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Test DeepAgents multi-agent systems","maintainers":[{"name":"btsowa","email":"tsowababangida@gmail.com"}],"readme":"# @artemiskit/adapter-deepagents\n\nDeepAgents.js adapter for ArtemisKit - Test and evaluate DeepAgents multi-agent systems.\n\n## Installation\n\n```bash\nbun add @artemiskit/adapter-deepagents\n# or\nnpm install @artemiskit/adapter-deepagents\n```\n\n## Quick Start\n\n### Testing a Multi-Agent Team\n\n```typescript\nimport { createDeepAgentsAdapter } from '@artemiskit/adapter-deepagents';\nimport { createTeam, Agent } from 'deepagents';\n\n// Create your DeepAgents team\nconst researcher = new Agent({\n  name: 'researcher',\n  role: 'Research specialist',\n  tools: [webSearchTool, documentReader],\n});\n\nconst writer = new Agent({\n  name: 'writer',\n  role: 'Content writer',\n});\n\nconst team = createTeam({\n  agents: [researcher, writer],\n  workflow: 'sequential',\n});\n\n// Wrap with ArtemisKit adapter\nconst adapter = createDeepAgentsAdapter(team, {\n  name: 'content-team',\n  captureTraces: true,\n  captureMessages: true,\n});\n\n// Use in ArtemisKit tests\nconst result = await adapter.generate({\n  prompt: 'Write an article about AI testing best practices',\n});\n\nconsole.log(result.text); // Final article content\n\n// Access execution metadata\nconst metadata = result.raw.metadata;\nconsole.log(metadata.agentsInvolved); // ['researcher', 'writer']\nconsole.log(metadata.totalToolCalls); // Number of tool invocations\nconsole.log(metadata.totalMessages); // Messages exchanged between agents\n```\n\n### Testing a Hierarchical Agent System\n\n```typescript\nimport { createDeepAgentsAdapter } from '@artemiskit/adapter-deepagents';\nimport { createHierarchy, Coordinator, Worker } from 'deepagents';\n\n// Create hierarchical system\nconst coordinator = new Coordinator({\n  name: 'manager',\n  strategy: 'delegate-and-synthesize',\n});\n\nconst workers = [\n  new Worker({ name: 'analyst', specialty: 'data analysis' }),\n  new Worker({ name: 'visualizer', specialty: 'chart creation' }),\n];\n\nconst hierarchy = createHierarchy({ coordinator, workers });\n\nconst adapter = createDeepAgentsAdapter(hierarchy, {\n  name: 'analysis-team',\n  executionTimeout: 120000, // 2 minutes\n});\n\nconst result = await adapter.generate({\n  prompt: 'Analyze sales data and create visualizations',\n});\n```\n\n### Testing with Execution Tracing\n\n```typescript\nconst adapter = createDeepAgentsAdapter(mySystem, {\n  name: 'traced-system',\n  captureTraces: true,\n  captureMessages: true,\n});\n\nconst result = await adapter.generate({ prompt: 'Complex task' });\n\n// Access detailed execution information\nconst { metadata } = result.raw;\n\n// See which agents were involved\nconsole.log('Agents:', metadata.agentsInvolved);\n\n// See tools used\nconsole.log('Tools:', metadata.toolsUsed);\n\n// See full trace of execution\nfor (const trace of metadata.traces) {\n  console.log(`${trace.agent}: ${trace.action}`, trace.output);\n}\n\n// See inter-agent communication\nfor (const msg of metadata.messages) {\n  console.log(`${msg.from} -> ${msg.to}: ${msg.content}`);\n}\n```\n\n## Configuration Options\n\n| Option              | Type      | Default    | Description                              |\n| ------------------- | --------- | ---------- | ---------------------------------------- |\n| `name`              | `string`  | -          | Identifier for the agent system          |\n| `captureTraces`     | `boolean` | `true`     | Capture agent execution traces           |\n| `captureMessages`   | `boolean` | `true`     | Capture inter-agent messages             |\n| `executionTimeout`  | `number`  | `300000`   | Max execution time (ms)                  |\n| `inputTransformer`  | `string`  | -          | Custom input transformer function        |\n| `outputTransformer` | `string`  | -          | Custom output transformer function       |\n\n## Supported System Types\n\nThe adapter supports DeepAgents systems that implement one of these methods:\n\n- `invoke(input, config)` - Primary execution method\n- `run(input, config)` - Alternative execution method\n- `execute(input, config)` - Legacy execution method\n\nAll methods should return a `DeepAgentsOutput` object.\n\n## Streaming Support\n\nIf your system supports streaming via `stream()`, the adapter will use it:\n\n```typescript\nfor await (const chunk of adapter.stream({ prompt: 'Long task' }, console.log)) {\n  // Process streaming events\n}\n```\n\n## Execution Metadata\n\nThe adapter captures rich metadata about multi-agent execution:\n\n```typescript\ninterface DeepAgentsExecutionMetadata {\n  name?: string;                  // System name\n  agentsInvolved: string[];       // All participating agents\n  totalAgentCalls: number;        // Total agent invocations\n  totalMessages: number;          // Messages exchanged\n  totalToolCalls: number;         // Tool invocations across agents\n  toolsUsed: string[];            // Unique tools used\n  traces?: DeepAgentsTrace[];     // Full execution traces\n  messages?: DeepAgentsMessage[]; // Full message log\n  executionTimeMs?: number;       // Total execution time\n}\n```\n\n## ArtemisKit Integration\n\nUse with ArtemisKit scenarios:\n\n```yaml\n# scenario.yaml\nname: multi-agent-test\nprovider: deepagents\nscenarios:\n  - name: Research Task\n    input: 'Research and summarize recent AI developments'\n    expected:\n      contains: ['AI', 'development']\n    metadata:\n      minAgents: 2\n```\n\n```typescript\n// Register adapter in your test setup\nimport { adapterRegistry } from '@artemiskit/core';\nimport { DeepAgentsAdapter } from '@artemiskit/adapter-deepagents';\n\nadapterRegistry.register('deepagents', async (config) => {\n  // Your system setup\n  return new DeepAgentsAdapter(config, myTeam);\n});\n```\n\n## License\n\nApache-2.0\n","readmeFilename":"README.md"}