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Build a consulting firm on an API.\n\n## Install\n\n```bash\nnpm install @chester-ai/sdk\n# or\npnpm add @chester-ai/sdk\n```\n\n## Quick Start\n\nThe high-level API uses consulting language that matches [chester.ai](https://chester.ai):\n\n```typescript\nimport { Chester, Step, Gate } from \"@chester-ai/sdk\";\n\nconst chester = new Chester({\n  url: \"http://localhost:8990\",\n  apiKey: \"cht_your_api_key_here\",\n});\n\n// Deploy consultants\nconst analyst = await chester.consultant(\"market-analyst\", {\n  identity: \"You are a market sizing specialist.\",\n  model: \"claude-sonnet-4-20250514\",\n});\n\n// Brief a consultant (async task)\nconst taskId = await analyst.brief(\"Estimate the TAM for AI consulting in 2026\");\n\n// Stream a conversation\nfor await (const text of analyst.chat(\"What methodology do you use?\")) {\n  process.stdout.write(text);\n}\n\n// Persistent memory\nawait analyst.remember(\"Client prefers conservative estimates\");\nconsole.log(await analyst.recall());\n```\n\n## Practice Groups (Teams)\n\n```typescript\nconst practice = await chester.practice(\"dd-practice\", {\n  coordinator: \"dd-lead\",\n  members: [\"market-analyst\", \"fin-analyst\", \"legal-reviewer\"],\n});\n\nconst result = await practice.run(\"Conduct due diligence on TargetCo\");\n```\n\n## Client Models (Digital Twins)\n\n```typescript\nconst typeId = await chester.clientModelType(\"enterprise-client\", {\n  description: \"Fortune 500 client model\",\n  baseAgent: \"analyst\",\n});\n\nconst acme = await chester.clientModel(\"Acme Corp\", {\n  typeId,\n  externalId: \"crm-12345\",\n});\n\nawait acme.learn(\"engagement_start\", '{\"deal_size\": \"500M\"}');\nconst answer = await acme.query(\"What is this client's risk appetite?\");\n```\n\n## Engagement Workflows\n\n```typescript\nconst templateId = await chester.engagement(\"due-diligence-flow\", {\n  steps: [\n    Step({ instruction: \"Market sizing\", agent: \"market-analyst\" }),\n    Step({ instruction: \"Financial review\", agent: \"fin-analyst\" }),\n    Gate({ name: \"partner-review\" }),\n    Step({ instruction: \"Executive summary\", agent: \"dd-lead\" }),\n  ],\n});\n\nawait chester.runEngagement(templateId, { entityId: \"acme-corp\" });\n```\n\n## Project Boards, Objectives, Pricing\n\n```typescript\n// Kanban board\nconst board = await chester.board(\"acme-pipeline\");\nawait board.addCard(\"Market Sizing\", { agentName: \"market-analyst\" });\nawait board.execute();\n\n// Cognitive objective\nconst obj = await chester.objective(\"analyst\", \"Complete market sizing\", {\n  budgetUsd: 5.0,\n});\n\n// Outcome pricing\nawait chester.pricingRule({\n  dimension: \"deliverable\",\n  rate: 2500,\n  notes: \"Per market sizing report\",\n});\n```\n\n## Raw gRPC Access\n\nThe full 27-service gRPC API is available via `chester.rpc`:\n\n```typescript\n// Escape hatch to any RPC\nconst { agents } = await chester.rpc.agents.listAgents({});\nawait chester.rpc.queue.sendTask({ agent: \"bot\", message: \"hello\" });\n\n// Or use ChesterClient directly\nimport { ChesterClient } from \"@chester-ai/sdk\";\nconst client = new ChesterClient({ url: \"http://localhost:8990\", apiKey: \"cht_...\" });\n```\n\n## Requirements\n\n- Node.js 18+ or modern browser\n- Chester daemon running with gRPC-web enabled (default on port 8990)\n","readmeFilename":"README.md"}