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Built with axllm.dev","maintainers":[{"name":"amitdeshmukh","email":"amit.deshmuk@gmail.com"}],"readme":"![AxCrew](axcrew.webp)\n\n[![npm version](https://img.shields.io/npm/v/@amitdeshmukh/ax-crew.svg)](https://www.npmjs.com/package/@amitdeshmukh/ax-crew) [![npm downloads](https://img.shields.io/npm/dm/@amitdeshmukh/ax-crew.svg)](https://www.npmjs.com/package/@amitdeshmukh/ax-crew)\n\n### AxCrew — build a crew of AI agents with shared state (powered by AxLLM)\n\nAxCrew lets you define a team of AI agents in config and run them together with shared state, tools, streaming, MCP, and built‑in metrics/cost tracking. Bring your own functions or use the provided registry.\n\n### Why AxCrew\n- **Config-first crews**: Declare agents once; instantiate on demand.\n- **ACE Learning**: Agents learn from human feedback with persistent playbooks.\n- **Sandboxed code execution**: AxJSRuntime with permission controls — data never leaves your environment.\n- **Shared state**: Simple key/value state all agents can read/write.\n- **Sub‑agents and tools**: Compose agents and functions cleanly.\n- **MCP**: Connect agents to MCP servers (STDIO, HTTP SSE, Streamable HTTP).\n- **Streaming**: Real‑time token streaming for responsive UX.\n- **Metrics & costs**: Per‑agent and crew snapshots, with estimated USD.\n- **Auto-installs Claude & Codex skills**: `npm install` installs 16 skill files that teach LLMs to build AxCrew agents.\n\n### Install\n\n```bash\nnpm install @amitdeshmukh/ax-crew @ax-llm/ax @ax-llm/ax-tools\n```\n\nThis also auto-installs 16 skill files for Claude Code (`~/.claude/skills/ax-crew/`) and Codex (`~/.agents/skills/ax-crew/`).\n\nRequirements: Node.js >= 21.\n\n### Environment\nSet provider keys in your environment. Example `.env`:\n```bash\nGEMINI_API_KEY=...\nANTHROPIC_API_KEY=...\nOPENAI_API_KEY=...\nAZURE_OPENAI_API_KEY=...\n```\nIn each agent config, set `providerKeyName` to the env var name. AxCrew resolves the key at runtime by reading `process.env[providerKeyName]` in Node or `globalThis[providerKeyName]` in the browser. Ensure your env is loaded (for example, import `dotenv/config` in Node) before creating the crew.\n\n#### Provider-specific arguments (`providerArgs`)\nSome providers require extra top-level configuration beyond `ai.model` (for example, Azure deployment details or Vertex AI project/region). Pass these via `providerArgs`. AxCrew forwards `providerArgs` to Ax unchanged; validation and semantics are handled by Ax. Refer to Ax provider docs for supported fields.\n\n### Quickstart\n\n```typescript\nimport { AxCrew, AxCrewFunctions, FunctionRegistryType, StateInstance } from '@amitdeshmukh/ax-crew';\nimport type { AxFunction } from '@ax-llm/ax';\n\nconst config = {\n  crew: [{\n    name: \"Planner\",\n    description: \"Creates a plan to complete a task\",\n    executionMode: \"axgen\", // \"axagent\" | \"axgen\"\n    signature: \"task:string \\\"a task to be completed\\\" -> plan:string \\\"a plan to execute the task\\\"\",\n    provider: \"google-gemini\",\n    providerKeyName: \"GEMINI_API_KEY\",\n    ai: {\n      model: \"gemini-1.5-pro\",\n      temperature: 0\n    }\n  }]\n};\n\n// Create custom functions with type safety\nclass MyCustomFunction {\n  constructor(private state: Record<string, any>) {}\n  \n  toFunction(): AxFunction {\n    return {\n      name: 'MyCustomFunction',\n      description: 'Does something useful',\n      parameters: {\n        type: 'object',\n        properties: {\n          inputParam: { type: 'string', description: \"input to the function\" }\n        }\n      },\n      func: async ({ inputParam }) => {\n        // Implementation\n        return inputParam;\n      }\n    };\n  }\n}\n\n// Type-safe function registry\nconst myFunctions: FunctionRegistryType = {\n  MyCustomFunction\n};\n\n// Create crew with type checking\nconst crew = new AxCrew(config, myFunctions);\n\n// Set and get state\ncrew.crewState.set('key', 'value');\nconst value: string = crew.crewState.get('key');\n\n// Add agents to the crew\nawait crew.addAgentsToCrew(['Planner']);\nconst planner = crew.agents?.get('Planner');\n\nif (planner) {\n  const response = await planner.forward({ task: \"Plan something\" });\n  \n  // Sub-agent usage - when used by another agent (AI is ignored and agent's own config is used)\n  const subAgentResponse = await planner.forward(ai, { task: \"Plan something\" });\n  \n  // Metrics (per-agent and per-crew)\n  const agentMetrics = (planner as any).getMetrics?.();\n  const crewMetrics = crew.getCrewMetrics();\n  console.log('Agent metrics:', agentMetrics);\n  console.log('Crew metrics:', crewMetrics);\n}\n```\n\nKey TypeScript features:\n- Full type definitions for all classes, methods, and properties\n- Type-safe configuration objects\n- Proper typing for function registries and custom functions\n- Type checking for state management\n- Comprehensive type safety for agent operations and responses\n- Usage cost tracking with proper types\n\n### Concepts at a glance\n- **Agent**: An LLM program with a `signature`, `provider`, `ai` model config, optional `examples`, and optional `mcpServers`.\n- **Sub‑agents**: List other agent names in `agents` to compose behaviors.\n- **Functions (tools)**: Register callable functions via a registry and reference by name in agent `functions`.\n- **State**: `crew.crewState.set/get/getAll()` shared across all agents.\n- **Persona**: Use `definition` (preferred) or `prompt` to set the system program. If both are present, `definition` wins.\n- **Execution mode**: Set `executionMode` to `axgen` (default) or `axagent` per agent.\n- **Streaming**: Use `streamingForward()` for token streams.\n- **Metrics**: Per‑agent `getMetrics()` + crew‑level `getCrewMetrics()` snapshots.\n\n## Usage\n\n### Initializing a Crew\nPass a JSON object to the `AxCrew` constructor:\n\n```javascript\n// Import the AxCrew class\nimport { AxCrew } from '@amitdeshmukh/ax-crew';\n\n// Create the configuration object\nconst config = {\n  crew: [\n    {\n      name: \"Planner\",\n      description: \"Creates a plan to complete a task\",\n      signature: \"task:string \\\"a task to be completed\\\" -> plan:string \\\"a plan to execute the task in 5 steps or less\\\"\",\n      provider: \"google-gemini\",\n      providerKeyName: \"GEMINI_API_KEY\",\n      ai: {\n        model: \"gemini-1.5-flash\",\n        temperature: 0\n      },\n      options: {\n        debug: false\n      }\n    }\n    // ... more agents\n  ]\n};\n\n// Create a new instance of AxCrew using the config object\nconst crew = new AxCrew(config);\n```\n\nAxCrew no longer supports reading from a configuration file to remain browser-compatible.\n\n### Agent Persona: definition and prompt\n\n- **definition**: Detailed persona/program description used as the system prompt. Recommended for precise control. Must be at least 100 characters.\n- **prompt**: An alias for `definition`. If both are provided, **definition** takes precedence.\n\nAdd either field to any agent config. The chosen value becomes the Ax agent's underlying program description (system prompt).\n\n```json\n{\n  \"name\": \"Planner\",\n  \"description\": \"Creates a plan to complete a task\",\n  \"prompt\": \"You are a meticulous planning assistant. Produce concise, executable step-by-step plans with clear justifications, constraints, and assumptions. Prefer brevity, avoid fluff, and ask for missing critical details before proceeding when necessary.\",\n  \"signature\": \"task:string -> plan:string\",\n  \"provider\": \"google-gemini\",\n  \"providerKeyName\": \"GEMINI_API_KEY\",\n  \"ai\": { \"model\": \"gemini-1.5-flash\", \"temperature\": 0 }\n}\n```\n\n### Execution Modes (`axagent` | `axgen`)\n\nEach agent can run in one of two execution modes supported by AxLLM:\n\n- `axgen` (default): Uses AxGen capabilities.\n- `axagent`: Uses AxAgent capabilities.\n\nSet mode in config:\n\n```json\n{\n  \"name\": \"Planner\",\n  \"description\": \"Creates plans\",\n  \"executionMode\": \"axgen\",\n  \"signature\": \"task:string -> plan:string\",\n  \"provider\": \"google-gemini\",\n  \"providerKeyName\": \"GEMINI_API_KEY\",\n  \"ai\": { \"model\": \"gemini-1.5-flash\", \"temperature\": 0 }\n}\n```\n\nExecution is routed internally by `executionMode` while you continue to call the same public APIs (`forward` / `streamingForward`).\nThere is no external API split and no caller-side branching for `axagent` vs `axgen`.\n\n### AxAgent RLM Support\n\n`AxAgent` in newer Ax versions moved to an RLM split architecture (Actor/Responder + runtime loop).\nAxCrew supports this directly so you can use modern AxAgent capabilities without changing the AxCrew call surface.\n\nTo configure those capabilities in AxCrew, use `axAgentOptions` on an agent (effective when `executionMode` is `axagent`):\n\n```typescript\nconst config = {\n  crew: [{\n    name: \"Researcher\",\n    description: \"Deep research agent\",\n    executionMode: \"axagent\",\n    signature: \"query:string, context:string? -> answer:string\",\n    provider: \"google-gemini\",\n    providerKeyName: \"GEMINI_API_KEY\",\n    ai: { model: \"gemini-1.5-pro\", temperature: 0 },\n    axAgentOptions: {\n      runtime,\n      contextFields: [\"context\"],\n      mode: \"simple\",\n      maxTurns: 12\n    }\n  }]\n};\n```\n\nRLM mode requirements:\n- `axAgentOptions.runtime`: Provide an `AxJSRuntime` instance for runtime execution.\n- `axAgentOptions.contextFields`: Required in RLM mode (can be an empty array when no context fields are needed).\n\nExample runtime wiring:\n\n```typescript\nimport { AxJSRuntime, AxJSRuntimePermission } from '@ax-llm/ax';\n\nconst runtime = new AxJSRuntime({\n  permissions: [AxJSRuntimePermission.TIMING],\n});\n\nconst config = {\n  crew: [{\n    name: \"Analyzer\",\n    executionMode: \"axagent\",\n    // ...\n    axAgentOptions: {\n      runtime,\n      contextFields: [\"context\"]\n    }\n  }]\n};\n```\n\nWhy this exists:\n- Upstream AxAgent changed to support richer RLM behaviors (runtime-backed decomposition, actor/responder separation, context management).\n- AxCrew needs to preserve compatibility while exposing these capabilities.\n- `executionMode` keeps this explicit per-agent and lets AxCrew route internally with zero external API change.\n\n`forward()` and `streamingForward()` remain unchanged; AxCrew routes internally based on `executionMode`.\n\n### RLM Examples (with cost and tracing verification)\n\nThe following examples in this repo demonstrate equivalent RLM behavior in AxCrew and print accumulated costs at the end:\n\n- [`examples/rlm-long-task.ts`](examples/rlm-long-task.ts): Long-task analysis with context management and RLM runtime.\n- [`examples/rlm-shared-fields.ts`](examples/rlm-shared-fields.ts): Shared-field propagation with parent/child AxAgent setup.\n\nBoth examples:\n- run through the same public `forward()` API\n- use AxAgent RLM options through `axAgentOptions`\n- print per-agent and crew metrics (`estimatedCostUSD`, token usage)\n- are compatible with telemetry/tracing instrumentation\n\n### Agent Examples\nYou can provide examples to guide the behavior of your agents using the `examples` field in the agent configuration. Examples help the agent understand the expected input/output format and improve its responses.\n\n```javascript\n{\n  \"name\": \"MathTeacher\",\n  \"description\": \"Solves math problems with step by step explanations\",\n  \"signature\": \"problem:string \\\"a math problem to solve\\\" -> solution:string \\\"step by step solution with final answer\\\"\",\n  \"provider\": \"google-gemini\",\n  \"providerKeyName\": \"GEMINI_API_KEY\",\n  \"ai\": {\n    \"model\": \"gemini-1.5-pro\",\n    \"temperature\": 0\n  },\n  \"examples\": [\n    {\n      \"problem\": \"what is the square root of 144?\",\n      \"solution\": \"Let's solve this step by step:\\n1. The square root of a number is a value that, when multiplied by itself, gives the original number\\n2. For 144, we need to find a number that when multiplied by itself equals 144\\n3. 12 × 12 = 144\\nTherefore, the square root of 144 is 12\"\n    },\n    {\n      \"problem\": \"what is the cube root of 27?\",\n      \"solution\": \"Let's solve this step by step:\\n1. The cube root of a number is a value that, when multiplied by itself twice, gives the original number\\n2. For 27, we need to find a number that when cubed equals 27\\n3. 3 × 3 × 3 = 27\\nTherefore, the cube root of 27 is 3\"\n    }\n  ]\n}\n```\n\nThe examples should:\n- Match the input/output signature of your agent\n- Demonstrate the desired format and style of responses\n- Include edge cases or specific patterns you want the agent to learn\n- Be clear and concise while showing the expected behavior\n\nExamples are particularly useful for:\n- Teaching agents specific response formats\n- Demonstrating step-by-step problem-solving approaches\n- Showing how to handle edge cases\n- Maintaining consistent output styles across responses\n\n### Function Registry\nFunctions (aka Tools) are the building blocks of agents. They are used to perform specific tasks, such as calling external APIs, databases, or other services.\n\nThe `FunctionRegistry` is a central place where all the functions that agents can use are registered. This allows for easy access and management of functions across different agents in the crew.\n\nTo use the `FunctionRegistry`, you need to either:\n- import and use the built-in functions from the `@amitdeshmukh/ax-crew` package, or\n- bring your own functions before initializing `AxCrew`. \n\nHere's an example of how to set up the `FunctionRegistry` with built-in functions:\n\n```javascript\nimport { AxCrewFunctions } from '@amitdeshmukh/ax-crew';\nconst crew = new AxCrew(config, AxCrewFunctions);\n```\n\nif you want to bring your own functions, you can do so by creating a new instance of `FunctionRegistry` and passing it to the `AxCrew` constructor.\n\n```typescript\nimport { FunctionRegistryType } from '@amitdeshmukh/ax-crew';\n\nconst myFunctions: FunctionRegistryType = {\n  GoogleSearch: googleSearchInstance.toFunction()\n};\n\nconst crew = new AxCrew(config, myFunctions);\n```\n\n### Adding Agents to the Crew\nThere are three ways to add agents to your crew, each offering different levels of control:\n\n#### Method 1: Add All Agents Automatically\nThis is the simplest method that automatically handles all dependencies:\n\n```javascript\n// Initialize all agents defined in the config\nawait crew.addAllAgents();\n\n// Get agent instances\nconst planner = crew.agents?.get(\"Planner\");\nconst manager = crew.agents?.get(\"Manager\");\n```\n\nThis method:\n- Reads all agents from your configuration\n- Automatically determines the correct initialization order based on dependencies\n- Initializes all agents in the proper sequence\n- Throws an error if there are circular dependencies\n\n#### Method 2: Add Multiple Agents with Dependencies\nThis method allows you to initialize a subset of agents while still handling dependencies automatically:\n\n```javascript\n// Add multiple agents - dependencies will be handled automatically\nawait crew.addAgentsToCrew(['Manager', 'Planner', 'Calculator']);\n\n// Or add them in multiple steps - order doesn't matter as dependencies are handled\nawait crew.addAgentsToCrew(['Calculator']); // Will be initialized first\nawait crew.addAgentsToCrew(['Manager']);    // Will initialize Planner first if it's a dependency\n```\n\nThis method:\n- Takes an array of agent names you want to initialize\n- Automatically handles dependencies even if not explicitly included\n- Initializes agents in the correct order regardless of the order specified\n- Throws an error if required dependencies are missing or if there are circular dependencies\n\n#### Method 3: Add Individual Agents\nThis method gives you the most control but requires manual dependency management:\n\n```javascript\n// Add agents one by one - you must handle dependencies manually\nawait crew.addAgent('Calculator'); // Add base agent first\nawait crew.addAgent('Planner');    // Then its dependent\nawait crew.addAgent('Manager');    // Then agents that depend on both\n```\n\nThis method:\n- Gives you full control over the initialization process\n- Requires you to handle dependencies manually\n- Throws an error if you try to initialize an agent before its dependencies\n\n#### Dependency Handling\nThe crew system automatically handles agent dependencies in the following ways:\n\n1. **Explicit Dependencies**: Defined in the agent config using the `agents` field:\n```javascript\n{\n  name: \"Manager\",\n  // ... other config ...\n  agents: [\"Planner\", \"Calculator\"] // Manager depends on these agents\n}\n```\n\n2. **Initialization Order**: \n   - Base agents (no dependencies) are initialized first\n   - Dependent agents are initialized only after their dependencies\n   - Circular dependencies are detected and reported\n\n3. **Error Handling**:\n   - Missing dependencies are reported with clear error messages\n   - Circular dependencies are detected and reported\n   - Invalid agent names or configurations are caught early\n\n4. **State Management**:\n   - All initialized agents within a crew share the same state\n   - Dependencies can access and modify shared state\n   - State persists across all initialization methods\n\nChoose the method that best fits your needs:\n- Use `addAllAgents()` for simple cases where you want all agents\n- Use `addAgentsToCrew()` when you need a subset of agents with automatic dependency handling\n- Use `addAgent()` when you need fine-grained control over the initialization process\n\n### State Management\n\nThe `StatefulAxAgent` class in `src/agents/index.js` allows for shared state functionality across agents. Sub-agents can be added to an agent to create complex behaviors. All agents in the crew have access to the shared state. State can also be shared with functions that are passed to the agents. To do this, pass the `state` object as an argument to the function class as shown here https://axllm.dev/guides/functions-1/\n\n\n```javascript\n// Set some state (key/value) for this crew\ncrew.crewState.set('name', 'Crew1');\ncrew.crewState.set('location', 'Earth');\n\n// Get the state for the crew\ncrew.crewState.get('name'); // 'Crew1'\ncrew.crewState.getAll(); // { name: 'Crew1', location: 'Earth' }\n``` \n\nState can also be set/get by individual agents in the crew. This state is shared with all agents. It is also passed to any functions expressed as a class in `FunctionsRegistry`.\n\n```javascript\nPlanner.crewState.set('plan', 'Fly to Mars');\nconsole.log(Manager.crewState.getAll()); // { name: 'Crew1', location: 'Earth', plan: 'Fly to Mars' }\n```\n\n## Example Agent task\n\nAn example of how to complete a task using the agents is shown below. The `Planner` agent is used to plan the task, and the `Manager` agent is used to execute the task.\n\n```javascript\nimport { AxCrew, AxCrewFunctions } from '@amitdeshmukh/ax-crew';\n\n// Create a new instance of AxCrew\nconst crew = new AxCrew(config, AxCrewFunctions);\nawait crew.addAgentsToCrew(['Planner', 'Calculator', 'Manager']);\n\n// Get agent instances\nconst Planner = crew.agents.get(\"Planner\");\nconst Manager = crew.agents.get(\"Manager\");\n\n// User query\nconst userQuery = \"whats the square root of the number of days between now and Christmas\";\n\nconsole.log(`\\n\\nQuestion: ${userQuery}`);\n\n// Forward the user query to the agents\nconst planResponse = await Planner.forward({ task: userQuery });\nconst managerResponse = await Manager.forward({ question: userQuery, plan: planResponse.plan });\n\n// Get and print the plan and answer from the agents\nconst plan = planResponse.plan;\nconst answer = managerResponse.answer;\n\nconsole.log(`\\n\\nPlan: ${plan}`);\nconsole.log(`\\n\\nAnswer: ${answer}`);\n```\n\n### Streaming Responses\n\nThe package supports streaming responses from agents, allowing you to receive and process agent outputs in real-time. This is particularly useful for long-running tasks or when you want to provide immediate feedback to users.\n\n```javascript\nimport { AxCrew, AxCrewFunctions } from '@amitdeshmukh/ax-crew';\n\n// Create and initialize crew as shown above\nconst crew = new AxCrew(config, AxCrewFunctions);\nawait crew.addAgentsToCrew(['Planner']);\n\nconst planner = crew.agents.get(\"Planner\");\n\n// Stream responses using the forward method\nawait planner.forward(\n  { task: \"Create a detailed plan for a website\" },\n  {\n    onStream: (chunk) => {\n      // Process each chunk of the response as it arrives\n      console.log('Received chunk:', chunk);\n    }\n  }\n);\n\n// You can also use streaming with sub-agents\nawait planner.forward(\n  ai,\n  { task: \"Create a detailed plan for a website\" },\n  {\n    onStream: (chunk) => {\n      process.stdout.write(chunk);\n    }\n  }\n);\n```\n\nKey streaming features:\n- Real-time response processing\n- Support for both direct and sub-agent usage\n- Customizable stream handling through callbacks\n- Compatible with all agent types and configurations\n- Maintains cost tracking and state management functionality\n\n### Model Context Protocol (MCP) Support\n\nAxCrew provides built-in support for the Model Context Protocol (MCP), allowing agents to connect to and use MCP servers for enhanced functionality. MCP enables agents to access external tools, data sources, and services in a standardized way.\n\n#### Supported Transport Types\n\nAxCrew supports three MCP transport types, replacing the deprecated `AxMCPHTTPTransport`:\n\n1. **AxMCPStdioTransport** - For standard input/output communication\n2. **AxMCPHTTPSSETransport** - For HTTP with Server-Sent Events\n3. **AxMCPStreambleHTTPTransport** - For streamable HTTP communication\n\n#### Configuration\n\nAdd MCP servers to your agent configuration using the `mcpServers` field:\n\n##### STDIO Transport Configuration\n\nFor MCP servers that communicate via standard input/output:\n\n```json\n{\n  \"name\": \"DataAnalyst\",\n  \"description\": \"Analyzes data using MCP tools\",\n  \"signature\": \"data:string -> analysis:string\",\n  \"provider\": \"openai\",\n  \"providerKeyName\": \"OPENAI_API_KEY\",\n  \"ai\": {\n    \"model\": \"gpt-4\",\n    \"temperature\": 0\n  },\n  \"mcpServers\": {\n    \"filesystem\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@modelcontextprotocol/server-filesystem\", \"/path/to/allowed/files\"],\n      \"env\": {\n        \"NODE_ENV\": \"production\"\n      }\n    },\n    \"brave-search\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@modelcontextprotocol/server-brave-search\"]\n    }\n  }\n}\n```\n\n##### HTTP SSE Transport Configuration\n\nFor MCP servers accessible via HTTP with Server-Sent Events:\n\n```json\n{\n  \"name\": \"WebAnalyst\",\n  \"description\": \"Analyzes web content using MCP tools\",\n  \"signature\": \"url:string -> analysis:string\",\n  \"provider\": \"anthropic\",\n  \"providerKeyName\": \"ANTHROPIC_API_KEY\",\n  \"ai\": {\n    \"model\": \"claude-3-haiku\",\n    \"temperature\": 0\n  },\n  \"mcpServers\": {\n    \"api-server\": {\n      \"sseUrl\": \"https://api.example.com/mcp/sse\"\n    }\n  }\n}\n```\n\n##### Streamable HTTP Transport Configuration\n\nFor MCP servers that support streamable HTTP communication:\n\n```json\n{\n  \"name\": \"StreamAnalyst\",\n  \"description\": \"Processes streaming data using MCP tools\",\n  \"signature\": \"stream:string -> results:string\",\n  \"provider\": \"google-gemini\",\n  \"providerKeyName\": \"GEMINI_API_KEY\",\n  \"ai\": {\n    \"model\": \"gemini-1.5-pro\",\n    \"temperature\": 0\n  },\n  \"mcpServers\": {\n    \"stream-processor\": {\n      \"mcpEndpoint\": \"http://localhost:3002/stream\",\n      \"options\": {\n        \"authorization\": \"Bearer ey.JhbGciOiJkaXIiLCJlbmMiOiJBMjU2R0NNIn0..-1234567890.1234567890\",\n        \"headers\": { \n          \"X-Custom-Header\": \"custom-value\"\n        }\n      }\n    }\n  }\n}\n```\n\n##### Mixed Transport Configuration\n\nYou can use multiple transport types within the same agent:\n\n```json\n{\n  \"name\": \"MultiModalAgent\",\n  \"description\": \"Uses multiple MCP servers with different transports\",\n  \"signature\": \"task:string -> result:string\",\n  \"provider\": \"openai\",\n  \"providerKeyName\": \"OPENAI_API_KEY\",\n  \"ai\": {\n    \"model\": \"gpt-4\",\n    \"temperature\": 0\n  },\n  \"mcpServers\": {\n    \"local-files\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@modelcontextprotocol/server-filesystem\", \"/workspace\"]\n    },\n    \"web-search\": {\n      \"sseUrl\": \"http://localhost:3001/sse\"\n    },\n    \"data-stream\": {\n      \"mcpEndpoint\": \"http://localhost:3002/stream\"\n    }\n  }\n}\n```\n\n#### MCP Server Examples\n\nHere are some popular MCP servers you can use:\n\n**Filesystem Server** (STDIO):\n```json\n\"filesystem\": {\n  \"command\": \"npx\",\n  \"args\": [\"-y\", \"@modelcontextprotocol/server-filesystem\", \"/allowed/path\"]\n}\n```\n\n**Brave Search Server** (STDIO):\n```json\n\"brave-search\": {\n  \"command\": \"npx\",\n  \"args\": [\"-y\", \"@modelcontextprotocol/server-brave-search\"],\n  \"env\": {\n    \"BRAVE_API_KEY\": \"your-brave-api-key\"\n  }\n}\n```\n\n**GitHub Server** (STDIO):\n```json\n\"github\": {\n  \"command\": \"npx\",\n  \"args\": [\"-y\", \"@modelcontextprotocol/server-github\"],\n  \"env\": {\n    \"GITHUB_PERSONAL_ACCESS_TOKEN\": \"your-github-token\"\n  }\n}\n```\n\n**PostgreSQL Server** (STDIO):\n```json\n\"postgres\": {\n  \"command\": \"npx\",\n  \"args\": [\"-y\", \"@modelcontextprotocol/server-postgres\"],\n  \"env\": {\n    \"POSTGRES_CONNECTION_STRING\": \"postgresql://user:pass@localhost/db\"\n  }\n}\n```\n\n#### Usage in Code\n\nMCP functions are automatically available to agents once the servers are configured:\n\n```javascript\nimport { AxCrew } from '@amitdeshmukh/ax-crew';\n\n// Create crew with MCP-enabled agents\nconst crew = new AxCrew(config);\nawait crew.addAgent('DataAnalyst'); // Agent with MCP servers configured\n\nconst analyst = crew.agents.get('DataAnalyst');\n\n// The agent can now use MCP functions automatically\nconst response = await analyst.forward({\n  data: \"Please analyze the sales data in /workspace/sales.csv\"\n});\n// The agent will automatically use the filesystem MCP server to read the file\n// and any other configured MCP tools for analysis\n```\n\n#### Best Practices\n\n1. **Environment Variables**: Store sensitive information like API keys in environment variables rather than in the configuration file.\n\n2. **Path Security**: For filesystem servers, always specify allowed paths to prevent unauthorized file access.\n\n3. **Server Health**: Implement health checks for HTTP-based MCP servers to ensure reliability.\n\n4. **Error Handling**: MCP server failures are handled gracefully - agents will continue to work with available tools.\n\n5. **Debugging**: Enable debug mode to see MCP server initialization and communication logs:\n```json\n{\n  \"debug\": true,\n  \"mcpServers\": { ... }\n}\n```\n\n#### Migration from Deprecated Transport\n\nIf you're upgrading from the deprecated `AxMCPHTTPTransport`, update your configuration:\n\n**Before (deprecated):**\n```json\n\"mcpServers\": {\n  \"my-server\": {\n    \"sseUrl\": \"http://localhost:3001/sse\"\n  }\n}\n```\n\n**After (current):**\nThe configuration remains the same - the transport type is automatically detected and `AxMCPHTTPSSETransport` is used for `sseUrl` configurations. No changes to your configuration files are needed.\n\nFor new streamable HTTP servers, use:\n```json\n\"mcpServers\": {\n  \"my-stream-server\": {\n    \"mcpEndpoint\": \"http://localhost:3002/stream\",\n    \"options\": {\n      \"timeout\": 30000\n    }\n  }\n}\n```\n\n### Tracking Usage Costs\n\nThe package provides metrics for monitoring API usage across individual agents and the entire crew. Estimated costs are accumulated precisely and rounded to 5 decimal places for reporting.\n\n```javascript\n// After running an agent's forward method\nawait Planner.forward({ task: userQuery });\n\n// Per-agent metrics\nconst agentMetrics = (Planner as any).getMetrics?.();\nconsole.log(agentMetrics);\n/* Example:\n{\n  provider: \"anthropic\",\n  model: \"claude-3-haiku-20240307\",\n  requests: { totalRequests, totalErrors, errorRate, totalStreamingRequests, durationMsSum, durationCount },\n  tokens: { promptTokens, completionTokens, totalTokens },\n  estimatedCostUSD: 0.00091, // rounded to 5 decimals\n  functions: { totalFunctionCalls, totalFunctionLatencyMs }\n}\n*/\n\n// Crew metrics\nconst crewMetrics = crew.getCrewMetrics();\nconsole.log(crewMetrics);\n/* Example:\n{\n  requests: { ... },\n  tokens: { promptTokens, completionTokens, totalTokens },\n  estimatedCostUSD: 0.00255, // rounded to 5 decimals\n  functions: { ... }\n}\n*/\n\n// Reset tracked metrics\ncrew.resetCosts();\n```\n\nNotes:\n- Legacy cost APIs (`getLastUsageCost`, `getAccumulatedCosts`, `getAggregatedCosts`) are superseded by metrics APIs.\n- Estimated cost values are numbers rounded to 5 decimal places.\n\n### Telemetry Support (OpenTelemetry)\n\nAxCrew provides optional OpenTelemetry integration for comprehensive observability. You can pass custom tracer and meter instances to monitor agent operations, track performance, and analyze behavior across your crew.\n\n#### Features\n\n- **Distributed Tracing**: Track agent execution flows, function calls, and dependencies\n- **Metrics Collection**: Monitor token usage, costs, latency, and error rates\n- **Multiple Exporters**: Support for console, Jaeger, Prometheus, and other OpenTelemetry backends\n\n#### Setup\n\nInstall OpenTelemetry dependencies:\n\n```bash\nnpm install @opentelemetry/api @opentelemetry/sdk-trace-node @opentelemetry/sdk-metrics\n```\n\nOptional: Install exporters for enhanced visualization:\n\n```bash\n# For Jaeger tracing UI\nnpm install @opentelemetry/exporter-jaeger\n\n# For Prometheus metrics\nnpm install @opentelemetry/exporter-prometheus\n```\n\n#### Basic Configuration\n\n```typescript\nimport { AxCrew } from '@amitdeshmukh/ax-crew';\nimport { trace, metrics } from '@opentelemetry/api';\nimport { NodeTracerProvider } from '@opentelemetry/sdk-trace-node';\nimport { MeterProvider } from '@opentelemetry/sdk-metrics';\nimport { ConsoleSpanExporter, SimpleSpanProcessor } from '@opentelemetry/sdk-trace-base';\n\n// Initialize OpenTelemetry\nconst tracerProvider = new NodeTracerProvider({\n  spanProcessors: [new SimpleSpanProcessor(new ConsoleSpanExporter())]\n});\ntracerProvider.register();\n\nconst meterProvider = new MeterProvider();\nmetrics.setGlobalMeterProvider(meterProvider);\n\n// Get tracer and meter instances\nconst tracer = trace.getTracer('my-app');\nconst meter = metrics.getMeter('my-app');\n\n// Pass to AxCrew\nconst crew = new AxCrew(\n  config,\n  AxCrewFunctions,\n  undefined,\n  {\n    telemetry: {\n      tracer,\n      meter\n    }\n  }\n);\n```\n\n#### What Gets Traced\n\nWhen telemetry is enabled, AxCrew automatically instruments:\n\n- **Agent Execution**: Each agent's `forward()` call creates a span with timing and metadata\n- **Function Calls**: Tool/function invocations are traced with parameters and results\n- **Provider Information**: Model name, provider, and configuration details\n- **Token Metrics**: Input/output tokens and estimated costs\n- **Errors**: Exceptions and failures are captured with full context\n\n#### Advanced Configuration with Jaeger\n\nFor enhanced visualization, you can export traces to Jaeger:\n\n```typescript\nimport { JaegerExporter } from '@opentelemetry/exporter-jaeger';\n\nconst tracerProvider = new NodeTracerProvider({\n  spanProcessors: [\n    new SimpleSpanProcessor(new ConsoleSpanExporter()),\n    new SimpleSpanProcessor(new JaegerExporter({\n      endpoint: 'http://localhost:14268/api/traces'\n    }))\n  ]\n});\ntracerProvider.register();\n\n// ... rest of setup\n```\n\nStart Jaeger with Docker:\n\n```bash\ndocker run -d --name jaeger \\\n  -p 16686:16686 \\\n  -p 14268:14268 \\\n  jaegertracing/all-in-one:latest\n```\n\nView traces at: http://localhost:16686\n\n#### Complete Example\n\nSee [examples/telemetry-demo.ts](examples/telemetry-demo.ts) for a full working example that demonstrates:\n\n- Setting up OpenTelemetry with console and Jaeger exporters\n- Configuring multiple agents with different providers\n- Running a multi-step workflow with telemetry tracking\n- Viewing traces and metrics in the console and Jaeger UI\n\nRun the example:\n\n```bash\n# With console output only\nnpm run dev examples/telemetry-demo.ts\n\n# With Jaeger (start Jaeger first)\ndocker run -d --name jaeger -p 16686:16686 -p 14268:14268 jaegertracing/all-in-one:latest\nnpm run dev examples/telemetry-demo.ts\n# Open http://localhost:16686 to view traces\n```\n\n#### Best Practices\n\n1. **Production Setup**: Use appropriate exporters for your infrastructure (Jaeger, Zipkin, Cloud providers)\n2. **Sampling**: Configure sampling strategies to control trace volume in production\n3. **Context Propagation**: OpenTelemetry automatically propagates trace context across agent calls\n4. **Custom Attributes**: Extend traces with custom attributes specific to your use case\n5. **Performance**: Telemetry adds minimal overhead when properly configured\n\n### ACE Support (Agentic Context Engineering)\n\nAxCrew integrates [Agentic Context Engineering (ACE)](https://www.youtube.com/watch?v=elgYgPo_vY4) from the Ax framework, enabling agents to learn and improve from human feedback. ACE maintains a \"playbook\" of learned rules that guide agent behavior, which can be persisted across sessions.\n\n#### Key Features\n\n- **Online Learning**: Agents learn from real-time feedback during conversations\n- **Playbook Persistence**: Save learned rules to JSON files or custom storage\n- **Teacher/Student Model**: Use a separate \"teacher\" LLM to distill feedback into actionable rules\n- **Feedback Routing**: Distribute feedback across agent dependency chains automatically\n\n#### Configuration\n\nAdd the `ace` field to any agent configuration:\n\n```typescript\n{\n  name: \"SupportAgent\",\n  description: \"Customer support agent\",\n  signature: \"ticket:string -> supportResponse:string, decision:string\",\n  provider: \"google-gemini\",\n  providerKeyName: \"GEMINI_API_KEY\",\n  ai: { model: \"gemini-flash-latest\", temperature: 0.7 },\n  ace: {\n    teacher: {\n      provider: \"google-gemini\",\n      providerKeyName: \"GEMINI_API_KEY\",\n      ai: { model: \"gemini-flash-latest\" }\n    },\n    options: {\n      maxEpochs: 1,\n      allowDynamicSections: true\n    },\n    persistence: {\n      playbookPath: \"playbooks/support-agent.json\",\n      autoPersist: true\n    },\n    metric: {\n      primaryOutputField: \"supportResponse\"\n    }\n  }\n}\n```\n\n#### ACE Configuration Options\n\n| Field | Type | Description |\n|-------|------|-------------|\n| `teacher` | object | Teacher model config (provider, model, apiURL) |\n| `persistence.playbookPath` | string | File path to save/load playbook |\n| `persistence.autoPersist` | boolean | Auto-save playbook after updates |\n| `persistence.onPersist` | function | Custom callback for saving playbook |\n| `persistence.onLoad` | function | Custom callback for loading playbook |\n| `options.maxEpochs` | number | Training epochs for offline compile |\n| `options.allowDynamicSections` | boolean | Allow playbook to create new sections |\n| `metric.primaryOutputField` | string | Output field to evaluate for quality |\n| `compileOnStart` | boolean | Run offline compile on agent init |\n\n#### Usage: Applying Feedback\n\n```typescript\nimport { AxCrew, AxCrewFunctions } from '@amitdeshmukh/ax-crew';\n\nconst crew = new AxCrew(config, AxCrewFunctions);\nawait crew.addAgentsToCrew(['SupportAgent']);\n\nconst agent = crew.agents.get('SupportAgent');\n\n// Run the agent\nconst result = await agent.forward({ ticket: \"Customer wants refund after 45 days\" });\nconst taskId = result._taskId;\n\n// Apply feedback to teach the agent\nawait crew.applyTaskFeedback({\n  taskId,\n  feedback: \"For loyal customers (5+ years), extend return window to 60 days\",\n  strategy: \"all\"  // Apply to all agents involved in this task\n});\n\n// View the learned playbook\nconst playbook = agent.getPlaybook?.();\nconsole.log(playbook);\n```\n\n#### Feedback Strategies\n\n| Strategy | Description |\n|----------|-------------|\n| `\"all\"` | Apply feedback to all agents involved in the task |\n| `\"primary\"` | Apply only to the primary (entry) agent |\n| `\"weighted\"` | Apply to all involved agents (reserved for weighted scoring flows) |\n\n#### Examples\n\nSee the ACE examples for complete demonstrations:\n\n- [`ace-customer-support.ts`](examples/ace-customer-support.ts) - Learn edge-case handling beyond standard policies\n- [`ace-flight-finder.ts`](examples/ace-flight-finder.ts) - Flight assistant with preference learning\n\n```bash\n# Run the customer support demo\nnpx tsx examples/ace-customer-support.ts\n```\n\n## Changelog\n\nSee [CHANGELOG.md](CHANGELOG.md) for a list of changes and version updates.\n","readmeFilename":"README.md"}