Context

Context supplies facts an agent may use. Choose the context path based on how large, dynamic, and permission-sensitive those facts are.

Static context

Use .context(text, id) for small documents that are safe and useful for every run of the agent.

const agent = new AgentBuilder('release-notes', model)
  .instructions('Answer questions about the current release.')
  .context(currentReleaseNotes, 'release-notes')
  .context(supportPolicySummary, 'support-policy')
  .build()

Static context is sent with every model request. Keep it short and stable.

Dynamic context

Use retrieval when relevant documents should be selected for each turn:

import { vectorFilter } from '@anvia/core/vector-store'

const agent = new AgentBuilder('docs-support', model)
  .instructions('Use retrieved documentation before answering.')
  .dynamicContext(docsIndex, {
    topK: 5,
    threshold: 0.72,
    filter: vectorFilter.eq('product', 'platform'),
  })
  .build()

Enforce tenant, product, language, and access filters in the index or retrieval adapter. Prompt instructions must not be the authorization boundary.

Request context

Keep user, tenant, conversation, and trace data on the request boundary:

const response = await agent
  .session(conversationId, {
    userId: user.id,
    metadata: { tenantId: user.tenantId },
  })
  .prompt(input.message)
  .withTrace({
    name: 'support-chat',
    userId: user.id,
  })
  .send()

Context	Best location
Small facts safe for all callers	.context(...)
Large or changing knowledge	.dynamicContext(...)
Conversation identity	.session(...)
Observability metadata	.withTrace(...)
Permissioned product state	Scoped tools and services

See Knowledges for ingestion, indexes, and retrieval patterns.