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Test LangChain chains and agents","maintainers":[{"name":"btsowa","email":"tsowababangida@gmail.com"}],"readme":"# @artemiskit/adapter-langchain\n\nLangChain.js adapter for ArtemisKit - Test and evaluate LangChain chains, agents, and runnables.\n\n## Installation\n\n```bash\nbun add @artemiskit/adapter-langchain\n# or\nnpm install @artemiskit/adapter-langchain\n```\n\n## Quick Start\n\n### Testing a Simple Chain\n\n```typescript\nimport { createLangChainAdapter } from '@artemiskit/adapter-langchain';\nimport { ChatOpenAI } from '@langchain/openai';\nimport { StringOutputParser } from '@langchain/core/output_parsers';\nimport { ChatPromptTemplate } from '@langchain/core/prompts';\n\n// Create your LangChain chain\nconst model = new ChatOpenAI({ model: 'gpt-4' });\nconst prompt = ChatPromptTemplate.fromTemplate('Answer concisely: {input}');\nconst chain = prompt.pipe(model).pipe(new StringOutputParser());\n\n// Wrap with ArtemisKit adapter\nconst adapter = createLangChainAdapter(chain, {\n  name: 'qa-chain',\n  runnableType: 'chain',\n});\n\n// Use in ArtemisKit tests\nconst result = await adapter.generate({ prompt: 'What is 2+2?' });\nconsole.log(result.text); // \"4\"\n```\n\n### Testing an Agent\n\n```typescript\nimport { createLangChainAdapter } from '@artemiskit/adapter-langchain';\nimport { AgentExecutor, createReactAgent } from 'langchain/agents';\nimport { ChatOpenAI } from '@langchain/openai';\nimport { Calculator } from '@langchain/community/tools/calculator';\n\n// Create agent\nconst model = new ChatOpenAI({ model: 'gpt-4' });\nconst tools = [new Calculator()];\nconst agent = createReactAgent({ llm: model, tools, prompt: agentPrompt });\nconst agentExecutor = new AgentExecutor({ agent, tools });\n\n// Wrap with ArtemisKit adapter\nconst adapter = createLangChainAdapter(agentExecutor, {\n  name: 'calculator-agent',\n  runnableType: 'agent',\n  captureIntermediateSteps: true,\n});\n\n// Use in ArtemisKit tests\nconst result = await adapter.generate({ prompt: 'Calculate 25 * 4' });\nconsole.log(result.text); // \"100\"\n\n// Access agent execution metadata\nconsole.log(result.raw.metadata.toolsUsed); // ['calculator']\nconsole.log(result.raw.metadata.totalToolCalls); // 1\n```\n\n### Testing RAG Chains\n\n```typescript\nimport { createLangChainAdapter } from '@artemiskit/adapter-langchain';\nimport { ChatOpenAI } from '@langchain/openai';\nimport { RetrievalQAChain } from 'langchain/chains';\n\n// Assume vectorstore is already set up\nconst retriever = vectorstore.asRetriever();\nconst chain = RetrievalQAChain.fromLLM(\n  new ChatOpenAI({ model: 'gpt-4' }),\n  retriever\n);\n\nconst adapter = createLangChainAdapter(chain, {\n  name: 'rag-qa',\n  inputKey: 'query',\n  outputKey: 'result',\n});\n\nconst result = await adapter.generate({\n  prompt: 'What does the document say about X?',\n});\n```\n\n## Configuration Options\n\n| Option                     | Type                                            | Default     | Description                              |\n| -------------------------- | ----------------------------------------------- | ----------- | ---------------------------------------- |\n| `name`                     | `string`                                        | -           | Identifier for the chain/agent           |\n| `runnableType`             | `'chain' \\| 'agent' \\| 'llm' \\| 'runnable'`     | auto-detect | Type of LangChain runnable               |\n| `captureIntermediateSteps` | `boolean`                                       | `true`      | Capture agent intermediate steps         |\n| `inputKey`                 | `string`                                        | `'input'`   | Custom input key for the runnable        |\n| `outputKey`                | `string`                                        | `'output'`  | Custom output key for the runnable       |\n\n## Supported Runnable Types\n\nThe adapter supports all LangChain runnables that implement `invoke()`:\n\n- **Chains**: LCEL chains, RetrievalQA, ConversationalRetrievalQA, etc.\n- **Agents**: ReAct agents, OpenAI Functions agents, Tool-calling agents\n- **LLMs**: Direct ChatOpenAI, ChatAnthropic, etc.\n- **Custom Runnables**: Any object with an `invoke()` method\n\n## Streaming Support\n\nIf your runnable supports streaming via `stream()`, the adapter will use it:\n\n```typescript\nfor await (const chunk of adapter.stream({ prompt: 'Tell me a story' }, console.log)) {\n  // Process streaming chunks\n}\n```\n\n## ArtemisKit Integration\n\nUse with ArtemisKit scenarios:\n\n```yaml\n# scenario.yaml\nname: langchain-qa-test\nprovider: langchain\nscenarios:\n  - name: Basic QA\n    input: 'What is the capital of France?'\n    expected:\n      contains: 'Paris'\n```\n\n```typescript\n// Register adapter in your test setup\nimport { adapterRegistry } from '@artemiskit/core';\nimport { LangChainAdapter } from '@artemiskit/adapter-langchain';\n\nadapterRegistry.register('langchain', async (config) => {\n  // Your chain/agent setup\n  return new LangChainAdapter(config, myChain);\n});\n```\n\n## License\n\nApache-2.0\n","readmeFilename":"README.md"}