apiKey });\n return (modelId) => gatewayProvider(modelId);\n }\n }\n});\n```\n\nWithout `providerFactory`, the provider will log a warning and skip gateway routing, sending requests directly to the original provider.\n\n### `cl.resolvePrompt(slug)`\n\nFetches and caches a prompt by slug from the backend. Prompts are cached for 60 seconds.\n\n```typescript\nconst prompt = await cl.resolvePrompt(\"customer-support\");\nconsole.log(prompt.content); // The prompt template text\nconsole.log(prompt.version); // Prompt version number\nconsole.log(prompt.promptId); // Unique prompt ID\n```\n\n**Returns:** `Promise`\n\n```typescript\ninterface CachedPrompt {\n promptId: string;\n slug: string;\n version: number;\n content: string;\n fetchedAt: number;\n gatewaySlug?: string;\n}\n```\n\n---\n\n### `cl.logConversation(payload)`\n\nManually logs a conversation to the backend for memory processing.\n\n```typescript\nawait cl.logConversation({\n userId: \"user-123\",\n projectId: \"my-project\",\n sessionId: \"session-abc\",\n messages: [\n { role: \"user\", content: \"Hello\" },\n { role: \"assistant\", content: \"Hi there!\" }\n ],\n modelId: \"gpt-4o\",\n});\n```\n\n**Parameters:** `LogConversationPayload`\n\n```typescript\ninterface LogConversationPayload {\n userId: string;\n projectId: string;\n sessionId: string;\n messages: any[];\n memorySystemPrompt?: string;\n modelId?: string;\n usage?: Record;\n promptSlug?: string;\n promptVersion?: number;\n promptId?: string;\n}\n```\n\n---\n\n### `cl.triggerProcessing(userId, projectId, sessionId)`\n\nManually triggers memory processing for a given session. This is normally called automatically after logging, but can be invoked independently.\n\n```typescript\ncl.triggerProcessing(\"user-123\", \"my-project\", \"session-abc\");\n```\n\n---\n\n### Managed guardrails\n\n`clConfig.guardrails` enables guardrails defined in the dashboard (Evaluate → Guardrails). `true` uses the\ndefaults; an object sets `agentName`, `stages`, `inputMode` / `outputMode` (`\"sync\"` | `\"async\"`),\n`blockedResponse`, `timeoutMs`, `failClosed` and `onVerdicts`. Semantics match guardrails on `createAgent`:\na sync input block skips the model (`[Guardrail] ` for `generateText`, `GuardrailViolationError`\nfor `streamText`, or a normal streamed refusal when `blockedResponse` is set); output guardrails replace\nthe text for `generateText` and end a stream in `GuardrailViolationError` / `GuardrailAbortError`.\nBlocked input is never logged or sent to memory extraction. See the docs page \"Managed Guardrails\".\n\n---\n\n### `cl.clearSessionCache(sessionKey?)`\n\nClears cached memory snapshots. Snapshots are cached per session for `snapshotCacheTtlMs` (default 5 minutes); failed fetches are never cached. Pass a specific session key (`\"userId:sessionId\"`, or `\"userId:default\"` when no session id is set) to clear one session, or omit to clear all.\n\n```typescript\n// Clear a specific session\ncl.clearSessionCache(\"user-123:session-abc\");\n\n// Clear all cached sessions\ncl.clearSessionCache();\n```\n\n---\n\n### Without Memory (Anonymous Users)\n\nSkip memory features by omitting `userId`:\n\n```typescript\nconst { text } = await generateText({\n model: cl(\"gpt-4o\"),\n prompt: \"General question without memory\"\n});\n```\n\n## How It Works\n\n### Memory Injection Flow\n\n1. **Request Interception**: When a request is made, the middleware fetches the user's memory snapshot\n2. **Context Injection**: Memory context is injected into the system prompt as `` block\n3. **Response Processing**: After the response, the conversation is logged\n4. **Background Processing**: Memory extraction and management runs asynchronously\n\n### Memory Context Format\n\nThe injected memory context follows this structure:\n\n```xml\n\nUse the following memory to stay consistent. Prefer UserContext facts for answers; AgentHeuristics guide style, safety, and priorities.\n\n- User prefers concise responses\n- Always greet user by name\n\n\n\n- User's name is John\n- Favorite color is blue\n\n\n- Currently working on a project\n\n\n\n```\n\n## Backend API Integration\n\nThe provider communicates with your Kognitive backend via these endpoints:\n\n| Endpoint | Method | Description |\n|----------|--------|-------------|\n| `/api/cognitive/snapshot` | GET | Fetches user's memory snapshot |\n| `/api/cognitive/log` | POST | Logs conversation for processing |\n| `/api/cognitive/process` | POST | Triggers memory extraction/management |\n| `/api/cognitive/prompt` | GET | Resolves a managed prompt by slug |\n| `/api/cognitive/gateway/{slug}` | * | Gateway proxy endpoint for routed requests |\n\n### Query Parameters for Snapshot\n\n```\nGET /api/cognitive/snapshot?userId={userId}&agentId={agentId}&appId={appId}\n```\n\n## Troubleshooting\n\n### Memory not being injected\n\n1. Ensure `userId` and `sessionId` are provided\n2. Check that the backend is running at the configured `baseUrl`\n3. Verify the snapshot endpoint returns data\n\n### Console warnings\n\n```\nCognitiveLayer: sessionId is required to log and process memories; skipping logging until provided.\n```\n\nThis warning appears when `userId` is provided but `sessionId` is missing. Add `sessionId` to enable logging.\n\n### Processing delay\n\nThe default 500ms delay before triggering memory processing allows database writes to settle. Adjust with `processDelayMs`:\n\n```typescript\nclConfig: {\n processDelayMs: 1000 // 1 second delay\n // processDelayMs: 0 // Immediate processing\n}\n```\n\n### Gateway not routing\n\nIf requests are not being routed through the gateway:\n\n1. Ensure `providerFactory` is set in your `clConfig`\n2. Verify the prompt has a `gatewaySlug` configured on the backend\n3. Set `logLevel: 'debug'` to inspect gateway resolution logs\n\n## License\n\nMIT\n","readmeFilename":"README.md"}