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Run YAML agent manifests in-process with a five-line code path, or call the hosted agntz API. The library you build agntz apps with.","maintainers":[{"name":"aparry3","email":"aaron.parry.18@gmail.com"}],"readme":"# @agntz/sdk\n\nOfficial agntz SDK. Embedded in-process YAML agent runner — five lines of code, one YAML file, and you're running an AI agent. No server, no signup, no infrastructure.\n\nWhen you outgrow embedded mode, the same manifest and client resource shape\nmove to the hosted\n[@agntz/client](https://www.npmjs.com/package/@agntz/client). Hosted workers\nalso add managed media, transcription, image generation, explicit retention,\nand normalized provider metadata.\n\n## Install\n\n```bash\npnpm add @agntz/sdk\n# or: npm install @agntz/sdk\n# or: yarn add @agntz/sdk\n```\n\n## Quick start\n\nCreate an agent YAML at `agents/support.yaml`:\n\n```yaml\nid: support\nkind: llm\nmodel:\n  provider: anthropic\n  name: claude-sonnet-5\ninstruction: |\n  You are a friendly customer support agent. Answer concisely.\n\n  {{userQuery}}\n```\n\nRun it:\n\n```ts\nimport { agntz } from \"@agntz/sdk\";\n\nconst client = await agntz({ agents: \"./agents\" });\ntry {\n  const result = await client.agents.run({\n    agentId: \"support\",\n    input: \"How do I reset my password?\",\n  });\n  console.log(result.output);\n} finally {\n  await client.close();\n}\n```\n\nSet `ANTHROPIC_API_KEY` (or `OPENAI_API_KEY`, etc. — whichever provider you used) in your environment and run the file. That's it.\n\n## Local tools\n\nThe model can call functions you define in code. Reference them in YAML by name, pass implementations at init:\n\n```yaml\n# agents/calculator.yaml\nid: calculator\nkind: llm\nmodel: { provider: openai, name: gpt-5.6-terra }\ninstruction: |\n  Use the `add` tool to answer math questions.\n\n  {{userQuery}}\ntools:\n  - kind: local\n    tools: [add]\n```\n\n```ts\nimport { agntz, tool, z } from \"@agntz/sdk\";\n\nconst client = await agntz({\n  agents: \"./agents\",\n  tools: [\n    tool({\n      name: \"add\",\n      description: \"Add two numbers and return the sum\",\n      input: z.object({\n        a: z.number().describe(\"First operand\"),\n        b: z.number().describe(\"Second operand\"),\n      }),\n      execute: async ({ a, b }) => a + b,\n    }),\n  ],\n});\n```\n\nEach tool is self-describing: the `name`, `description`, and Zod `input` schema all flow through to the model's tool list, so it knows when to call the tool and how to shape arguments. `z` is re-exported from `@agntz/sdk` — no separate `zod` install needed.\n\nNames referenced in YAML but missing from the `tools` array fail at load time, not on first model call.\n\n## Invocation-scoped client tools\n\nFor application state that varies per call, declare the contract in the\nmanifest and pass only the handler on `run()` or `stream()`:\n\n```yaml\ntools:\n  - kind: client\n    name: get_current_selection\n    description: Read the current editor selection\n    inputSchema:\n      type: object\n      properties: {}\n      additionalProperties: false\n```\n\n```ts\nawait client.agents.run({\n  agentId: \"editor-assistant\",\n  input: \"Explain my selection\",\n  clientTools: {\n    get_current_selection: async (_input, ctx) =>\n      editor.getSelection({ signal: ctx.signal }),\n  },\n});\n```\n\nThe manifest owns the model-facing contract; the handler exists only for that\ninvocation. Missing handlers fail before Run creation. `runs.start()` is\nrejected because client tools are attached-only. Handler failures and timeouts\nbecome model-visible tool errors, and outputs must be JSON-serializable with a\n40,000-character serialized limit.\n\n## HTTP tools with credentials\n\n### Static credentials — templated headers\n\nReference env vars with `{{env.NAME}}` — resolved from `process.env` automatically:\n\n```yaml\ntools:\n  - kind: http\n    name: get_user\n    url: \"https://api.example.com/users/{userId}\"\n    headers:\n      Authorization: \"Bearer {{env.MY_API_TOKEN}}\"\n```\n\nMissing env vars throw at invoke time with a clear error so misconfigurations surface fast.\n\n`{{secrets.NAME}}` works the same way when a `SecretStore` is wired (typically through `@agntz/stores`).\n\n### POST / PUT / PATCH with a request body\n\nHTTP tools support `GET`, `POST`, `PUT`, `PATCH`, and `DELETE`. For methods that accept a body, set `body_type` (`json` is the default when `body` is present) and provide a templated `body`:\n\n```yaml\ntools:\n  - kind: http\n    name: create_user\n    url: \"https://api.example.com/users\"\n    method: POST\n    body_type: json\n    body:\n      name: \"{{userName}}\"\n      email: \"{{userEmail}}\"\n```\n\n`body_type: form` serializes the body as `application/x-www-form-urlencoded`; `body_type: query` appends the fields to the URL.\n\n### Dynamic auth — OAuth2 client credentials\n\nWhen an API requires fetching a short-lived access token before each request, declare an `auth:` block. The runner fetches the token, caches it (in-memory by default; refreshes on `401`), and applies it to every request — you don't need to write any code.\n\nThe `oauth2_client_credentials` preset covers the standard RFC 6749 §4.4 flow:\n\n```yaml\ntools:\n  - kind: http\n    name: send_message\n    url: \"https://api.salesforce.com/services/data/v60.0/sobjects/Message\"\n    method: POST\n    body_type: json\n    body:\n      content: \"{{message}}\"\n    auth:\n      type: oauth2_client_credentials\n      token_url: \"https://login.salesforce.com/services/oauth2/token\"\n      client_id: \"{{secrets.sf_client_id}}\"\n      client_secret: \"{{secrets.sf_client_secret}}\"\n      scope: \"messages:write\"             # optional\n      creds_location: basic_header        # default; or \"body\"\n```\n\n### Dynamic auth — generic token exchange\n\nFor login endpoints that don't match RFC 6749 (custom shapes, different field names, plain-text token bodies, etc.) use the parametric `token_exchange` form:\n\n```yaml\ntools:\n  - kind: http\n    name: list_things\n    url: \"https://api.example.com/things\"\n    auth:\n      type: token_exchange\n      request:\n        url: \"https://api.example.com/auth/login\"\n        method: POST\n        body_type: json\n        body:\n          username: \"{{secrets.api_user}}\"\n          password: \"{{secrets.api_pass}}\"\n      extract:\n        response_format: json             # default; or \"text\" for raw-body tokens\n        token_path: \"$.access_token\"      # JSONPath; e.g. \"$.token\", \"$.data.accessToken\"\n        expires_path: \"$.expires_in\"      # optional, seconds\n      apply:\n        location: header                  # default; or \"query\"\n        name: Authorization               # header or query parameter name\n        format: \"Bearer {token}\"          # default for header; \"{token}\" for query\n      cache_ttl: 3000                     # optional, seconds (overrides expires_path)\n      refresh_on: [401]                   # default; statuses that trigger refresh + retry\n```\n\nEvery shape is configurable: `{access_token}`, `{token}`, `{data: {accessToken}}`, raw text bodies, headers vs query, \"Bearer\" prefix vs raw token. See the test fixtures in `packages/core/tests/auth/` for more examples.\n\n### What you get for free\n\n- **Token caching** per `(auth shape, ownerId)` so two tenants sharing the same OAuth app don't share a token.\n- **Single-flight** dedup: a burst of N tool calls fires one token request, not N.\n- **Refresh-on-401**: on `401` (configurable via `refresh_on`), the runner invalidates the cache and retries exactly once. A second `401` surfaces normally — no infinite loops.\n- **Credential redaction**: known tokens and `state.secrets` values are scrubbed from response bodies and auth-error messages before they reach the LLM, traces, or logs.\n\n### Persistent token cache (advanced)\n\nThe default in-memory cache is lost on process restart. To plug in a persistent backend (Redis, SQL, etc.), pass `tokenCache` when constructing the runner:\n\n```ts\nimport { agntz } from \"@agntz/sdk\";\n\nconst client = await agntz({\n  agents: \"./agents\",\n  tokenCache: myPersistentCache,  // any object implementing TokenCache\n});\n```\n\n## Sessions\n\nBy default, sessions are in-memory and reset on process restart. For persistence, install `@agntz/stores` and use the `sqlite` subpath:\n\n```bash\npnpm add @agntz/stores\n```\n\n```ts\nimport { agntz } from \"@agntz/sdk\";\nimport { sqliteStore } from \"@agntz/sdk/sqlite\";\n\nconst client = await agntz({\n  agents: \"./agents\",\n  store: sqliteStore(\"./agntz.db\"),\n});\n\n// Pass the same sessionId across runs to continue a conversation:\nawait client.agents.run({ agentId: \"support\", input: \"hi\", sessionId: \"user-42\" });\nawait client.agents.run({ agentId: \"support\", input: \"follow-up\", sessionId: \"user-42\" });\n\n// Flush pending trace writes and close the owned SQLite connection.\nawait client.close();\n```\n\nCall `close()` during process shutdown when the client owns persistent stores or\nMCP connections. It is safe to call more than once.\n\n## Runs and traces\n\nWithout a configured store, invocations use bounded in-memory ring buffers\n(default 1000 entries). When `store` is configured, runs and full trace trees\nare persisted and remain queryable after a restart:\n\n```ts\nconst { rows } = await client.runs.list({ limit: 10 });\nfor (const run of rows) {\n  console.log(run.agentId, run.status, run.result?.output);\n}\n\nconst traces = await client.traces.list({ limit: 10 });\nconst trace = await client.traces.get(traces.rows[0].traceId);\nconsole.log(trace?.spans);\n```\n\nFor real-time observability during streaming, pass `onEvent`:\n\n```ts\nconst client = await agntz({\n  agents: \"./agents\",\n  onEvent: (event) => {\n    if (event.type === \"tool-call-start\") console.log(\"→\", event.toolCall.name);\n    if (event.type === \"text-delta\") process.stdout.write(event.text);\n  },\n});\n```\n\n## Imports, memory, and eval records\n\nThe embedded client mirrors the hosted resource shape so local state can move to\na worker later:\n\n```ts\nawait client.agents.import({ agents: [{ id: \"support\", manifest: yaml }] });\nawait client.sessions.import({ sessions });\nawait client.memory?.import({ entries });\n\nawait client.datasets.create(dataset);\nawait client.evals.create(definition);\nconst run = await client.evals.run({ evalId: \"support-quality\" });\nconst scores = await client.evals.listLatestScores({ evalId: \"support-quality\" });\n```\n\nUse `agntz publish` to migrate local manifests, sessions, and memrez entries to\na hosted or self-hosted worker:\n\n```bash\nagntz login --key ar_live_...\nagntz publish agents --agents-dir ./agents --dry-run\nagntz publish agents --agents-dir ./agents --yes\nagntz publish sessions memory --db ./agntz.db --memory-db ./memory.db --yes\n```\n\nExisting agent ids create new hosted versions by default. Use\n`--skip-existing` or `--fail-existing` to select a different conflict policy.\nThe CLI publishes manifests and persisted state, not arbitrary local\ntool-handler code.\n\n## Streaming\n\n```ts\nfor await (const event of client.agents.stream({ agentId: \"support\", input: \"...\" })) {\n  if (event.type === \"complete\") {\n    console.log(\"\\nfinal:\", event.output);\n  } else if (event.type === \"reply\") {\n    console.log(\"partial:\", event.text);\n  }\n}\n```\n\n## Graduating to the hosted API\n\nWhen you outgrow embedded mode — multi-user isolation, durable run history, hosted observability, agent push from CI — swap to `@agntz/client`:\n\n```diff\n- import { agntz } from \"@agntz/sdk\";\n+ import { AgntzClient } from \"@agntz/client\";\n\n- const client = await agntz({ agents: \"./agents\", tools });\n+ const client = new AgntzClient({ apiKey: process.env.AGNTZ_API_KEY!, baseUrl: \"https://api.agntz.co\" });\n```\n\nThe `client.agents.run / .stream`, `client.runs.list / .get`, and\n`client.traces.list / .get` resource shapes stay familiar. YAML manifests move\nto the hosted registry. In-process local tool handlers become hosted MCP/HTTP\ntools or typed signed callback endpoints. Add an explicit hosted retention\npolicy and treat `traceId` / `sessionId` as optional when using `none` or\n`result`.\n\n## What's supported in embedded mode\n\n| Feature | Embedded | Hosted (@agntz/client) |\n|---|---|---|\n| LLM agents | ✓ | ✓ |\n| Sequential / parallel / tool agent kinds | ✓ | ✓ |\n| Transcription / image agent kinds | × | ✓ (managed operations) |\n| Canonical recursive input/output JSON Schema | ✓ | ✓ |\n| Ordered text/image/audio run content | × | ✓ |\n| Managed input/output artifacts | × | ✓ |\n| Explicit `none` / `result` / `session` retention | × | ✓ |\n| Local tools (registered at client creation) | ✓ | (use MCP/HTTP instead) |\n| Client tools (passed per invocation) | ✓ | ✓ |\n| HTTP tools | ✓ | ✓ |\n| MCP tools (raw URL + headers) | ✓ | ✓ |\n| Signed callback tools | ✓ (with `SecretStore`) | ✓ |\n| Agent-as-tool (subagent calls) | ✓ | ✓ |\n| Spawnable subagents | ✓ | ✓ |\n| Sessions | ✓ (memory or sqlite) | ✓ (managed) |\n| Runs / traces | ✓ (bounded memory or persisted store) | ✓ (retention-aware) |\n| Agent/session/memory import | ✓ | ✓ |\n| Memory admin with memrez | ✓ | ✓ |\n| Datasets / eval records | ✓ | ✓ |\n| Streaming for LLM agents | ✓ (full event stream) | ✓ |\n| Streaming for pipelines | ✓ (single `complete` event) | ✓ |\n| `{{env.X}}` template refs | ✓ | (opt-in per server) |\n| `{{secrets.X}}` template refs | ✓ (configured SecretStore) | ✓ |\n| Skills | ✓ | ✓ |\n| Multi-user isolation | × | ✓ |\n\n## MCP tools\n\nMCP servers work via raw URL + optional headers. No connection store\nrequired for embedded mode:\n\n```yaml\ntools:\n  - kind: mcp\n    server: \"https://search.example.com/mcp\"\n    tools: [search, fetch_url]\n    headers:\n      Authorization: \"Bearer {{env.SEARCH_API_KEY}}\"\n```\n\nThe runner connects lazily on first tool call and reuses the connection\nfor the lifetime of the process.\n\n## License\n\nMIT\n","readmeFilename":"README.md"}