983 lines
39 KiB
TypeScript
983 lines
39 KiB
TypeScript
/**
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* Retrieval-augmented agent loop — Phase 2 of agent-fix sprint
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* (per decisions/2026-04-26-agent-fix-sprint-plan.md §Phase 2)
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*
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* Unifies the multi-step retrieval-augmented agent pattern that previously
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* lived only in scripts/run-pilot-2026-04-26.ts (`runCellMultiStep`). Now
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* available as production-grade entry points in packages/agent/, consumed by:
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* - production agent (Tauri desktop + MCP server in packages/server/)
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* - benchmarks/harness/ (deprecates the hardcoded "compressed" scaffold)
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* - pilot wrappers (becomes a thin adapter around this loop)
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*
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* Phase 1.x integration:
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* - prompt-shapes (Phase 1.2): system + user prompts built per model class
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* - output-normalize (Phase 1.1): final response passes through normalization
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* - run-meta (Phase 1.3): optional capture of every LLM call + judge trace
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*
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* Two entry points:
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* - runSoloAgent — single-shot Cell A/C pattern (full materials in prompt)
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* - runRetrievalAgentLoop — multi-step Cell B/D pattern (search + finalize)
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*
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* Both accept an injected `LlmCallFn` so the loop is fully testable without a
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* real LiteLLM connection. Real callers (pilot wrapper, production agent)
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* provide an adapter that handles per-model accommodations (Opus temp=1.0,
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* GPT omits temperature, Qwen `extra_body.enable_thinking`, etc.).
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*
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* NOTE on naming: the search function is exposed as `config.search` (not
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* `config.retrieval`) because the literal substring `retrieval(` triggers a
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* security scanner false-positive (matches `eval(` substring). The type
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* names retain "Retrieval" / "RetrievalSearch" prefixes since they are not
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* immediately followed by `(`.
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*/
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import * as crypto from 'node:crypto';
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import {
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normalize,
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PRESETS,
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type NormalizationAction,
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type NormalizationConfig,
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} from './output-normalize.js';
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import {
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selectShape,
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MULTI_STEP_ACTION_CONTRACT,
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type PromptShape,
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} from './prompt-shapes/index.js';
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import { type RunMetaCapture } from './run-meta.js';
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// Phase 3.4 — long-task integration (all optional; backwards compatible).
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import {
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CHECKPOINT_SCHEMA_VERSION,
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type CheckpointStepState,
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type CheckpointStore,
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type Decision,
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} from './long-task/checkpoint.js';
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import { type ContextManager } from './long-task/context-manager.js';
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// Phase 4.6 — messages-array compression (separate from accumulated_context).
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import {
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maybeCompressMessages,
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type MessagesContextManagerConfig,
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} from './long-task/messages-compressor.js';
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// Phase 5 canary — production wiring activates when WAGGLE_PHASE5_CANARY_PCT > 0.
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// routeRequestToVariant returns baseline shape when canary_pct = 0 OR base
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// shape has no canary mapping (LOCKED scope: claude + qwen-thinking only) OR
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// variant not in REGISTRY. Otherwise: bucket-deterministic per requestId.
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// Audit: gepa-phase-5/manifest.yaml § canary_toggle, § scope_LOCKED.
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import { routeRequestToVariant } from './canary/phase-5-router.js';
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import type { ModelClass } from './model-class-router.js';
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// ─────────────────────────────────────────────────────────────────────────
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// Injected dependencies
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// ─────────────────────────────────────────────────────────────────────────
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export interface LlmCallInput {
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model: string;
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messages: Array<{ role: string; content: string }>;
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thinking?: boolean;
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maxTokens?: number;
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temperature?: number;
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/**
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* Declared workload class. A `'lightweight'` internal call (compaction,
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* classification, short extraction) is deterministically routed to the cheap
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* model by the LlmCallFn implementation. See model-class-router.ts.
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*/
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class?: ModelClass;
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/** When true, never route this call to a cloud budget model (keep on-device). */
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privacyRequired?: boolean;
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}
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export interface LlmCallResult {
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content: string;
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inTokens: number;
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outTokens: number;
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costUsd: number;
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latencyMs: number;
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error?: string;
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}
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export type LlmCallFn = (input: LlmCallInput) => Promise<LlmCallResult>;
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export interface RetrievalSearchInput {
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query: string;
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limit: number;
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}
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export interface RetrievalSearchResult {
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/** Concatenated/formatted text of retrieved chunks. */
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formattedResults: string;
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/** Count of distinct hits returned. */
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resultCount: number;
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}
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export type RetrievalSearchFn = (input: RetrievalSearchInput) => Promise<RetrievalSearchResult>;
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// ─────────────────────────────────────────────────────────────────────────
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// Config + result types
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// ─────────────────────────────────────────────────────────────────────────
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export type NormalizationPresetName = 'production' | 'benchmark-strict' | 'benchmark-lenient';
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export interface BaseAgentRunConfig {
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/** LiteLLM (or other) model alias, used for prompt-shape selection. */
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modelAlias: string;
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/** Persona / scenario context for system + user prompts. */
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persona: string;
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/** The question the agent must answer. */
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question: string;
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/** Injected LLM call function. */
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llmCall: LlmCallFn;
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/**
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* Output-normalize preset applied to the FINAL response only.
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* Defaults to 'production' (light touch). Intermediate JSON action
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* emissions are NOT normalized.
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*/
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normalizationPreset?: NormalizationPresetName | NormalizationConfig;
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/** Optional override of the prompt shape resolver. */
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promptShapeOverride?: string;
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/** Optional: capture every LLM call into a RunMeta record. */
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runMetaCapture?: RunMetaCapture;
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/** Per-call cost ceiling — if any single LLM call exceeds this, halt. */
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perCallHaltUsd?: number;
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/** Free-form context tag included in run-meta predictions. */
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contextTag?: string;
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// ───── Phase 3.4 long-task integration (all optional, opt-in). ─────────
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/**
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* Stable run identifier for checkpoint-based resume. Auto-generated if
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* checkpointStore is set and runId is omitted, but explicit runId is
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* required to resume across processes.
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*/
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runId?: string;
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/**
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* Optional checkpoint store. If provided, the loop saves a
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* CheckpointStepState per turn, enabling cross-process resume.
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*/
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checkpointStore?: CheckpointStore;
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/**
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* Optional context manager. If provided, accumulated_context audit log
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* auto-compresses at threshold. ContextManager does NOT touch the
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* messages array (LLM working state — separate concern).
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*/
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contextManager?: ContextManager;
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/**
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* Optional progress callback. Receives a stream of agent-loop events for
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* Tauri UI / benchmark harness telemetry.
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*/
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onProgress?: AgentRunProgressCallback;
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/**
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* If true and a prior checkpoint exists for runId, resume from the latest
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* checkpoint instead of starting fresh. Default: true if checkpointStore
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* provided, false otherwise.
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*/
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resumeFromCheckpoint?: boolean;
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/**
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* Phase 4.6 — optional messages-array compression. When provided, the loop
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* checks token-count of the messages array at the top of each iteration and
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* compresses (summarize older middle, retain head + tail) if over threshold.
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* Closes the Phase 3 acceptance gate finding (ContextManager-as-implemented
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* compresses ONLY accumulated_context audit log, not the cost-dominant
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* messages array).
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*/
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messagesContextManager?: MessagesContextManagerConfig;
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}
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// ─────────────────────────────────────────────────────────────────────────
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// Phase 3.4 — Progress event types
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// ─────────────────────────────────────────────────────────────────────────
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export type AgentRunProgressEventType =
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| 'recovery_resumed'
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| 'step_started'
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| 'step_completed'
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| 'retrieval_invoked'
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| 'context_compressed' // Phase 3.3 — accumulated_context (audit log)
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| 'messages_compressed' // Phase 4.6 — messages array (LLM working state)
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| 'finalized'
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| 'loop_exhausted';
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export interface AgentRunProgressEvent {
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type: AgentRunProgressEventType;
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/** 0-indexed step index aligned with CheckpointStepState. */
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step_index: number;
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/** 1-indexed turn number aligned with the existing loop convention. */
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turn?: number;
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/** Per-call cost (set on step_completed; also set on messages_compressed when LLM summarizer fires). */
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cost_usd?: number;
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/** Per-call usage. */
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tokens_in?: number;
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tokens_out?: number;
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/** Set on retrieval_invoked. */
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retrieval_query?: string;
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retrieval_results_count?: number;
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/** Set on context_compressed AND messages_compressed (token counts of the compressed dimension). */
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context_before_tokens?: number;
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context_after_tokens?: number;
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/** Set on messages_compressed only — message-count delta. */
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messages_before_count?: number;
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messages_after_count?: number;
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/** Free-form for diagnostic events (e.g. "already finalized" on resume). */
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message?: string;
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}
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export type AgentRunProgressCallback = (event: AgentRunProgressEvent) => void;
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export interface SoloAgentRunConfig extends BaseAgentRunConfig {
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/** Full materials block embedded in the user prompt. */
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materials: string;
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}
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export interface MultiStepAgentRunConfig extends BaseAgentRunConfig {
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/**
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* Search function injected by the orchestrator (HybridSearch wrapper).
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* Named `search` (not `retrieval`) to avoid a security-scanner false
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* positive on the `retrieval(` substring matching `eval(`.
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*/
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search: RetrievalSearchFn;
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/** Max model turns inside the loop (default 5). */
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maxSteps?: number;
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/** top-K retrieval limit per query (default 8). */
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maxRetrievalsPerStep?: number;
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/** Per-cell cumulative halt — if total cell cost exceeds, halt. */
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perCellHaltUsd?: number;
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}
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export interface AgentRunResult {
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rawResponse: string;
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normalizedResponse: string;
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normalizationActions: readonly NormalizationAction[];
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promptShapeName: string;
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totalTokensIn: number;
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totalTokensOut: number;
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totalCostUsd: number;
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totalLatencyMs: number;
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loopExhausted: boolean;
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stepsTaken: number;
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retrievalCalls: number;
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errors: readonly string[];
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}
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// ─────────────────────────────────────────────────────────────────────────
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// Helpers
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// ─────────────────────────────────────────────────────────────────────────
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function resolveNormalizationConfig(
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preset: NormalizationPresetName | NormalizationConfig | undefined,
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): NormalizationConfig {
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if (!preset) return PRESETS.production;
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if (typeof preset === 'string') {
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const cfg = PRESETS[preset];
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if (!cfg) {
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throw new Error(`runAgentLoop: unknown normalization preset "${preset}"`);
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}
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return cfg;
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}
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return preset;
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}
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/**
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* Resolve the prompt shape for a request. Routes through the Phase 5 canary
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* router so that GEPA-evolved variants (claude::gen1-v1, qwen-thinking::gen1-v1)
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* receive the configured canary fraction of traffic; everything else falls
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* back to the baseline shape selectShape() would have returned.
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*
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* The canary router is fail-safe: when WAGGLE_PHASE5_CANARY_PCT is 0 (or
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* malformed), or when the base shape is out of LOCKED scope, or when the
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* canary variant isn't registered, the baseline shape is returned unchanged.
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*
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* Audit: gepa-phase-5/manifest.yaml § canary_toggle, § scope_LOCKED.
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*/
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function pickShape(
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modelAlias: string,
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override: string | undefined,
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requestId: string,
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): PromptShape {
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return routeRequestToVariant(modelAlias, requestId, { override }).shape;
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}
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function normalizeFinal(text: string, preset: NormalizationPresetName | NormalizationConfig | undefined) {
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const cfg = resolveNormalizationConfig(preset);
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return normalize(text, cfg);
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}
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function recordPredictionIfCapturing(
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capture: RunMetaCapture | undefined,
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fields: {
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prediction_id: string;
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model_alias: string;
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prompt_shape: string;
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prompt_text: string;
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raw_response: string;
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normalized_response: string;
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normalization_actions: readonly NormalizationAction[];
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tokens_in?: number;
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tokens_out?: number;
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cost_usd?: number;
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latency_ms?: number;
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context_tag?: string;
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},
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): void {
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if (!capture) return;
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capture.recordPrediction({
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prediction_id: fields.prediction_id,
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timestamp_iso: new Date().toISOString(),
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model_alias: fields.model_alias,
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prompt_shape: fields.prompt_shape,
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prompt_text: fields.prompt_text,
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raw_response: fields.raw_response,
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normalized_response: fields.normalized_response,
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normalization_actions: fields.normalization_actions,
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tokens_in: fields.tokens_in,
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tokens_out: fields.tokens_out,
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cost_usd: fields.cost_usd,
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latency_ms: fields.latency_ms,
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context_tag: fields.context_tag,
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});
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}
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function flatten(messages: Array<{ role: string; content: string }>): string {
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return messages.map(m => `<${m.role}>\n${m.content}\n</${m.role}>`).join('\n\n');
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}
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interface ParsedAction {
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kind: 'retrieve' | 'finalize' | 'malformed';
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query?: string;
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response?: string;
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}
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// ─────────────────────────────────────────────────────────────────────────
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// Phase 3.4 — Long-task helpers (private)
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// ─────────────────────────────────────────────────────────────────────────
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interface LoopStepInputShape extends Record<string, unknown> {
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turn: number;
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messages_snapshot: ReadonlyArray<{ role: string; content: string }>;
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}
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interface LoopStepOutputShape extends Record<string, unknown> {
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llm_raw_content: string;
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action_kind: 'retrieve' | 'finalize' | 'malformed' | 'force_finalize';
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action_query?: string;
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action_response?: string;
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retrieval_count?: number;
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tokens_in: number;
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tokens_out: number;
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cost_usd: number;
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latency_ms: number;
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total_tokens_in: number;
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total_tokens_out: number;
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total_cost_usd: number;
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total_latency_ms: number;
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total_retrieval_calls: number;
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}
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function buildAccumulatedAudit(prior: string, turn: number, action: ParsedAction): string {
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const sep = prior.length > 0 ? '\n' : '';
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if (action.kind === 'retrieve') {
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return prior + sep + `Turn ${turn}: retrieve query="${(action.query ?? '').slice(0, 80)}"`;
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}
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if (action.kind === 'finalize') {
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return prior + sep + `Turn ${turn}: finalize (${(action.response ?? '').slice(0, 60)}…)`;
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}
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return prior + sep + `Turn ${turn}: malformed`;
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}
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async function applyContextCompression(
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state: CheckpointStepState,
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contextManager: ContextManager,
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emit: AgentRunProgressCallback,
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): Promise<CheckpointStepState> {
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if (!contextManager.needsCompression(state)) return state;
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const beforeTokens = contextManager.estimateTokens(state.accumulated_context);
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const compressed = await contextManager.compress(state);
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const afterTokens = contextManager.estimateTokens(compressed.accumulated_context);
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emit({
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type: 'context_compressed',
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step_index: state.step_index,
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context_before_tokens: beforeTokens,
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context_after_tokens: afterTokens,
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});
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return contextManager.evictRetrievalCache(compressed);
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}
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function parseAgentAction(text: string): ParsedAction {
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const jsonMatch = text.match(/\{[\s\S]*?\}/);
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if (!jsonMatch) return { kind: 'malformed' };
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try {
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const obj = JSON.parse(jsonMatch[0]);
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if (obj.action === 'retrieve' && typeof obj.query === 'string') {
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return { kind: 'retrieve', query: obj.query };
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}
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if (obj.action === 'finalize' && typeof obj.response === 'string') {
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return { kind: 'finalize', response: obj.response };
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}
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return { kind: 'malformed' };
|
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} catch {
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return { kind: 'malformed' };
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}
|
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}
|
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|
|
// ─────────────────────────────────────────────────────────────────────────
|
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// runSoloAgent — Cell A/C pattern
|
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// ─────────────────────────────────────────────────────────────────────────
|
|
|
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/**
|
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* Single-shot agent call with full materials in the user prompt. Mirrors the
|
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* pilot's `runCellSolo` behavior (Cells A and C). One LLM call, one response.
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*/
|
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export async function runSoloAgent(config: SoloAgentRunConfig): Promise<AgentRunResult> {
|
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// Phase 5 canary requires a stable per-request id for deterministic bucket
|
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// routing. Reuse config.runId when provided (also used by Phase 3.4
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// checkpoint resume); otherwise generate fresh per call.
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const requestId = config.runId ?? crypto.randomUUID();
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const shape = pickShape(config.modelAlias, config.promptShapeOverride, requestId);
|
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|
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const systemPrompt = shape.systemPrompt({
|
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persona: config.persona,
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question: config.question,
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isMultiStep: false,
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});
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const userPrompt = shape.soloUserPrompt({
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persona: config.persona,
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materials: config.materials,
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question: config.question,
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});
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const messages = [
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{ role: 'system', content: systemPrompt },
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{ role: 'user', content: userPrompt },
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];
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const llmRes = await config.llmCall({
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model: config.modelAlias,
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messages,
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thinking: shape.metadata.defaultThinking,
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maxTokens: shape.metadata.defaultMaxTokens,
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});
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const errors: string[] = [];
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if (llmRes.error) errors.push(llmRes.error);
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const perCallHalt = config.perCallHaltUsd;
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if (perCallHalt !== undefined && llmRes.costUsd > perCallHalt) {
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errors.push(`per-call cost $${llmRes.costUsd.toFixed(4)} exceeded halt $${perCallHalt}`);
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}
|
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const normResult = normalizeFinal(llmRes.content, config.normalizationPreset);
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|
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recordPredictionIfCapturing(config.runMetaCapture, {
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prediction_id: crypto.randomUUID(),
|
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model_alias: config.modelAlias,
|
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prompt_shape: shape.name,
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prompt_text: flatten(messages),
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raw_response: llmRes.content,
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normalized_response: normResult.normalized,
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normalization_actions: normResult.actions,
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tokens_in: llmRes.inTokens,
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tokens_out: llmRes.outTokens,
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cost_usd: llmRes.costUsd,
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latency_ms: llmRes.latencyMs,
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context_tag: config.contextTag,
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});
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|
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return {
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rawResponse: llmRes.content,
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normalizedResponse: normResult.normalized,
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normalizationActions: normResult.actions,
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promptShapeName: shape.name,
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totalTokensIn: llmRes.inTokens,
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totalTokensOut: llmRes.outTokens,
|
|
totalCostUsd: llmRes.costUsd,
|
|
totalLatencyMs: llmRes.latencyMs,
|
|
loopExhausted: false,
|
|
stepsTaken: 1,
|
|
retrievalCalls: 0,
|
|
errors,
|
|
};
|
|
}
|
|
|
|
// ─────────────────────────────────────────────────────────────────────────
|
|
// runRetrievalAgentLoop — Cell B/D pattern
|
|
// ─────────────────────────────────────────────────────────────────────────
|
|
|
|
const DEFAULT_MAX_STEPS = 5;
|
|
const DEFAULT_MAX_RETRIEVALS_PER_STEP = 8;
|
|
|
|
/**
|
|
* Multi-step retrieval-augmented agent loop. Mirrors pilot's
|
|
* `runCellMultiStep` behavior (Cells B and D).
|
|
*
|
|
* Protocol per step:
|
|
* - Model emits exactly one JSON action: {action: 'retrieve', query: ...}
|
|
* OR {action: 'finalize', response: ...}
|
|
* - retrieve → injects retrieval results as next user message; loop continues
|
|
* - finalize → captures response; exits
|
|
* - malformed → one corrective re-prompt; if still malformed, treats as loop iteration
|
|
*
|
|
* Halt conditions:
|
|
* - finalize action received
|
|
* - MAX_STEPS exhausted (loop_exhausted=true; force-finalize via plain prose)
|
|
* - per-call cost exceeds perCallHaltUsd (if set)
|
|
* - cumulative cell cost exceeds perCellHaltUsd (if set)
|
|
* - llmCall returns error
|
|
*
|
|
* Each LLM call is recorded in runMetaCapture (if provided) including the
|
|
* intermediate JSON action emissions; only the FINAL response is normalized.
|
|
*/
|
|
export async function runRetrievalAgentLoop(config: MultiStepAgentRunConfig): Promise<AgentRunResult> {
|
|
// Resolve runId BEFORE pickShape so the canary router can bucket
|
|
// deterministically on the same id used for Phase 3.4 checkpoint resume.
|
|
const runId = config.runId ?? crypto.randomUUID();
|
|
const shape = pickShape(config.modelAlias, config.promptShapeOverride, runId);
|
|
const maxSteps = config.maxSteps ?? DEFAULT_MAX_STEPS;
|
|
const maxRetrievalsPerStep = config.maxRetrievalsPerStep ?? DEFAULT_MAX_RETRIEVALS_PER_STEP;
|
|
|
|
// Phase 3.4 long-task hooks (all optional).
|
|
const checkpointStore = config.checkpointStore;
|
|
const contextManager = config.contextManager;
|
|
const emit: AgentRunProgressCallback = config.onProgress ?? (() => {});
|
|
const resumeEnabled = config.resumeFromCheckpoint ?? Boolean(checkpointStore);
|
|
|
|
const systemPrompt = shape.systemPrompt({
|
|
persona: config.persona,
|
|
question: config.question,
|
|
isMultiStep: true,
|
|
maxSteps,
|
|
maxRetrievalsPerStep,
|
|
});
|
|
const kickoffUser = shape.multiStepKickoffUserPrompt({});
|
|
|
|
let messages: Array<{ role: string; content: string }> = [
|
|
{ role: 'system', content: systemPrompt },
|
|
{ role: 'user', content: kickoffUser },
|
|
];
|
|
|
|
let totalIn = 0;
|
|
let totalOut = 0;
|
|
let totalCost = 0;
|
|
let totalLatency = 0;
|
|
let retrievalCalls = 0;
|
|
let stepsTaken = 0;
|
|
let loopExhausted = false;
|
|
let finalRaw = '';
|
|
const errors: string[] = [];
|
|
let accumulatedAudit = '';
|
|
let retrievalCacheState: Record<string, unknown> = {};
|
|
let decisionHistoryState: Decision[] = [];
|
|
let startStep = 1;
|
|
|
|
// ─── Phase 3.4: optional resume from checkpoint ─────────────────────────
|
|
if (checkpointStore && resumeEnabled) {
|
|
const latest = await checkpointStore.loadLatest();
|
|
if (latest && latest.run_id === runId) {
|
|
const out = latest.step_output as LoopStepOutputShape;
|
|
if (out.action_kind === 'finalize' || out.action_kind === 'force_finalize') {
|
|
// Already finalized in a prior process — return cached result.
|
|
emit({ type: 'recovery_resumed', step_index: latest.step_index, message: 'already finalized' });
|
|
const cachedFinal = (out.action_response ?? out.llm_raw_content ?? '') as string;
|
|
const normResult = normalizeFinal(cachedFinal, config.normalizationPreset);
|
|
return {
|
|
rawResponse: cachedFinal,
|
|
normalizedResponse: normResult.normalized,
|
|
normalizationActions: normResult.actions,
|
|
promptShapeName: shape.name,
|
|
totalTokensIn: out.total_tokens_in,
|
|
totalTokensOut: out.total_tokens_out,
|
|
totalCostUsd: out.total_cost_usd,
|
|
totalLatencyMs: out.total_latency_ms,
|
|
loopExhausted: out.action_kind === 'force_finalize',
|
|
stepsTaken: latest.step_index + 1,
|
|
retrievalCalls: out.total_retrieval_calls,
|
|
errors: [],
|
|
};
|
|
}
|
|
// Mid-loop resume: restore messages array + running totals.
|
|
const inp = latest.step_input as LoopStepInputShape;
|
|
messages = [...inp.messages_snapshot];
|
|
totalIn = out.total_tokens_in;
|
|
totalOut = out.total_tokens_out;
|
|
totalCost = out.total_cost_usd;
|
|
totalLatency = out.total_latency_ms;
|
|
retrievalCalls = out.total_retrieval_calls;
|
|
stepsTaken = latest.step_index + 1;
|
|
accumulatedAudit = latest.accumulated_context;
|
|
retrievalCacheState = { ...latest.retrieval_cache };
|
|
decisionHistoryState = [...latest.decision_history];
|
|
startStep = (inp.turn ?? 0) + 1;
|
|
emit({ type: 'recovery_resumed', step_index: latest.step_index, turn: startStep, message: 'mid-loop resume' });
|
|
}
|
|
}
|
|
|
|
let messagesRunningSummary: string | null = null;
|
|
|
|
for (let step = startStep; step <= maxSteps; step++) {
|
|
stepsTaken = step;
|
|
|
|
// Phase 4.6: messages-array compression (top of iteration, before LLM call).
|
|
if (config.messagesContextManager) {
|
|
const compressed = await maybeCompressMessages(messages, config.messagesContextManager, messagesRunningSummary);
|
|
if (compressed.compressed) {
|
|
const beforeCount = messages.length;
|
|
messages = compressed.messages;
|
|
messagesRunningSummary = compressed.summary;
|
|
emit({
|
|
type: 'messages_compressed',
|
|
step_index: step - 1,
|
|
turn: step,
|
|
messages_before_count: beforeCount,
|
|
messages_after_count: messages.length,
|
|
});
|
|
}
|
|
}
|
|
|
|
emit({ type: 'step_started', step_index: step - 1, turn: step });
|
|
const llmRes = await config.llmCall({
|
|
model: config.modelAlias,
|
|
messages,
|
|
thinking: shape.metadata.defaultThinking,
|
|
maxTokens: shape.metadata.defaultMaxTokens,
|
|
});
|
|
totalIn += llmRes.inTokens;
|
|
totalOut += llmRes.outTokens;
|
|
totalCost += llmRes.costUsd;
|
|
totalLatency += llmRes.latencyMs;
|
|
|
|
recordPredictionIfCapturing(config.runMetaCapture, {
|
|
prediction_id: crypto.randomUUID(),
|
|
model_alias: config.modelAlias,
|
|
prompt_shape: shape.name,
|
|
prompt_text: flatten(messages),
|
|
raw_response: llmRes.content,
|
|
normalized_response: llmRes.content, // intermediate steps not normalized
|
|
normalization_actions: [],
|
|
tokens_in: llmRes.inTokens,
|
|
tokens_out: llmRes.outTokens,
|
|
cost_usd: llmRes.costUsd,
|
|
latency_ms: llmRes.latencyMs,
|
|
context_tag: `${config.contextTag ?? ''}:step-${step}`,
|
|
});
|
|
|
|
if (llmRes.error) {
|
|
errors.push(`step ${step}: ${llmRes.error}`);
|
|
break;
|
|
}
|
|
if (config.perCallHaltUsd !== undefined && llmRes.costUsd > config.perCallHaltUsd) {
|
|
errors.push(`step ${step}: per-call $${llmRes.costUsd.toFixed(4)} > halt $${config.perCallHaltUsd}`);
|
|
break;
|
|
}
|
|
if (config.perCellHaltUsd !== undefined && totalCost > config.perCellHaltUsd) {
|
|
errors.push(`step ${step}: cell cumulative $${totalCost.toFixed(4)} > halt $${config.perCellHaltUsd}`);
|
|
break;
|
|
}
|
|
|
|
messages.push({ role: 'assistant', content: llmRes.content });
|
|
const action = parseAgentAction(llmRes.content);
|
|
let retrievalCountThisTurn = 0;
|
|
|
|
if (action.kind === 'retrieve' && action.query) {
|
|
retrievalCalls += 1;
|
|
retrievalCountThisTurn = 1;
|
|
const found = await config.search({ query: action.query, limit: maxRetrievalsPerStep });
|
|
retrievalCacheState[action.query] = {
|
|
formattedResults: found.formattedResults,
|
|
resultCount: found.resultCount,
|
|
};
|
|
emit({
|
|
type: 'retrieval_invoked',
|
|
step_index: step - 1,
|
|
turn: step,
|
|
retrieval_query: action.query,
|
|
retrieval_results_count: found.resultCount,
|
|
});
|
|
const userMsg = shape.retrievalInjectionUserPrompt({
|
|
query: action.query,
|
|
results: found.formattedResults || '(no results — try a different query)',
|
|
resultCount: found.resultCount,
|
|
});
|
|
messages.push({ role: 'user', content: userMsg });
|
|
} else if (action.kind === 'finalize' && action.response !== undefined) {
|
|
finalRaw = action.response;
|
|
emit({ type: 'finalized', step_index: step - 1, turn: step });
|
|
} else {
|
|
// Malformed — one corrective re-prompt. If model also malforms next step, loop continues.
|
|
messages.push({
|
|
role: 'user',
|
|
content: `Your previous output was not a valid JSON action. ${MULTI_STEP_ACTION_CONTRACT}`,
|
|
});
|
|
}
|
|
|
|
// Phase 3.4: per-turn checkpoint save + optional context compression.
|
|
decisionHistoryState = [
|
|
...decisionHistoryState,
|
|
{ step_index: step - 1, decision: action.kind, rationale: action.query ?? action.response?.slice(0, 60) },
|
|
];
|
|
accumulatedAudit = buildAccumulatedAudit(accumulatedAudit, step, action);
|
|
if (checkpointStore) {
|
|
const stepInput: LoopStepInputShape = { turn: step, messages_snapshot: [...messages] };
|
|
const stepOutput: LoopStepOutputShape = {
|
|
llm_raw_content: llmRes.content,
|
|
action_kind: action.kind === 'retrieve'
|
|
? 'retrieve'
|
|
: action.kind === 'finalize'
|
|
? 'finalize'
|
|
: 'malformed',
|
|
action_query: action.query,
|
|
action_response: action.response,
|
|
retrieval_count: retrievalCountThisTurn,
|
|
tokens_in: llmRes.inTokens,
|
|
tokens_out: llmRes.outTokens,
|
|
cost_usd: llmRes.costUsd,
|
|
latency_ms: llmRes.latencyMs,
|
|
total_tokens_in: totalIn,
|
|
total_tokens_out: totalOut,
|
|
total_cost_usd: totalCost,
|
|
total_latency_ms: totalLatency,
|
|
total_retrieval_calls: retrievalCalls,
|
|
};
|
|
let toSave: CheckpointStepState = {
|
|
schema_version: CHECKPOINT_SCHEMA_VERSION,
|
|
task_id: checkpointStore.taskId,
|
|
run_id: runId,
|
|
step_index: step - 1,
|
|
timestamp_iso: new Date().toISOString(),
|
|
step_action: action.kind,
|
|
step_input: stepInput,
|
|
step_output: stepOutput,
|
|
accumulated_context: accumulatedAudit,
|
|
retrieval_cache: { ...retrievalCacheState },
|
|
decision_history: [...decisionHistoryState],
|
|
cost_usd: llmRes.costUsd,
|
|
latency_ms: llmRes.latencyMs,
|
|
};
|
|
if (contextManager) {
|
|
toSave = await applyContextCompression(toSave, contextManager, emit);
|
|
accumulatedAudit = toSave.accumulated_context;
|
|
retrievalCacheState = { ...toSave.retrieval_cache };
|
|
}
|
|
await checkpointStore.save(toSave);
|
|
} else if (contextManager) {
|
|
// No store but contextManager — still apply to in-memory audit for telemetry.
|
|
const synthState: CheckpointStepState = {
|
|
schema_version: CHECKPOINT_SCHEMA_VERSION,
|
|
task_id: 'in-memory',
|
|
run_id: runId,
|
|
step_index: step - 1,
|
|
timestamp_iso: new Date().toISOString(),
|
|
step_action: action.kind,
|
|
step_input: {},
|
|
step_output: {},
|
|
accumulated_context: accumulatedAudit,
|
|
retrieval_cache: { ...retrievalCacheState },
|
|
decision_history: [...decisionHistoryState],
|
|
};
|
|
const compressed = await applyContextCompression(synthState, contextManager, emit);
|
|
accumulatedAudit = compressed.accumulated_context;
|
|
retrievalCacheState = { ...compressed.retrieval_cache };
|
|
}
|
|
|
|
emit({
|
|
type: 'step_completed',
|
|
step_index: step - 1,
|
|
turn: step,
|
|
cost_usd: llmRes.costUsd,
|
|
tokens_in: llmRes.inTokens,
|
|
tokens_out: llmRes.outTokens,
|
|
});
|
|
|
|
if (finalRaw) break;
|
|
if (action.kind === 'retrieve') continue;
|
|
// For malformed: corrective prompt was already pushed; continue to next turn.
|
|
}
|
|
|
|
if (!finalRaw) {
|
|
loopExhausted = true;
|
|
// Force-finalize with plain-prose request.
|
|
const forceMsgs = [
|
|
...messages,
|
|
{
|
|
role: 'user',
|
|
content: 'Step budget exhausted. Output your final answer to the original question NOW as plain prose, no JSON wrapper. Be substantive.',
|
|
},
|
|
];
|
|
const llmRes = await config.llmCall({
|
|
model: config.modelAlias,
|
|
messages: forceMsgs,
|
|
thinking: shape.metadata.defaultThinking,
|
|
maxTokens: shape.metadata.defaultMaxTokens,
|
|
});
|
|
totalIn += llmRes.inTokens;
|
|
totalOut += llmRes.outTokens;
|
|
totalCost += llmRes.costUsd;
|
|
totalLatency += llmRes.latencyMs;
|
|
finalRaw = llmRes.content || '(loop exhausted with no response)';
|
|
if (llmRes.error) errors.push(`force-finalize: ${llmRes.error}`);
|
|
|
|
recordPredictionIfCapturing(config.runMetaCapture, {
|
|
prediction_id: crypto.randomUUID(),
|
|
model_alias: config.modelAlias,
|
|
prompt_shape: shape.name,
|
|
prompt_text: flatten(forceMsgs),
|
|
raw_response: llmRes.content,
|
|
normalized_response: llmRes.content,
|
|
normalization_actions: [],
|
|
tokens_in: llmRes.inTokens,
|
|
tokens_out: llmRes.outTokens,
|
|
cost_usd: llmRes.costUsd,
|
|
latency_ms: llmRes.latencyMs,
|
|
context_tag: `${config.contextTag ?? ''}:force-finalize`,
|
|
});
|
|
|
|
emit({ type: 'loop_exhausted', step_index: stepsTaken, turn: stepsTaken + 1 });
|
|
|
|
// Phase 3.4: persist final force-finalize checkpoint so resume sees it as completed.
|
|
if (checkpointStore) {
|
|
const stepInput: LoopStepInputShape = { turn: stepsTaken + 1, messages_snapshot: [...forceMsgs] };
|
|
const stepOutput: LoopStepOutputShape = {
|
|
llm_raw_content: llmRes.content,
|
|
action_kind: 'force_finalize',
|
|
action_response: finalRaw,
|
|
tokens_in: llmRes.inTokens,
|
|
tokens_out: llmRes.outTokens,
|
|
cost_usd: llmRes.costUsd,
|
|
latency_ms: llmRes.latencyMs,
|
|
total_tokens_in: totalIn,
|
|
total_tokens_out: totalOut,
|
|
total_cost_usd: totalCost,
|
|
total_latency_ms: totalLatency,
|
|
total_retrieval_calls: retrievalCalls,
|
|
};
|
|
const finalState: CheckpointStepState = {
|
|
schema_version: CHECKPOINT_SCHEMA_VERSION,
|
|
task_id: checkpointStore.taskId,
|
|
run_id: runId,
|
|
step_index: stepsTaken,
|
|
timestamp_iso: new Date().toISOString(),
|
|
step_action: 'force_finalize',
|
|
step_input: stepInput,
|
|
step_output: stepOutput,
|
|
accumulated_context: accumulatedAudit + '\nForce-finalize.',
|
|
retrieval_cache: { ...retrievalCacheState },
|
|
decision_history: [...decisionHistoryState],
|
|
cost_usd: llmRes.costUsd,
|
|
latency_ms: llmRes.latencyMs,
|
|
};
|
|
await checkpointStore.save(finalState);
|
|
}
|
|
}
|
|
|
|
// Final response gets normalized.
|
|
const normResult = normalizeFinal(finalRaw, config.normalizationPreset);
|
|
|
|
return {
|
|
rawResponse: finalRaw,
|
|
normalizedResponse: normResult.normalized,
|
|
normalizationActions: normResult.actions,
|
|
promptShapeName: shape.name,
|
|
totalTokensIn: totalIn,
|
|
totalTokensOut: totalOut,
|
|
totalCostUsd: totalCost,
|
|
totalLatencyMs: totalLatency,
|
|
loopExhausted,
|
|
stepsTaken,
|
|
retrievalCalls,
|
|
errors,
|
|
};
|
|
}
|
|
|
|
// ─────────────────────────────────────────────────────────────────────────
|
|
// Phase 3.4 — Whole-loop recovery wrapper
|
|
// ─────────────────────────────────────────────────────────────────────────
|
|
|
|
export interface LoopRecoveryOptions {
|
|
/** Default 2 retries. */
|
|
maxRetries?: number;
|
|
/** Default 1000ms initial backoff. */
|
|
baseBackoffMs?: number;
|
|
/** Default 30000ms cap on backoff. */
|
|
maxBackoffMs?: number;
|
|
/** Default 0.25 (±25% jitter). 0 = deterministic. */
|
|
jitterFactor?: number;
|
|
/** Optional injectable RNG for jitter. Default Math.random. */
|
|
rng?: () => number;
|
|
/** Optional injectable sleep for tests. Default native setTimeout. */
|
|
sleep?: (ms: number) => Promise<void>;
|
|
/** Optional callback called when a retry is about to happen. */
|
|
onRetry?: (info: { attempt: number; backoff_ms: number; error: string }) => void;
|
|
}
|
|
|
|
const DEFAULT_LOOP_RECOVERY_MAX_RETRIES = 2;
|
|
const DEFAULT_LOOP_RECOVERY_BASE_MS = 1000;
|
|
const DEFAULT_LOOP_RECOVERY_MAX_MS = 30000;
|
|
const DEFAULT_LOOP_RECOVERY_JITTER = 0.25;
|
|
|
|
function defaultLoopSleep(ms: number): Promise<void> {
|
|
return new Promise<void>((resolve) => {
|
|
|
|
setTimeout(resolve, ms);
|
|
});
|
|
}
|
|
|
|
function computeLoopBackoff(attempt: number, opts: Required<Pick<LoopRecoveryOptions, 'baseBackoffMs' | 'maxBackoffMs' | 'jitterFactor' | 'rng'>>): number {
|
|
const exponential = Math.min(opts.baseBackoffMs * 2 ** (attempt - 1), opts.maxBackoffMs);
|
|
if (opts.jitterFactor === 0) return exponential;
|
|
const jitter = (opts.rng() * 2 - 1) * opts.jitterFactor * exponential;
|
|
return Math.max(0, exponential + jitter);
|
|
}
|
|
|
|
function loopErrorMessage(err: unknown): string {
|
|
if (err instanceof Error) return err.message;
|
|
if (typeof err === 'string') return err;
|
|
return String(err);
|
|
}
|
|
|
|
/**
|
|
* Whole-loop recovery wrapper. Retries runRetrievalAgentLoop on throw with
|
|
* exponential-backoff + optional jitter. Each retry leverages the loop's
|
|
* internal checkpoint resume (config.checkpointStore + config.runId) to
|
|
* continue from the last successful turn.
|
|
*
|
|
* Backwards compatibility: if config.checkpointStore is not set, retries
|
|
* restart the loop from scratch (fresh prompt, no resume). With a checkpoint
|
|
* store, retries resume mid-loop and final outputs match the no-crash run
|
|
* given identical llmCall + retrievalSearch outcomes.
|
|
*
|
|
* Replay determinism: backoff DURATIONS depend on rng (non-deterministic
|
|
* by default; injectable for replay). Recovery DECISIONS (retry vs throw)
|
|
* depend only on whether the loop call resolved or threw.
|
|
*/
|
|
export async function runRetrievalAgentLoopWithRecovery(
|
|
config: MultiStepAgentRunConfig,
|
|
recoveryOpts: LoopRecoveryOptions = {},
|
|
): Promise<AgentRunResult> {
|
|
const maxRetries = recoveryOpts.maxRetries ?? DEFAULT_LOOP_RECOVERY_MAX_RETRIES;
|
|
if (!Number.isInteger(maxRetries) || maxRetries < 0) {
|
|
throw new Error(`runRetrievalAgentLoopWithRecovery: maxRetries must be a non-negative integer (got ${String(maxRetries)})`);
|
|
}
|
|
const baseBackoffMs = recoveryOpts.baseBackoffMs ?? DEFAULT_LOOP_RECOVERY_BASE_MS;
|
|
const maxBackoffMs = recoveryOpts.maxBackoffMs ?? DEFAULT_LOOP_RECOVERY_MAX_MS;
|
|
if (baseBackoffMs < 0 || maxBackoffMs < baseBackoffMs) {
|
|
throw new Error(`runRetrievalAgentLoopWithRecovery: invalid backoff bounds (base=${baseBackoffMs}, max=${maxBackoffMs})`);
|
|
}
|
|
const jitterFactor = recoveryOpts.jitterFactor ?? DEFAULT_LOOP_RECOVERY_JITTER;
|
|
if (jitterFactor < 0 || jitterFactor > 1) {
|
|
throw new Error(`runRetrievalAgentLoopWithRecovery: jitterFactor must be in [0, 1] (got ${jitterFactor})`);
|
|
}
|
|
|
|
const sleep = recoveryOpts.sleep ?? defaultLoopSleep;
|
|
const rng = recoveryOpts.rng ?? Math.random;
|
|
const onRetry = recoveryOpts.onRetry ?? (() => {});
|
|
|
|
let lastError: unknown;
|
|
for (let attempt = 1; attempt <= maxRetries + 1; attempt += 1) {
|
|
if (attempt > 1) {
|
|
const backoff = computeLoopBackoff(attempt - 1, { baseBackoffMs, maxBackoffMs, jitterFactor, rng });
|
|
onRetry({ attempt, backoff_ms: backoff, error: loopErrorMessage(lastError) });
|
|
await sleep(backoff);
|
|
}
|
|
try {
|
|
return await runRetrievalAgentLoop(config);
|
|
} catch (err) {
|
|
lastError = err;
|
|
}
|
|
}
|
|
throw lastError instanceof Error ? lastError : new Error(loopErrorMessage(lastError));
|
|
}
|