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Contributors"},"license":"MIT","homepage":"https://www.agentskit.io/docs/reference/packages/adapters","keywords":["agentskit","ai","agents","llm","chat","typescript","openai","anthropic","claude","gemini","chatgpt","embeddings","providers","ai-agents","ollama","deepseek","grok","langchain","vercel-ai-sdk","provider-adapter","langchain"],"repository":{"url":"git+https://github.com/AgentsKit-io/agentskit.git","type":"git","directory":"packages/adapters"},"description":"Provider adapters for AgentsKit.","maintainers":[{"name":"emersonbraun","email":"emersonfbraun@gmail.com"}],"readme":"# @agentskit/adapters\n\nProfile: <code>major-package</code>\n\n<p align=\"center\"><img alt=\"AgentsKit\" src=\"https://raw.githubusercontent.com/AgentsKit-io/agentskit/main/apps/docs-next/public/brand/logo-wordmark.svg\" width=\"180\" /></p>\n\nConnect to any LLM provider — and swap between them — without touching your app code.\n\n[![npm version](https://img.shields.io/npm/v/@agentskit/adapters?color=blue)](https://www.npmjs.com/package/@agentskit/adapters)\n[![npm downloads](https://img.shields.io/npm/dm/@agentskit/adapters)](https://www.npmjs.com/package/@agentskit/adapters)\n[![bundle size](https://img.shields.io/bundlejs/size/@agentskit/adapters?label=bundle)](https://bundlejs.com/?q=@agentskit/adapters)\n[![license](https://img.shields.io/badge/license-MIT-blue.svg)](../../LICENSE)\n[![stability](https://img.shields.io/badge/stability-beta-yellow)](../../docs/STABILITY.md)\n[![GitHub stars](https://img.shields.io/github/stars/AgentsKit-io/agentskit?style=social)](https://github.com/AgentsKit-io/agentskit)\n\n**Tags:** `ai` · `agents` · `llm` · `agentskit` · `openai` · `anthropic` · `claude` · `gemini` · `chatgpt` · `ollama` · `embeddings` · `providers`\n\n## Verified proof\n\n- Package metadata and tests live under `packages/adapters/`.\n- Package guide: https://www.agentskit.io/docs/reference/packages/adapters\n- Stability map: [docs/STABILITY.md](../../docs/STABILITY.md)\n\n## How this fits the ecosystem\n\n@agentskit/adapters is the provider layer: swap OpenAI, Anthropic, Gemini, Ollama, local models, and embedding providers without rewriting your agent.\n\n- **AgentsKit**: compose it with the other packages in this repo to build agents from small, swappable parts.\n- **Registry**: look for ready agents and templates that already use this layer at [registry.agentskit.io](https://registry.agentskit.io).\n- **Playbook**: learn the production patterns behind this layer at [playbook.agentskit.io](https://playbook.agentskit.io).\n- **AKOS**: run the same concepts with enterprise deployment, governance, and observability at [akos.agentskit.io](https://akos.agentskit.io).\n\nDocs: [package guide](https://www.agentskit.io/docs/reference/packages/adapters) · [agent handoff](https://github.com/AgentsKit-io/agentskit/blob/main/llms.txt)\n\n## Why adapters\n\n- **Vendor independence** — switch from OpenAI to Anthropic to a local Ollama model by changing one line; your hooks, runtime, and tools stay untouched\n- **25 native adapters in the catalog** — Anthropic, OpenAI, Gemini, Ollama, DeepSeek, Grok, Kimi, Mistral, Cohere, Together, Groq, Fireworks, OpenRouter, Hugging Face, LM Studio, vLLM, llama.cpp, LangChain, Vercel AI SDK, and additional compatible providers\n- **Embedder functions built in** — the same adapter pattern covers text embeddings, so you can reuse provider config for both chat and RAG\n- **One-line local AI** — `ollama({ model: 'llama3.1' })` for fully offline agents with no API key required\n- **CLI-backed agents** — `@agentskit/adapters/cli` normalizes text, JSON, and ACP-based local LLM CLIs\n- **LangChain bridge** — `@agentskit/adapters/langchain-bridge` turns any adapter into a LangChain `BaseChatModel` for `createAgent`\n\n## Install\n\n<!-- readme-command:install -->\n```bash\nnpm install @agentskit/adapters\n```\n\nThe runtime example below also needs `@agentskit/runtime`. The RAG example\nalso needs `@agentskit/rag`, `@agentskit/memory`, and the optional `vectra`\npeer (`npm install @agentskit/rag @agentskit/memory vectra`).\n\n## Quick example\n\n<!-- readme-example:quickstart -->\n```ts\nimport { anthropic } from '@agentskit/adapters'\nimport { createRuntime } from '@agentskit/runtime'\n\n// Switch provider by swapping one import\nconst adapter = anthropic({ apiKey: process.env.ANTHROPIC_API_KEY, model: 'claude-sonnet-4-6' })\n// const adapter = openai({ apiKey: process.env.OPENAI_API_KEY, model: 'gpt-4o' })\n// const adapter = ollama({ model: 'llama3.1' })\n\nconst runtime = createRuntime({ adapter })\nconst result = await runtime.run('Summarize the latest AI news')\nconsole.log(result.content)\n```\n\n## Embeddings (for RAG)\n\nUse the same package for vector embeddings — wire `openaiEmbedder`, `geminiEmbedder`, or `ollamaEmbedder` into [`@agentskit/rag`](https://www.npmjs.com/package/@agentskit/rag):\n\n```ts\nimport { openaiEmbedder } from '@agentskit/adapters'\nimport { createRAG } from '@agentskit/rag'\nimport { fileVectorMemory } from '@agentskit/memory'\n\nconst rag = createRAG({\n  embed: openaiEmbedder({ apiKey: process.env.OPENAI_API_KEY! }),\n  store: fileVectorMemory({ path: './vectors' }),\n})\n```\n\n## Features\n\n- Providers: Anthropic, OpenAI, Gemini, Ollama, DeepSeek, Grok, Kimi, Mistral, Cohere, Together, Groq, Fireworks, OpenRouter, Hugging Face, LM Studio, vLLM, llama.cpp, LangChain, LangGraph, Vercel AI SDK, generic `ReadableStream`\n- Embedders: `openaiEmbedder`, `geminiEmbedder`, `ollamaEmbedder`, `deepseekEmbedder`, `grokEmbedder`, `kimiEmbedder`, `createOpenAICompatibleEmbedder`\n- Fetch-backed adapters run against the shared `Adapter` contract v1 suite (ADR 0001); SDK-backed adapters have provider-specific contract and resilience coverage\n- Custom adapter authoring via `createAdapter()`\n- Higher-order adapters: `createRouter` (cost/latency/classifier), `createEnsembleAdapter` (fan-out + merge), `createFallbackAdapter` (ordered try-next)\n\n## CLI-backed adapters\n\nThe Node-only `@agentskit/adapters/cli` subpath runs an explicitly selected\nlocal LLM executable without a shell:\n\n```ts\nimport { createCliAdapter, getCliProviderManifest, resolveCliManifest } from '@agentskit/adapters/cli'\n\nconst manifest = getCliProviderManifest('codex')\nif (!manifest) throw new Error('provider manifest is unavailable')\nconst adapter = createCliAdapter(resolveCliManifest(manifest, { mode: 'review-safe' }))\n```\n\nThe generic factories are `createCliAdapter` (`exec-text`),\n`createJsonCliAdapter` (`exec-json`), and `createAcpCliAdapter` (ACP v1 over\nJSON lines). The built-in manifests cover Codex, Claude Code (text and JSON\noutput), Grok CLI, and OpenCode. `resolveCliManifest` keeps command, argv, protocol, provider id, and\nmode explicit; `diagnoseCliProviderManifest` verifies availability and an\noptional version pattern. `review-safe` is the default: no shell, automatic\ninstallation, native login, MCP, plugins, or terminal tools. Use\n`trusted-local` explicitly when a developer intentionally wants the CLI's local\nauthentication and environment. `requiredCapabilities` is checked before\nspawning, and `onDiagnostic` receives redacted exit, timeout, abort, and\noutput-limit data. Structured output fails closed; timeouts, aborts, output\nlimits, and non-zero exits produce terminal adapter errors. Process termination\nis awaited before the adapter finishes, including when input or output fails.\n`restricted-environment` is an explicit environment allowlist mode; it is not\nan OS, filesystem, process, or network sandbox. Use `@agentskit/sandbox` when a\nreal isolation boundary is required.\n\n### `exec-text` vs `exec-json`: what each protocol can return\n\nThe protocol decides which stream chunks a manifest can produce. Pick the\nmanifest by what you need back, not by the CLI name:\n\n| Protocol    | Factory                | Chunks emitted                          | Not available                                  |\n| ----------- | ---------------------- | --------------------------------------- | ---------------------------------------------- |\n| `exec-text` | `createCliAdapter`     | `text` (streamed), `done`               | `structuredOutput`, `reasoning`, `tool_call`, usage |\n| `exec-json` | `createJsonCliAdapter` | `text`, `reasoning`, `tool_call`, `usage`, `done` | streaming (one response per process)   |\n| `acp`       | `createAcpCliAdapter`  | `text`, `reasoning` (streamed), `done`  | tool calls (rejected), MCP, plugins, terminal  |\n\n`validateCliProviderManifest` rejects a manifest that claims a capability its\nprotocol cannot deliver, and `requiredCapabilities` fails before spawning for\nthe same reason. In practice: the `claude-code` and `codex` manifests are\n`exec-text` and only ever yield plain text. For tool calls, usage, or\nstructured output from Claude Code use `claude-code-json` instead.\n\nThe same table is enforced at compile time. `CliProviderManifest` is a union\ndiscriminated by `protocol`, `capabilities` is typed as\n`CliCapabilitiesFor<protocol>`, and `getCliProviderManifest('claude-code')`\nreturns `CliProviderManifestFor<'exec-text'>`, so this does not type-check:\n\n```ts\nconst manifest = getCliProviderManifest('claude-code')!\nresolveCliManifest(manifest, { requiredCapabilities: { tools: true } })\n//                                                    ^^^^^ not assignable: exec-text has no tools/structuredOutput\n```\n\n### Claude Code\n\nTwo first-party manifests wrap the `claude` executable:\n\n- `claude-code` — `claude -p`, `exec-text`. Streams the final answer as text.\n- `claude-code-json` — `claude -p --output-format json`, `exec-json`. Parses\n  the result envelope into `text`, `tool_call`, and `usage` chunks with\n  `session_id`, `total_cost_usd`, `duration_ms`, and `num_turns` as metadata.\n  `is_error` or a non-`success` subtype fails closed.\n\n```ts\nimport { createJsonCliAdapter, getCliProviderManifest, resolveCliManifest } from '@agentskit/adapters/cli'\n\nconst manifest = getCliProviderManifest('claude-code-json')\nif (!manifest) throw new Error('provider manifest is unavailable')\nconst adapter = createJsonCliAdapter(resolveCliManifest(manifest, { mode: 'trusted-local' }))\n```\n\nWhen the request carries `context.tools`, the tool list and a\n`{ text?, toolCalls }` reply contract are appended to Claude Code's system\nprompt via `--append-system-prompt`; `claude-code-json` recognizes that object\n(fenced or bare) in the `result` string and emits `tool_call` chunks. Plain prose results stay `text`.\n`structured_output` from `--json-schema` is honored the same way. Tool\nexecution itself remains the consumer's responsibility: the adapter never\nenables Claude Code's own tools, MCP, or plugins.\n\n### Prompt serialization\n\n`createCliAdapter` and `createJsonCliAdapter` default to writing the raw\n`AdapterRequest` as one JSON line on stdin. That is the right contract for a\npurpose-built CLI, but agentic CLIs read stdin as a user prompt, and Claude\nCode refuses a raw JSON request containing `systemPrompt` as an apparent\nprompt-injection attempt. The first-party manifests therefore use two\nexported serializers:\n\n- `serializeCliPrompt` (used by `codex`) writes labelled blocks:\n\n  ```text\n  [system]\n  You are a reviewer.\n\n  [user]\n  review this\n  ```\n\n- `serializeCliMessages` plus `claudeCodeRequestArgs` (used by `claude-code`\n  and `claude-code-json`) writes only the conversation to stdin (a single user\n  message is written bare) and passes the system prompt and the tool contract\n  through `--append-system-prompt`. Verified live against Claude Code 2.1:\n  the same instructions inside the user prompt, even as labelled `[system]`\n  blocks, are flagged as an injection attempt, whereas through the system\n  flag a request with `context.tools` comes back as a `tool_call` chunk.\n\nA manifest may declare `serializeRequest`, `requestArgs` (extra per-request\nargv, appended after `args`), `parseOutput`, and `parse`; `resolveCliManifest`\nforwards them to the factory. `buildCliSystemPrompt(request)` returns the\nsystem prompt plus tool contract for other CLIs with a system-prompt flag.\n\nUse `buildArgs(request)` only for CLIs that require the prompt in argv; it is\nrequest-aware and still uses direct, shell-free spawning. Set\n`serializeRequest: () => ''` when the provider does not consume stdin. For\nJSONL or event-wrapped output, `parseOutput(stdout)` can decode raw stdout\nbefore the normal `parse(value)` callback runs.\nFor CLIs that write the final response to a file, `outputFile` reads that file\nafter process completion with the same byte limit and abort handling.\n\n## LangChain bridge: use any adapter as a LangChain chat model\n\n`langchain()` and `langgraph()` expose a LangChain runnable as an\n`AdapterFactory`. The `@agentskit/adapters/langchain-bridge` subpath goes the\nother way: `adapterToLangChainModel(adapter)` wraps any `AdapterFactory`\n(mock, Ollama, a CLI-backed adapter, ...) as a real `BaseChatModel`, so it can\nreplace `ChatAnthropic`/`ChatOpenAI` in `createAgent`, `AgentNode`, or a plain\nchain. It needs `@langchain/core` (optional peer dependency).\n\n```ts\nimport { createAgent } from 'langchain'\nimport { tool } from '@langchain/core/tools'\nimport { mockAdapter } from '@agentskit/adapters'\nimport { adapterToLangChainModel } from '@agentskit/adapters/langchain-bridge'\n\nconst add = tool(async ({ a, b }) => String(a + b), {\n  name: 'add',\n  description: 'Add two numbers',\n  schema: { type: 'object', properties: { a: { type: 'number' }, b: { type: 'number' } }, required: ['a', 'b'] },\n})\nconst model = adapterToLangChainModel(mockAdapter({ response: [/* ... */] }), { modelName: 'mock' })\nconst agent = createAgent({ model, tools: [add] })\n```\n\n- `bindTools()` forwards LangChain tools (structured tools, OpenAI-format\n  definitions, runnable tools) as AgentsKit `ToolDefinition`s on\n  `context.tools`; `tool_choice` lands in `context.metadata.toolChoice`.\n- System messages become both a `system` message and `context.systemPrompt`;\n  AI messages keep `tool_calls`; tool messages keep `tool_call_id`.\n- `tool_call` chunks become `AIMessage.tool_calls` (args parsed from JSON),\n  `usage` chunks become `usage_metadata`, `reasoning` chunks land in\n  `additional_kwargs.reasoning`, `error` chunks throw, and `.stream()` yields\n  `AIMessageChunk`s with `tool_call_chunks`.\n- Every response is a real `AIMessage`/`AIMessageChunk` instance, so\n  `wrapModelCall` middleware and `responseFormat` work together. The default\n  `profile` reports `toolCalling` and no native `structuredOutput`, so\n  `createAgent` uses its tool strategy, which any tool-calling adapter can\n  satisfy. Pass `profile: { structuredOutput: true }` only for adapters that\n  honour a provider-side JSON schema.\n\n## Stream guarantees\n\n- A stream terminates exactly once with `done` or `error`; terminal errors carry an `Error` in `metadata.error`.\n- Provider streams that close before their native completion marker are treated as truncated, not successful.\n- `abort(reason)` propagates to active fetch readers and SDK requests and terminates with the same error semantics.\n- Native tool histories preserve call/result correlation. Parallel tool results are encoded in a single provider turn where required.\n- Credentials stay in provider headers when the protocol supports them; Gemini API keys are never placed in request URLs.\n- `vercelAI` consumes the Vercel AI SDK UI message stream v1 protocol, including its required response header and `[DONE]` marker.\n\n## Higher-order adapters\n\n```ts\nimport { createRouter, anthropic, openai } from '@agentskit/adapters'\n\n// Auto-pick cheapest capable candidate per request.\nconst router = createRouter({\n  candidates: [\n    { id: 'haiku', adapter: anthropic({ model: 'claude-haiku-4-5' }), cost: 0.25 },\n    { id: 'sonnet', adapter: anthropic({ model: 'claude-sonnet-4-6' }), cost: 3 },\n    { id: 'gpt-mini', adapter: openai({ model: 'gpt-4o-mini' }), cost: 0.15 },\n  ],\n})\n```\n\nSee [Adapter router](https://www.agentskit.io/docs/reference/recipes/adapter-router), [Ensemble](https://www.agentskit.io/docs/reference/recipes/adapter-ensemble), and [Fallback chain](https://www.agentskit.io/docs/reference/recipes/fallback-chain).\n\n## Ecosystem\n\n| Package | Role |\n|---------|------|\n| [@agentskit/core](https://www.npmjs.com/package/@agentskit/core) | `Adapter`, `EmbedFn`, types |\n| [@agentskit/runtime](https://www.npmjs.com/package/@agentskit/runtime) | Headless `createRuntime` |\n| [@agentskit/rag](https://www.npmjs.com/package/@agentskit/rag) | `createRAG` + embedders |\n| [@agentskit/memory](https://www.npmjs.com/package/@agentskit/memory) | Vector + chat memory backends |\n\n## Testing Adapters\n\nThree built-in utilities let you test agents without hitting a real LLM.\n\n### `mockAdapter` — deterministic responses\n\n```ts\nimport { mockAdapter } from '@agentskit/adapters'\n\nconst adapter = mockAdapter({\n  response: [\n    { type: 'text', content: 'Hello!' },\n    { type: 'done' },\n  ],\n})\n```\n\nPass a function to make responses request-aware, or pass an array of arrays to return different chunks on each call (sequenced mode). Use the optional `history` array to capture every request for assertions.\n\n### `recordingAdapter` + `inMemorySink` — capture real calls\n\n```ts\nimport { recordingAdapter, inMemorySink, anthropic } from '@agentskit/adapters'\n\nconst sink = inMemorySink()\nconst adapter = recordingAdapter(\n  anthropic({ apiKey: process.env.ANTHROPIC_API_KEY!, model: 'claude-sonnet-4-6' }),\n  sink,\n)\n// Runs the real LLM and captures every chunk to sink.fixture\n```\n\n### `replayAdapter` — replay captured fixtures\n\n```ts\nimport { replayAdapter } from '@agentskit/adapters'\nimport fixture from './fixture.json'\n\nconst adapter = replayAdapter(fixture) // no network calls\n```\n\nTypical workflow: record once in dev → commit JSON fixture → replay in CI.\n\n## Contributors\n\n<a href=\"https://github.com/AgentsKit-io/agentskit/graphs/contributors\">\n  <img src=\"https://contrib.rocks/image?repo=AgentsKit-io/agentskit\" alt=\"AgentsKit contributors\" />\n</a>\n\n## License\n\nMIT — see [LICENSE](../../LICENSE).\n\n## Docs\n\n[Full documentation](https://www.agentskit.io) · [GitHub](https://github.com/AgentsKit-io/agentskit)\n\n## Maturity and compatibility\n\n- Stability: **beta** — see [docs/STABILITY.md](../../docs/STABILITY.md)\n- The implementation is hardened for a future freeze, but promotion still requires the 90-day beta window, two released minor lines, accepted package RFC, and repository evidence required by ADR 0024.\n- **Node.js 20+** and **TypeScript** strict mode\n- Published as `@agentskit/adapters`\n\n## Contributing\n\nSee [CONTRIBUTING.md](../../CONTRIBUTING.md) and the monorepo [LICENSE](../../LICENSE).\n","readmeFilename":"README.md"}