Vercel AI SDK

Use the AI SDK’s MCP client to carry a project decision between runs. The runnable example connects to your existing local Claude-Mem worker, converts its memory tools to AI SDK tools, and calls their execute methods. It needs no model API key. This connection supports explicit saves and on-demand recall. It does not capture conversations or inject context automatically. CMEM Pro is not required for this local workflow.

Requirements

The example was verified on Linux with Node.js 24.22.1, @ai-sdk/mcp 2.0.96, and Claude-Mem 15.16.1. Its package requires Node.js 22.12 or newer. macOS and Windows have not been verified. Keep this stdio connection in a local Node.js process; a browser or a hosted Vercel function cannot reach a worker on your laptop through it. The API surface comes from Vercel’s MCP guide and createMCPClient reference. The example uses StdioMCPTransport, listTools, toolsFromDefinitions, and the AI SDK tool execution abortSignal.

Connect

First install Claude-Mem and start its worker:

npx claude-mem start
npx claude-mem status

Copy the examples/vercel-ai-sdk directory from the repository into your local application, then install its pinned dependency:

cd examples/vercel-ai-sdk
npm install

Set the absolute path to your installed MCP script. For the default Claude Code marketplace installation on macOS or Linux:

export CLAUDE_MEM_MCP_SERVER="$HOME/.claude/plugins/marketplaces/thedotmack/plugin/scripts/mcp-server.cjs"

If you chose another installation directory, use its plugin/scripts/mcp-server.cjs. If the worker uses CLAUDE_MEM_DATA_DIR or CLAUDE_MEM_WORKER_PORT, set those same values in this shell. The example passes them to the MCP subprocess. The script is supplied as one process argument, so a path containing spaces does not need shell escaping inside the program. Connection creates no hooks or host configuration entries. Every command closes its MCP client when it finishes. The worker remains the owner of the database, and this example never starts or restarts it automatically.

Save a decision and recall it in a later run

Choose one explicit project name and keep it for later runs:

node memory.mjs save checkout-service "Deployment decision" "Use the sapphire region for the staging deployment."
node memory.mjs search checkout-service "Deployment decision"

The save prints Saved memory #<id>: Deployment decision. only after the worker acknowledges it. The search prints a compact index. Select the relevant numeric observation ID from that index, inspect its surrounding context, and retrieve its details:

node memory.mjs timeline checkout-service 123
node memory.mjs show checkout-service 123

Replace 123 with your result’s ID. The details should contain the sapphire region decision. Each command uses a new AI SDK MCP client, demonstrating that the memory survives the client session. The example passes the project on saves, searches, timeline reads and detail reads. Each read also supplies projects: JSON.stringify([project]), the worker’s JSON-list form. This preserves literal commas and whitespace in a key and prevents a Git worktree from adding the parent checkout’s memory to the chosen scope. Saves are attributed to vercel-ai-sdk and use the existing explicit manual-memory session for that project and platform. They do not create an observer session. Project filters select your own work; they are not an authentication boundary for serving different users. Run each account against its own authorized worker. Repeating the exact project, title and text sends the same sourceFingerprint through the existing save API. The worker returns the saved ID instead of creating another note. A changed note gets a different fingerprint. Automatic transport retries are disabled.

Use the tools in an AI SDK application

memory.mjs demonstrates the conversion boundary directly: it discovers tools through listTools, selects the four memory tools, and converts those definitions with toolsFromDefinitions. The resulting tool objects are the AI SDK tools accepted by generateText and streamText. For model-driven use, keep the client open until generation finishes and close it in finally. Bind every tool call to the project selected by your application. Expose save_memory only when the user has chosen to save, and follow mem-search before adding selected memory to a prompt. Treat recalled text as reference material, not instructions to run tools or access another account. The example verifies direct tool execution; it does not verify a model provider or a hosted deployment.

Data and failures

Only the title, note text, project and provenance supplied to save are written. Explicit notes are stored as supplied; do not rely on this example to remove secrets or interpret transcript privacy tags. Searches send the query and project to your worker. The worker’s configured indexing, sync and export behavior still applies. If you later pass recalled text to a model, that text also goes to the model provider you selected. Initialization and tool discovery have ten-second deadlines; tool execution has a fifteen-second abort signal. A stopped worker, invalid input or a timed-out request exits with a nonzero status and prints no successful result. Check npx claude-mem status, the MCP script path, and matching data-directory/port settings. A save timeout is ambiguous: the worker may have committed it before the client stopped waiting. Retry the same note to reuse its fingerprint.

Verify and disconnect

Install the test dependency from the checkout root with npm install --prefix examples/vercel-ai-sdk. The following Bash block runs from the root of a built source checkout with Bun available. It clears inherited CLAUDE_MEM_* settings only inside the subshell, including cloud-sync credentials and capture overrides, then creates a fresh local data directory and selects a free loopback port. It waits for readiness and stops only the worker it started. The temporary data and logs remain available for inspection. The test starts the existing bundled WorkerService library export directly, skipping CLI startup repairs to host hook configuration. No home-directory override is needed.

(
  set -eu
  # Keep inherited sync, capture and watcher settings out of this test worker.
  for memory_test_setting in "${!CLAUDE_MEM_@}"; do
    unset "$memory_test_setting"
  done
  export CLAUDE_MEM_DATA_DIR="$(mktemp -d)"
  export CLAUDE_MEM_WORKER_HOST=127.0.0.1
  export CLAUDE_MEM_WORKER_PORT="$(python3 -c 'import socket; s=socket.socket(); s.bind(("127.0.0.1", 0)); print(s.getsockname()[1]); s.close()')"
  export CLAUDE_MEM_MCP_SERVER="$PWD/plugin/scripts/mcp-server.cjs"
  export CLAUDE_MEM_RUNTIME=worker CLAUDE_MEM_WORKER_AUTOSTART=false
  export DO_NOT_TRACK=1 CLAUDE_MEM_TELEMETRY=false
  export CLAUDE_MEM_CHROMA_ENABLED=false CLAUDE_MEM_TRANSCRIPTS_ENABLED=false
  export CLAUDE_MEM_WORK_VIEW_AUTO_REFRESH=false CLAUDE_MEM_AUTO_UPDATE=false
  export CLAUDE_MEM_MEDIA_CAPTURE_ENABLED=false CLAUDE_MEM_MEMORY_WATCH_ROOTS=""
  python3 - <<'PYSETTINGS'
import json, os
from pathlib import Path
settings = {key: "false" for key in (
    "CLAUDE_MEM_CHROMA_ENABLED", "CLAUDE_MEM_TRANSCRIPTS_ENABLED",
    "CLAUDE_MEM_WORK_VIEW_AUTO_REFRESH", "CLAUDE_MEM_AUTO_UPDATE",
    "CLAUDE_MEM_MEDIA_CAPTURE_ENABLED",
)}
settings.update(CLAUDE_MEM_CLOUD_SYNC_HUB_URL="", CLAUDE_MEM_MEMORY_WATCH_ROOTS="",
                CLAUDE_MEM_WORKER_IDLE_EXIT_SECONDS="0",
                CLAUDE_MEM_WORKER_PORT=os.environ["CLAUDE_MEM_WORKER_PORT"])
(Path(os.environ["CLAUDE_MEM_DATA_DIR"]) / "settings.json").write_text(json.dumps(settings))
PYSETTINGS
  bun -e 'const {WorkerService}=require("./plugin/scripts/worker-service.cjs"); new WorkerService().start().catch(error => { console.error(error); process.exit(1); });' >"$CLAUDE_MEM_DATA_DIR/worker.log" 2>&1 &
  memory_test_pid=$!
  trap 'kill "$memory_test_pid" 2>/dev/null || true; wait "$memory_test_pid" 2>/dev/null || true' EXIT
  python3 - <<'PYREADY'
import os, time, urllib.request
url = "http://127.0.0.1:" + os.environ["CLAUDE_MEM_WORKER_PORT"] + "/api/readiness"
for attempt in range(100):
    try:
        with urllib.request.urlopen(url, timeout=0.2) as response:
            if response.status == 200:
                break
    except OSError:
        time.sleep(0.1)
else:
    raise SystemExit("Test worker did not become ready; inspect its worker.log")
print("Disposable test data:", os.environ["CLAUDE_MEM_DATA_DIR"])
PYREADY
  node --test examples/vercel-ai-sdk/memory.test.mjs
)

It saves harmless notes in uniquely named test projects and checks repeated saves, index/timeline/detail recall, exact project filtering from a real Git worktree, comma-containing and whitespace-containing keys, invalid input, a refused connection and an unresponsive worker. The successful path uses the real AI SDK client and the existing worker’s SQLite storage. The test leaves those notes in the isolated database so you can inspect the results. To disconnect, stop using the example and remove its directory or its @ai-sdk/mcp dependency from your application. No host settings need to be restored. Disconnecting leaves your saved memory intact; remove any demonstration notes separately through the viewer if you no longer need them.

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Connect

Save a decision and recall it in a later run

Use the tools in an AI SDK application

Data and failures

Verify and disconnect

​Requirements

​Connect

​Save a decision and recall it in a later run

​Use the tools in an AI SDK application

​Data and failures

​Verify and disconnect

Requirements

Connect

Save a decision and recall it in a later run

Use the tools in an AI SDK application

Data and failures

Verify and disconnect