refactor(agent-core-v2): clarify human-layer names

packages/agent-core-v2/docs/en/llm.md

@@ -6,9 +6,9 @@ llm is a standalone LLM request library inside the human layer (`src/human/llm/`
 
 1. **Minimal boundary: llm = "a single request"**. llm only handles request encoding/decoding and event emission. auth, usage accounting, HistoryMessage/meta, compaction, switch, the media file system, and Tool Message assembly are all out of scope — they either move up to the turn/agent layer or plug in as contribution points.
 2. **Streaming-native; events are the contract**. The only outward surface is a single, purely serializable event stream (requester level: `llm.sent / streaming.headers / streaming.part / streaming.usage / streaming.finish / streaming.message_id / failed.syntax / failed.remote / done`; the turn level adds `llm.retrying / llm.recovering`, and `llm.sent` carries the most recent recovery record). Streaming and non-streaming are isomorphic (non-streaming also accumulates over the stream, just without deltas). Events are emitted as they arrive — no caching, no fallback.
-3. **format masks inter-protocol differences; traits express provider customizations**. format lives at the protocol layer and handles encoding/decoding of requests, responses, errors, usage, and finish. Each protocol owns a typed trait interface (`OpenAITrait` / `OpenAIResponsesTrait` / `AnthropicTrait` / `GoogleGenAITrait`) exposing only the customization points that protocol actually consumes — a hook a protocol ignores is unrepresentable, never silently dead. format and trait never import each other: both speak only the neutral wire/chunk types in the protocol's `contract.ts`. The requester is the composition root — `generate` runs a fixed per-protocol pipeline (`planOpenAIRequest` and friends) that alternates pure format stages (lower → assemble → encode → stream parser) with trait hooks (cacheKey/thinking → convertMessage → mergeHistory → convertTool → buildParams → extractUsage), so customization is explicit data flow instead of a closure captured inside format. Endpoint/env resolution and default headers form the provider `connection`, error classification is a requester option, and model capability is a provider-variant field — none of them are format business. Each base's public seam is contract + trait + requester; format, lower, and patterns are internal to the requester pipeline — only bases code and tests may import them (lint-enforced). Protocol differences must not leak into the turn or into requester decorators.
+3. **format masks inter-protocol differences; traits express provider customizations**. format lives at the protocol layer and handles encoding/decoding of requests, responses, errors, usage, and finish. Each protocol owns a typed trait interface (`OpenAITrait` / `OpenAIResponsesTrait` / `AnthropicTrait` / `GoogleGenAITrait`) exposing only the customization points that protocol actually consumes — a hook a protocol ignores is unrepresentable, never silently dead. format and trait never import each other: both speak only the neutral wire/chunk types in the protocol's `contract.ts`. The requester is the composition root — `generate` runs a fixed per-protocol pipeline (`planOpenAIRequest` and friends) that alternates pure format stages (lower → assemble → encode → stream parser) with trait hooks (cacheKey/thinking → convertMessage → mergeHistory → convertTool → buildParams → extractUsage), so customization is explicit data flow instead of a closure captured inside format. Endpoint/env resolution and default headers form the provider `connection`, error classification is a requester option, and model capability is a provider-binding field — none of them are format business. Each base's public seam is contract + trait + requester; format, lower, and patterns are internal to the requester pipeline — only bases code and tests may import them (lint-enforced). Protocol differences must not leak into the turn or into requester decorators.
 4. **Two-layer error model**. Internally, code throws the SDK's native errors; local request validation throws the shared `SyntaxRequestFormatError` (`llm/syntax-errors.ts`), which the requester converts uniformly via `toLlmSyntaxErrorMessage`, with no intermediate layer. Externally there are only `llm.failed.syntax` (local message syntax errors, never retried) and `llm.failed.remote` (remote streaming errors, subdivided into connection / timeout / rate_limit / quota_exhausted / context_overflow / request_structure, etc.), converted by format at the boundary.
-5. **Stateless core + turn-driven orchestration**. `generate(config, content, control)` is a stateless function; errors are delivered via onEvent, never thrown. The turn machine invokes the request actor (`createRequestActor`) directly: the actor wraps a single request (messageResolvers, abort scope, event sendBack), and the turn drives retry and recovery through the pure policy functions in retry.ts / recovery.ts: recovery is a strategy chain composed by the caller (the engine tries `credentialsRecovery` before the configured replacement-message strategies such as media degradation); each strategy's pure `propose` returns a self-describing record (`strategy`/`action`, optional replacement `messages`, optional opaque `prepare` effect) — the turn runs `prepare` and/or swaps messages and re-enters `thinking` with attempt reset to 1; retry backs off in the `retrying` state (honoring Retry-After), and the turn emits `llm.recovering / llm.retrying` for each. Empty response is judged by the turn at `llm.done` via the pure `emptyResponseError` and re-raised as `llm.failed.remote`, entering the same failure cascade. Abort is carried by an AbortController owned by the turn: the controller is passed into the request actor via `LlmInput.signal`, and the turn aborts it directly on `turn.abort`, with the request ending as `llm.failed.remote`; the request actor neither creates its own controller nor touches any signal on teardown, so a finished request can never abort a shared signal. The accumulator is held by the turn and fed by the event stream; on `llm.retrying / llm.recovering` the turn rolls it back and recreates it, so every attempt accumulates from zero while as much interrupted state as possible is preserved (the turn finishes the complete message out of the accumulator at `llm.done`).
+5. **Stateless core + turn-driven orchestration**. `generate(config, content, control)` is a stateless function; errors are delivered via onEvent, never thrown. The turn machine invokes the request actor (`createRequestActor`) directly: the actor wraps a single request (messageResolvers, abort scope, event sendBack), and the turn drives retry and recovery through the pure policy functions in retry.ts / recovery.ts: recovery is a strategy chain composed by the caller (the engine tries `credentialsRecovery` before the configured replacement-message strategies such as media degradation); each strategy's pure `propose` returns a self-describing record (`strategy`/`action`, optional replacement `messages`, optional opaque `beforeRetry` effect) — the turn runs `beforeRetry` and/or swaps messages and re-enters `thinking` with attempt reset to 1; retry backs off in the `retrying` state (honoring Retry-After), and the turn emits `llm.recovering / llm.retrying` for each. Empty response is judged by the turn at `llm.done` via the pure `emptyResponseError` and re-raised as `llm.failed.remote`, entering the same failure cascade. Abort is carried by an AbortController owned by the turn: the controller is passed into the request actor via `LlmInput.signal`, and the turn aborts it directly on `turn.abort`, with the request ending as `llm.failed.remote`; the request actor neither creates its own controller nor touches any signal on teardown, so a finished request can never abort a shared signal. The accumulator is held by the turn and fed by the event stream; on `llm.retrying / llm.recovering` the turn rolls it back and recreates it, so every attempt accumulates from zero while as much interrupted state as possible is preserved (the turn finishes the complete message out of the accumulator at `llm.done`).
 6. **No silent fallback**. Configuration is taken exactly as given. For beta features, thinking, empty response, and similar scenarios, define explicit error conditions first, fail at request time, and guide the user to fix the configuration — never fall back silently.
 7. **Every variable capability is a contribution point**. Providers, media upload/degradation, usage, traceId, and error recovery (compaction / media degradation) all plug in through extension points; the llm core contains none of these concepts.
 8. **Data is data**. A model is pure, function-free data (endpoint url + model uniquely identifies a model), serializable and directly usable as generate input. The catalog is a derived `provider -> models` cache; the dependency direction only goes from models-dev into llm internals, never the reverse.
@@ -37,7 +37,7 @@ llm/
 │   │                     LlmRequestConfig.credentials: credential contribution point
 │   │                     (resolve/canRecover/invalidate), resolved per attempt by the caller;
 │   │                     factories and the credentialsRecovery strategy live in human/credentials
-│   │                     (staticCredentials / oauthCredentials; kimiOAuthCredentialProvider adapts
+│   │                     (createStaticCredentialProvider / createOAuthCredentialProvider; kimiOAuthCredentialProvider adapts
 │   │                     Kimi OAuth tokens); the runWithCredentialRecovery /
 │   │                     streamWithCredentialRecovery executors for direct callers live in
 │   │                     llm-adapter/model/credential-recovery
@@ -60,7 +60,7 @@ llm/
 └── media/                media contribution points: cache / degrade / ref / resolver / store / upload
 ```
 
-Request lifecycle: `generate` receives (config, content, control) → the caller resolves `config.credentials` into a fully-credentialed model before each attempt (the request actor on the machine path), so requests always carry fresh credentials and a credential-refresh recovery (recoverable 401 → `credentials.invalidate()`, emitted as `llm.recovering` with strategy `credentials`) naturally re-resolves on the re-send (direct callers outside the state machines — ping, generate, full compaction, media upload — share the same single-retry recovery through `runWithCredentialRecovery` / `streamWithCredentialRecovery`) → the requester's `plan*` function composes pure format stages with trait hooks into protocol requestParams (format lowers the generic Message[] through the Pattern Rewriter; trait adjusts kwargs, converted messages, history, tools, and final params in between) → internalGenerate calls the official SDK → streaming chunks are converted by the stateless parser callbacks into `llm.streaming.part / streaming.usage / streaming.finish / streaming.message_id` events → errors are converted by format into `llm.failed.*`; on success the requester emits `llm.done`, on failure it ends with `llm.failed.syntax / llm.failed.remote` and never emits `llm.done`. At `llm.done` the turn judges empty responses via `emptyResponseError` and re-raises them as `llm.failed.remote`; the turn machine first tries recovery on `llm.failed.remote` (the engine-composed strategy chain — credential refresh on a recoverable 401 first, then replacement-message strategies — each pure `propose` returning a record whose opaque `prepare` effect the turn executes, emitting `llm.recovering`), then retries with backoff (honoring Retry-After, emitting `llm.retrying`), and only fails the turn once attempts are exhausted. The turn holds the HistoryAccumulator, fed by the event stream, rolls it back and recreates it on `llm.retrying / llm.recovering`, and finishes the complete message at `llm.done`; usage accounting, tracing, compaction, and media degradation all attach to the event stream as plugins/contribution points.
+Request lifecycle: `generate` receives (config, content, control) → the caller resolves `config.credentials` into a fully-credentialed model before each attempt (the request actor on the machine path), so requests always carry fresh credentials and a credential-refresh recovery (recoverable 401 → `credentials.invalidate()`, emitted as `llm.recovering` with strategy `credentials`) naturally re-resolves on the re-send (direct callers outside the state machines — ping, generate, full compaction, media upload — share the same single-retry recovery through `runWithCredentialRecovery` / `streamWithCredentialRecovery`) → the requester's `plan*` function composes pure format stages with trait hooks into protocol requestParams (format lowers the generic Message[] through the Pattern Rewriter; trait adjusts kwargs, converted messages, history, tools, and final params in between) → internalGenerate calls the official SDK → streaming chunks are converted by the stateless parser callbacks into `llm.streaming.part / streaming.usage / streaming.finish / streaming.message_id` events → errors are converted by format into `llm.failed.*`; on success the requester emits `llm.done`, on failure it ends with `llm.failed.syntax / llm.failed.remote` and never emits `llm.done`. At `llm.done` the turn judges empty responses via `emptyResponseError` and re-raises them as `llm.failed.remote`; the turn machine first tries recovery on `llm.failed.remote` (the engine-composed strategy chain — credential refresh on a recoverable 401 first, then replacement-message strategies — each pure `propose` returning a record whose opaque `beforeRetry` effect the turn executes, emitting `llm.recovering`), then retries with backoff (honoring Retry-After, emitting `llm.retrying`), and only fails the turn once attempts are exhausted. The turn holds the HistoryAccumulator, fed by the event stream, rolls it back and recreates it on `llm.retrying / llm.recovering`, and finishes the complete message at `llm.done`; usage accounting, tracing, compaction, and media degradation all attach to the event stream as plugins/contribution points.
 
 ## Rejected Schemes (do not reintroduce)