feat: post-consumption tool result compression (#116)
Replace consumed tool results with compact markers before each LLM call, freeing context budget in multi-turn agent runs. A tool result is "consumed" once the assistant has produced a response after seeing it. - Add `compressToolResults` option to AgentConfig / RunnerOptions - Runs before contextStrategy (lightweight, no LLM calls) - Error results and short results (< minChars, default 500) are skipped - 9 test cases covering default off, compression, parallel tools, 4+ turn compounding, error exemption, custom threshold, and contextStrategy coexistence
This commit is contained in:
JackChen
parent
c3ead26677
commit
1529dd1346
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@ -154,6 +154,7 @@ export class Agent {
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loopDetection: this.config.loopDetection,
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maxTokenBudget: this.config.maxTokenBudget,
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contextStrategy: this.config.contextStrategy,
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compressToolResults: this.config.compressToolResults,
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}
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this.runner = new AgentRunner(
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@ -98,6 +98,11 @@ export interface RunnerOptions {
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readonly maxTokenBudget?: number
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/** Optional context compression strategy for long multi-turn runs. */
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readonly contextStrategy?: ContextStrategy
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/**
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* Compress tool results that the agent has already processed.
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* See {@link AgentConfig.compressToolResults} for details.
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*/
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readonly compressToolResults?: boolean | { readonly minChars?: number }
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}
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/**
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@ -176,6 +181,9 @@ function addTokenUsage(a: TokenUsage, b: TokenUsage): TokenUsage {
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const ZERO_USAGE: TokenUsage = { input_tokens: 0, output_tokens: 0 }
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/** Default minimum content length before tool result compression kicks in. */
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const DEFAULT_MIN_COMPRESS_CHARS = 500
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/**
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* Prepends synthetic framing text to the first user message so we never emit
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* consecutive `user` turns (Bedrock) and summaries do not concatenate onto
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@ -569,6 +577,12 @@ export class AgentRunner {
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turns++
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// Compress consumed tool results before context strategy (lightweight,
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// no LLM calls) so the strategy operates on already-reduced messages.
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if (this.options.compressToolResults && turns > 1) {
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conversationMessages = this.compressConsumedToolResults(conversationMessages)
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}
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// Optionally compact context before each LLM call after the first turn.
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if (this.options.contextStrategy && turns > 1) {
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const compacted = await this.applyContextStrategy(
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@ -846,6 +860,75 @@ export class AgentRunner {
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// Private helpers
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// -------------------------------------------------------------------------
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/**
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* Replace consumed tool results with compact markers.
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*
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* A tool_result is "consumed" when the assistant has produced a response
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* after seeing it (i.e. there is an assistant message following the user
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* message that contains the tool_result). The most recent user message
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* with tool results is always kept intact — the LLM is about to see it.
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*
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* Error results and results shorter than `minChars` are never compressed.
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*/
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private compressConsumedToolResults(messages: LLMMessage[]): LLMMessage[] {
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const config = this.options.compressToolResults
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if (!config) return messages
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const minChars = typeof config === 'object'
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? (config.minChars ?? DEFAULT_MIN_COMPRESS_CHARS)
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: DEFAULT_MIN_COMPRESS_CHARS
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// Find the last user message that carries tool_result blocks.
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let lastToolResultUserIdx = -1
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for (let i = messages.length - 1; i >= 0; i--) {
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if (
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messages[i]!.role === 'user' &&
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messages[i]!.content.some(b => b.type === 'tool_result')
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) {
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lastToolResultUserIdx = i
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break
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}
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}
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// Nothing to compress if there's at most one tool-result user message.
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if (lastToolResultUserIdx <= 0) return messages
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let anyChanged = false
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const result = messages.map((msg, idx) => {
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// Only compress user messages that appear before the last one.
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if (msg.role !== 'user' || idx >= lastToolResultUserIdx) return msg
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const hasToolResult = msg.content.some(b => b.type === 'tool_result')
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if (!hasToolResult) return msg
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let msgChanged = false
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const newContent = msg.content.map((block): ContentBlock => {
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if (block.type !== 'tool_result') return block
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// Never compress error results — they carry diagnostic value.
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if (block.is_error) return block
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// Skip short results — the marker itself has overhead.
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if (block.content.length < minChars) return block
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msgChanged = true
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return {
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type: 'tool_result',
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tool_use_id: block.tool_use_id,
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content: `[Tool output compressed — ${block.content.length} chars, already processed]`,
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} satisfies ToolResultBlock
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})
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if (msgChanged) {
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anyChanged = true
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return { role: msg.role, content: newContent } as LLMMessage
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}
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return msg
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})
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return anyChanged ? result : messages
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}
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/**
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* Build the {@link ToolUseContext} passed to every tool execution.
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* Identifies this runner as the invoking agent.
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15
src/types.ts
15
src/types.ts
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@ -270,6 +270,21 @@ export interface AgentConfig {
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* takes priority over this value.
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*/
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readonly maxToolOutputChars?: number
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/**
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* Compress tool results that the agent has already processed.
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*
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* In multi-turn runs, tool results persist in the conversation even after the
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* agent has acted on them. When enabled, consumed tool results (those followed
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* by an assistant response) are replaced with a short marker before the next
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* LLM call, freeing context budget for new reasoning.
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*
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* - `true` — enable with default threshold (500 chars)
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* - `{ minChars: N }` — only compress results longer than N characters
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* - `false` / `undefined` — disabled (default)
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*
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* Error tool results are never compressed.
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*/
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readonly compressToolResults?: boolean | { readonly minChars?: number }
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/**
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* Optional Zod schema for structured output. When set, the agent's final
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* output is parsed as JSON and validated against this schema. A single
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@ -0,0 +1,456 @@
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import { describe, it, expect } from 'vitest'
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import { z } from 'zod'
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import { AgentRunner } from '../src/agent/runner.js'
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import { ToolRegistry, defineTool } from '../src/tool/framework.js'
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import { ToolExecutor } from '../src/tool/executor.js'
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import type { LLMAdapter, LLMMessage, LLMResponse } from '../src/types.js'
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// ---------------------------------------------------------------------------
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// Helpers
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// ---------------------------------------------------------------------------
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function textResponse(text: string): LLMResponse {
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return {
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id: `resp-${Math.random().toString(36).slice(2)}`,
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content: [{ type: 'text', text }],
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model: 'mock-model',
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stop_reason: 'end_turn',
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usage: { input_tokens: 10, output_tokens: 20 },
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}
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}
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function toolUseResponse(toolName: string, input: Record<string, unknown>): LLMResponse {
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return {
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id: `resp-${Math.random().toString(36).slice(2)}`,
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content: [{
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type: 'tool_use',
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id: `tu-${Math.random().toString(36).slice(2)}`,
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name: toolName,
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input,
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}],
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model: 'mock-model',
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stop_reason: 'tool_use',
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usage: { input_tokens: 15, output_tokens: 25 },
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}
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}
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function buildRegistryAndExecutor(
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toolOutput: string = 'x'.repeat(600),
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): { registry: ToolRegistry; executor: ToolExecutor } {
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const registry = new ToolRegistry()
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registry.register(
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defineTool({
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name: 'echo',
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description: 'Echo input',
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inputSchema: z.object({ message: z.string() }),
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async execute() {
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return { data: toolOutput }
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},
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}),
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)
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return { registry, executor: new ToolExecutor(registry) }
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}
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function buildErrorRegistryAndExecutor(): { registry: ToolRegistry; executor: ToolExecutor } {
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const registry = new ToolRegistry()
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registry.register(
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defineTool({
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name: 'fail',
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description: 'Always fails',
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inputSchema: z.object({ message: z.string() }),
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async execute() {
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return { data: 'E'.repeat(600), isError: true }
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},
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}),
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)
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return { registry, executor: new ToolExecutor(registry) }
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}
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/** Extract all tool_result content strings from messages sent to the LLM. */
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function extractToolResultContents(messages: LLMMessage[]): string[] {
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return messages.flatMap(m =>
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m.content
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.filter((b): b is { type: 'tool_result'; tool_use_id: string; content: string; is_error?: boolean } =>
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b.type === 'tool_result')
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.map(b => b.content),
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)
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}
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// ---------------------------------------------------------------------------
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// Tests
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// ---------------------------------------------------------------------------
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describe('AgentRunner compressToolResults', () => {
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it('does NOT compress when compressToolResults is not set (default)', async () => {
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const calls: LLMMessage[][] = []
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const longOutput = 'x'.repeat(600)
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const responses = [
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toolUseResponse('echo', { message: 't1' }),
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toolUseResponse('echo', { message: 't2' }),
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textResponse('done'),
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]
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let idx = 0
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const adapter: LLMAdapter = {
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name: 'mock',
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async chat(messages) {
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calls.push(messages.map(m => ({ role: m.role, content: [...m.content] })))
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return responses[idx++]!
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},
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async *stream() { /* unused */ },
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}
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const { registry, executor } = buildRegistryAndExecutor(longOutput)
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const runner = new AgentRunner(adapter, registry, executor, {
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model: 'mock-model',
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allowedTools: ['echo'],
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maxTurns: 5,
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// compressToolResults not set
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})
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await runner.run([{ role: 'user', content: [{ type: 'text', text: 'start' }] }])
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// Turn 3 should still see full tool results from turn 1
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const turn3Messages = calls[2]!
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const allToolResults = extractToolResultContents(turn3Messages)
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expect(allToolResults.every(c => c === longOutput)).toBe(true)
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})
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it('compresses consumed tool results on turn 3+', async () => {
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const calls: LLMMessage[][] = []
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const longOutput = 'x'.repeat(600)
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const responses = [
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toolUseResponse('echo', { message: 't1' }),
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toolUseResponse('echo', { message: 't2' }),
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textResponse('done'),
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]
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let idx = 0
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const adapter: LLMAdapter = {
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name: 'mock',
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async chat(messages) {
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calls.push(messages.map(m => ({ role: m.role, content: [...m.content] })))
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return responses[idx++]!
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},
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async *stream() { /* unused */ },
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}
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const { registry, executor } = buildRegistryAndExecutor(longOutput)
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const runner = new AgentRunner(adapter, registry, executor, {
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model: 'mock-model',
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allowedTools: ['echo'],
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maxTurns: 5,
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compressToolResults: true,
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})
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await runner.run([{ role: 'user', content: [{ type: 'text', text: 'start' }] }])
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// Turn 3: the LLM should see a compressed marker for turn 1 results
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// but the full output for turn 2 results (most recent, not yet consumed).
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const turn3Messages = calls[2]!
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const allToolResults = extractToolResultContents(turn3Messages)
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expect(allToolResults).toHaveLength(2)
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// First result (turn 1) should be compressed
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expect(allToolResults[0]).toContain('compressed')
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expect(allToolResults[0]).toContain('600 chars')
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// Second result (turn 2, most recent) should be preserved in full
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expect(allToolResults[1]).toBe(longOutput)
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})
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it('preserves tool_use_id on compressed results', async () => {
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const calls: LLMMessage[][] = []
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const longOutput = 'x'.repeat(600)
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const responses = [
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toolUseResponse('echo', { message: 't1' }),
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toolUseResponse('echo', { message: 't2' }),
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textResponse('done'),
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]
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let idx = 0
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const adapter: LLMAdapter = {
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name: 'mock',
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async chat(messages) {
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calls.push(messages.map(m => ({ role: m.role, content: [...m.content] })))
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return responses[idx++]!
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},
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async *stream() { /* unused */ },
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}
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const { registry, executor } = buildRegistryAndExecutor(longOutput)
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const runner = new AgentRunner(adapter, registry, executor, {
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model: 'mock-model',
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allowedTools: ['echo'],
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maxTurns: 5,
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compressToolResults: true,
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})
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await runner.run([{ role: 'user', content: [{ type: 'text', text: 'start' }] }])
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// Turn 3: verify compressed result still has tool_use_id
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const turn3Messages = calls[2]!
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const toolResultBlocks = turn3Messages.flatMap(m =>
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m.content.filter(b => b.type === 'tool_result'),
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)
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for (const block of toolResultBlocks) {
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expect(block).toHaveProperty('tool_use_id')
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expect((block as { tool_use_id: string }).tool_use_id).toBeTruthy()
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}
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})
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it('skips short tool results below minChars threshold', async () => {
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const calls: LLMMessage[][] = []
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const shortOutput = 'short' // 5 chars, well below 500 default
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const responses = [
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toolUseResponse('echo', { message: 't1' }),
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toolUseResponse('echo', { message: 't2' }),
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textResponse('done'),
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]
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let idx = 0
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const adapter: LLMAdapter = {
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name: 'mock',
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async chat(messages) {
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calls.push(messages.map(m => ({ role: m.role, content: [...m.content] })))
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return responses[idx++]!
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},
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async *stream() { /* unused */ },
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}
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const { registry, executor } = buildRegistryAndExecutor(shortOutput)
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const runner = new AgentRunner(adapter, registry, executor, {
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model: 'mock-model',
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allowedTools: ['echo'],
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maxTurns: 5,
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compressToolResults: true,
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})
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await runner.run([{ role: 'user', content: [{ type: 'text', text: 'start' }] }])
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// Turn 3: short results should NOT be compressed
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const turn3Messages = calls[2]!
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const allToolResults = extractToolResultContents(turn3Messages)
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expect(allToolResults.every(c => c === shortOutput)).toBe(true)
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})
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it('respects custom minChars threshold', async () => {
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const calls: LLMMessage[][] = []
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const output = 'x'.repeat(200)
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const responses = [
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toolUseResponse('echo', { message: 't1' }),
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toolUseResponse('echo', { message: 't2' }),
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textResponse('done'),
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]
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let idx = 0
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const adapter: LLMAdapter = {
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name: 'mock',
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async chat(messages) {
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calls.push(messages.map(m => ({ role: m.role, content: [...m.content] })))
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return responses[idx++]!
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},
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async *stream() { /* unused */ },
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}
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const { registry, executor } = buildRegistryAndExecutor(output)
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const runner = new AgentRunner(adapter, registry, executor, {
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model: 'mock-model',
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allowedTools: ['echo'],
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maxTurns: 5,
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compressToolResults: { minChars: 100 },
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})
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await runner.run([{ role: 'user', content: [{ type: 'text', text: 'start' }] }])
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// With minChars=100, the 200-char output should be compressed
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const turn3Messages = calls[2]!
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const allToolResults = extractToolResultContents(turn3Messages)
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expect(allToolResults[0]).toContain('compressed')
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expect(allToolResults[0]).toContain('200 chars')
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})
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it('never compresses error tool results', async () => {
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const calls: LLMMessage[][] = []
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const responses = [
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toolUseResponse('fail', { message: 't1' }),
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toolUseResponse('fail', { message: 't2' }),
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textResponse('done'),
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]
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let idx = 0
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const adapter: LLMAdapter = {
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name: 'mock',
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async chat(messages) {
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calls.push(messages.map(m => ({ role: m.role, content: [...m.content] })))
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return responses[idx++]!
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},
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async *stream() { /* unused */ },
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}
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const { registry, executor } = buildErrorRegistryAndExecutor()
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const runner = new AgentRunner(adapter, registry, executor, {
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model: 'mock-model',
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allowedTools: ['fail'],
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maxTurns: 5,
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compressToolResults: true,
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})
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await runner.run([{ role: 'user', content: [{ type: 'text', text: 'start' }] }])
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// Error results should never be compressed even if long
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const turn3Messages = calls[2]!
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const allToolResults = extractToolResultContents(turn3Messages)
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expect(allToolResults.every(c => c === 'E'.repeat(600))).toBe(true)
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})
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it('compresses selectively in multi-block tool_result messages (parallel tool calls)', async () => {
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const calls: LLMMessage[][] = []
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// Two tools: one returns long output, one returns short output
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const registry = new ToolRegistry()
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registry.register(
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defineTool({
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name: 'long_tool',
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description: 'Returns long output',
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inputSchema: z.object({ msg: z.string() }),
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async execute() { return { data: 'L'.repeat(600) } },
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}),
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)
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registry.register(
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defineTool({
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name: 'short_tool',
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description: 'Returns short output',
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inputSchema: z.object({ msg: z.string() }),
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async execute() { return { data: 'S'.repeat(50) } },
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}),
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)
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const executor = new ToolExecutor(registry)
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// Turn 1: model calls both tools in parallel
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const parallelResponse: LLMResponse = {
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id: 'resp-parallel',
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content: [
|
||||
{ type: 'tool_use', id: 'tu-long', name: 'long_tool', input: { msg: 'a' } },
|
||||
{ type: 'tool_use', id: 'tu-short', name: 'short_tool', input: { msg: 'b' } },
|
||||
],
|
||||
model: 'mock-model',
|
||||
stop_reason: 'tool_use',
|
||||
usage: { input_tokens: 15, output_tokens: 25 },
|
||||
}
|
||||
const responses = [
|
||||
parallelResponse,
|
||||
toolUseResponse('long_tool', { msg: 't2' }),
|
||||
textResponse('done'),
|
||||
]
|
||||
let idx = 0
|
||||
const adapter: LLMAdapter = {
|
||||
name: 'mock',
|
||||
async chat(messages) {
|
||||
calls.push(messages.map(m => ({ role: m.role, content: [...m.content] })))
|
||||
return responses[idx++]!
|
||||
},
|
||||
async *stream() { /* unused */ },
|
||||
}
|
||||
|
||||
const runner = new AgentRunner(adapter, registry, executor, {
|
||||
model: 'mock-model',
|
||||
allowedTools: ['long_tool', 'short_tool'],
|
||||
maxTurns: 5,
|
||||
compressToolResults: true,
|
||||
})
|
||||
|
||||
await runner.run([{ role: 'user', content: [{ type: 'text', text: 'start' }] }])
|
||||
|
||||
// Turn 3: the parallel results from turn 1 should be selectively compressed.
|
||||
// The long_tool result (600 chars) → compressed. The short_tool result (50 chars) → kept.
|
||||
const turn3Messages = calls[2]!
|
||||
const turn1ToolResults = turn3Messages.flatMap(m =>
|
||||
m.content.filter((b): b is { type: 'tool_result'; tool_use_id: string; content: string } =>
|
||||
b.type === 'tool_result'),
|
||||
)
|
||||
// Find the results from turn 1 (first user message with tool_results)
|
||||
const firstToolResultMsg = turn3Messages.find(
|
||||
m => m.role === 'user' && m.content.some(b => b.type === 'tool_result'),
|
||||
)!
|
||||
const blocks = firstToolResultMsg.content.filter(
|
||||
(b): b is { type: 'tool_result'; tool_use_id: string; content: string } =>
|
||||
b.type === 'tool_result',
|
||||
)
|
||||
|
||||
// One should be compressed (long), one should be intact (short)
|
||||
const compressedBlocks = blocks.filter(b => b.content.includes('compressed'))
|
||||
const intactBlocks = blocks.filter(b => !b.content.includes('compressed'))
|
||||
expect(compressedBlocks).toHaveLength(1)
|
||||
expect(compressedBlocks[0]!.content).toContain('600 chars')
|
||||
expect(intactBlocks).toHaveLength(1)
|
||||
expect(intactBlocks[0]!.content).toBe('S'.repeat(50))
|
||||
})
|
||||
|
||||
it('compounds compression across 4+ turns', async () => {
|
||||
const calls: LLMMessage[][] = []
|
||||
const longOutput = 'x'.repeat(600)
|
||||
const responses = [
|
||||
toolUseResponse('echo', { message: 't1' }),
|
||||
toolUseResponse('echo', { message: 't2' }),
|
||||
toolUseResponse('echo', { message: 't3' }),
|
||||
textResponse('done'),
|
||||
]
|
||||
let idx = 0
|
||||
const adapter: LLMAdapter = {
|
||||
name: 'mock',
|
||||
async chat(messages) {
|
||||
calls.push(messages.map(m => ({ role: m.role, content: [...m.content] })))
|
||||
return responses[idx++]!
|
||||
},
|
||||
async *stream() { /* unused */ },
|
||||
}
|
||||
const { registry, executor } = buildRegistryAndExecutor(longOutput)
|
||||
const runner = new AgentRunner(adapter, registry, executor, {
|
||||
model: 'mock-model',
|
||||
allowedTools: ['echo'],
|
||||
maxTurns: 6,
|
||||
compressToolResults: true,
|
||||
})
|
||||
|
||||
await runner.run([{ role: 'user', content: [{ type: 'text', text: 'start' }] }])
|
||||
|
||||
// Turn 4: turns 1 and 2 should both be compressed, turn 3 should be intact
|
||||
const turn4Messages = calls[3]!
|
||||
const allToolResults = extractToolResultContents(turn4Messages)
|
||||
expect(allToolResults).toHaveLength(3)
|
||||
|
||||
// First two are compressed (turns 1 & 2)
|
||||
expect(allToolResults[0]).toContain('compressed')
|
||||
expect(allToolResults[1]).toContain('compressed')
|
||||
|
||||
// Last one (turn 3, most recent) preserved
|
||||
expect(allToolResults[2]).toBe(longOutput)
|
||||
})
|
||||
|
||||
it('works together with contextStrategy', async () => {
|
||||
const calls: LLMMessage[][] = []
|
||||
const longOutput = 'x'.repeat(600)
|
||||
const responses = [
|
||||
toolUseResponse('echo', { message: 't1' }),
|
||||
toolUseResponse('echo', { message: 't2' }),
|
||||
textResponse('done'),
|
||||
]
|
||||
let idx = 0
|
||||
const adapter: LLMAdapter = {
|
||||
name: 'mock',
|
||||
async chat(messages) {
|
||||
calls.push(messages.map(m => ({ role: m.role, content: [...m.content] })))
|
||||
return responses[idx++]!
|
||||
},
|
||||
async *stream() { /* unused */ },
|
||||
}
|
||||
const { registry, executor } = buildRegistryAndExecutor(longOutput)
|
||||
const runner = new AgentRunner(adapter, registry, executor, {
|
||||
model: 'mock-model',
|
||||
allowedTools: ['echo'],
|
||||
maxTurns: 5,
|
||||
compressToolResults: true,
|
||||
contextStrategy: { type: 'sliding-window', maxTurns: 10 },
|
||||
})
|
||||
|
||||
const result = await runner.run([
|
||||
{ role: 'user', content: [{ type: 'text', text: 'start' }] },
|
||||
])
|
||||
|
||||
// Should complete without error; both features coexist
|
||||
expect(result.output).toBe('done')
|
||||
|
||||
// Turn 3 should have compressed turn 1 results
|
||||
const turn3Messages = calls[2]!
|
||||
const allToolResults = extractToolResultContents(turn3Messages)
|
||||
expect(allToolResults[0]).toContain('compressed')
|
||||
})
|
||||
})
|
||||
Loading…
Reference in New Issue