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multi-agent orchestration system with MCP server support","maintainers":[{"name":"anonx3247","email":"anas@lecaillon.com"}],"readme":"# Universal Agent Harness\n\nA multi-agent orchestration system with MCP (Model Context Protocol) server support. Run AI agents with configurable tools and capabilities.\n\n## Features\n\n- 🤖 Multi-agent orchestration\n- 🔌 MCP server integration for extensible tool support\n- 💾 SQLite-based run tracking\n- 💰 Cost and token usage tracking\n- 📊 Support for multiple LLM providers (Anthropic, OpenAI, Google, Mistral, Deepseek, etc.)\n- 📚 Library and CLI interfaces\n- 📁 Dynamic profile and problem discovery from directories\n\n## Installation\n\n```bash\nnpm install universal-agent-harness\n```\n\n## Library Usage\n\n```typescript\nimport { createRun, run } from 'universal-agent-harness';\n\n// Create a run\nconst result = await createRun({\n  name: \"solve-math-problem\",\n  problemId: \"factorial-problem\",  // References ./problems/factorial-problem/\n  model: \"claude-sonnet-4-5\",\n  agentCount: 1,\n  profile: \"example\"\n});\n\n// Run (single tick)\nawait run({\n  runName: \"solve-math-problem\",\n  singleTick: true,\n  onMessage: (msg) => console.log(\"Agent output:\", msg)\n});\n```\n\n### Profiles and Problems\n\n**Profiles** define agent behavior and are stored in `./profiles/` (configurable via `PROFILES_DIR`):\n- `prompt.md` - System prompt for the agent\n- `settings.json` - MCP server configuration (optional)\n- Any additional files accessible via `getProfilePath(profile, relativePath)`\n\n**Problems** are stored in `./problems/` (configurable via `PROBLEMS_DIR`):\n- Each problem is a directory with a unique ID\n- Must contain `problem.md` with the problem statement\n- Can include additional files accessible via `getProblemPath(problemId, relativePath)`\n\nExample structure:\n```\nproblems/\n  factorial-problem/\n    problem.md\n    test-cases.json\nprofiles/\n  example/\n    prompt.md\n    settings.json\n```\n\n### MCP Server Configuration\n\nConfigure MCP servers in `profiles/{profile}/settings.json`:\n\n```json\n{\n  \"mcpServers\": [\n    {\n      \"name\": \"filesystem\",\n      \"transport\": \"stdio\",\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@modelcontextprotocol/server-filesystem\", \"/tmp\"],\n      \"enabled\": true\n    },\n    {\n      \"name\": \"github\",\n      \"transport\": \"stdio\",\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@modelcontextprotocol/server-github\"],\n      \"env\": {\n        \"GITHUB_PERSONAL_ACCESS_TOKEN\": \"${GITHUB_TOKEN}\"\n      },\n      \"enabled\": true\n    }\n  ]\n}\n```\n\n## CLI Usage\n\n```bash\n# Create a run\nnpx agent-harness create my-run \\\n  -p factorial-problem \\\n  -m claude-sonnet-4-5 \\\n  -n 1 \\\n  --profile example\n\n# Run (single tick)\nnpx agent-harness run my-run --tick 0\n\n# Run continuously\nnpx agent-harness run my-run\n\n# Run specific agent\nnpx agent-harness run my-run --agent 0\n\n# Run with cost limit\nnpx agent-harness run my-run --max-cost 5.0\n\n# List runs\nnpx agent-harness list\n\n# Clean up run\nnpx agent-harness clean my-run\n```\n\n## API Reference\n\n### `createRun(config)`\n\nCreate a new run.\n\n**Parameters:**\n- `name` (string): Unique run name\n- `problemId` (string): Problem ID (directory name in `./problems/`)\n- `model` (Model): AI model to use\n- `agentCount` (number, optional): Number of agents (default: 1)\n- `profile` (string, optional): Profile name (default: \"example\")\n\n**Returns:** `Promise<Result<RunResource>>`\n\n### `run(config)`\n\nRun or continue a run.\n\n**Parameters:**\n- `runName` (string): Run name\n- `agentIndex` (number, optional): Run specific agent\n- `singleTick` (boolean, optional): Run one tick only\n- `maxCost` (number, optional): Maximum cost limit\n- `thinking` (boolean, optional): Enable extended thinking (default: true)\n- `onMessage` (function, optional): Message callback\n- `onCostUpdate` (function, optional): Cost update callback\n\n**Returns:** `Promise<Result<{ cost: number } | void>>`\n\n### `getRun(name)`\n\nGet run by name.\n\n**Returns:** `Promise<Result<RunResource>>`\n\n### `listRuns()`\n\nList all runs.\n\n**Returns:** `Promise<RunResource[]>`\n\n### `getRunCost(run)`\n\nGet total cost for a run.\n\n**Returns:** `Promise<number>`\n\n### `deleteRun(name)`\n\nDelete a run and all its data.\n\n**Returns:** `Promise<Result<void>>`\n\n### Profile Utilities\n\n- `listProfiles()` - Get all available profiles\n- `getProfileDir(profile)` - Get profile directory path\n- `getProfilePath(profile, relativePath)` - Resolve file path within profile\n\n### Problem Utilities\n\n- `listProblems()` - Get all available problems\n- `getProblemDir(problemId)` - Get problem directory path\n- `getProblemPath(problemId, relativePath)` - Resolve file path within problem\n- `getProblemContent(problemId)` - Read problem.md content\n\n### Model Utilities\n\n- `createLLM(model, config?)` - Create an LLM instance for a given model\n- `MODELS` - Record of all available models\n\n**Example:**\n```typescript\nimport { createLLM } from 'universal-agent-harness';\n\nconst llm = createLLM('claude-sonnet-4-5', {\n  maxTokens: 4096,\n  thinking: true\n});\n```\n\n### Exported Types\n\nThe library exports all core types for TypeScript users:\n\n**Model Types:**\n- `Model` - Union type of all supported models\n- `Message` - Agent message structure\n- `TextContent`, `ToolUse`, `ToolResult`, `Thinking` - Message content types\n- `Tool` - Tool definition structure\n- `TokenUsage` - Token usage tracking\n- `ModelConfig` - LLM configuration options\n- `LLM` - Abstract LLM base class\n- `ProviderData` - Provider-specific data\n\n**Configuration Types:**\n- `MCPServerConfig` - MCP server configuration\n- `ProfileConfig` - Profile configuration with MCP servers\n- `Result<T>` - Success/error result type\n\n## Supported Models\n\n- **Anthropic**: claude-opus-4-5, claude-sonnet-4-5, claude-haiku-4-5\n- **OpenAI**: gpt-5.2-pro, gpt-5.2, gpt-5.1, gpt-5, gpt-4.1, gpt-5-mini, etc.\n- **Google**: gemini-3-pro-preview, gemini-2.5-pro, gemini-2.5-flash\n- **Mistral**: mistral-large-latest, devstral-medium-latest, codestral-latest\n- **Deepseek**: deepseek-chat, deepseek-reasoner\n- **Moonshot AI**: kimi-k2-thinking\n- **RedPill AI**: kimi-k2.5, glm-4.7, llama-3.3-70b-instruct, qwen-2.5-7b-instruct\n\n## Development\n\n```bash\n# Install dependencies\nnpm install\n\n# Run type checking\nnpm run typecheck\n\n# Run linting\nnpm run lint\n\n# Build\nnpm run build\n```\n\n## Database\n\nThe system uses SQLite with Drizzle ORM:\n\n```bash\n# Generate migrations\nnpx drizzle-kit generate\n\n# Run migrations\nnpx drizzle-kit migrate\n```\n\n## License\n\nMIT\n","readmeFilename":"README.md"}