Common Errors

Symptom → cause → fix for the most frequent Mastra failure modes. Grep this file by error-message fragment before searching source.

For longer narrative coverage, see the sibling skill's reference at ../../mastra/references/common-errors.md.

Cannot find module '@mastra/core' / Cannot use import statement outside a module

Cause. Mastra requires native ES modules. CommonJS tsconfig.json or a missing "type": "module" in package.json breaks the import graph.

Fix.

// tsconfig.json
{
  "compilerOptions": {
    "target": "ES2022",
    "module": "ES2022",
    "moduleResolution": "bundler"
  }
}

// package.json
{ "type": "module" }

Property 'tools' does not exist on type 'Agent' / Property 'X' does not exist on type 'AgentConfig'

Cause. The API changed between Mastra versions; training data is stale.

Fix. Read the SKILL.md and reference shipped with the installed version, not memory:

cat node_modules/@mastra/core/dist/docs/SKILL.md
cat node_modules/@mastra/core/dist/docs/assets/SOURCE_MAP.json | jq '."Agent"'
cat node_modules/@mastra/core/dist/<path-from-SOURCE_MAP>

Agent replies "I don't have access to that tool" even though a tool was defined

Cause. Tool created but not registered on the Mastra instance, or the key used in agent.tools doesn't match the key used in mastra.tools.

Fix. Register the tool in both places with the same key:

const mastra = new Mastra({
  agents: { supportAgent },
  tools: { weatherTool },     // same key as below
});

const supportAgent = new Agent({
  id: "support-agent",
  tools: { weatherTool },      // same key as above
  /* ... */
});

Cannot read property 'then' of undefined when running a workflow

Cause. .commit() was not called on the workflow chain. Until .commit() runs, the workflow is a builder, not a runnable object.

Fix.

const workflow = createWorkflow({ /* ... */ })
  .then(step1)
  .then(step2)
  .commit();                    // REQUIRED

const run = await workflow.createRun();
await run.start({ inputData });

Agent forgets previous messages across turns

Cause. One of: (a) no storage on Memory, (b) memory.thread differs between calls, (c) memory.resource missing or changing.

Fix. Keep both IDs stable across a single user's conversation:

await agent.generate("…", {
  memory: {
    thread: "user-42::support",
    resource: "user-42",
  },
});

If storage is missing, instantiate one (e.g. new PostgresStore({ connectionString }) from @mastra/pg, or new LibSQLStore(...) from @mastra/libsql) and pass it into new Memory({ storage, ... }).

Storage is required for Memory

Cause. new Memory({ }) was called without storage.

Fix. Always pass a storage adapter:

const memory = new Memory({
  id: "chat-memory",
  storage,                       // REQUIRED
  options: { lastMessages: 10 },
});

Semantic recall returns only recent messages (no semantic hits)

Cause. semanticRecall: true enabled without vector + embedder configured.

Fix. Semantic recall needs all three:

const memory = new Memory({
  id: "semantic-memory",
  storage,
  vector: chromaVectorStore,     // REQUIRED
  embedder: openaiEmbedder,      // REQUIRED
  options: {
    lastMessages: 10,
    semanticRecall: true,        // REQUIRED
  },
});

Tool input throws ZodError: Expected X, received Y

Cause. Tool invocation does not match inputSchema. Also — the field is inputSchema, not parameters (that's the AI SDK name).

Fix. Align the call site with the schema. If a field should be optional, mark it z.X().optional().

Model 'gpt-4' not found / Invalid model format

Cause. Model string missing the provider prefix.

Fix. Always use provider/model:

const agent = new Agent({ model: "openai/gpt-5.4" });         // ✅
const agent = new Agent({ model: "anthropic/claude-sonnet-4-5" }); // ✅
const agent = new Agent({ model: "gpt-4" });                   // ❌

Enumerate valid providers and current model names with the script shipped in the sibling skill:

node plugins/mastra/.agents/skills/mastra/scripts/provider-registry.mjs --list
node plugins/mastra/.agents/skills/mastra/scripts/provider-registry.mjs --provider openai

Never use model IDs from memory — they change frequently.

connect ECONNREFUSED against Postgres / LibSQL

Cause. Database not running, wrong connection string, or storage.init() not called.

Fix.

1. Start the database (Docker, local process, managed service, …).
2. Verify DATABASE_URL / equivalent.
3. Call await storage.init() at app startup so schema tables exist.

Tool suspension never resumes

Cause. context.suspend(...) called, but nothing later calls run.resume({ resumeData }) or the resume payload doesn't match resumeSchema.

Fix. Define both schemas and resume with matching data:

const approvalTool = createTool({
  id: "approval",
  inputSchema: z.object({ request: z.string() }),
  suspendSchema: z.object({ requestId: z.string() }),
  resumeSchema: z.object({ approved: z.boolean() }),
  execute: async ({ context }) => {
    if (!context.resumeData) {
      context.suspend({ requestId: crypto.randomUUID() });
      return;
    }
    return { approved: context.resumeData.approved };
  },
});

await run.resume({ resumeData: { approved: true } });

When the symptom isn't listed

1. Grep the installed source for the exact error string:

   rg -n "error string fragment" node_modules/@mastra/core/dist
   rg -n "error string fragment" node_modules/@mastra/<subpkg>/dist

2. Check the bundled reference docs for the nearest API:

   ls node_modules/@mastra/<pkg>/dist/docs/references/

3. Fall back to https://mastra.ai/docs or the @mastra/mcp-docs-server MCP tool.
4. Verify all @mastra/* versions match: pnpm list | grep @mastra/. Version skew across the scope is a common silent cause.