#Context

A3S Code treats context as a budgeted resource. The model should see the smallest useful context for the current decision, not every available file, skill, memory, and tool log.

#Sources

Context can come from:

• user prompt and conversation history
• AGENTS.md project instructions
• skills and agent definitions
• memory stores
• file search and direct tool results
• MCP tools and context providers
• delegated task summaries
• trace events

AGENTS.md injection, skills discovery, memory APIs, direct tool results, delegated task helpers, and traceEvents() are part of the documented Node SDK surface. Validate MCP context behavior against your own live integrations before documenting it as product behavior.

#Assembly

sources -> ContextItem -> rank -> dedupe -> budget -> render

Long grep output, logs, and child transcripts should be preserved outside the prompt and summarized into prompt-safe evidence. Use session.traceEvents() for compact runtime evidence.

#Compaction

Enable automatic compaction for long sessions:

const session = agent.session('/repo', {
  autoCompact: true,
  autoCompactThreshold: 0.75,
  maxContextTokens: 128_000,
});

When maxContextTokens is omitted, Core uses the selected model's declared context window when available. Before each model request, Core accounts for the system prompt, conversation, tool calls and results, and exposed tool schemas. At the configured threshold it bounds oversized tool output, summarizes the older safe prefix, keeps recent messages, and continues the same task. The summary participates in later compactions, so long sessions can roll forward through repeated compression; this does not enlarge the model's physical single-request context window. A successful context_compacted event includes the cumulative summary so hosts that supply external history can persist the same compact generation across turns.

Python exposes the same override as max_context_tokens.