docs(integrations): rewrite Vercel AI SDK guide as cookbook style (DEV-1485) (#635)
* docs(integrations): add @honcho-ai/vercel-ai-sdk guide Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> * docs(integrations): rewrite Vercel AI SDK guide as cookbook style (DEV-1485) Reshapes the guide to cookbook formula, adds Full Script section, fixes maxSteps → stopWhen for ai-sdk v5, renames package, and prunes stale notes. See PR for full decision log. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> * docs(integrations): lead Vercel AI SDK verification with direct-inspection check - Restructure Verifying section: direct inspection (token delta + dashboard) is now step 1 so readers isolate Honcho's contribution before grading model behavior - Behavioral tests (first turn, multi-turn, cross-session, tool calling) follow as steps 2-5 - Note `result.toolCalls` as the way to confirm which Honcho tool fired (tool names don't appear in `result.text`) - Signpost the Full Script from Complete Example so the two snippets read as a staircase, not a duplicate Addresses review comments on PR #635. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(tests): satisfy basedpyright in test_representation_manager The save-representation tests added in #615 were structurally correct but failed strict typing in two places. Static Analysis has been red on main since the merge. - `mock_save.await_args` is `_Call | None`; assert it's not None before reading `.kwargs` / `.args` so basedpyright can narrow the type - `SimpleNamespace(...)` passed as `message_level_configuration` is an intentional duck-typed mock (only `.dream.enabled` is read by `save_representation`), so opt out at the call site with `# pyright: ignore[reportArgumentType]` rather than constructing a full `ResolvedConfiguration` (matches the existing `reportPrivateUsage` ignore pattern in this file) No runtime behavior changes; `uv run basedpyright` is now clean project-wide. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(tests): pad timestamp windows in test_messages for clock skew Three timestamp tests captured `before_request` / `after_request` with `datetime.now(UTC)` on the host and asserted the server's `created_at` fell within. Under Docker, the Postgres container's clock can skew tens of ms from the macOS host, flipping the assertion intermittently under parallel pytest load. Pad each window by 1 second on both sides — wide enough to absorb realistic skew, narrow enough that the test still proves the timestamp is server-current. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs(integrations): tighten Verifying section after end-to-end smoke Smoke-tested all five verification steps against a fresh Sonnet 4.6 + Honcho integration. Three findings, all reflected here: - Cross-session recall (#4): added Note about DERIVER_REPRESENTATION_BATCH_MAX_TOKENS=1024 — short warmups don't accumulate enough content to flush observations, so cross-session recall returns empty even on a working integration. - Tool calling prompt (#5): replaced the honcho_chat patterns prompt with a verbatim-retrieval honcho_search prompt. Sonnet skips honcho_chat when middleware-injected context already answers; verbatim retrieval forces a fire. - Tool inspection (#5): replaced result.toolCalls reference with result.steps[i].toolCalls + flatMap snippet. Top-level toolCalls is empty in multi-step calls (stopWhen: stepCountIs(N)) — the fires are nested inside steps. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs(integrations): make Step 4 cross-session test durable via honcho_search Replace the prose-recall test ("Based on what we've talked about, what do you know about me?") with a forced honcho_search call. Prose recall depended on the model getting deriver-built representation/peer-card in its system prompt, which is gated behind DERIVER_REPRESENTATION_BATCH_MAX_TOKENS=1024 — short tutorial-length conversations don't trigger it, producing false negatives on a working integration. honcho_search hits message embeddings, which are computed synchronously at message persist time (src/crud/message.py:262-276), so peer-scoped retrieval works regardless of how short the prior session was. Also folds the result.steps[i].toolCalls inspection snippet from the old Step 5 into Step 4 — same prompt, no need for two sections. Drops Step 5 entirely. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
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@ -104,6 +104,7 @@
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"pages": [
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"v3/guides/integrations/claude-code",
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"v3/guides/integrations/opencode",
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"v3/guides/integrations/vercel-ai-sdk",
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"v3/guides/integrations/crewai",
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"v3/guides/integrations/langgraph",
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"v3/guides/integrations/mcp",
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@ -0,0 +1,377 @@
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---
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title: "Vercel AI SDK"
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icon: "triangle"
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iconType: "solid"
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description: "Add persistent user memory and reasoning to any Vercel AI SDK app with Honcho"
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sidebarTitle: "Vercel AI SDK"
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---
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Integrate Honcho with the Vercel AI SDK to build AI apps that remember users across sessions. The [Vercel AI SDK](https://sdk.vercel.ai) is an open-source TypeScript toolkit for building AI-powered apps with a unified API across providers. This guide shows you how to wrap any `generateText` or `streamText` call with Honcho's memory middleware and reasoning tools.
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<Note>
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The full package source and examples are available on [GitHub](https://github.com/plastic-labs/vercel-ai-sdk-package).
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</Note>
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## What We're Building
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We'll wire Honcho into a Vercel AI SDK app so the model receives context from past conversations and can query what it knows about the user mid-generation. Here's how the pieces fit together:
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- **Vercel AI SDK** handles model calls and streaming
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- **Honcho** stores messages and retrieves user context before each generation
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- **Your model provider** can be Anthropic, OpenAI, Google, etc.
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The key benefit: you don't manually manage conversation history across sessions. Honcho handles persistence and context injection — the model always has a rich picture of who it's talking to. (New to Honcho's primitives? See [peers and sessions](/v3/documentation/core-concepts/architecture).)
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## Setup
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Install the package:
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<CodeGroup>
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```bash npm
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npm install @honcho-ai/vercel-ai-sdk
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```
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```bash pnpm
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pnpm add @honcho-ai/vercel-ai-sdk
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```
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```bash yarn
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yarn add @honcho-ai/vercel-ai-sdk
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```
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```bash bun
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bun add @honcho-ai/vercel-ai-sdk
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```
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</CodeGroup>
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Get your API key at [app.honcho.dev](https://app.honcho.dev).
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```bash
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HONCHO_API_KEY=your-api-key
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HONCHO_WORKSPACE_ID=your-workspace-id
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```
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## Create a Provider Instance
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`createHoncho()` is the entry point. It reads your API key and workspace from environment variables and returns a provider object with `middleware()`, `tools()`, and `send()`.
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```typescript
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import { createHoncho } from '@honcho-ai/vercel-ai-sdk';
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const honcho = createHoncho();
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```
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You can set a stable `defaultAssistantId` on the provider to identify the AI peer across all calls:
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```typescript
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const honcho = createHoncho({
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defaultAssistantId: 'my-assistant',
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});
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```
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## Add Middleware
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`honcho.middleware()` is compatible with `wrapLanguageModel`. Two things happen on each call:
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1. **Before generation** — Honcho fetches the user's representation, peer card, session summary, and recent messages and injects them into the system prompt
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2. **After generation** — the user message and assistant response are stored back in Honcho with correct peer attribution
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```typescript
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import { createHoncho } from '@honcho-ai/vercel-ai-sdk';
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import { wrapLanguageModel, generateText } from 'ai';
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import { anthropic } from '@ai-sdk/anthropic';
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const honcho = createHoncho();
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const model = wrapLanguageModel({
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model: anthropic('claude-sonnet-4-6'),
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middleware: honcho.middleware({
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userId: 'user-abc',
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sessionId: 'session-123',
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}),
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});
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const { text } = await generateText({
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model,
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prompt: 'What should I focus on today?',
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});
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```
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Pass `userId` and `sessionId` per request — no session handles to construct. Both default to lazily generated IDs if omitted, which is fine for local scripts but not for multi-user server traffic.
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## Add Tools
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`honcho.tools()` gives the model six tools it can call mid-generation to query or update what it knows about the user:
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| Tool | What it does |
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| --- | --- |
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| `honcho_chat` | Dialectic reasoning — ask natural-language questions about the user; answers synthesized from full interaction history |
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| `honcho_context` | Short summary of recent context within the session |
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| `honcho_search` | Semantic search over stored conversation messages |
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| `honcho_search_conclusions` | Query derived conclusions: personality traits, preferences, behavioral patterns |
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| `honcho_get_representation` | Full synthesized profile of the user |
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| `honcho_save_conclusion` | Persist an observation about the user for future sessions |
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Pass the same `userId` and `sessionId` to `honcho.tools()` so tool calls bind to the same peers as the middleware:
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```typescript
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import { generateText, stepCountIs } from 'ai';
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const { text } = await generateText({
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model,
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tools: honcho.tools({
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userId: 'user-abc',
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sessionId: 'session-123',
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}),
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stopWhen: stepCountIs(3),
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prompt: 'Based on our conversations, what do I care about most?',
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});
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```
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## Complete Example
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Here's a full working example combining middleware and tools.
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Want a runnable end-to-end version? See the [Full Script](#full-script).
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```typescript
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import { createHoncho } from '@honcho-ai/vercel-ai-sdk';
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import { wrapLanguageModel, generateText, stepCountIs } from 'ai';
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import { anthropic } from '@ai-sdk/anthropic';
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const honcho = createHoncho({
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defaultAssistantId: 'assistant',
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});
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const userId = 'user-abc';
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const sessionId = 'session-123';
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const model = wrapLanguageModel({
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model: anthropic('claude-sonnet-4-6'),
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middleware: honcho.middleware({ userId, sessionId }),
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});
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const { text } = await generateText({
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model,
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tools: honcho.tools({ userId, sessionId }),
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stopWhen: stepCountIs(3),
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prompt: 'What should we work on today?',
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});
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console.log(text);
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```
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## Streaming
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`streamText` works the same way — middleware handles persistence after the stream completes:
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```typescript
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import { createHoncho } from '@honcho-ai/vercel-ai-sdk';
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import { wrapLanguageModel, streamText } from 'ai';
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import { openai } from '@ai-sdk/openai';
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const honcho = createHoncho();
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const userId = 'user-abc';
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const sessionId = 'session-456';
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const model = wrapLanguageModel({
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model: openai('gpt-4o'),
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middleware: honcho.middleware({ userId, sessionId }),
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});
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const result = streamText({
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model,
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tools: honcho.tools({ userId, sessionId }),
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prompt: 'What should we work on today?',
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});
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for await (const chunk of result.textStream) {
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process.stdout.write(chunk);
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}
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```
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## Using with `messages`
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If your app already manages conversation history and passes a `messages` array directly, set `injectHistory: false` to prevent Honcho from prepending duplicate history:
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```typescript
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honcho.middleware({
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userId,
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sessionId,
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injectHistory: false, // don't prepend history — we're passing messages directly
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})
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```
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Honcho still injects the user's representation and peer card into the system prompt, and still persists messages after generation. With `injectHistory: false` you must pass a `messages` array — without either `messages` or `prompt`, the Vercel AI SDK throws `Invalid prompt: prompt or messages must be defined`.
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## Verifying the Integration
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### 1. Isolate Honcho's Contribution
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Let's confirm the memory is actually coming from Honcho and not your app's existing conversation history.
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Two ways to check: 1) through a developer method 2) through the UI.
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**Token delta (developer check).** On a session with a few prior turns, run the same prompt twice — once with `injectHistory: false` and once without.
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Compare `result.usage.inputTokens`:
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```typescript
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const baseline = await generateText({
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model: wrapLanguageModel({
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model: anthropic('claude-sonnet-4-6'),
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middleware: honcho.middleware({ userId, sessionId, injectHistory: false }),
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}),
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prompt: 'What do you know about my preferences?',
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});
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const injected = await generateText({
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model: wrapLanguageModel({
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model: anthropic('claude-sonnet-4-6'),
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middleware: honcho.middleware({ userId, sessionId }),
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}),
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prompt: 'What do you know about my preferences?',
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});
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console.log(injected.usage.inputTokens - baseline.usage.inputTokens);
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```
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A positive delta is Honcho's representation, peer card, and session summary being injected into the system prompt. Expect ~0 on a fresh peer — the deriver runs asynchronously after messages persist, so injected context only populates after a few prior turns.
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**Dashboard (UI check).** Open [app.honcho.dev/explore](https://app.honcho.dev/explore), select your workspace, and confirm your peer and session appear under the Peers and Sessions tables.
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With Honcho's contribution isolated, the rest of this section shows what the integration feels like in practice.
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### 2. First turn
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Send any message. The model responds normally — nothing is stored yet. Context injection returns empty on the first turn.
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### 3. Build memory across turns
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Have a multi-turn conversation and share something about yourself:
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```text
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I prefer concise answers and I mostly work in TypeScript.
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```
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After a few turns, ask:
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```text
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What do you know about my preferences?
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```
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If the model references TypeScript and concise answers without being told again in this session, memory is working.
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### 4. Cross-session recall
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Start a new session (new `sessionId`) with the same `userId`. Ask:
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```text
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Call your honcho_search tool with the query 'TypeScript' and quote the exact verbatim message that contained TypeScript. Do not paraphrase.
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```
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If the search returns a message from the prior session word-for-word, peer-scoped retrieval is crossing session boundaries. `honcho_search` queries the user's messages across all their sessions and doesn't depend on the deriver, so it works regardless of how short the prior session was.
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To confirm the tool actually fired, inspect `result.steps[i].toolCalls`:
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```typescript
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const toolFires = result.steps?.flatMap((step, i) =>
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(step.toolCalls ?? []).map((tc) => ({ step: i, tool: tc.toolName, input: tc.input }))
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) ?? [];
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console.log(toolFires);
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// [{ step: 0, tool: "honcho_search", input: { query: "TypeScript", limit: 10 } }]
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```
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When the model takes more than one turn (call a tool, see the result, then answer), the top-level `result.toolCalls` is empty — check inside each `step`.
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## Full Script
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<Accordion title="honcho_vercel_chat.ts">
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```typescript
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/**
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* Multi-turn chat with Honcho memory + Vercel AI SDK.
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*
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* Prerequisites:
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* 1. Install dependencies:
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* npm install @honcho-ai/vercel-ai-sdk ai @ai-sdk/anthropic dotenv
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* 2. Set environment variables in `.env`:
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* HONCHO_API_KEY=your-honcho-api-key
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* HONCHO_WORKSPACE_ID=your-workspace-id
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* ANTHROPIC_API_KEY=your-anthropic-api-key
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* 3. Run with: npx tsx honcho_vercel_chat.ts
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*
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* Pass a stable userId from your auth system and a sessionId for the conversation
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* thread; Honcho handles persistence and context injection on every turn.
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*/
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import 'dotenv/config';
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import { createHoncho } from '@honcho-ai/vercel-ai-sdk';
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import { wrapLanguageModel, generateText, stepCountIs } from 'ai';
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import { anthropic } from '@ai-sdk/anthropic';
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import * as readline from 'node:readline/promises';
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import { stdin as input, stdout as output } from 'node:process';
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const honcho = createHoncho({
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defaultAssistantId: 'assistant',
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});
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const userId = process.env.USER_ID ?? 'demo-user';
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const sessionId = process.env.SESSION_ID ?? `session-${Date.now()}`;
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const model = wrapLanguageModel({
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model: anthropic('claude-sonnet-4-6'),
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middleware: honcho.middleware({ userId, sessionId }),
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});
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async function chat(prompt: string): Promise<string> {
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const { text } = await generateText({
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model,
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tools: honcho.tools({ userId, sessionId }),
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stopWhen: stepCountIs(3),
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prompt,
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});
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return text;
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}
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async function main() {
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const rl = readline.createInterface({ input, output });
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console.log(`Honcho session: ${sessionId} (user: ${userId})`);
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console.log('Type a message, or "exit" to quit.\n');
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while (true) {
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const userMessage = (await rl.question('you > ')).trim();
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if (!userMessage || userMessage === 'exit') break;
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const reply = await chat(userMessage);
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console.log(`bot > ${reply}\n`);
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}
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rl.close();
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}
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main().catch((err) => {
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console.error(err);
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process.exit(1);
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});
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```
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</Accordion>
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## Next Steps
|
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|
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<CardGroup cols={2}>
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<Card title="Github Repository" icon="github" href="https://github.com/plastic-labs/vercel-ai-sdk-package">
|
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Source, tests, and full API reference for @honcho-ai/vercel-ai-sdk.
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</Card>
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|
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<Card title="Honcho Architecture" icon="sitemap" href="/v3/documentation/core-concepts/architecture">
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||||
Learn about peers, sessions, and dialectic reasoning.
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</Card>
|
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|
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<Card title="Self-Hosting Guide" icon="server" href="/v3/contributing/self-hosting">
|
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Run Honcho locally with your Vercel AI SDK app.
|
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</Card>
|
||||
|
||||
<Card title="Vercel AI SDK Docs" icon="book" href="https://sdk.vercel.ai">
|
||||
wrapLanguageModel, middleware, and tool use reference.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
|
@ -24,6 +24,7 @@ async def _fake_tracked_db(_name: str):
|
|||
|
||||
def _saved_observations(mock_save: AsyncMock):
|
||||
call = mock_save.await_args
|
||||
assert call is not None, "mock was not awaited"
|
||||
if "all_observations" in call.kwargs:
|
||||
return call.kwargs["all_observations"]
|
||||
if len(call.args) > 1:
|
||||
|
|
@ -162,7 +163,9 @@ class TestRepresentationManagerSoftDelete:
|
|||
|
||||
class TestRepresentationManagerSave:
|
||||
@pytest.mark.asyncio
|
||||
async def test_save_representation_filters_blank_observations_before_embedding(self):
|
||||
async def test_save_representation_filters_blank_observations_before_embedding(
|
||||
self,
|
||||
):
|
||||
manager = RepresentationManager(
|
||||
"workspace",
|
||||
observer="observer",
|
||||
|
|
@ -202,7 +205,7 @@ class TestRepresentationManagerSave:
|
|||
message_ids=[1],
|
||||
session_name="session",
|
||||
message_created_at=datetime.now(timezone.utc),
|
||||
message_level_configuration=SimpleNamespace(
|
||||
message_level_configuration=SimpleNamespace( # pyright: ignore[reportArgumentType]
|
||||
dream=SimpleNamespace(enabled=False)
|
||||
),
|
||||
)
|
||||
|
|
@ -258,7 +261,7 @@ class TestRepresentationManagerSave:
|
|||
message_ids=[1],
|
||||
session_name="session",
|
||||
message_created_at=datetime.now(timezone.utc),
|
||||
message_level_configuration=SimpleNamespace(
|
||||
message_level_configuration=SimpleNamespace( # pyright: ignore[reportArgumentType]
|
||||
dream=SimpleNamespace(enabled=False)
|
||||
),
|
||||
)
|
||||
|
|
@ -311,7 +314,7 @@ class TestRepresentationManagerSave:
|
|||
message_ids=[1],
|
||||
session_name="session",
|
||||
message_created_at=datetime.now(timezone.utc),
|
||||
message_level_configuration=SimpleNamespace(
|
||||
message_level_configuration=SimpleNamespace( # pyright: ignore[reportArgumentType]
|
||||
dream=SimpleNamespace(enabled=False)
|
||||
),
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1001,8 +1001,10 @@ async def test_create_message_without_timestamp_uses_default(
|
|||
db_session.add(test_session)
|
||||
await db_session.commit()
|
||||
|
||||
# Record time before request
|
||||
before_request = datetime.datetime.now(datetime.timezone.utc)
|
||||
# Pad the window to absorb client/Postgres clock skew under Docker.
|
||||
before_request = datetime.datetime.now(datetime.timezone.utc) - datetime.timedelta(
|
||||
seconds=1
|
||||
)
|
||||
|
||||
response = client.post(
|
||||
f"/v3/workspaces/{test_workspace.name}/sessions/{test_session.name}/messages",
|
||||
|
|
@ -1017,8 +1019,9 @@ async def test_create_message_without_timestamp_uses_default(
|
|||
},
|
||||
)
|
||||
|
||||
# Record time after request
|
||||
after_request = datetime.datetime.now(datetime.timezone.utc)
|
||||
after_request = datetime.datetime.now(datetime.timezone.utc) + datetime.timedelta(
|
||||
seconds=1
|
||||
)
|
||||
|
||||
assert response.status_code == 201
|
||||
data = response.json()
|
||||
|
|
@ -1053,8 +1056,9 @@ async def test_create_batch_messages_with_mixed_timestamps(
|
|||
timestamp1 = datetime.datetime(2023, 1, 1, 12, 0, 0, tzinfo=datetime.timezone.utc)
|
||||
timestamp2 = datetime.datetime(2023, 1, 2, 12, 0, 0, tzinfo=datetime.timezone.utc)
|
||||
|
||||
# Record time before request for default timestamp
|
||||
before_request = datetime.datetime.now(datetime.timezone.utc)
|
||||
before_request = datetime.datetime.now(datetime.timezone.utc) - datetime.timedelta(
|
||||
seconds=1
|
||||
)
|
||||
|
||||
response = client.post(
|
||||
f"/v3/workspaces/{test_workspace.name}/sessions/{test_session.name}/messages",
|
||||
|
|
@ -1081,7 +1085,9 @@ async def test_create_batch_messages_with_mixed_timestamps(
|
|||
},
|
||||
)
|
||||
|
||||
after_request = datetime.datetime.now(datetime.timezone.utc)
|
||||
after_request = datetime.datetime.now(datetime.timezone.utc) + datetime.timedelta(
|
||||
seconds=1
|
||||
)
|
||||
|
||||
assert response.status_code == 201
|
||||
data = response.json()
|
||||
|
|
@ -1124,8 +1130,9 @@ async def test_create_message_with_null_timestamp(
|
|||
db_session.add(test_session)
|
||||
await db_session.commit()
|
||||
|
||||
# Record time before request
|
||||
before_request = datetime.datetime.now(datetime.timezone.utc)
|
||||
before_request = datetime.datetime.now(datetime.timezone.utc) - datetime.timedelta(
|
||||
seconds=1
|
||||
)
|
||||
|
||||
response = client.post(
|
||||
f"/v3/workspaces/{test_workspace.name}/sessions/{test_session.name}/messages",
|
||||
|
|
@ -1141,7 +1148,9 @@ async def test_create_message_with_null_timestamp(
|
|||
},
|
||||
)
|
||||
|
||||
after_request = datetime.datetime.now(datetime.timezone.utc)
|
||||
after_request = datetime.datetime.now(datetime.timezone.utc) + datetime.timedelta(
|
||||
seconds=1
|
||||
)
|
||||
|
||||
assert response.status_code == 201
|
||||
data = response.json()
|
||||
|
|
|
|||
Loading…
Reference in New Issue