# Request context

Agents, tools, and workflows can all accept `RequestContext` as a parameter, making request-specific values available to the underlying primitives.

## When to use `RequestContext`

Use `RequestContext` when a primitive's behavior should change based on runtime conditions. For example, you might switch models or storage backends based on user attributes, or adjust instructions and tool selection based on language.

> **Note:** `RequestContext` is primarily used for passing data into specific requests. It's distinct from agent memory, which handles conversation history and state persistence across multiple calls.

## Setting values

Pass `requestContext` into an agent, network, workflow, or tool call to make values available to all underlying primitives during execution. Use `.set()` to define values before making the call.

The `.set()` method takes two arguments:

1. **key**: The name used to identify the value.
2. **value**: The data to associate with that key.

```typescript
import { RequestContext } from '@mastra/core/request-context'

export type UserTier = {
  'user-tier': 'enterprise' | 'pro'
}

const requestContext = new RequestContext<UserTier>()
requestContext.set('user-tier', 'enterprise')

const agent = mastra.getAgent('weatherAgent')
await agent.generate("What's the weather in London?", {
  requestContext,
})

const routingAgent = mastra.getAgent('routingAgent')
routingAgent.network("What's the weather in London?", {
  requestContext,
})

const run = await mastra.getWorkflow('weatherWorkflow').createRun()
await run.start({
  inputData: {
    location: 'London',
  },
  requestContext,
})
await run.resume({
  resumeData: {
    city: 'New York',
  },
  requestContext,
})

await weatherTool.execute({ location: 'London' }, { requestContext })
```

### Setting values based on request headers

You can populate `requestContext` dynamically in server middleware by extracting information from the request. In this example, the `temperature-unit` is set based on the Cloudflare `CF-IPCountry` header to ensure responses match the user's locale.

```typescript
import { Mastra } from '@mastra/core'
import { RequestContext } from '@mastra/core/request-context'
import { testWeatherAgent } from './agents/test-weather-agent'

export const mastra = new Mastra({
  agents: { testWeatherAgent },
  server: {
    middleware: [
      async (context, next) => {
        const country = context.req.header('CF-IPCountry')
        const requestContext = context.get('requestContext')

        requestContext.set('temperature-unit', country === 'US' ? 'fahrenheit' : 'celsius')

        await next()
      },
    ],
  },
})
```

> **Info:** Visit [Middleware](https://mastra.ai/docs/server/middleware) for how to use server middleware.

## Studio

When developing locally, you can define named presets in a JSON file and load them into [Studio](https://mastra.ai/docs/studio/overview) with the [`--request-context-presets`](https://mastra.ai/reference/cli/mastra) CLI flag. This adds a dropdown to the request context editor in Studio so you can quickly switch between configurations without manually editing JSON each time.

```bash
mastra dev --request-context-presets ./presets.json
```

```json
{
  "development": { "userId": "dev-user", "env": "development" },
  "production": { "userId": "prod-user", "env": "production" }
}
```

When you select a preset from the dropdown, the JSON editor populates with that preset's values. Editing the JSON manually switches the dropdown back to **"Custom"**.

## Accessing values with agents

You can access the `requestContext` argument from any supported configuration options in agents. These functions can be sync or `async`. Use the `.get()` method to read values from `requestContext`.

```typescript
export type UserTier = {
  'user-tier': 'enterprise' | 'pro'
}

export const weatherAgent = new Agent({
  id: 'weather-agent',
  name: 'Weather Agent',
  instructions: async ({ requestContext }) => {
    const userTier = requestContext.get('user-tier') as UserTier['user-tier']

    if (userTier === 'enterprise') {
    }
  },
  model: ({ requestContext }) => {},
  tools: ({ requestContext }) => {},
  memory: ({ requestContext }) => {},
})
```

You can also use `requestContext` with other options like `agents`, `workflows`, `scorers`, `inputProcessors`, and `outputProcessors`.

### Dynamic instructions

Agent instructions can be provided as an async function, enabling you to resolve prompts dynamically at runtime. Combined with `requestContext`, this enables patterns like:

- **Personalization**: Tailor instructions based on user attributes, preferences, or tier
- **Localization**: Adjust tone, language, or behavior based on locale
- **A/B testing**: Serve different prompt variants for experimentation
- **External prompt management**: Fetch prompts from registry services without redeploying

```typescript
import { Agent } from '@mastra/core/agent'

export const dynamicAgent = new Agent({
  id: 'dynamic-agent',
  name: 'Dynamic Agent',
  instructions: async ({ requestContext }) => {
    const userTier = requestContext?.get('user-tier')
    const locale = requestContext?.get('locale')

    // Personalize based on user tier
    const basePrompt =
      userTier === 'enterprise'
        ? 'You are a premium support agent. Provide detailed, thorough responses with technical depth.'
        : 'You are a helpful assistant. Be concise and friendly.'

    // Localize behavior
    const localeInstructions = locale === 'ja' ? 'Respond in Japanese using formal keigo.' : ''

    return `${basePrompt} ${localeInstructions}`.trim()
  },
  model: 'openai/gpt-5.5',
})
```

#### Fetching from a prompt registry

If your organization uses a prompt registry service for central prompt management, you can fetch instructions at runtime. This allows you to update prompts without redeploying, run experiments with variants, and track prompt usage across your agents.

```typescript
import { Agent } from '@mastra/core/agent'

// Your prompt registry client
import { promptRegistry } from '../lib/prompt-registry'

export const registryAgent = new Agent({
  id: 'registry-agent',
  name: 'Registry Agent',
  instructions: async ({ requestContext }) => {
    const prompt = await promptRegistry.getPrompt({
      promptId: 'customer-support-agent',
      // Pass context for variant selection or tracking
      variant: requestContext?.get('experiment-variant'),
      userId: requestContext?.get('user-id'),
    })

    return prompt.content
  },
  model: 'openai/gpt-5.5',
})
```

> **Info:** Visit [Agent](https://mastra.ai/reference/agents/agent) for a full list of configuration options.

## Accessing values from workflow steps

You can access the `requestContext` argument from a workflow step's `execute` function. This function can be sync or async. Use the `.get()` method to read values from `requestContext`.

```typescript
export type UserTier = {
  'user-tier': 'enterprise' | 'pro'
}

const stepOne = createStep({
  id: 'step-one',
  execute: async ({ requestContext }) => {
    const userTier = requestContext.get('user-tier') as UserTier['user-tier']

    if (userTier === 'enterprise') {
    }
  },
})
```

> **Info:** Visit [createStep()](https://mastra.ai/reference/workflows/step) for a full list of configuration options.

## Accessing values with tools

You can access the `requestContext` argument from a tool's `execute` function. This function is `async`. Use the `.get()` method to read values from `requestContext`.

```typescript
export type UserTier = {
  'user-tier': 'enterprise' | 'pro'
}

export const weatherTool = createTool({
  id: 'weather-tool',
  execute: async (inputData, context) => {
    const userTier = context?.requestContext?.get('user-tier') as UserTier['user-tier'] | undefined

    if (userTier === 'enterprise') {
    }
  },
})
```

> **Info:** Visit [createTool()](https://mastra.ai/reference/tools/create-tool) for a full list of configuration options.

## Reserved keys

Mastra reserves special context keys for security purposes. When set, these keys take precedence over client-provided values. The server automatically validates ownership and returns 403 errors when users attempt to access resources they don't own.

The easiest way to set `MASTRA_RESOURCE_ID_KEY` is via the `mapUserToResourceId` callback in auth config:

```typescript
auth: {
  authenticateToken: async token => verifyToken(token),
  mapUserToResourceId: user => user.id,
}
```

You can also set these keys manually in middleware:

```typescript
import { MASTRA_RESOURCE_ID_KEY, MASTRA_THREAD_ID_KEY } from '@mastra/core/request-context'

// In middleware: force memory operations to use authenticated user's ID
requestContext.set(MASTRA_RESOURCE_ID_KEY, user.id)

// In middleware: set validated thread ID
requestContext.set(MASTRA_THREAD_ID_KEY, threadId)
```

| Key                      | Purpose                                                                                                                                          |
| ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------ |
| `MASTRA_RESOURCE_ID_KEY` | Forces all memory operations to use this resource ID. The server validates that accessed threads belong to this resource and returns 403 if not. |
| `MASTRA_THREAD_ID_KEY`   | Forces thread operations to use this thread ID, overriding client-provided values                                                                |

These keys are used to implement user isolation in multi-tenant applications. See [Authorization middleware](https://mastra.ai/docs/server/middleware) for usage examples.

## TypeScript support

When you provide a type parameter to `RequestContext`, all methods are fully typed:

```typescript
import { RequestContext } from '@mastra/core/request-context'

type MyContext = {
  userId: string
  maxTokens: number
  isPremium: boolean
}

const ctx = new RequestContext<MyContext>()

// set() enforces correct value types
ctx.set('userId', 'user-123') // ✓ valid
ctx.set('maxTokens', 4096) // ✓ valid
ctx.set('maxTokens', 'wrong') // ✗ TypeScript error: expected number

// get() returns the correct type automatically
const tokens = ctx.get('maxTokens') // inferred as number
const id = ctx.get('userId') // inferred as string

// keys() returns typed keys
for (const key of ctx.keys()) {
  // key is "userId" | "maxTokens" | "isPremium"
}

// entries() supports type narrowing
for (const [key, value] of ctx.entries()) {
  if (key === 'maxTokens') {
    // TypeScript knows value is number here
    console.log(value.toFixed(2))
  }
  if (key === 'userId') {
    // TypeScript knows value is string here
    console.log(value.toUpperCase())
  }
}
```

## Schema validation

Use `requestContextSchema` to define a [Standard JSON Schema](https://standardschema.dev/json-schema) ([Zod](https://zod.dev/), [Valibot](https://valibot.dev/), [ArkType](https://arktype.io/), etc.) that validates request context values at runtime. This catches missing or invalid context values early, provides clear error messages, and gives you type inference within your component.

### Agent schema validation

When you define `requestContextSchema` on an agent, the context is validated at the start of `generate()` or `stream()`. If validation fails, the agent throws a `MastraError` before any LLM calls are made.

```typescript
import { Agent } from '@mastra/core/agent'
import { z } from 'zod'

export const validatedAgent = new Agent({
  id: 'validated-agent',
  name: 'Validated Agent',
  requestContextSchema: z.object({
    userId: z.string(),
    apiKey: z.string(),
  }),
  instructions: ({ requestContext }) => {
    // Access all values as a typed object
    const { userId, apiKey } = requestContext.all
    // { userId: string; apiKey: string }

    // Or retrieve individual values with .get()
    const id = requestContext.get('userId')
    // string

    return `You are helping user ${userId}`
  },
  model: 'openai/gpt-5.5',
})
```

When validation fails, the error includes the agent ID and details about which fields failed:

```text
Request context validation failed for agent 'validated-agent':
- apiKey: Required
```

### Tool schema validation

When you define `requestContextSchema` on a tool, the context is validated before `execute()` runs. Unlike agents, tools return a validation error object instead of throwing:

```typescript
import { createTool } from '@mastra/core/tools'
import { z } from 'zod'

export const validatedTool = createTool({
  id: 'validated-tool',
  description: 'A tool that requires authenticated context',
  inputSchema: z.object({
    query: z.string(),
  }),
  requestContextSchema: z.object({
    userId: z.string(),
  }),
  execute: async (inputData, context) => {
    // Access all values as a typed object
    const { userId } = context.requestContext?.all ?? {}
    // { userId: string }

    // Or retrieve individual values with .get()
    const id = context.requestContext?.get('userId')
    // string | undefined

    return { result: `Processed for ${userId}` }
  },
})
```

When validation fails, the tool returns an error object instead of throwing:

```json
{
  "error": true,
  "message": "Request context validation failed for validated-tool. Please fix the following errors and try again:\n- userId: Required\n\nProvided context: {}"
}
```

### Workflow schema validation

When you define `requestContextSchema` on a workflow, the context is validated at the start of `run.start()`. If validation fails, the workflow throws an error before any steps execute.

```typescript
import { createWorkflow, createStep } from '@mastra/core/workflows'
import { z } from 'zod'

// Define schema once and share between workflow and steps
const workflowContextSchema = z.object({
  tenantId: z.string(),
})

const step1 = createStep({
  id: 'step-1',
  inputSchema: z.object({ message: z.string() }),
  outputSchema: z.object({ result: z.string() }),
  // Add schema to step for type inference
  requestContextSchema: workflowContextSchema,
  execute: async ({ inputData, requestContext }) => {
    // Access all values as a typed object
    const { tenantId } = requestContext.all
    // { tenantId: string }

    // Or retrieve individual values with .get()
    const id = requestContext.get('tenantId')
    // string

    return { result: `Processed for tenant ${tenantId}` }
  },
})

export const validatedWorkflow = createWorkflow({
  id: 'validated-workflow',
  inputSchema: z.object({ message: z.string() }),
  outputSchema: z.object({ result: z.string() }),
  requestContextSchema: workflowContextSchema,
})
  .then(step1)
  .commit()
```

When validation fails, the workflow throws an error:

```text
Request context validation failed for workflow 'validated-workflow':
- tenantId: Required
```

Steps can also define their own `requestContextSchema` for step-level validation. Step validation runs before the step's `execute()` function.

### Validation behavior

| Component | Property               | Validation timing                  | On failure            |
| --------- | ---------------------- | ---------------------------------- | --------------------- |
| Agent     | `requestContextSchema` | Start of `generate()` / `stream()` | Throws `MastraError`  |
| Tool      | `requestContextSchema` | Before `execute()`                 | Returns error object  |
| Workflow  | `requestContextSchema` | Start of `run.start()`             | Throws `Error`        |
| Step      | `requestContextSchema` | Before step `execute()`            | Step fails with error |

### Best practices

**Match your middleware**: Define the same required fields in your schema that your middleware sets. This ensures the contract between middleware and components is explicit and validated.

```typescript
// Middleware sets these fields
requestContext.set('userId', user.id)
requestContext.set('tenantId', tenant.id)

// Schema validates they exist
requestContextSchema: z.object({
  userId: z.string(),
  tenantId: z.string(),
})
```

**Use optional fields for conditional context**: Use `.optional()` for values that may not always be present.

```typescript
requestContextSchema: z.object({
  userId: z.string(), // Always required
  experimentVariant: z.string().optional(), // May not be set
})
```

**Handle tool validation errors**: Since tools return error objects instead of throwing, check for errors in your agent or workflow logic when tool execution is critical.

## Related

- [Agent Request Context](https://mastra.ai/docs/memory/overview)
- [Workflow Request Context](https://mastra.ai/docs/workflows/overview)
- [Server Middleware](https://mastra.ai/docs/server/middleware)
- [Authorization Middleware](https://mastra.ai/docs/server/middleware)