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academy developers, Sergii Nosachenko"},"license":"MIT","description":"A gateway package for LLM services.","maintainers":[{"name":"mateacademy","email":"dev@mate.academy"}],"readme":"# LLM Gateway\n\nA standardized interface for interacting with various Large Language Model (LLM) providers.\n\n## Table of Contents\n\n- [Overview](#overview)\n- [Installation](#installation)\n- [Features](#features)\n- [Usage](#usage)\n  - [Logger Interface](#logger-interface)\n  - [Reporter Interface](#reporter-interface)\n  - [Basic Setup](#basic-setup)\n  - [Real-world Example](#real-world-example)\n  - [Usage Examples](#usage-examples)\n    - [Basic Text Completion](#basic-text-completion)\n    - [Chat-based Assistance](#chat-based-assistance)\n    - [Direct Image Analysis](#direct-image-analysis)\n    - [Audio by URL](#audio-by-url)\n    - [Structured Output](#structured-output)\n    - [Model Configuration](#model-configuration)\n    - [File-based Assistance](#file-based-assistance)\n    - [Speech-to-Text Transcription](#speech-to-text-transcription)\n    - [Text-to-Speech Generation](#text-to-speech-generation)\n    - [Token Counting for Context Management](#token-counting-for-context-management)\n    - [Abort Signal Support](#abort-signal-support)\n- [API Reference](#api-reference)\n  - [LLMProviders](#llmproviders)\n  - [LLMPurposes](#llmpurposes)\n  - [LLMServiceFactory](#llmservicefactory)\n  - [LLMCompletionService](#llmcompletionservice)\n  - [LLMAssistanceService](#llmassistanceservice)\n  - [LLMSpeechToTextService](#llmspeechtotext-service)\n  - [LLMTextToSpeechService](#llmtexttospeech-service)\n  - [Prompt Builder](#prompt-builder)\n- [Supported Providers](#supported-providers)\n  - [OpenAI](#openai)\n  - [Google Generative AI](#google-generative-ai)\n- [Testing](#testing)\n  - [Test Structure](#test-structure)\n  - [Running Tests](#running-tests)\n  - [Test Configuration](#test-configuration)\n  - [Test Coverage](#test-coverage)\n  - [Test Helpers](#test-helpers)\n  - [Writing Custom Tests](#writing-custom-tests)\n- [Developer Guide](#developer-guide)\n  - [Schema Architecture Overview](#schema-architecture-overview)\n  - [Adding a New Provider](#adding-a-new-provider)\n\n## Overview\n\nThe `@mate-academy/llm-gateway` package provides a unified way to work with different LLM providers like OpenAI and Google Generative AI. It abstracts the implementation details of each provider, allowing you to easily switch between them without changing your application code.\n\n## Installation\n\n```bash\nnpm install @mate-academy/llm-gateway\n```\n\n## Features\n\n- Support for multiple LLM providers (OpenAI, Google Generative AI)\n- **Tracing port**: The gateway defines an `LLMGatewayTracer` port and captures generations, tool observations, usage, and cost automatically at every LLM call; fire-and-forget so a tracing failure never blocks or fails one. `@mate-academy/llm-tracer` provides the Langfuse-backed implementation; the gateway itself carries no tracing SDK. Tracing is opt-in per `LLMGateway` instance — `LLMServiceFactory` keeps working untraced.\n- **Structured Output**: Type-safe JSON responses with schema validation\n- **Extensible Schema Architecture**: Driver pattern with provider-specific adapters\n- **LLM Metrics & Reporting**: Comprehensive metrics collection with automatic cost calculation\n- **Agent invocation reporting**: `runAgent` exposes the numeric invocation id and its parent to reporter context builders for per-agent accounting.\n- **Cost Tracking**: Real-time cost calculation for all LLM operations with flexible pricing functions supporting text and audio tokens, including cached token discounts and reasoning tokens\n- **Cached Token Support**: Automatic detection and separate pricing for cached tokens (prompt caching) with provider-specific discount rates\n- **Instance Caching**: Intelligent SDK instance pooling with LRU eviction to prevent memory leaks\n- Standardized completion service interface\n- Standardized assistance service interface with file handling and direct chat\n- Speech-to-text transcription capabilities\n- Text-to-speech generation capabilities\n- Abort signal support for cancelling long-running operations\n- Per-subagent timeouts that fail only the bounded delegation call\n- Lightweight token counting using character-based approximation (no heavyweight tokenizer dependencies)\n- Type-safe prompt templates with dynamic replacements\n- Factory pattern for easy provider selection\n- Consistent logging across all providers\n- Comprehensive testing suite with integration tests and shared test utilities\n- Full backward compatibility\n\n## Usage\n\n### Tracing\n\nThe gateway defines the tracer port (`LLMGatewayTracer`) but carries no tracing\nSDK of its own. `@mate-academy/llm-tracer` provides `createLangfuseTracer`, the\nLangfuse-backed implementation of that port. Build an `LLMGateway` with it at\nthe composition root (the consumer's DI container / serverless core) and take\nevery traced service from that instance — feature code never touches Langfuse\ncredentials, and the package installs no process-wide state:\n\n```typescript\nimport { LLMGateway } from '@mate-academy/llm-gateway';\nimport { createLangfuseTracer } from '@mate-academy/llm-tracer';\n\nconst tracer = createLangfuseTracer({\n  baseUrl: process.env.LANGFUSE_BASE_URL,\n  publicKey: process.env.LANGFUSE_PUBLIC_KEY,\n  secretKey: process.env.LANGFUSE_SECRET_KEY,\n  environment: process.env.APP_ENV,\n  // 'batched' for long-running processes, 'immediate' for Lambda\n  exportMode: 'batched',\n});\n\nconst llmGateway = new LLMGateway({ tracer });\n\nconst completionService = llmGateway.getCompletionService({\n  provider: LLMProviders.OpenAI,\n  logger,\n  reporter,\n});\n```\n\n`LLMServiceFactory`'s static methods stay available and stay untraced, so call\nsites that know nothing about tracing keep working unchanged. Building\n`new LLMGateway()` without a tracer is equally valid — it hands out services\nwired to the no-op tracer, so nothing breaks before rollout.\n`createLangfuseTracer` likewise falls back to a no-op when credentials are\nmissing.\n\nEvery completion and speech-to-text call made through a gateway-built service\nemits exactly one `generation` observation (prompt/completion content, model,\nparams, usage, cost) at the same success/error points the metrics reporter\nalready wraps — one on success, one (level `ERROR`) on failure. An assistance\ncall emits one `generation` per **model round** of its tool loop, each carrying\nthe conversation as it stood for that round and that round's assistant output,\nwith the round's own usage and cost. Inside an active trace scope, a legacy\n`assistInChat` caller that runs an agent-style tool loop can pass `agentName` to\ngroup all of its generation and tool observations under one `agent`\nobservation. The v2 `runAgent` API opens that agent observation automatically.\nThe run still writes a single aggregate metrics point, unchanged. Calls that\ncarry no per-call trace context (text-to-speech, file and chat management) emit\nnone, on either branch.\nObservation input/output are recorded in full — the prompt and the completion\nare what a trace exists to show, so they are never truncated. All of this is\nfire-and-forget — a tracing failure never blocks or fails an LLM call.\n\nTool observations use the tool definition's bare `name`. This is a breaking\nchange for consumers that filter on the former\n`AssistanceService/tool:<name>` observation names. A tool may declare\n`tracing.observation` as `LLMToolTracingObservation.Tool`, `.Retriever`, or\n`.None`; the default is `.Tool`. `.None` is intended for delegation tools whose\nexecution already opens an agent observation. Unknown and gate-denied calls are\nstill traced, at warning level, with exactly the result returned to the model.\nFailed calls likewise record the same `Error executing tool '<name>': ...`\nmessage the model receives.\n\nUse `tracing.mapInput` when a tool's provider-facing arguments are intentionally\nencoded differently from the shape people should inspect in a trace. The mapper\nchanges only the recorded observation input: `canExecute` and `execute` still\nreceive the original arguments. If the mapper throws, tracing falls back to the\noriginal input and tool execution continues normally.\n\nEvery agent-loop generation carries a `tools` metadata field containing the\nserialized definitions offered in that round. A delegated run uses its agent\nname consistently for its agent observation and generation descendants, and\nits tools nest below that single agent observation rather than a duplicate\ndelegation-tool observation.\n\nPass `traceContext.prompt` (`{ name, version, isFallback }`) to name the\nobservation after the managed prompt and link it to that prompt version in\nLangfuse; a fallback prompt intentionally produces no link. The v2 client builds\nthis context for every call it makes, so `generate` and `runAgent` need nothing\nat the call site.\n\n`runAgent` also passes an optional `invocation` object to the reporter context\nbuilder. It identifies the root agent or delegated subagent within the current\nagent tree; ordinary `generate` calls leave it absent.\n\n`userId`, `sessionId` and `tags` travel as typed fields on\n`LLMCallTraceContext` and on the generation record, never as metadata keys: they\nare first-class Langfuse trace attributes, and the tracer promotes them to the\ntrace itself. The per-call context bag stays the metadata path, so a user named\nin it still appears in metadata exactly as before.\n\nA generation records the same provider-native message array on both branches:\neach service prepares its request messages before the provider call is\nattempted, so a failed round renders as a role-labelled transcript instead of a\nraw `{ message, history }` object. Preparing those messages is local work, so\nits own failure is a configuration error and propagates rather than being folded\ninto the provider's error result. An assistance run that fails before its first\nround ever starts has no transcript to show, and falls back to a single error\ngeneration carrying the call's message.\n\n`LLMGatewayTracer.getActiveTraceContext()` returns the context the surrounding\nscope was opened with (`undefined` outside a scope). The v2 client passes it to\n`reporting.buildContext` as `scopeContext`, so a composition root can fall back\nto what the operation declared when a call names nothing of its own.\n\nThe observation also carries its real time span. Each provider service captures\n`startedAt` (epoch ms) before the provider call and the base service stamps\n`endedAt` when it hands the record to the tracer, so the exported generation has\nthe actual call latency instead of a zero-width span. Both fields are required\non `LLMGenerationTraceRecord`, and `startedAt` is required on the\n`LLMGenerationTrace` a provider builds — a new provider call site cannot compile\nwithout threading a start timestamp.\n\nTo group several calls under one trace, wrap the work so nested gateway calls\nattach to it automatically:\n\n```typescript\nawait llmGateway.withTrace(\n  'my-operation',\n  { metadata: { feature: 'city-prediction' } },\n  async () => {\n    // gateway calls here nest under the trace\n  },\n);\n```\n\nShort-lived processes (Lambdas, scripts) must flush before exit, since buffered\nevents are lost otherwise:\n\n```typescript\nawait llmGateway.flush(); // safe no-op when tracing is disabled\n```\n\nThe CloudWatch fallback-serving metric (`LangfuseFallbackServed`, namespace\n`LLMGateway`) is emitted through `llmGateway.emitFallbackServed(...)`; pass a\nreal emitter as `fallbackMetricEmitter` when building the gateway (it defaults\nto a no-op so the package carries no AWS dependency).\n\nTo assert emitted observations in tests, build the gateway with a test double\nimplementing `LLMGatewayTracer` and assert what it captured (see\n`@mate-academy/llm-tracer`'s README for asserting real Langfuse spans).\n\n### v2 Client (`createLLMClient`)\n\nThe v2 client is the prompt-name-first call surface. A call site names the\noperation (the Langfuse prompt name); everything else — prompt text, model,\nprovider, params, credentials, reporter wiring, tracing, structured output —\nresolves from it inside the package. Build it once at the composition root and\ninject it via DI; feature code never sees configuration or credentials.\n\nLive prompt fetching is behind the `LLMGatewayPromptClient` port, not a\nLangfuse SDK client: the gateway carries no `@langfuse/*` dependency of its\nown. `@mate-academy/prompt-client`'s `LLMPromptClient` satisfies this port\nstructurally — pass its `promptClient` straight through. Omitting\n`promptClient` runs the client on the build-time snapshot alone (the same\nno-op-by-omission story as the tracer port).\n\n```typescript\nimport {\n  createLLMClient,\n  defineLLMPrompts,\n  LLMSchema,\n} from '@mate-academy/llm-gateway';\nimport { LLMPrompt, langfusePromptSnapshot } from './langfusePrompts.generated';\n\nconst llmPromptRegistry = defineLLMPrompts({\n  [LLMPrompt.CityPredictionPredictCityName]: {\n    schema: LLMSchema.object({ cityName: LLMSchema.string().nullable() }),\n    variables: ['locationName', 'countryName'],\n  },\n});\n\nconst llm = createLLMClient({\n  registry: llmPromptRegistry,\n  snapshot: langfusePromptSnapshot,\n  credentials: async ({ provider, promptName, product }) => providerOptions,\n  reporting: { reporter },\n  logger,\n  context: { product, appEnvironment },\n  promptClient, // an `@mate-academy/prompt-client` bundle's `promptClient`, or omit for snapshot-only\n});\n\nconst { cityName } = await llm.generate(\n  LLMPrompt.CityPredictionPredictCityName,\n  { variables: { locationName, countryName }, context: { userId } },\n);\n```\n\n- **Typed end to end.** The client is generic over `typeof registry`. Required\n  variable names come from each binding's code-owned `variables` list, while the\n  return type is the bound schema's inferred type, or `string` when no schema is\n  bound. Langfuse prompt edits never change TypeScript types.\n- **The reporter's context is type-tied to its mapper.** `createLLMClient`'s third\n  type parameter is the reporter context, so a `reporter` typed\n  `LLMReporterInterface<T>` forces `reporting.buildContext` to return exactly `T` —\n  a mapper that drops or mistypes a reported column fails the build instead of\n  corrupting the dashboard downstream of it. It is inferred when the call site\n  passes no explicit type arguments; TypeScript has no partial inference, so a call\n  site that spells out `Registry`/`VariableMap` must spell the context out too:\n  `createLLMClient<typeof registry, VariableMap, ReporterContext>({ … })`.\n- **Product is per call when the caller knows better.** A client is built once per\n  process, so its `context.product` can only carry the product of whatever built it\n  — a request host, or nothing at all on a message-bus or WebSocket entry point.\n  Pass `context: { product }` to `generate`/`runAgent` and it wins over the static\n  one for credentials resolution, and reaches `buildContext` for the reported row.\n- **Sessions and tags are top-level call options.** Pass `sessionId` to\n  `generate`/`runAgent` to group the traces of one conversation, review or agent\n  run into a single Langfuse session (`runAgent` copies it onto delegated\n  subagent runs), and `traceTags` for Langfuse trace tags. Both are typed\n  Langfuse trace fields rather than metadata, and the anchor format\n  `<domain>:<entity>:<id>` must stay stable — changing it splits a session's\n  history in two.\n- **Config validation on every fetch.** The client checks `config`, provider,\n  model, param ceilings, and the resolved model's capabilities against registry\n  `requires`. Any failure serves the last-known-good snapshot and emits the\n  `LangfuseFallbackServed` metric; prompt variables remain a manually maintained\n  contract between the registry and Langfuse.\n- **Prompt bodies render with Mustache.** A fetched body (and every message of a\n  chat body) is rendered in a single pass, so `{{#key}}…{{/key}}` renders while\n  its variable is a non-empty string and `{{^key}}…{{/key}}` renders while it is\n  not — a prompt author can make a paragraph conditional in Langfuse without a\n  code change. One pass also means a value is never re-scanned: a `{{leadName}}`\n  a user types into a transcript reaches the model as those characters instead of\n  being expanded. A supplied `{{key}}` is replaced by its raw value (no HTML\n  escaping, and `$&` or `$$` inside it stay literal), and a `{{key}}` the call\n  site does not supply is left in the body rather than dropped. A body Mustache\n  cannot parse, or one carrying a comment, a partial, a delimiter change or a\n  dotted path, is substituted variable by variable instead, so it degrades rather\n  than throws. Note that `{{#!key}}`, the negation\n  [`createPromptTemplate`](#prompt-builder) accepts, is not Mustache — write\n  `{{^key}}…{{/key}}` in a Langfuse body. A body that fails to parse warns once\n  per distinct body text, not once per call, so a broken live Langfuse body does\n  not flood the logs.\n\n  Three differences from the old variable-by-variable replacement, worth\n  knowing before editing a live Langfuse body: an unsupplied `{{y}}` that\n  shares its name with a `{{#y}}`/`{{^y}}` section in the same body now renders\n  empty instead of the literal tag; `{{{x}}}` and `{{&x}}` (Mustache's\n  unescaped-variable syntax) now render `x`'s value instead of being left as\n  literal text; and two unsupplied spellings of the same tag that differ only\n  in internal whitespace (`{{ y }}` and `{{y}}`) collapse to one spelling in\n  the output.\n- **Errors throw with a taxonomy** (`LLMGatewayError` base + `LLMProviderError`,\n  `LLMSchemaValidationError`, `LLMAbortedError`, `LLMConfigError`); validated\n  `data` is the return value, never an `'error' in response` union.\n- **Chat-type prompts.** A prompt seeded as a Langfuse `chat` prompt binds with\n  `kind: 'chat'`. The runtime produces a role-tagged message array with variables\n  substituted across every message; at call time a leading `system` message maps\n  to the provider's instructions slot and the final `user` message is the request,\n  preserving the message structure the prompt declares.\n- **Per-call schema override.** Pass `schema` in the `generate` options to override\n  the registry binding's schema for one call; the return type follows the call-site\n  schema. Use it when the output contract is dynamic (e.g. a schema whose shape\n  depends on runtime input). A binding may omit `schema` when callers always pass\n  one. The schema is the code-owned output contract, honored in every mode.\n- **Per-call routing overrides.** `overrides` on `generate`, `runAgent`,\n  `compilePrompt`, `generateSpeech`, `generateTranscript`, `generateImage` and\n  `describePrompt`\n  carries `{ provider, model, reasoningEffort, promptVersion, promptLabel }`.\n  `promptVersion`/`promptLabel` pick which Langfuse version is fetched; the other\n  three replace the fetched config's routing before the validation gate runs, so\n  an overridden model is checked against `LLM_SERVICE_MODELS` and the binding's\n  `requires` exactly like one the prompt itself names — a model outside the map\n  is rejected rather than silently ignored. An agent run passes its overrides\n  down to every delegated sub-agent. Use them for evaluation harnesses and\n  model comparisons, not for production routing, which belongs in the prompt's\n  own `config`.\n\n  ```typescript\n  const result = await llm.runAgent(AGENT_PROMPT, {\n    variables,\n    overrides: { model: 'gemini-3.7-flash', reasoningEffort: LLMReasoningEffort.Medium },\n  });\n  ```\n\n- **`describePrompt(name, { purpose, overrides })`.** Read-only accessor returning\n  the `{ provider, model, promptVersion, isFallback }` the runtime would serve\n  right now (same resolve/cache/fallback path as `generate`, no provider call).\n  Use it where analytics events need the resolved model name — keeping it off\n  `generate`'s data-only return. `purpose` picks the model map to read, defaulting\n  to `Completion`; `overrides` reports what the same overrides would route to.\n- **`compilePrompt(name, { variables })`.** Compiles a prompt to text instead of\n  generating from it — for a sub-prompt injected as a variable into a parent\n  prompt. An outage serves the snapshot rather than throwing, and no provider is\n  called, so a compile produces no usage row. Two traps: Mustache sections are not\n  evaluated, so conditional content needs its own sub-prompt (compiled, or `''`);\n  and a sub-prompt whose `config` names no real model is skipped by the snapshot\n  codegen and left with no fallback.\n\n  ```typescript\n  const generalInstructions = await llm.compilePrompt(SECTION_PROMPT, {\n    variables: { aiContextSection },\n  });\n\n  await llm.generate(WELCOME_PROMPT, { variables: { generalInstructions } });\n  ```\n\n#### Audio prompts (`generateSpeech` / `generateTranscript`)\n\nThe prompt body is the provider instructions and `config.params.speech` carries\n`voice`, `speed` and `format`. Both reuse the completion pipeline and return the\naudio or transcript directly; each resolves against its own model map and\nrequires `tts` / `stt`, so a completion prompt fails the gate. `variables` is\nrequired as it is on `generate` — pass `{}` for a prompt that declares none.\n\n```typescript\nconst { audio, mimeType } = await llm.generateSpeech(\n  LLMPrompt.AutoTechChecksTextToSpeech,\n  { text: questionText, variables: {}, context: { userId }, sessionId },\n);\n\nconst { text } = await llm.generateTranscript(\n  LLMPrompt.AutoTechChecksSpeechToText,\n  {\n    audio: { pathToAudio, mimeType: LLMUploadFileMimeTypes.AUDIO_OGG },\n    variables: { userLanguage },\n    context: { userId },\n  },\n);\n```\n\n#### Image prompts (`generateImage`)\n\nThe prompt resolves against the image model map and requires `imageGeneration`,\nso a completion prompt fails the gate. The images endpoints take a single\nprompt string, so the compiled prompt body is the preamble and `prompt` — the\ndescription of the wanted picture — is appended to it: the house style stays\neditable in Langfuse while the caller supplies the subject. `imageOptions` is\nthe wire shape LLMAPI validates against the model's supported sizes: `size` is\n`'auto'` or pixel dimensions (`1328x1328` on `qwen-image`, `3840x2160` on\n`gpt-image-2` — a per-model list no single provider's own union covers), and\n`output_format` is an `LLMAPIImageOutputFormat` member. Passing\n`referenceImages` switches the call from `/v1/images/generations` to\n`/v1/images/edits`.\n\nThe result carries the decoded bytes, their MIME type, the dimensions the\nprovider reported, and `cost`, where the picture is billed as output image\ntokens at its own rate — so an image call lands on the same usage ledger and\nLangfuse trace as a text one.\n\n```typescript\nconst { image, mimeType, cost } = await llm.generateImage(\n  LLMPrompt.ContentEditorIllustrateTopic,\n  {\n    prompt: illustrationDescription,\n    variables: {},\n    imageOptions: { size: '1024x1536' },\n    context: { agentRunId },\n  },\n);\n```\n\n#### Agent runs (`runAgent`)\n\n`llm.runAgent` runs a tool-calling agent whose instructions are a registry\nprompt: the Langfuse prompt text is the agent's system instructions and its\n`config` routes the model, so an agent is versioned and re-routed exactly like\nany other operation. The provider assistance loop executes the model⇄tool\nrounds; subagents are exposed to the model as tools (one level deep — a\nsubagent cannot declare its own subagents).\n\n```typescript\nconst turn = await llm.runAgent(LLMPrompt.ContentEditorRootAgent, {\n  variables: { courseName },\n  input: userMessage,\n  history: persistedMessages, // resume from stored session state\n  tools: [createTopicTool, updateTheoryTool, askUserTool],\n  subagents: [{\n    prompt: LLMPrompt.ContentEditorResearchSubagent,\n    name: 'researcher',\n    description: 'Researches sources for course content',\n    variables: { courseName },\n    tools: [searchTool],\n    reportCap: {\n      maxLength: 8192,\n      truncationNote: '\\n\\nCheck the persisted artifact.',\n    },\n    // Optional: flags this specialist's report when it never searched.\n    grounding: {\n      groundingToolNames: ['web_search', 'web_fetch'],\n      ungroundedReportNote: '[UNGROUNDED - no web access was used.]\\n\\n',\n    },\n  }],\n  context: { userId },\n  // Optional: omitted for backward-compatible retry/delegation behaviour.\n  failurePolicy: {\n    identicalToolCallNudgeAfter: 2,\n    identicalToolCallAbortAfter: 5,\n  },\n  onEvent: (event) => publishAndPersist(event), // WS updates + run history\n});\n\nif (turn.stopReason === LLMToolLoopStopReasons.TerminalTool) {\n  await askTheUser(turn.terminalTool.output); // agent handed control back\n} else {\n  await applyStructure(turn.output); // the model's answer\n}\n```\n\n`failurePolicy` is opt-in. It tracks consecutive failures of the exact same\ntool name and canonicalized input per agent run, adds a corrective hint at the\nconfigured nudge threshold, and stops only the affected delegation at the abort\nthreshold. Only a call that ran and produced a result resets that tool's\nstreak: a refused call and a delegation the policy stopped both keep it\nrunning, and both carry the escalation in the text the model reads. The\nthresholds are validated, so a nudge that could never fire is a configuration\nerror rather than silence.\n\n`reportCap` is declared per subagent, because how much prose a specialist may\nhand back belongs to that delegation's contract — a specialist that names every\nentity it touched legitimately reports more than one summarising a document it\npersisted elsewhere. A capped text report is cut on a code-point boundary and\nthe note is appended; a capped structured report is replaced by a valid JSON\nenvelope carrying an excerpt, never by half a JSON document.\n\nA subagent's `grounding` is opt-in too, and it is a counter over the trajectory\nrather than a reading of the report: the runner already dispatches every call\nthe specialist makes, so a delegation that executed none of\n`groundingToolNames` gets `ungroundedReportNote` prepended to the report the\nparent model reads. A refused or failed call does not ground anything — only an\nexecution does. A prose report is noted before it is capped, so a long report\ncannot bury the warning; a structured one is noted after, because its cap\nreplaces the document with an envelope that has to stay whole. Declaring\n`grounding` on a subagent whose report is structured therefore means an\nungrounded delegation returns note-then-JSON rather than JSON — capped or not —\nso do not declare it on a subagent whose delegation result you parse.\n\n`grounding.citations` raises that floor by one step, for the specialist that\ngrounded itself with one real call and then cited pages it never opened. It\nnames the tool whose executions count as reading (`sourceToolName`), the\nargument carrying the url (`sourceUrlArgument`), and the note\n(`unreadCitationsNote`) prepended with the urls the report cites and the\ntrajectory never read. A url is compared on scheme, host and path, so a\ntracking query string or a trailing slash is not a different page, and the url\na fetch reports landing on counts alongside the one it was asked for. Urls are\nread out of prose, so the scan stops at the characters markdown puts around a\nurl and a url may not carry unescaped — `)`, `]`, `}`, `<`, `>`, a quote, a\nbacktick and a pipe. Trailing sentence punctuation is stripped afterwards,\ntogether with the asterisks of a bold run; the asterisk is not excluded from\nthe scan itself, because RFC 3986 makes it a legal sub-delimiter and a listing\nurl can carry one mid-path. A citation that fails to parse as a url is left\nunflagged: the note accuses the model of inventing a source, so a miss is\ncheaper than a false accusation.\n\n- **Structured progress events.** Every step emits an `LLMAgentEvent`\n  (`agent_started/step_started/message/completed/failed`,\n  `tool_call_started/completed/failed/rejected`,\n  `subagent_started/completed/failed`) through `onEvent`, for live subscription\n  updates and durable run persistence. Events carry correlation ids from one\n  sequence per root run — `runId` on every event, `invocationId` on started,\n  completed and failed tool and subagent events, and `subagentRunId` linking a\n  delegation to the nested run's events — so parallel tool calls and repeated\n  delegations reconstruct into an unambiguous run tree. A\n  `tool_call_rejected` event is the explicit exception: it has no\n  `invocationId`, because the provider call never entered the wrapped tool\n  lifecycle and not every provider exposes a stable call identifier. Listeners\n  may be async; a throw or rejection is logged and never affects the run.\n  `agent_step_started` is emitted immediately before every provider model call,\n  including failed calls and nested subagent rounds. `tool_call_rejected`\n  preserves the provider's ordered call when its name is unknown or its JSON\n  arguments cannot be parsed, even though no wrapped tool invocation starts.\n- **Full-fidelity history.** `history` restores the conversation as it happened,\n  not a prose retelling of it. An entry is a plain message\n  (`{ role, text }`), a past tool round\n  (`{ type: LLMHistoryEntryTypes.ToolCall, calls, text? }`), the result that\n  answered one of its calls\n  (`{ type: LLMHistoryEntryTypes.ToolResult, toolCallId, result }`), or\n  mid-conversation context (`{ type: LLMHistoryEntryTypes.Context, text }`).\n  Each maps to the provider's own representation, so a resumed run reads its\n  own past calls as calls. The union is additive — an existing\n  `LLMPlainMessage[]` is already a valid history and maps exactly as before.\n\n  ```typescript\n  await llm.runAgent(agent, {\n    variables,\n    input: userMessage,\n    history: [\n      { role: 'user', text: 'Rewrite the flaky-selectors theory' },\n      { type: LLMHistoryEntryTypes.Context, text: carriedPlan },\n      {\n        type: LLMHistoryEntryTypes.ToolCall,\n        text: 'Looking it up.',\n        calls: [{\n          id: 'call-1',\n          name: 'find_content',\n          arguments: { query: 'flaky selectors' },\n        }],\n      },\n      {\n        type: LLMHistoryEntryTypes.ToolResult,\n        toolCallId: 'call-1',\n        result: 'theory-1',\n      },\n    ],\n  });\n  ```\n\n  Per provider: LLMAPI replays a round as an assistant message carrying\n  `tool_calls` plus `tool`-role results and context as a mid-conversation\n  `system` message; OpenAI replays `function_call` / `function_call_output`\n  items and context as a `developer` item; Gemini replays a `model` turn of\n  `functionCall` parts answered by a `user` turn of `functionResponse` parts,\n  and — having no mid-conversation system role — carries context as a user\n  turn. On Gemini a restored call carries no `thought_signature`, so it replays\n  under the documented validator-bypass marker, and a multi-call round is split\n  into sequential single-call turns exactly as the live loop records one; a\n  call that kept its signature passes it in `signature` and it is echoed\n  verbatim.\n\n  Where the restored conversation then lives differs by provider, and no\n  provider spends a generation to materialize it. LLMAPI keeps the converted\n  messages in local chat state and resends the whole array each round; Gemini\n  holds them in the SDK chat session; OpenAI writes them to a Responses\n  **conversation object** (`POST /v1/conversations`), a non-generative call, and\n  every round of that chat then passes `conversation: <chatId>` — so the chat id\n  *is* the conversation id, `deleteChat` deletes it server-side, and reasoning\n  items stay in the chain across tool rounds exactly as they did under\n  `previous_response_id`. `conversation` and `previous_response_id` are mutually\n  exclusive; the package uses the former. OpenAI accepts at most 20 items per\n  conversation request (`OPENAI_CONVERSATION_ITEMS_PER_REQUEST_LIMIT`), so the\n  conversation is created with the first 20 converted items and the rest are\n  appended in order, 20 at a time, through `POST /v1/conversations/{id}/items`.\n  A history of any length restores; a failed or aborted append deletes the\n  half-restored conversation and returns `{ error }` from `createChat` like a\n  failed create, so nothing is left on OpenAI (conversations have no TTL).\n\n  A call the history leaves unanswered is answered by the package with a real\n  tool result carrying `LLM_UNANSWERED_TOOL_CALL_RESULT` — every provider\n  rejects an unanswered call, so the repair is native rather than left to the\n  caller to fake in prose. A result naming no preceding call, a second result\n  for an already-answered call, and a round that called nothing are dropped.\n  Same-role entries need no merging: the contract carries the structure.\n\n- **Images on a turn and in its history.** `inputImages` sends images as content\n  parts of the same user message as `input`, and a plain history message carries\n  its own `images` so a replayed turn reads what the person actually attached.\n  Each image is `{ url, detail? }`, `detail` being an `LLMImageDetail` member —\n  the gateway builds the provider's own image part from it, so a caller never\n  writes one. Only a `user` turn carries images: the role discriminates\n  `LLMPlainMessage`, so an `assistant` turn with `images` does not compile,\n  which is what every provider would have rejected at runtime.\n\n  ```typescript\n  await llm.runAgent(agent, {\n    variables,\n    input: 'What is wrong with this screen?',\n    inputImages: [{ url: signedScreenshotUrl, detail: LLMImageDetail.High }],\n    history: [\n      {\n        role: 'user',\n        text: 'Here is the previous version',\n        images: [{ url: signedPreviousUrl }],\n      },\n    ],\n  });\n  ```\n\n  `messageImagesToContentParts` is the same conversion, exported so a caller\n  assembling its own content array (or asserting that the installed version\n  carries images at all) does not have to reimplement it.\n\n  ```typescript\n  import { messageImagesToContentParts } from '@mate-academy/llm-gateway';\n\n  messageImagesToContentParts([{ url: signedScreenshotUrl }]);\n  // [{ type: LLMMessageContentType.IMAGE_URL, image_url: { url: ... } }]\n  ```\n\n- **Intermediate narration.** When the model returns visible assistant text\n  alongside tool calls in a round that will continue, that text is emitted as\n  `agent_message` (attributed to the emitting run, so a subagent's narration\n  carries the subagent's identity) before the round's tools run. Reasoning /\n  thinking content and the final answer are never sent this way — the final\n  answer is `agent_completed.output`.\n- **Terminal tools end the turn.** A tool declaring `terminal: true` (an\n  `ask_user` tool, say) hands control back to the application. The round it\n  appears in still runs in full — every call of that round, including parallel\n  ones, executes and emits its `tool_call_*` events — but the results are not\n  fed back and no further model round starts. The run then returns\n  `stopReason: 'terminal_tool'` with `terminalTool` carrying the tool's name\n  and output; if several terminal calls land in one round, the first in the\n  round's order is the outcome (all still execute). This is ordinary control\n  flow — no exception, no abort — and `maxToolIterations` is untouched. A\n  terminal tool that throws still ends the turn, carrying its failure message\n  as the outcome. Delegation tools are never terminal, so a subagent that stops\n  on its own terminal tool returns that output as its delegation result and the\n  parent's loop continues.\n- **Subagent names are tool names.** A subagent's `name` must match\n  `[a-zA-Z0-9_-]+` (it is exposed to the model as a tool); an invalid name\n  fails the run upfront with `LLMConfigError` instead of being silently\n  rewritten.\n- **Typed delegation inputs.** A subagent declaring an `inputSchema` is exposed\n  to the model with that schema instead of the default single `prompt` string,\n  and the arguments the parent model fills join the subagent's own `variables`\n  (winning over them), so its prompt can reference them as `{{topicRef}}` and\n  the validation gate checks them like any other variable. Use it whenever the\n  delegated work needs identifiers the parent must not paraphrase — an entity\n  ref, a changeset id, an enum choice. Without `inputSchema` nothing changes:\n  one free-form `prompt` argument, which is also the nested run's input.\n\n  ```typescript\n  const theoryWriter = llm.defineAgent({\n    prompt: LLMPrompt.ContentEditorWriteTheory,\n    name: 'write_theory',\n    description: 'Authors one theory document into the current changeset',\n    inputSchema: LLMSchema.object({\n      topicRef: LLMSchema.string().describe('Changeset entity ref of the topic'),\n      audienceLevel: LLMSchema.enum(['beginner', 'intermediate', 'advanced']),\n      outline: LLMSchema.array(LLMSchema.string()),\n    }),\n    variables: { changesetId },\n    tools: [getEntityTool, saveTheoryTool],\n  });\n  ```\n- **One place normalizes tool output.** Every tool result and every delegation\n  result passes through the package's coercion before the model sees it, so a\n  tool that returns an object, a number or nothing cannot put `[object Object]`\n  into the conversation. A client may add its own `normalizeToolResult` on top —\n  the natural place to wrap untrusted material (a fetched web page) so no tool\n  author can forget to:\n\n  ```typescript\n  const llm = createLLMClient({\n    // ...\n    normalizeToolResult: ({ tool, result }) => (\n      UNTRUSTED_TOOLS.has(tool)\n        ? { content: `<untrusted source=\"${tool}\">\\n${toText(result)}\\n</untrusted>` }\n        : result\n    ),\n  });\n  ```\n\n  The normalized value is what reaches both the model and the\n  `tool_call_completed` event, so a persisted run history records exactly what\n  the model read.\n- **Preconditions gate a call before it runs.** A tool may declare\n  `canExecute(args)` returning `{ allowed: true }` or\n  `{ allowed: false, reason }`. It is checked before `execute`, so a refused\n  call never has its side effect. A refusal is ordinary control flow, not an\n  error: the model is told the reason as that call's result, the run continues,\n  and the iteration budget is untouched — so the model can ask the user or take\n  another path. The invocation reports `tool_call_denied` instead of\n  `tool_call_completed`, and a refused delegation reports the same event with\n  `tool` naming the subagent. Use it for permission, quota and state checks that\n  depend on the arguments (an authorization check before a write, a stale-hash\n  guard, a per-run write budget) — enforcement then lives in the tool's\n  definition rather than in prompt instructions the model may ignore.\n\nThe plain-object form above is complete on its own. `llm.defineAgent` is\noptional sugar for reusable, composable definitions: it returns an immutable\n`LLMAgent` instance (typed against the registry) that `runAgent` accepts in\nplace of a prompt key, and that other agents accept as a subagent. Unlike\nplain-object subagents, `LLMAgent` subagents may declare their own\n`subagents` — instances are immutable, so a delegation cycle cannot be\nconstructed. Agent-level `variables` are a base merged under the per-run\n`variables`; a subagent used as a delegation tool must carry `name` and\n`description`.\n\n```typescript\nconst researcher = llm.defineAgent({\n  prompt: LLMPrompt.ContentEditorResearchSubagent,\n  name: 'researcher',\n  description: 'Researches sources for course content',\n  variables: { courseName },\n  tools: [searchTool],\n});\n\nconst rootAgent = llm.defineAgent({\n  prompt: LLMPrompt.ContentEditorRootAgent,\n  tools: [createTopicTool, updateTheoryTool],\n  subagents: [researcher],\n});\n\nconst turn = await llm.runAgent(rootAgent, {\n  variables: { courseName },\n  input: userMessage,\n  context: { userId },\n  onEvent: (event) => publishAndPersist(event),\n});\n```\n- **Typed final output.** A run returns a result discriminated by `stopReason`.\n  For an answered turn (`completed`, or `max_iterations`) the prompt's binding\n  drives `output` exactly like `generate`: the bound schema's inferred type, or\n  the final assistant text when no schema is bound. A `terminal_tool` turn has\n  no model answer, so the bound schema is deliberately not applied — `output`\n  is absent and `terminalTool` is the outcome instead. Both carry `text`, the\n  visible assistant text of the final round.\n- **Tool-capability gate.** When a run has tools or subagents, the validation\n  gate additionally requires the resolved model to declare the `tools`\n  capability, so a Langfuse re-route to a tool-less model serves the fallback\n  instead of failing mid-run.\n- **Langfuse for free.** Provider services already record generation and tool\n  observations under the active trace; wrap the run with the tracer's\n  `withTrace` at the call site to group the whole agent session.\n- Overrides, credentials, reporter context, and the error taxonomy behave\n  exactly as in `generate`.\n\n#### Prompt snapshot codegen\n\n`langfusePrompts.generated.ts` is produced by `npm run langfuse:generate` (root),\nwhich fetches every labeled prompt and emits only the `LLMPrompt` enum and the\nsnapshot used as the outage fallback. Variable types are not inferred from\nLangfuse; they stay in the application registry. The file is gitignored exactly\nlike GraphQL generated files: regenerated locally on demand, in CI before image\nbuilds, and refreshed non-blocking at API pod boot.\n\nThe generated declarations are built with ts-morph through its structural API,\nnot by string concatenation. Ts-morph is a dependency of the `codegen` entry\npoint only, so importing the package's main entry never pulls it in.\n\nThe snapshot is plain JSON, so an enum-typed config field is stored as the string\nbehind its member. `LLMPromptConfigSnapshot` types those fields as\n`EnumOrValue<LLMProviders>` and `EnumOrValue<LLMReasoningEffort>` — the enum plus\nits own literals — so the emitted value type-checks while a value outside the\nenum still fails the compiler. The runtime gate re-parses the config through the\nzod schema into the strict `LLMPromptConfig` before it is ever served.\n\n`generateSnapshot` (exported from `@mate-academy/llm-gateway/codegen`) takes an\ninjected `LLMPromptCatalog` instead of constructing a Langfuse SDK client\nitself — the caller owns the catalog's credentials and its Langfuse project.\n`@mate-academy/prompt-client`'s `LLMPromptClient` satisfies this port too, so\nthe same instance a runtime composition root builds can be reused for codegen:\n\n```typescript\nimport { generateSnapshot } from '@mate-academy/llm-gateway/codegen';\n\nawait generateSnapshot({\n  catalog: promptClient, // an `@mate-academy/prompt-client` bundle's `promptClient`\n  label: 'production',\n  outputPath: './src/langfusePrompts.generated.ts',\n  onLog: (message) => console.log(message),\n});\n```\n\n#### Escape hatch\n\n`@mate-academy/llm-gateway/advanced` re-exports `LLMServiceFactory`, the provider\nservices, and the model maps for the rare call site needing raw control. It\nbypasses prompt management, the validation gate, and config-in-prompt routing —\nprefer the v2 client.\n\n### Logger Interface\n\nThe package accepts an optional logger that implements the `LLMLoggerInterface` interface. Most logging libraries are compatible (`@mate-academy/logger`, winston, pino, etc.). If no logger is provided, no logging will occur.\n\n```typescript\n// Using @mate-academy/logger (recommended)\nimport { logger } from '@mate-academy/logger';\n\n// Or simple console logger\nconst logger = {\n  info: (msg, meta) => console.log(msg, meta),\n  error: (msg, meta) => console.error(msg, meta),\n  warn: (msg, meta) => console.warn(msg, meta),\n  child: (context) => logger,\n};\n```\n\n**Available Metrics:**\n\nThe reporter automatically collects the following metrics:\n\n```typescript\ninterface LLMMetrics {\n  // Provider & Model Information\n  provider: 'OpenAI' | 'GoogleGenerativeAI';\n  purpose: 'completion' | 'assistance' | 'speech_to_text' | 'text_to_speech';\n  model: string | null;\n  modelConfig: Record<string, any> | null;\n\n  // Method Context\n  method: string; // e.g., 'sendMessage', 'assistInChat', 'transcribe'\n  status: 'success' | 'error' | 'cancelled';\n\n  // Token Usage\n  tokens: {\n    input: number;  // Regular input tokens (excludes cached tokens)\n    output: number; // Output tokens, reasoning tokens included\n    total: number;  // Total tokens (input + output)\n    lastRequestInput?: number; // Input of the most recent request, not the tool loop's sum\n    outputReasoning?: number;  // How much of `output` was reasoning, omitted when zero\n  };\n\n  // Cost Tracking (automatically calculated with cached token discounts)\n  costs: {\n    input: number;   // Cost for input tokens (text + audio), with cached token discounts applied\n    output: number;  // Cost for output tokens (text + audio + reasoning tokens)\n    total: number;   // Total cost\n    currency: string; // 'USD'\n  };\n}\n```\n\n**Reasoning tokens:** `tokens.output` and `costs.output` always carry the\nvisible and reasoning halves together, because every provider bills reasoning at\nits output rate. Each provider service maps its own response, since OpenAI counts\nreasoning inside its output-token field and LLMAPI reports it beside, and checks\n`total_tokens` so a provider that changes that is followed rather than mispriced.\n\n**TypeScript Behavior:**\n\nThe `reporterContext` parameter behavior depends on your reporter configuration:\n\n```typescript\n// Case 1: Reporter with defined context type - reporterContext is REQUIRED\nconst reporterWithContext: LLMReporterInterface<{ userId: string }> = new MyReporter();\n\n// Case 2: Reporter without context - reporterContext is OPTIONAL\nconst reporterWithoutContext: LLMReporterInterface<undefined> = new SimpleReporter();\n\n// Case 3: No reporter - reporterContext is OPTIONAL\nconst service = LLMServiceFactory.getCompletionService({\n  provider, options\n  // no reporter\n});\n```\n\n### Basic Setup\n\n```typescript\nimport {\n  LLMProviders,\n  LLMServiceFactory,\n  LLMCompletionService,\n  LLMAssistanceService,\n  LLMSpeechToTextService,\n  LLMTextToSpeechService,\n  LLMRoles,\n  LLMMessageContentType,\n  LLMUploadFileMimeTypes,\n  LLMCompletionMessage,\n  LLMModel,\n  LLMSchema,\n  createPromptTemplate,\n  LLMLoggerInterface,\n  LLMReporterInterface,\n} from '@mate-academy/llm-gateway';\n\n// Define provider options\nconst llmProviderOptions = LLMServiceFactory.resolveProviderOptions(\n  LLMProviders.OpenAI, // chosen provider\n  {\n    [LLMProviders.OpenAI]: {\n      apiKey: 'your-openai-api-key',\n      organization: 'your-organization-id', // optional\n      baseURL: 'https://api.openai.com/v1', // optional\n    },\n    [LLMProviders.GoogleGenerativeAI]: {\n      apiKey: 'your-google-ai-api-key',\n    },\n  },\n);\n\n// Get completion service\nconst completionService = LLMServiceFactory.getCompletionService({\n  provider: LLMProviders.OpenAI,\n  options: llmProviderOptions, // optional, but recommended - otherwise must be set later via service.setOptions()\n  logger, // optional - omit for no logging\n  reporter, // optional - omit for no metrics collection\n});\n\n// Get assistance service\nconst assistanceService = LLMServiceFactory.getAssistanceService({\n  provider: LLMProviders.OpenAI,\n  options: llmProviderOptions, // optional, but recommended - otherwise must be set later via service.setOptions()\n  logger, // optional - omit for no logging\n  reporter, // optional - omit for no metrics collection\n});\n\n// Get speech-to-text service\nconst speechToTextService = LLMServiceFactory.getSpeechToTextService({\n  provider: LLMProviders.OpenAI,\n  options: llmProviderOptions, // optional, but recommended - otherwise must be set later via service.setOptions()\n  logger, // optional - omit for no logging\n  reporter, // optional - omit for no metrics collection\n});\n\n// Get text-to-speech service\nconst textToSpeechService = LLMServiceFactory.getTextToSpeechService({\n  provider: LLMProviders.OpenAI,\n  options: llmProviderOptions, // optional, but recommended - otherwise must be set later via service.setOptions()\n  logger, // optional - omit for no metrics collection\n  reporter, // optional - omit for no metrics collection\n});\n```\n\n### Real-world Example\n\nHere's how you might integrate the LLM Gateway in a use case class:\n\n```typescript\nimport {\n  LLMProviders,\n  LLMServiceFactory,\n  LLMCompletionService,\n  LLMAssistanceService,\n  LLMSpeechToTextService,\n  LLMTextToSpeechService,\n} from '@mate-academy/llm-gateway';\n\nclass MyUseCase {\n  private llmCompletionService: LLMCompletionService<LLMProviders>;\n  private llmAssistanceService: LLMAssistanceService<LLMProviders>;\n  private llmSpeechToTextService: LLMSpeechToTextService<LLMProviders>;\n  private llmTextToSpeechService: LLMTextToSpeechService<LLMProviders>;\n\n  constructor(logger, config) {\n    const llmProviderOptions = LLMServiceFactory.resolveProviderOptions(\n      config.llmProvider,\n      {\n        [LLMProviders.OpenAI]: {\n          apiKey: config.openAIApiKey,\n          organization: config.openAIOrgId,\n          baseURL: config.openAIBaseUrl,\n        },\n        [LLMProviders.GoogleGenerativeAI]: {\n          apiKey: config.googleAIApiKey,\n        },\n      },\n    );\n\n    this.llmCompletionService = LLMServiceFactory.getCompletionService({\n      provider: config.llmProvider,\n      options: llmProviderOptions,\n      logger,\n      reporter,\n    });\n\n    this.llmAssistanceService = LLMServiceFactory.getAssistanceService({\n      provider: config.llmProvider,\n      options: llmProviderOptions,\n      logger,\n      reporter,\n    });\n\n    this.llmSpeechToTextService = LLMServiceFactory.getSpeechToTextService({\n      provider: config.llmProvider,\n      options: llmProviderOptions,\n      logger,\n      reporter,\n    });\n\n    this.llmTextToSpeechService = LLMServiceFactory.getTextToSpeechService({\n      provider: config.llmProvider,\n      options: llmProviderOptions,\n      logger,\n      reporter,\n    });\n  }\n\n  async processRequest(prompt) {\n    // Use completion service\n    const completion = await this.llmCompletionService.sendMessage({\n      message: {\n        role: LLMRoles.User,\n        content: [\n          {\n            type: LLMMessageContentType.TEXT,\n            text: prompt,\n          }\n        ],\n      },\n      model: this.getPreferredModel(),\n    });\n\n    return completion;\n  }\n\n  async transcribeAudio(audioPath) {\n    // Use speech-to-text service\n    const transcription = await this.llmSpeechToTextService.transcribe({\n      pathToAudio: audioPath,\n      model: this.getPreferredModel(),\n    });\n\n    return transcription;\n  }\n\n  async generateSpeech(text) {\n    // Use text-to-speech service\n    const speech = await this.llmTextToSpeechService.createSpeech({\n      text: text,\n      model: this.getPreferredModel(),\n      speechOptions: {\n        voice: 'alloy', // OpenAI voice option\n        response_format: 'mp3',\n      },\n    });\n\n    return speech;\n  }\n\n  private getPreferredModel() {\n    // Get the appropriate model from the service's available models\n    const models = Object.values(this.llmCompletionService.models);\n    return models[0]; // Use the first available model\n  }\n}\n```\n\n### Usage Examples\n\n#### Basic Text Completion\n\n```typescript\nconst result = await completionService.sendMessage({\n  message: {\n    role: LLMRoles.User,\n    content: [\n      {\n        type: LLMMessageContentType.TEXT,\n        text: 'Hello, how are you?',\n      }\n    ],\n  },\n  model: preferredModel,\n  reporterContext: { // required if reporter is configured with context type\n    userId: 12345,\n    feature: 'chat',\n    environment: 'production',\n  },\n});\n\nconsole.log(result.text); // AI response\n```\n\n#### Chat-based Assistance\n\n```typescript\n// Create a chat session\nconst chat = await assistanceService.createChat({\n  model: preferredModel,\n  instructions: 'You are a helpful coding assistant.',\n});\n\n// Send messages directly to the chat\nconst result = await assistanceService.assistInChat({\n  model: preferredModel,\n  message: {\n    role: LLMRoles.User,\n    content: [{\n      type: LLMMessageContentType.TEXT,\n      text: 'Help me understand React hooks',\n    }],\n  },\n  chatId: chat.chatId,\n  reporterContext: { // required if reporter is configured with context type\n    userId: 12345,\n    feature: 'coding_assistance',\n    environment: 'production',\n  },\n});\n\nconsole.log(result.text); // Assistant response\n```\n\n#### Direct Image Analysis\n\n```typescript\n// Send image directly in message content (OpenAI)\nconst result = await assistanceService.assistInChat({\n  model: preferredModel,\n  message: {\n    role: LLMRoles.User,\n    content: [\n      {\n        type: LLMMessageContentType.TEXT,\n        text: 'What do you see in this image?',\n      },\n      {\n        type: LLMMessageContentType.IMAGE_URL,\n        image_url: {\n          url: 'https://example.com/image.png',\n          detail: LLMImageDetail.High,\n        },\n      },\n    ],\n  },\n  chatId: chat.chatId,\n});\n\nconsole.log(result.text); // Image analysis response\n```\n\n#### Audio by URL\n\nLong audio reaches a multimodal model as a URL the provider fetches itself,\ninstead of being inlined as base64. Only LLMAPI Gemini routes read it; any\nother model rejects the request rather than answering about audio it never\nreceived.\n\n```typescript\nconst result = await completionService.sendMessage({\n  model: geminiModel,\n  message: {\n    role: LLMRoles.User,\n    content: [\n      {\n        type: LLMMessageContentType.TEXT,\n        text: 'Summarise what was agreed on this call',\n      },\n      {\n        type: LLMMessageContentType.AUDIO_URL,\n        audio_url: {\n          url: presignedRecordingUrl,\n        },\n      },\n    ],\n  },\n});\n```\n\nThe URL has to be reachable by the provider for the length of the request; a\npresigned S3 URL works, so a recording never needs public exposure. The\nprovider caps the fetched file at 20 MiB.\n\nA response that reports no audio tokens means the model never read the\nrecording and answered from nothing, so the call comes back as an error rather\nthan as text.\n\n### Structured Output\n\nThe LLM Gateway supports structured output, allowing you to request type-safe, validated JSON responses from LLM providers. Built on Zod v4 with native JSON Schema generation for optimal performance.\n\n#### Basic Structured Output\n\n```typescript\nimport { LLMSchema } from '@mate-academy/llm-gateway';\n\n// Define your output schema\nconst personSchema = LLMSchema.object({\n  name: LLMSchema.string(),\n  age: LLMSchema.number().min(1),\n  email: LLMSchema.string().email(),\n  isActive: LLMSchema.boolean(),\n});\n\n// Request structured output\nconst result = await completionService.sendMessage({\n  message: {\n    role: LLMRoles.User,\n    content: [{\n      type: LLMMessageContentType.TEXT,\n      text: 'Create a person profile for John Smith, 30 years old, email john@example.com'\n    }]\n  },\n  model: preferredModel,\n  schema: personSchema  // Add schema for structured output\n});\n\n// Type-safe access to structured data\nif (result.data) {\n  console.log(result.data.name);        // string\n  console.log(result.data.age);         // number\n  console.log(result.data.email);       // string\n  console.log(result.data.isActive);    // boolean\n}\n\n// Always available as text fallback\nconsole.log(result.text);\n```\n\n#### Complex Schema Example\n\n```typescript\nconst analysisSchema = LLMSchema.object({\n  sentiment: LLMSchema.enum(['positive', 'negative', 'neutral']),\n  confidence: LLMSchema.number().min(0).max(1),\n  keywords: LLMSchema.array(LLMSchema.string()),\n  summary: LLMSchema.string(),\n  metadata: LLMSchema.object({\n    processedAt: LLMSchema.string(),\n    modelVersion: LLMSchema.string(),\n  }),\n});\n\nconst result = await completionService.sendMessage({\n  message: {\n    role: LLMRoles.User,\n    content: [{\n      type: LLMMessageContentType.TEXT,\n      text: 'Analyze this text: \"I love this new feature!\"'\n    }]\n  },\n  model: preferredModel,\n  schema: analysisSchema\n});\n\n// Fully type-safe access\nif (result.data) {\n  console.log(result.data.sentiment);         // 'positive' | 'negative' | 'neutral'\n  console.log(result.data.confidence);        // number (0-1)\n  console.log(result.data.keywords);          // string[]\n  console.log(result.data.summary);           // string\n  console.log(result.data.metadata.processedAt); // string\n}\n```\n\n#### Schema API Reference\n\nThe `LLMSchema` builder provides a fluent API for defining output schemas:\n\n**Basic Types:**\n- `LLMSchema.string()` - String values\n- `LLMSchema.number()` - Numeric values\n- `LLMSchema.boolean()` - Boolean values\n- `LLMSchema.array(itemSchema)` - Arrays of items\n- `LLMSchema.object({ ... })` - Object with specified properties\n- `LLMSchema.enum(['option1', 'option2'])` - Enumerated values\n- `LLMSchema.literal('exact_value')` - Exact literal values\n\n**Modifiers:**\n- `.optional()` - Makes field optional and nullable (compatible with all providers)\n- `.nullable()` - Allows null values\n- `.default(value)` - Sets default value\n- `.describe(text)` - Adds description\n\n**String Modifiers:**\n- `.min(length)` - Minimum string length\n- `.max(length)` - Maximum string length\n- `.email()` - Email validation\n- `.url()` - URL validation\n- `.uuid()` - UUID validation\n\n**Number Modifiers:**\n- `.int()` - Integer values only\n- `.positive()` - Positive numbers (Note: Use `.min(1)` for better OpenAI compatibility)\n- `.negative()` - Negative numbers\n- `.min(value)` - Minimum value\n- `.max(value)` - Maximum value\n\n#### Error Handling\n\n```typescript\nconst result = await completionService.sendMessage({\n  message: { /* ... */ },\n  model: preferredModel,\n  schema: mySchema\n});\n\nif (result.data) {\n  // Successfully parsed structured output\n  console.log('Structured data:', result.data);\n} else if (result.parseError) {\n  // Parsing failed, but text is still available\n  console.error('Parse error:', result.parseError);\n  console.log('Raw text:', result.text);\n} else if (result.error) {\n  // Request failed entirely\n  console.error('Request error:', result.error);\n}\n```\n\n#### Token Usage and Cost\n\nSuccessful completion and transcription results expose two optional fields: `usage`\n(`LLMModelUsage`) with the provider-reported token counts, and `cost`\n(`LLMCostsResult`) with the absolute USD cost for the call, derived from that usage\nand the model's pricing. Both are present only on the success branch (absent when\n`result.error` is set), so read them after narrowing:\n\n```typescript\nconst result = await completionService.sendMessage({\n  message: { /* ... */ },\n  model: preferredModel,\n  schema: mySchema,\n});\n\nif (!('error' in result) && result.usage) {\n  console.log('Input tokens:', result.usage.inputTextTokens);\n  console.log('Output tokens:', result.usage.outputTextTokens);\n  console.log('Total cost (USD):', result.cost?.total);\n}\n```\n\n\n#### Provider Support\n\n- **OpenAI**: Uses native `response_format` with `json_schema` for optimal performance\n- **Google Generative AI**: Uses native `responseSchema` parameter for structured output\n- **LLMAPI**: Uses native `response_format` for the models that accept it. Anthropic-backed\n  `claude-*` models are not among them — LLMAPI forwards `response_format` verbatim and\n  Anthropic only honours its own `output_config`, so those entries declare\n  `structuredOutputs: false` and `jsonOutput: false`. The gateway then injects the JSON\n  schema into the system message and validates the response, rather than sending a\n  parameter the upstream provider discards.\n  GPT-5.x models reached through LLMAPI occasionally answer a strict `json_schema` request\n  with two complete JSON values back to back (the same object twice, or two different ones).\n  The gateway accepts the first value, validates it against the schema and reports the\n  recovery on the result as `recovery: { kind: 'first_of_concatenated_json', discardedValues,\n  discardedChars }`, logging a warning with the model name and those counts. Anything else —\n  prose around the JSON, a truncated value, a syntax error — still fails with `parseError`;\n  nothing repairs JSON.\n- **Backward Compatibility**: All existing code continues to work unchanged\n\n#### Provider Compatibility Notes\n\n**OpenAI Structured Output:**\n- The `.optional()` modifier now automatically converts to required+nullable for OpenAI compatibility\n- Use `.min(1)` instead of `.positive()` for better compatibility\n- Nested objects and arrays are fully supported\n\n**Google Generative AI:**\n- Supports all schema types and modifiers\n- Handles optional fields natively\n- More flexible with schema variations\n\n**Best Practices for Cross-Provider Compatibility:**\n- Use `.optional()` for optional fields (automatically handled for all providers)\n- Use `.min()` and `.max()` instead of `.positive()`, `.negative()`\n- Both providers now have unified schema handling\n\n#### Schema Architecture\n\nThe package uses a **driver pattern** for schema conversion, automatically adapting schemas to each provider's specific format while maintaining a unified API.\n\nSchema adapters are automatically registered when providers are imported, so no manual configuration is needed. For detailed architecture information and extending with new providers, see the [Developer Guide](#developer-guide).\n\n### Model Configuration\n\nEach model comes with default configuration values that can be customized for your specific needs.\n\n#### Accessing and Customizing Models\n\n```typescript\n// Get available models from the service\nconst models = completionService.models;\n\n// Get a specific model\nconst model = models[OpenAIModelNames.GPT_4_1];\n\n// Customize model configuration\nconst customModel = {\n  ...model,\n  config: {\n    ...model.config,\n    temperature: 0.8,  // Override temperature (0-2, controls randomness)\n    top_p: 0.9,       // Override top_p (nucleus sampling)\n  }\n};\n\n// Use customized model in requests\nconst result = await completionService.sendMessage({\n  message: {\n    role: LLMRoles.User,\n    content: [{ type: LLMMessageContentType.TEXT, text: 'Hello!' }]\n  },\n  model: customModel,\n});\n```\n\n#### Model-Specific Configuration\n\n**GPT-5 Models** have special configuration options:\n\n```typescript\nconst gpt5Model = models[OpenAIModelNames.GPT_5];\n\nconst customGPT5Model = {\n  ...gpt5Model,\n  config: {\n    ...gpt5Model.config,\n    temperature: 1,  // Note: GPT-5 only supports temperature=1\n    reasoning_effort: 'high',  // 'minimal' | 'low' | 'medium' | 'high' | 'xhigh'\n    verbosity: 'low',  // 'low' | 'medium' | 'high'\n  }\n};\n\n// GPT-5.1 and GPT-5.2 supports 'none' reasoning_effort\nconst gpt51Model = models[OpenAIModelNames.GPT_5_1];\n\nconst customGPT51Model = {\n  ...gpt51Model,\n  config: {\n    ...gpt51Model.config,\n    temperature: 1,  // Note: GPT-5.1 only supports temperature=1\n    reasoning_effort: 'none',  // 'none' | 'minimal' | 'low' | 'medium' | 'high' | 'xhigh'\n    verbosity: 'low',  // 'low' | 'medium' | 'high'\n  }\n};\n```\n\n**Note:** All GPT-5 models require `temperature: 1` and cannot be changed. GPT-5, GPT-5-MINI, and GPT-5-NANO support reasoning_effort values of 'minimal', 'low', 'medium', 'high', or 'xhigh'. GPT-5.1, GPT-5.2, and GPT-5.4 additionally support 'none'.\n\n#### Per-Prompt Reasoning Effort (`params.reasoningEffort`)\n\nA prompt's `config.params` can carry generation knobs that override the static\nmodel-map defaults for that prompt's calls only. `reasoningEffort` is the first\nsupported knob — the v2 client resolves it from the prompt config (live\nLangfuse config or the snapshot fallback) and passes it to the provider on\nevery round of the call, agent tool loops included:\n\n```typescript\n// In the prompt snapshot (TypeScript):\nconfig: {\n  provider: LLMProviders.OpenAI,\n  model: 'gpt-5.1',\n  params: { reasoningEffort: LLMReasoningEffort.Medium },\n}\n```\n\nA live Langfuse prompt config is JSON, so it spells the same value out as a\nlowercase string:\n\n```json\n{\n  \"provider\": \"OpenAI\",\n  \"model\": \"gpt-5.1\",\n  \"params\": { \"reasoningEffort\": \"medium\" }\n}\n```\n\nThe accepted values are exactly `'none'`, `'minimal'`, `'low'`, `'medium'`,\n`'high'` and `'xhigh'`. `LLMReasoningEffort.None`, `.Minimal`, `.Low`,\n`.Medium`, `.High` and `.Xhigh` are the TypeScript names for those lowercase\nstrings, not values themselves — the runtime gate validates the config against\nthe enum, so a capitalized `\"Medium\"` in a live Langfuse config fails the gate\nand the prompt falls back to the snapshot.\n\nWhich of the six a given model accepts is the model's own business, and the gate\ndoes not know it: per the GPT-5 note above, GPT-5, GPT-5-MINI and GPT-5-NANO\ntake `'minimal'` through `'xhigh'` but not `'none'`, and the whole\n`'minimal'`/`'xhigh'` vocabulary belongs to the GPT-5.x family in the first\nplace. A combination the model does not support is rejected by the provider API\nat call time, not by the config gate.\n\n- **OpenAI (Responses API):** sent as `reasoning: { effort }`. Without\n  `params`, no reasoning field is sent and the OpenAI API default applies.\n- **LLMAPI / OpenAI-compatible completions:** sent as `reasoning_effort`,\n  overriding the model map's hardcoded value.\n- **Google Generative AI:** not applied (Gemini uses `thinkingLevel`, a\n  different dialect).\n\nA model whose map entry declares `capabilities.reasoning: false` never receives\nthe field at all — the effort is dropped rather than sent to a model that would\nreject it, and the traced model parameters report what was sent rather than the\neffort that was asked for.\n\nDirect service callers can pass the same knob per call via\n`generationParams: { reasoningEffort }` on `assistInChat` / `sendMessage`\noptions. A prompt or call that carries no params behaves exactly as before.\n\n**Gemini 3.x thinking models** (gemini-3-flash-preview, gemini-3.1-pro-preview) have special configuration options:\n\n```typescript\nimport { ThinkingLevel } from '@google/genai';\n\nconst gemini31Model = models[GoogleGenerativeAIModelNames.GEMINI_3_1_PRO_PREVIEW];\n\nconst customGemini31Model = {\n  ...gemini31Model,\n  config: {\n    ...gemini31Model.config,\n    tem","readmeFilename":"README.md"}