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manifest-based LLM executor for agnt prompts with CLI","maintainers":[{"name":"anindya007","email":"anindya@skej.com"}],"readme":"# @agnt-sdk/studio\n\nV2 manifest-native LLM executor for [Agnt](https://agnt.ai) prompts, with a CLI for pulling/running agent manifests locally and for pulling task/chat run detail from the platform for debugging.\n\n## Installation\n\n```bash\nnpm install @agnt-sdk/studio\n```\n\nFor the CLI:\n\n```bash\nnpm install -g @agnt-sdk/studio\nagnt --help\n```\n\n## Configuration\n\nCreate `agnt.config.js` at your project root. See [`@agnt-sdk/config`](../config) for the full reference.\n\n```js\n// agnt.config.js\nexport default {\n  privateKey: `-----BEGIN PRIVATE KEY-----\\n...\\n-----END PRIVATE KEY-----`,\n  kid: 'your-key-id',\n  apiUrl: 'https://api.agnt.ai',\n  serviceKey: '',\n  outputDir: './agnt/prompts',\n  apiMode: true, // false = load from local files (after agnt pull)\n};\n```\n\n## CLI\n\n### `agnt pull`\n\nPull one or all prompt manifests from the Agnt platform into your local `outputDir`:\n\n```bash\n# Pull a specific prompt\nagnt pull myaccount/flight-planner\n\n# Pull all public prompts for an account\nagnt pull myaccount/*\n```\n\nManifests are saved to `outputDir/accountSlug/promptSlug.json`. Set `apiMode: false` in your config to execute from these local files instead of fetching from the API on every run.\n\n### `agnt init`\n\nScaffold an `agnt.config.js` in the current directory:\n\n```bash\nagnt init\n```\n\n### `agnt configure` / `agnt run` — pull task/chat run detail from the DB\n\nFor debugging what an agent actually did on a task or chat — every tool call and result, not just the message transcript — without tunneling into the database or scraping logs. Uses the standard `/tasks` and `/chats` API, so it needs a real API key, not the project-level `agnt.config.js`.\n\n**Setup — mint an API key, then save it as a named profile** (AWS-CLI style; profiles live in `~/.agnt/credentials`, independent of any project):\n\n```bash\nagnt configure --profile production --api-url https://api.agnt.ai --api-key ak_live_...\nagnt configure --profile staging --api-url https://staging-api.agnt.ai --api-key ak_live_...\n```\n\nSelect a profile per command with `--profile`, or set `AGNT_PROFILE` in your shell. Defaults to a profile named `default`.\n\n```bash\n# List recent tasks/chats\nagnt run list --since 24h --profile production\nagnt run list --status active --profile production\n\n# Full activity timeline for one task/chat (tool calls + results, paginates\n# through everything automatically — capped at 1000 activities by default)\nagnt run task <taskId> --profile production\nagnt run chat <chatId> --profile production\n\n# No cap — fetch every activity, however many pages that takes\nagnt run task <taskId> --all --profile production\n\n# Raw JSON for piping into other tools (e.g. `jq`, or Claude Code via Bash)\nagnt run task <taskId> --json --profile production\n```\n\nAn account-level API key (one created without a specific `userId`) sees everything in its account. A user-scoped key only sees that one user's own tasks/chats.\n\n### `agnt eval` — read Run Review evaluations\n\nRun Review scores finished runs from the user's point of view (did they get what they asked for, how did it feel) and is what agnt-console's Evaluation page shows. `agnt eval` puts the same three views in the terminal, so an agent can go from \"how are runs doing\" to one evaluation to the run behind it. Same profile setup as `agnt run`, but it **needs an account-level API key** (one created without a user and without an org): an evaluation is a cross-user view, so a key tied to a user or an org is refused. A key's scopes are not checked: nothing enforces them on any route, and every key the console mints carries some.\n\n```bash\n# How runs are doing: averages, outcome and ending buckets, and the by-task-type table\nagnt eval summary --days 30\n\n# The evaluations, worst first. Filter to a task type from the table above,\n# an outcome, how the user ended, or a score range\nagnt eval list --task-class schedule_meeting_multi_participant\nagnt eval list --max-score 2 --outcome failed --sort newest\nagnt eval list --unclassified\nagnt eval list --page 2\n\n# One evaluation in full\nagnt eval get <reviewId>\n\n# Raw JSON for piping into other tools\nagnt eval list --max-score 2 --json\n```\n\nEvery review names the task and chat it is about, in full. `agnt eval get` prints the commands that follow it to the run: `agnt run task <taskId>` for the tool-call timeline, `agnt run chat <chatId>` for the conversation, and the LangSmith query for the trace.\n\n### `agnt workflow` — push, pull and list workflow skills\n\nKeep a workflow (a Skill with `kind: \"workflow\"`) in git as a JSON file. Uses the same routes as the console (`POST /skills`, `PATCH /skills/:id`), so triggers are stamped and the workflow is scheduled; it does not use the manifest import route.\n\n```bash\nagnt workflow list [--json]\nagnt workflow pull daily-digest -o daily-digest.json    # omit -o to print to stdout\nagnt workflow push daily-digest.json                    # create; fails if the name exists\nagnt workflow push daily-digest.json --update           # update in place (name cannot change)\n# all accept --profile <name>\n```\n\nExample definition (`kind` defaults to `workflow`; `name` is a lowercase slug):\n\n```json\n{\n  \"name\": \"daily-digest\",\n  \"title\": \"Daily digest\",\n  \"description\": \"Summarise the day each morning\",\n  \"scheduleType\": \"trigger-based\",\n  \"workflowStatus\": \"active\",\n  \"hidden\": false,\n  \"triggers\": [{ \"on\": \"cron\", \"schedule\": \"0 9 * * *\" }]\n}\n```\n\nServer-managed fields (`id`, `origin`, `tier`, `createdBy`, `account`, `followers`, `billedTo`, `triggerSources`, timestamps, run counters) are dropped on pull and ignored on push.\n\n**Known limits (verified against staging, 2026-09-29)**\n\n- `list`, `pull` and create-`push` work with an API key. A created workflow round-trips through `pull` (the server adds trigger `_id`, `intelligenceTier` and `subTriggers`).\n- `push --update` was refused by the API with `403 You can only edit skills you own` on a workflow the same key had just created. Until the backend accepts it, change an existing workflow in the console.\n- `userFacingPlan` is accepted on update but dropped on create (it is missing from the create schema), so it comes back `null` after a first `push`.\n- `user.*` system triggers can only be created from the console.\n- A user-scoped key cannot see hidden or draft skills in `list`; use an account-level key for those.\n- `workflowStatus` is honoured on create (verified on staging: `\"paused\"` comes back `\"disabled\"` and is not scheduled). `status`, `followers`, `silentOnNoOp`, `processingBufferMs` and `skillCollection` are, however, ignored on create (a warning is printed when they are non-default).\n- `list` shows one row per install, so a workflow installed more than once can appear twice; this comes from `GET /skills`, not the CLI.\n\n## Programmatic use\n\n### `AgntExecutor`\n\nExecute a prompt by address (`accountSlug/promptSlug`). Fetches the manifest from the API or a local file depending on `apiMode`.\n\n```ts\nimport { AgntExecutor } from '@agnt-sdk/studio';\n\nconst executor = await AgntExecutor.create({\n  credentials: {\n    anthropic: { apiKey: process.env.ANTHROPIC_API_KEY },\n  },\n});\n\nconst result = await executor.execute(\n  'myaccount/flight-planner',\n  { destination: 'New York', departDate: '2025-06-15' },\n  {\n    // optional tool implementations\n    get_flights: {\n      execute: async (args) => { /* ... */ }\n    }\n  }\n);\n\nconsole.log(result.result);   // final output\nconsole.log(result.messages); // full message history\nconsole.log(result.usage);    // token usage + cost\n```\n\n### `createExecutor`\n\nLower-level factory — takes a V2 `PromptManifestV2` object directly:\n\n```ts\nimport { createExecutor } from '@agnt-sdk/studio';\n\nconst executor = await createExecutor({\n  manifest,\n  credentials: {\n    anthropic: { apiKey: process.env.ANTHROPIC_API_KEY },\n  },\n  variables: { key: 'value' },\n  toolRouter: { /* tool implementations */ },\n});\n\nconst result = await executor.execute();\n```\n\n## Failure results\n\nA failed `execute()` returns `{ ok: false, error, failure }`. `error` is the message string (unchanged);\n`failure` is structured, derived from the provider's typed error (HTTP status, error class, machine code) and\nnever from message text, so it is language-independent:\n\n```ts\nif (!result.ok) {\n  const { kind, status, provider, model, fallbackTrail, retryable } = result.failure!;\n  // kind: 'quota' | 'timeout' | 'aborted' | 'unsupported' | 'auth' | 'provider_error' | 'bad_request' | 'unknown'\n  // fallbackTrail: [{ provider, model, kind, status }] — every model-chain member tried, in order\n}\n```\n\n- `quota`: HTTP 429, or a quota/rate-limit class/code (OpenAI/Azure Foundry `rate_limit_exceeded`, `insufficient_quota`,\n  `RateLimitReached`; Anthropic `rate_limit_error`; Kimi `exceeded_current_quota_error`; Google `RESOURCE_EXHAUSTED`;\n  Bedrock `ThrottlingException`). Quota is checked before timeout. **Quota may be permanent**: `insufficient_quota` and\n  `exceeded_current_quota_error` are `quota` with `retryable: false`.\n- `timeout`: idle/backstop stream abort, HTTP 408, timeout error class or code (Node codes are read through `.cause`,\n  so `fetch failed` TypeErrors classify). `aborted`: the caller stopped the run.\n- `unsupported`: HTTP 501/405/415 or an `unsupported_*`/`DeploymentNotFound` style code; also a chain member that was\n  skipped in the trail because no `executorFactory` could build it.\n- `auth`: 401/403, `AuthenticationError`/`PermissionDeniedError`, AccessDenied codes (a revoked key is not a bad request).\n- `provider_error`: 5xx (including 529 overloaded), network failures. TLS/certificate errors stay `unknown`.\n- `bad_request`: any other 4xx. A 402 with no code is `bad_request`; a 402 carrying a quota code is `quota`.\n- `unknown`: no typed signal, **and every error that did not come through the model chain** (a tool handler's\n  rethrown error, a variable-validation error): a tool error's `.status` says nothing about the LLM provider.\n\n**`kind` is not a retry instruction.** Use `retryable` (true for 5xx/529, transient 429, timeouts; false for permanent\nquota, auth, unsupported, bad_request; absent for `unknown`).\n\nThe SDK deliberately does NOT match message text. A transient failure a provider reports only in prose (for example\nAzure's \"no deployments ready\", an HTTP 400 with no code) is `bad_request`; a consumer that needs to catch it must do\nits own text match.\n\nBecause a 429 is retried by the provider client, `streamWithRetry`, and then the next chain member, a 429 storm\noften ends as a `timeout` whose `fallbackTrail` shows only `quota` entries. Inspect the trail, not just `kind`.\n\nOn success, a result has `fallbackTrail` (same entry shape) only when at least one earlier member failed, so a\nquota-then-success is visible. A cancel that does not throw returns `ok: false` with no `failure`.\n\n## Logging\n\nPass `logLevel` to control output verbosity:\n\n```ts\nconst executor = await createExecutor({\n  manifest,\n  credentials,\n  logLevel: 'debug',  // 'debug' | 'info' | 'silent'  (default: 'info')\n});\n```\n\n- `'info'` — lifecycle events (model selection, tool calls)\n- `'debug'` — full request/response payloads sent to the LLM\n- `'silent'` — no output\n\n## V2 Manifest format\n\n```json\n{\n  \"$schema\": \"https://agnt.ai/schemas/manifest/v2.json\",\n  \"kind\": \"PromptManifest\",\n  \"apiVersion\": \"v2\",\n  \"metadata\": {\n    \"name\": \"flight-planner\",\n    \"title\": \"Flight Planner\",\n    \"description\": \"Books flights based on user preferences.\"\n  },\n  \"spec\": {\n    \"routingStrategy\": \"fallback\",\n    \"enableToolCalls\": true,\n    \"variables\": [],\n    \"models\": [\n      { \"provider\": \"anthropic\", \"model\": \"claude-sonnet-4-5\" }\n    ],\n    \"tools\": [],\n    \"files\": [],\n    \"dependencies\": []\n  }\n}\n```\n\n## Supported providers\n\n| Provider | Credentials key |\n|---|---|\n| Anthropic | `credentials.anthropic.apiKey` |\n| OpenAI | `credentials.openai.apiKey` |\n| AWS Bedrock | `credentials.bedrock.{ region, accessKeyId, secretAccessKey }` |\n| DeepSeek | `credentials.deepseek.apiKey` |\n| Google Gemini | `credentials.google.apiKey` |\n\n## License\n\nMIT\n\n### Native reasoning replay and observation\n\nProvider `invoke()` results can carry `message.nativeState` with the producing\nprovider, model, wire format and complete ordered native output items. Persist\nthis envelope unchanged when resuming an unfinished tool exchange. The compatible\nadapter replays it once instead of reconstructing a second copy of the assistant\ntext and calls. OpenAI and Azure Responses preserve IDs, phase, summaries and\nopaque encrypted content; Gemini preserves signed parts on tool and final-answer\nturns; Anthropic preserves the complete content block order. Compatibility is\nconservatively exact provider/model/format. It does not promise cross-model or\ncross-provider portability. Older `rawParts` snapshots still work for recognizable\nAnthropic/Gemini shapes; an incompatible envelope never falls back to those parts.\n\n`message.reasoningSummary` contains only supported public summary text, separate\nfrom the answer. Summary collection remains opt-in through the provider's settings\n(`reasoning.summary`, `thinking.display`, or `includeThoughts`). No setting exposes\na universal raw reasoning transcript. Opaque state and legacy signed parts are\nexcluded from SDK traces and `llm_output` hook payloads. Traces report state\npresence, public summaries, actual constructed `reasoningConfig` settings and\nprovider-reported reasoning counts. A missing count is unknown; zero is retained.\nThe count is a detail of inclusive output usage, not an extra billing bucket.\n\nGemini's generic `reasoning_effort` mapping applies only to supported model\nfamilies. Generate Content Gemini 3 uses supported `thinkingLevel` values;\nGemini 2.5 uses SDK budget choices of 1024/8192/16384 for low/medium/high within the\nprovider's ranges. `none` maps to budget zero only on 2.5 Flash, where disabling\nthinking is supported. Explicit native budgets or levels win. Unsupported tiers,\nunknown models and specialized image/audio/live models keep their native defaults.\n\nAnthropic manual thinking drops incompatible forced tool choices, temperature,\nand top_k; top_p is retained only within 0.95–1. Supported adaptive models retain\nforced tool choices, except models that reject all forcing. The exported\n`anthropicSupportsForcedTools(model, thinking)` predicate uses the actual request\nthinking mode. Requested `thinking.display` and `thinking.block_binding` survive\neffort translation.\n\nNew Anthropic models also bind thinking to the system prompt, tool definitions and\nconversation prefix. Exact model provenance alone does not prove that a replayed\nblock is valid after a prefix edit. The adapter retries the exact documented\nprefix-binding 400 once with the provider's `drop_block` policy and the\n`thinking-binding-controls-2026-08-01` beta header, when adaptive thinking is\ncompatible. This is degraded recovery: stale reasoning can be dropped by the\nprovider. It does not restore continuity lost through a prefix edit. Generic or\ntampered signature errors and unsupported thinking modes are not retried this way.\nAn explicit caller `error` or `drop_block` policy takes precedence. A successful\nrecovery is logged, traced as `prefixBindingRecovery`, and retained in the\ncompatible `nativeState.replayPolicy` for checkpoint resume and in the executor\ninstance for subsequent requests. `inputTransformations` reports the provider's\nblock paths/types/reasons without opaque contents.\n\nTo observe prefix mismatch reports without selecting a new policy, set\n`metadata.anthropic_beta` to `['thinking-binding-controls-2026-08-01']`. This maps\nto the header, and alone leaves the provider's existing mismatch behavior intact.\nExisting beta values are merged during recovery. Preserving reasoning across\ncompaction and changing lazy tools requires preserving the valid prefix or using\nthe provider's server-side context editing; that broader harness policy is not\nimplemented by this SDK replay envelope.\n\nSources: [OpenAI reasoning](https://developers.openai.com/api/docs/guides/reasoning),\n[Anthropic thinking](https://platform.claude.com/docs/en/build-with-claude/thinking),\n[Anthropic preserved thinking](https://platform.claude.com/docs/en/build-with-claude/preserved-thinking),\n[Gemini Generate Content thinking](https://ai.google.dev/gemini-api/docs/generate-content/thinking),\n[Gemini thought signatures](https://ai.google.dev/gemini-api/docs/generate-content/thought-signatures).\n\nThe adapter reconciles canonical tool-call ID repairs, call removals/additions and\nvisible content edits with stored native output. Unedited items retain their exact\norder, IDs, phases, argument spelling and opaque values. Removed function calls\nare not replayed, repaired IDs match their results, and added calls appear once.\nSuch edits can still degrade provider continuity. For a compatible Gemini native\nturn, if a signed function call's arguments changed, or a removed signed call\nleaves an unsigned survivor, only the affected reconstructed call uses Gemini's\ndocumented imported-history `skip_thought_signature_validator` sentinel. Traces\nreport `inputTransformations` with `type: imported_history` and\n`reason: canonical_tool_history_changed`; an unchanged turn uses its original\nsignature. Recognizable legacy Gemini parts use the same reconciliation. Gemini's\npreexisting function-name IDs are not unique routing IDs; the adapter matches\narguments before pairing duplicate names and never transfers another call's\nsignature to a surviving sibling. Cross-model/provider fallback into an ongoing\nsigned tool exchange can still need a restarted or explicitly imported history.\nAutomatic Anthropic prefix recovery in this release is bounded to adaptive/default\ncompatible modes; explicit manual and between_tools modes are not auto-recovered.\n\nThis change is a 0.0.65 release candidate. Publish the reviewed SDK package first,\nthen update the backend's exact dependency and lockfile to that published artifact,\nbuild/deploy it, and verify real provider traces. A locally packed tarball is for\nreview/integration proof and does not establish a deployed or published version.\n","readmeFilename":"README.md"}