Claude Managed Agents

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic();

// Create a vault (one per workspace, reusable across agents)
const vault = await client.beta.vaults.create({
  metadata: { description: "3ngram memory credentials" },
});

// Register the 3ngram OAuth credential
await client.beta.vaults.credentials.create(vault.id, {
  display_name: "3ngram Memory",
  auth: {
    type: "mcp_oauth",
    mcp_server_url: "https://mcp.3ngram.ai/mcp",
    access_token: "<your-access-token>",
    expires_at: "2026-05-01T00:00:00Z",
    refresh: {
      refresh_token: "<your-refresh-token>",
      client_id: "<your-client-id>",
      token_endpoint: "https://mcp.3ngram.ai/mcp/oauth/token",
      token_endpoint_auth: { type: "none" },
    },
  },
});

console.log(`Vault ID: ${vault.id}`); // Save this for session creation

const agent = await client.beta.agents.create({
  name: "Knowledge Worker",
  model: "claude-sonnet-4-6",
  system: `You are a knowledge worker with persistent memory via 3ngram.

At the start of every session:
1. Use the briefing prompt to load open commitments, blockers, and recent decisions
2. Review what's overdue or stale

During work:
- Use remember() to persist decisions, context, and learnings
- Use search_memories() and search_content() to find relevant history
- Use propose_action() before taking irreversible actions

At the end of every session:
- Use the debrief prompt to extract and persist what was accomplished
- Use resolve() to mark completed commitments`,
  mcp_servers: [
    {
      type: "url",
      name: "3ngram",
      url: "https://mcp.3ngram.ai/mcp",
    },
  ],
  tools: [
    { type: "agent_toolset_20260401", default_config: { enabled: true } },
    {
      type: "mcp_toolset",
      mcp_server_name: "3ngram",
      default_config: {
        permission_policy: { type: "always_allow" },
      },
    },
  ],
});

console.log(`Agent ID: ${agent.id}`); // Save this — reuse across sessions

const environment = await client.beta.environments.create({
  name: "3ngram-env",
  config: {
    type: "cloud",
    networking: { type: "unrestricted" },
  },
});

const session = await client.beta.sessions.create({
  agent: agent.id,
  environment_id: environment.id,
  vault_ids: [vault.id],
});

// Stream-first: open stream before sending the kickoff message
const stream = await client.beta.sessions.stream(session.id);

await client.beta.sessions.events.send(session.id, {
  events: [
    {
      type: "user.message",
      content: [
        { type: "text", text: "Run my briefing, then help me plan today's work." },
      ],
    },
  ],
});

for await (const event of stream) {
  if (event.type === "agent.message") {
    for (const block of event.content) {
      if (block.type === "text") process.stdout.write(block.text);
    }
  }
  if (event.type === "session.status_terminated") break;
  if (event.type === "session.status_idle" && event.stop_reason?.type !== "requires_action") break;
}

{
  "type": "mcp_toolset",
  "mcp_server_name": "3ngram",
  "default_config": {
    "permission_policy": { "type": "always_allow" }
  },
  "configs": [
    { "name": "archive_memories", "permission_policy": { "type": "always_ask" } },
    { "name": "delete_prompt", "permission_policy": { "type": "always_ask" } },
    { "name": "delete_schedule", "permission_policy": { "type": "always_ask" } },
    { "name": "delete_trigger", "permission_policy": { "type": "always_ask" } },
    { "name": "create_schedule", "permission_policy": { "type": "always_ask" } },
    { "name": "create_trigger", "permission_policy": { "type": "always_ask" } }
  ]
}

At the start of this session, use the 3ngram briefing prompt to load your
current context. Review open commitments, blockers, and overdue items before
beginning any work.

When you learn something worth remembering, use remember() with an appropriate
memory type (decision, context, commitment, blocker, or learning).

Before making irreversible changes, use propose_action() to get user approval.

Use search_memories() to check if relevant context exists before starting
new research.

Before ending this session, use the 3ngram debrief prompt to extract and
persist decisions, commitments, and learnings from this conversation.
Mark completed work with resolve().