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agent library","maintainers":[{"name":"yt902","email":"ty29022@gmail.com"}],"readme":"# @aseity/lyra\n\nLyra is a TypeScript library for running tool-using agents across OpenAI,\nAnthropic, and compatible model providers. It includes streaming, sessions,\nhandoffs, structured outputs, tracing, MCP clients, and sandbox capabilities.\n\n## Changelog\n\nSee the [release history](https://github.com/Ty026/max/blob/main/packages/lyra/CHANGELOG.md)\nfor changes in each version. `CHANGELOG.md` is also included in the npm package.\n\n## Install\n\n```sh\npnpm add @aseity/lyra zod\n```\n\n## Quick start\n\n```ts\nimport { Agent, OpenAIProvider, run } from '@aseity/lyra';\n\nconst provider = new OpenAIProvider({\n  apiKey: process.env.OPENAI_API_KEY,\n});\nconst model = await provider.getModel('gpt-5.4-mini');\n\nconst agent = new Agent({\n  name: 'Assistant',\n  instructions: 'Answer clearly and concisely.',\n  model,\n});\n\nconst result = await run(agent, 'Hello!');\nconsole.log(result.finalOutput);\n```\n\nPass concrete `Model` instances when model selection is owned by your\napplication. Use `Runner` when you need reusable workflow, tracing, or session\nconfiguration.\n\n## OpenRouter\n\n`OpenRouterProvider` uses OpenRouter's Chat Completions endpoint while\npreserving OpenRouter routing metadata, cost data, structured reasoning, and\nin-band stream errors:\n\n```ts\nimport { OpenRouterProvider } from '@aseity/lyra';\n\nconst provider = new OpenRouterProvider({\n  apiKey: process.env.OPENROUTER_API_KEY,\n  httpReferer: 'https://example.com',\n  appTitle: 'Example Agent',\n  defaultRequest: {\n    provider: { require_parameters: true },\n  },\n});\n\nconst model = await provider.getModel('anthropic/claude-sonnet-4.6');\n```\n\nPer-request OpenRouter extensions such as `provider`, `models`, `plugins`,\n`reasoning`, and `session_id` can also be passed through\n`modelSettings.providerData`.\n\n## License\n\nMIT\n\n## Model parameter compatibility\n\nSee [model parameter transport](docs/model-parameters.md) for modern effort support,\nlegacy thinking budgets, explicit output validation, and migration from aliased\neffort values. Provider/model support is distinct from transport support.\n\n## MCP\n\nConnect stdio, Streamable HTTP or SSE MCP servers and expose their tools to any\nLyra tool-capable model provider. See [MCP clients](docs/mcp.md) for connection\nmanagement, authentication, filtering, resources and upstream compatibility.\n\n### Image validation\n\n`view_image` validates inline PNG/JPEG/WebP/GIF bytes before native image results\nor vision fallback requests, even when compression is disabled. It performs a\nstrict Sharp decode, including all animation frames; GIF block boundaries are\nalso checked because the decoder tolerates some truncated GIFs. Invalid images\nreturn `toolError` text without image content, so the model can regenerate the\nfile and retry within the same turn. Source files are never repaired or rewritten.\nA provider's later HTTP 400 still ends the turn; local validation does not guarantee\nprovider acceptance and does not add model retries. Remote image references from\ncustom sandboxes cannot be locally validated and return a tool error.\n\nByte and dimension limits are checked before full decoding. Host ceilings are\n32 MiB per input, 8000 pixels per side and 64 million pixels summed across all\nframes; stricter model limits also apply. At most two decodes run concurrently\nper process. Validation uses streaming pixel statistics instead of returning a\nfull raw pixel buffer. Queued calls retain their bounded input bytes, so callers\nmust still bound tool-call concurrency. Sharp loads lazily on the first image.\nStatic compression doubles as validation instead of decoding the image twice.\n\nOffline regression: `pnpm --filter @aseity/lyra exec vitest run test/view-image-validation.test.ts`.\nSet `LYRA_TEST_DOCKER_IMAGE` to an existing image with bash to include real Docker\nRunner recovery. `bun packages/lyra/test/manual/image-validation.ts` measures four\nconcurrent calls at roughly 1, 16 and 64 million pixels; optional file arguments\nreplay local failures without contacting a model. Local 64-million-pixel runs took\n6.8 seconds for four calls with sampled process RSS peaking near 232 MiB; these\nmeasurements depend on codecs, pixels, runtime, and machine, and are not a memory guarantee.\n\n### Optional view_image compression\n\n`view_image` preserves original image bytes by default. To reduce inline image payloads:\n\n```ts\nfilesystem({ viewImageCompression: true })\n```\n\nThis applies to both native vision and `viewImageCaptioner` requests. Static inline\nimages are encoded as JPEG at quality 90 with 4:4:4 chroma, without downscaling;\nEXIF orientation is applied and transparent backgrounds become white. If encoding\nwould increase the payload, the original is retained. Animated images retain their original bytes after validation. Source files are never overwritten; returned\nMIME type, byte size and dimensions describe the actual transmitted image.\n\nValidation and compression share the installed `sharp` dependency.\nExisting input and model image limits still apply. This reduces individual image\npayloads but does not limit accumulated conversation size or guarantee avoidance\nof HTTP 413 responses. Lossy compression can affect fine text; retain original\nfiles for detailed crops and verification.\n\n## Docker lifecycle\n\nDocker startup failures are cleaned up before the error returns. If cleanup also fails, catch `DockerSandboxStartupError`, retain the execution-resource reservation, and retry `error.cleanup()` after Docker recovers. See [Docker startup and cleanup](./docs/docker-lifecycle.md) for the error contract, cancellation, and shutdown behavior.\n\nSee [explicit skill input](docs/skills.md) for manual selection, discovery flags,\noriginal message display and frozen model history.\n","readmeFilename":"README.md"}