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The batteries-included agent harness.
\n\n\n\n\nUsing an LLM to call tools in a loop is the simplest form of an agent. This architecture, however, can yield agents that are \"shallow\" and fail to plan and act over longer, more complex tasks.\n\nApplications like \"Deep Research\", \"Manus\", and \"Claude Code\" have gotten around this limitation by implementing a combination of four things:\na **planning tool**, **sub agents**, access to a **file system**, and a **detailed prompt**.\n\n`deepagents` is a TypeScript package that implements these in a general purpose way so that you can easily create a Deep Agent for your application.\n\n> 💡 **Tip:** Looking for the Python version of this package? See [langchain-ai/deepagents](https://github.com/langchain-ai/deepagents)\n\n\n\n[Documentation](https://docs.langchain.com/oss/javascript/deepagents/overview) | [Examples](./examples) | [Report Bug](https://github.com/langchain-ai/deepagentsjs/issues) | [Request Feature](https://github.com/langchain-ai/deepagentsjs/issues)\n\n
\n\n## 📖 Overview\n\nUsing an LLM to call tools in a loop is the simplest form of an agent. However, this architecture can yield agents that are \"shallow\" and fail to plan and act over longer, more complex tasks.\n\nApplications like **Deep Research**, **Manus**, and **Claude Code** have overcome this limitation by implementing a combination of four key components:\n\n1. **Planning Tool** - Strategic task decomposition\n2. **Sub-Agents** - Specialized agents for subtasks\n3. **File System Access** - Persistent state and memory\n4. **Detailed Prompts** - Context-rich instructions\n\n**Deep Agents** is a TypeScript package that implements these patterns in a general-purpose way, enabling you to easily create sophisticated agents for your applications.\n\n## ✨ Features\n\n- 🎯 **Task Planning & Decomposition** - Break complex tasks into manageable steps\n- 🤖 **Sub-Agent Architecture** - Delegate specialized work to focused agents\n- 💾 **File System Integration** - Persistent memory and state management\n- 🌊 **Streaming Support** - Real-time updates, token streaming, and progress tracking\n- 🔄 **LangGraph Powered** - Built on the robust LangGraph framework\n- 📝 **TypeScript First** - Full type safety and IntelliSense support\n- 🔌 **Extensible** - Easy to customize and extend for your use case\n\n## Installation\n\n```bash\n# npm\nnpm install deepagents\n\n# yarn\nyarn add deepagents\n\n# pnpm\npnpm add deepagents\n```\n\n## Usage\n\n(To run the example below, you will need to `npm install @langchain/tavily`).\n\nMake sure to set `TAVILY_API_KEY` in your environment. You can generate one [here](https://www.tavily.com/).\n\n```typescript\nimport { tool } from \"langchain\";\nimport { TavilySearch } from \"@langchain/tavily\";\nimport { createDeepAgent } from \"deepagents\";\nimport { z } from \"zod\";\n\n// Web search tool\nconst internetSearch = tool(\n async ({\n query,\n maxResults = 5,\n topic = \"general\",\n includeRawContent = false,\n }: {\n query: string;\n maxResults?: number;\n topic?: \"general\" | \"news\" | \"finance\";\n includeRawContent?: boolean;\n }) => {\n const tavilySearch = new TavilySearch({\n maxResults,\n tavilyApiKey: process.env.TAVILY_API_KEY,\n includeRawContent,\n topic,\n });\n return await tavilySearch._call({ query });\n },\n {\n name: \"internet_search\",\n description: \"Run a web search\",\n schema: z.object({\n query: z.string().describe(\"The search query\"),\n maxResults: z\n .number()\n .optional()\n .default(5)\n .describe(\"Maximum number of results to return\"),\n topic: z\n .enum([\"general\", \"news\", \"finance\"])\n .optional()\n .default(\"general\")\n .describe(\"Search topic category\"),\n includeRawContent: z\n .boolean()\n .optional()\n .default(false)\n .describe(\"Whether to include raw content\"),\n }),\n },\n);\n\n// System prompt to steer the agent to be an expert researcher\nconst researchInstructions = `You are an expert researcher. Your job is to conduct thorough research, and then write a polished report.\n\nYou have access to an internet search tool as your primary means of gathering information.\n\n> [!TIP]\n> For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home).\n\n## \\`internet_search\\`\n\nUse this to run an internet search for a given query. You can specify the max number of results to return, the topic, and whether raw content should be included.\n`;\n\n// Create the deep agent\nconst agent = createDeepAgent({\n tools: [internetSearch],\n systemPrompt: researchInstructions,\n});\n\n// Invoke the agent\nconst result = await agent.invoke({\n messages: [{ role: \"user\", content: \"What is langgraph?\" }],\n});\n```\n\nSee [examples/research/research-agent.ts](examples/research/research-agent.ts) for a more complex example.\n\nThe agent created with `createDeepAgent` is just a LangGraph graph - so you can interact with it (streaming, human-in-the-loop, memory, studio)\nin the same way you would any LangGraph agent.\n\n## Core Capabilities\n\n**Planning & Task Decomposition**\n\nDeep Agents include a built-in `write_todos` tool that enables agents to break down complex tasks into discrete steps, track progress, and adapt plans as new information emerges.\n\n**Context Management**\n\nFile system tools (`ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`) allow agents to offload large context to memory, preventing context window overflow and enabling work with variable-length tool results.\n\n**Subagent Spawning**\n\nA built-in `task` tool enables agents to spawn specialized subagents for context isolation. This keeps the main agent's context clean while still going deep on specific subtasks.\n\n**Long-term Memory**\n\nExtend agents with persistent memory across threads using LangGraph's Store. Agents can save and retrieve information from previous conversations.\n\n## Customizing Deep Agents\n\nThere are several parameters you can pass to `createDeepAgent` to create your own custom deep agent.\n\n### `model`\n\nBy default, `deepagents` uses `\"claude-sonnet-4-5-20250929\"`. You can customize this by passing any [LangChain model object](https://js.langchain.com/docs/integrations/chat/).\n\n```typescript\nimport { ChatAnthropic } from \"@langchain/anthropic\";\nimport { ChatOpenAI } from \"@langchain/openai\";\nimport { createDeepAgent } from \"deepagents\";\n\n// Using Anthropic\nconst agent = createDeepAgent({\n model: new ChatAnthropic({\n model: \"claude-sonnet-4-20250514\",\n temperature: 0,\n }),\n});\n\n// Using OpenAI\nconst agent2 = createDeepAgent({\n model: new ChatOpenAI({\n model: \"gpt-5\",\n temperature: 0,\n }),\n});\n```\n\n### `systemPrompt`\n\nDeep Agents come with a built-in system prompt. This is relatively detailed prompt that is heavily based on and inspired by [attempts](https://github.com/kn1026/cc/blob/main/claudecode.md) to [replicate](https://github.com/asgeirtj/system_prompts_leaks/blob/main/Anthropic/claude-code.md)\nClaude Code's system prompt. It was made more general purpose than Claude Code's system prompt. The default prompt contains detailed instructions for how to use the built-in planning tool, file system tools, and sub agents.\n\nEach deep agent tailored to a use case should include a custom system prompt specific to that use case as well. The importance of prompting for creating a successful deep agent cannot be overstated.\n\n```typescript\nimport { createDeepAgent } from \"deepagents\";\n\nconst researchInstructions = `You are an expert researcher. Your job is to conduct thorough research, and then write a polished report.`;\n\nconst agent = createDeepAgent({\n systemPrompt: researchInstructions,\n});\n```\n\n### `tools`\n\nJust like with tool-calling agents, you can provide a deep agent with a set of tools that it has access to.\n\n```typescript\nimport { tool } from \"langchain\";\nimport { TavilySearch } from \"@langchain/tavily\";\nimport { createDeepAgent } from \"deepagents\";\nimport { z } from \"zod\";\n\nconst internetSearch = tool(\n async ({\n query,\n maxResults = 5,\n topic = \"general\",\n includeRawContent = false,\n }: {\n query: string;\n maxResults?: number;\n topic?: \"general\" | \"news\" | \"finance\";\n includeRawContent?: boolean;\n }) => {\n const tavilySearch = new TavilySearch({\n maxResults,\n tavilyApiKey: process.env.TAVILY_API_KEY,\n includeRawContent,\n topic,\n });\n return await tavilySearch._call({ query });\n },\n {\n name: \"internet_search\",\n description: \"Run a web search\",\n schema: z.object({\n query: z.string().describe(\"The search query\"),\n maxResults: z.number().optional().default(5),\n topic: z\n .enum([\"general\", \"news\", \"finance\"])\n .optional()\n .default(\"general\"),\n includeRawContent: z.boolean().optional().default(false),\n }),\n },\n);\n\nconst agent = createDeepAgent({\n tools: [internetSearch],\n});\n```\n\n### `middleware`\n\n`createDeepAgent` is implemented with middleware that can be customized. You can provide additional middleware to extend functionality, add tools, or implement custom hooks.\n\n```typescript\nimport { tool } from \"langchain\";\nimport { createDeepAgent } from \"deepagents\";\nimport type { AgentMiddleware } from \"langchain\";\nimport { z } from \"zod\";\n\nconst getWeather = tool(\n async ({ city }: { city: string }) => {\n return `The weather in ${city} is sunny.`;\n },\n {\n name: \"get_weather\",\n description: \"Get the weather in a city.\",\n schema: z.object({\n city: z.string().describe(\"The city to get weather for\"),\n }),\n },\n);\n\nconst getTemperature = tool(\n async ({ city }: { city: string }) => {\n return `The temperature in ${city} is 70 degrees Fahrenheit.`;\n },\n {\n name: \"get_temperature\",\n description: \"Get the temperature in a city.\",\n schema: z.object({\n city: z.string().describe(\"The city to get temperature for\"),\n }),\n },\n);\n\nclass WeatherMiddleware implements AgentMiddleware {\n tools = [getWeather, getTemperature];\n}\n\nconst agent = createDeepAgent({\n model: \"claude-sonnet-4-20250514\",\n middleware: [new WeatherMiddleware()],\n});\n```\n\n### `subagents`\n\nA main feature of Deep Agents is their ability to spawn subagents. You can specify custom subagents that your agent can hand off work to in the subagents parameter. Sub agents are useful for context quarantine (to help not pollute the overall context of the main agent) as well as custom instructions.\n\n`subagents` should be a list of objects that follow the `SubAgent` interface:\n\n```typescript\ninterface SubAgent {\n name: string;\n description: string;\n systemPrompt: string;\n tools?: StructuredTool[];\n model?: LanguageModelLike | string;\n middleware?: AgentMiddleware[];\n interruptOn?: Record;\n skills?: string[];\n}\n```\n\n**SubAgent fields:**\n\n- **name**: This is the name of the subagent, and how the main agent will call the subagent\n- **description**: This is the description of the subagent that is shown to the main agent\n- **systemPrompt**: This is the prompt used for the subagent\n- **tools**: This is the list of tools that the subagent has access to.\n- **model**: Optional model name or model instance.\n- **middleware**: Additional middleware to attach to the subagent. See [here](https://docs.langchain.com/oss/typescript/langchain/middleware) for an introduction into middleware and how it works with createAgent.\n- **interruptOn**: A custom interrupt config that specifies human-in-the-loop interactions for your tools.\n- **skills**: Skill source paths for the subagent (e.g., `[\"/skills/research/\"]`). See skills inheritance below.\n\n#### Skills Inheritance\n\nWhen you configure `skills` on the main agent via `createDeepAgent`, the behavior differs between subagent types:\n\n- **General-purpose subagent**: Automatically inherits skills from the main agent. This subagent has access to all the same skills as the main agent.\n- **Custom subagents**: Do NOT inherit skills from the main agent by default. If you want a custom subagent to have access to skills, you must explicitly define the `skills` property on that subagent.\n\n```typescript\nconst agent = createDeepAgent({\n model: \"claude-sonnet-4-20250514\",\n skills: [\"/skills/\"], // Main agent and general-purpose subagent get these skills\n subagents: [\n {\n name: \"researcher\",\n description: \"Research assistant\",\n systemPrompt: \"You are a researcher.\",\n // This subagent will NOT have access to /skills/ from the main agent\n },\n {\n name: \"coder\",\n description: \"Coding assistant\",\n systemPrompt: \"You are a coder.\",\n skills: [\"/skills/coding/\"], // This subagent has its own skills\n },\n ],\n});\n```\n\nThis design ensures context isolation - custom subagents only have access to the skills they explicitly need, preventing unintended skill leakage between specialized agents.\n\n#### Using SubAgent\n\n```typescript\nimport { tool } from \"langchain\";\nimport { TavilySearch } from \"@langchain/tavily\";\nimport { createDeepAgent, type SubAgent } from \"deepagents\";\nimport { z } from \"zod\";\n\nconst internetSearch = tool(\n async ({\n query,\n maxResults = 5,\n topic = \"general\",\n includeRawContent = false,\n }: {\n query: string;\n maxResults?: number;\n topic?: \"general\" | \"news\" | \"finance\";\n includeRawContent?: boolean;\n }) => {\n const tavilySearch = new TavilySearch({\n maxResults,\n tavilyApiKey: process.env.TAVILY_API_KEY,\n includeRawContent,\n topic,\n });\n return await tavilySearch._call({ query });\n },\n {\n name: \"internet_search\",\n description: \"Run a web search\",\n schema: z.object({\n query: z.string(),\n maxResults: z.number().optional().default(5),\n topic: z\n .enum([\"general\", \"news\", \"finance\"])\n .optional()\n .default(\"general\"),\n includeRawContent: z.boolean().optional().default(false),\n }),\n },\n);\n\nconst researchSubagent: SubAgent = {\n name: \"research-agent\",\n description: \"Used to research more in depth questions\",\n systemPrompt: \"You are a great researcher\",\n tools: [internetSearch],\n model: \"gpt-4o\", // Optional override, defaults to main agent model\n};\n\nconst subagents = [researchSubagent];\n\nconst agent = createDeepAgent({\n model: \"claude-sonnet-4-20250514\",\n subagents: subagents,\n});\n```\n\n### `interruptOn`\n\nA common reality for agents is that some tool operations may be sensitive and require human approval before execution. Deep Agents supports human-in-the-loop workflows through LangGraph's interrupt capabilities. You can configure which tools require approval using a checkpointer.\n\nThese tool configs are passed to our prebuilt [HITL middleware](https://docs.langchain.com/oss/typescript/langchain/middleware#human-in-the-loop) so that the agent pauses execution and waits for feedback from the user before executing configured tools.\n\n```typescript\nimport { tool } from \"langchain\";\nimport { createDeepAgent } from \"deepagents\";\nimport { z } from \"zod\";\n\nconst getWeather = tool(\n async ({ city }: { city: string }) => {\n return `The weather in ${city} is sunny.`;\n },\n {\n name: \"get_weather\",\n description: \"Get the weather in a city.\",\n schema: z.object({\n city: z.string(),\n }),\n },\n);\n\nconst agent = createDeepAgent({\n model: \"claude-sonnet-4-20250514\",\n tools: [getWeather],\n interruptOn: {\n get_weather: {\n allowedDecisions: [\"approve\", \"edit\", \"reject\"],\n },\n },\n});\n```\n\n### `backend`\n\nDeep Agents use backends to manage file system operations and memory storage. You can configure different backends depending on your needs:\n\n```typescript\nimport {\n createDeepAgent,\n StateBackend,\n StoreBackend,\n FilesystemBackend,\n LocalShellBackend,\n CompositeBackend,\n} from \"deepagents\";\nimport { MemorySaver } from \"@langchain/langgraph\";\nimport { InMemoryStore } from \"@langchain/langgraph-checkpoint\";\n\n// Default: StateBackend (in-memory, ephemeral)\nconst agent1 = createDeepAgent({\n // No backend specified - uses StateBackend by default\n});\n\n// StoreBackend: Persistent storage using LangGraph Store\nconst agent2 = createDeepAgent({\n backend: (config) => new StoreBackend(config),\n store: new InMemoryStore(), // Provide a store\n checkpointer: new MemorySaver(), // Optional: for conversation persistence\n});\n\n// FilesystemBackend: Store files on actual filesystem\nconst agent3 = createDeepAgent({\n backend: (config) => new FilesystemBackend({ rootDir: \"./agent-workspace\" }),\n});\n\n// LocalShellBackend: Filesystem access + local shell command execution\nconst agent4 = createDeepAgent({\n backend: new LocalShellBackend({\n rootDir: \"./agent-workspace\",\n inheritEnv: true,\n }),\n});\n\n// CompositeBackend: Combine multiple backends\nconst agent5 = createDeepAgent({\n backend: (config) =>\n new CompositeBackend({\n state: new StateBackend(config),\n store: config.store ? new StoreBackend(config) : undefined,\n }),\n store: new InMemoryStore(),\n checkpointer: new MemorySaver(),\n});\n```\n\nSee [examples/backends/](examples/backends/) for detailed examples of each backend type.\n\n### Sandbox Execution\n\nFor agents that need to run shell commands, you can create a sandbox backend by extending `BaseSandbox`. This enables the `execute` tool which allows agents to run arbitrary shell commands in an isolated environment.\n\n```typescript\nimport {\n createDeepAgent,\n BaseSandbox,\n type ExecuteResponse,\n type FileUploadResponse,\n type FileDownloadResponse,\n} from \"deepagents\";\nimport { spawn } from \"child_process\";\n\n// Create a concrete sandbox by extending BaseSandbox\nclass LocalShellSandbox extends BaseSandbox {\n readonly id = \"local-shell\";\n private readonly workingDirectory: string;\n\n constructor(workingDirectory: string) {\n super();\n this.workingDirectory = workingDirectory;\n }\n\n // Only execute() is required - BaseSandbox implements all file operations\n async execute(command: string): Promise {\n return new Promise((resolve) => {\n const child = spawn(\"/bin/bash\", [\"-c\", command], {\n cwd: this.workingDirectory,\n });\n\n const chunks: string[] = [];\n child.stdout.on(\"data\", (data) => chunks.push(data.toString()));\n child.stderr.on(\"data\", (data) => chunks.push(data.toString()));\n\n child.on(\"close\", (exitCode) => {\n resolve({\n output: chunks.join(\"\"),\n exitCode,\n truncated: false,\n });\n });\n });\n }\n\n async uploadFiles(\n files: Array<[string, Uint8Array]>,\n ): Promise {\n // Implement file upload logic\n return files.map(([path]) => ({ path, error: null }));\n }\n\n async downloadFiles(paths: string[]): Promise {\n // Implement file download logic\n return paths.map((path) => ({\n path,\n content: null,\n error: \"file_not_found\",\n }));\n }\n}\n\n// Use the sandbox with your agent\nconst sandbox = new LocalShellSandbox(\"./workspace\");\n\nconst agent = createDeepAgent({\n backend: sandbox,\n systemPrompt: \"You can run shell commands using the execute tool.\",\n});\n```\n\nWhen using a sandbox backend, the agent gains access to an `execute` tool that can run shell commands. The tool automatically returns the command output, exit code, and whether the output was truncated.\n\nSee [examples/sandbox/local-sandbox.ts](examples/sandbox/local-sandbox.ts) for a complete implementation.\n\n## Deep Agents Middleware\n\nDeep Agents are built with a modular middleware architecture. As a reminder, Deep Agents have access to:\n\n- A planning tool\n- A filesystem for storing context and long-term memories\n- The ability to spawn subagents\n\nEach of these features is implemented as separate middleware. When you create a deep agent with `createDeepAgent`, we automatically attach **todoListMiddleware**, **FilesystemMiddleware** and **SubAgentMiddleware** to your agent.\n\nMiddleware is a composable concept, and you can choose to add as many or as few middleware to an agent depending on your use case. That means that you can also use any of the aforementioned middleware independently!\n\n### TodoListMiddleware\n\nPlanning is integral to solving complex problems. If you've used claude code recently, you'll notice how it writes out a To-Do list before tackling complex, multi-part tasks. You'll also notice how it can adapt and update this To-Do list on the fly as more information comes in.\n\n**todoListMiddleware** provides your agent with a tool specifically for updating this To-Do list. Before, and while it executes a multi-part task, the agent is prompted to use the write_todos tool to keep track of what its doing, and what still needs to be done.\n\n```typescript\nimport { createAgent, todoListMiddleware } from \"langchain\";\n\n// todoListMiddleware is included by default in createDeepAgent\n// You can customize it if building a custom agent\nconst agent = createAgent({\n model: \"claude-sonnet-4-20250514\",\n middleware: [\n todoListMiddleware({\n // Optional: Custom addition to the system prompt\n systemPrompt: \"Use the write_todos tool to...\",\n }),\n ],\n});\n```\n\n### FilesystemMiddleware\n\nContext engineering is one of the main challenges in building effective agents. This can be particularly hard when using tools that can return variable length results (ex. web_search, rag), as long ToolResults can quickly fill up your context window.\n\n**FilesystemMiddleware** provides tools to your agent to interact with both short-term and long-term memory:\n\n- **ls**: List the files in your filesystem\n- **read_file**: Read an entire file, or a certain number of lines from a file\n- **write_file**: Write a new file to your filesystem\n- **edit_file**: Edit an existing file in your filesystem\n- **glob**: Find files matching a pattern\n- **grep**: Search for text within files\n- **execute**: Run shell commands (only available when using a `SandboxBackendProtocol`)\n\n```typescript\nimport { createAgent } from \"langchain\";\nimport { createFilesystemMiddleware } from \"deepagents\";\n\n// FilesystemMiddleware is included by default in createDeepAgent\n// You can customize it if building a custom agent\nconst agent = createAgent({\n model: \"claude-sonnet-4-20250514\",\n middleware: [\n createFilesystemMiddleware({\n backend: ..., // Optional: customize storage backend\n systemPrompt: \"Write to the filesystem when...\", // Optional custom system prompt override\n customToolDescriptions: {\n ls: \"Use the ls tool when...\",\n read_file: \"Use the read_file tool to...\",\n }, // Optional: Custom descriptions for filesystem tools\n }),\n ],\n});\n```\n\n### SubAgentMiddleware\n\nHanding off tasks to subagents is a great way to isolate context, keeping the context window of the main (supervisor) agent clean while still going deep on a task. The subagents middleware allows you supply subagents through a task tool.\n\nA subagent is defined with a name, description, system prompt, and tools. You can also provide a subagent with a custom model, or with additional middleware. This can be particularly useful when you want to give the subagent an additional state key to share with the main agent.\n\n```typescript\nimport { tool } from \"langchain\";\nimport { createAgent } from \"langchain\";\nimport { createSubAgentMiddleware, type SubAgent } from \"deepagents\";\nimport { z } from \"zod\";\n\nconst getWeather = tool(\n async ({ city }: { city: string }) => {\n return `The weather in ${city} is sunny.`;\n },\n {\n name: \"get_weather\",\n description: \"Get the weather in a city.\",\n schema: z.object({\n city: z.string(),\n }),\n },\n);\n\nconst weatherSubagent: SubAgent = {\n name: \"weather\",\n description: \"This subagent can get weather in cities.\",\n systemPrompt: \"Use the get_weather tool to get the weather in a city.\",\n tools: [getWeather],\n model: \"gpt-4o\",\n middleware: [],\n};\n\nconst agent = createAgent({\n model: \"claude-sonnet-4-20250514\",\n middleware: [\n createSubAgentMiddleware({\n defaultModel: \"claude-sonnet-4-20250514\",\n defaultTools: [],\n subagents: [weatherSubagent],\n }),\n ],\n});\n```\n\n## ACP (Agent Client Protocol) Support\n\nDeep Agents can be exposed as an [Agent Client Protocol](https://agentclientprotocol.com) server, enabling integration with IDEs like [Zed](https://zed.dev), JetBrains, and other ACP-compatible clients through a standardized JSON-RPC 2.0 protocol over stdio.\n\nThe `deepagents-acp` package wraps your Deep Agent with ACP support:\n\n```bash\nnpm install deepagents-acp\n```\n\nThe quickest way to get started is via the CLI:\n\n```bash\nnpx deepagents-acp --name my-agent --workspace /path/to/project\n```\n\nOr programmatically:\n\n```typescript\nimport { startServer } from \"deepagents-acp\";\n\nawait startServer({\n agents: {\n name: \"coding-assistant\",\n description: \"AI coding assistant with filesystem access\",\n skills: [\"./skills/\"],\n },\n workspaceRoot: process.cwd(),\n});\n```\n\nTo use with Zed, add the following to your Zed settings:\n\n```json\n{\n \"agent\": {\n \"profiles\": {\n \"deepagents\": {\n \"name\": \"DeepAgents\",\n \"command\": \"npx\",\n \"args\": [\"deepagents-acp\"]\n }\n }\n }\n}\n```\n\nSee the [deepagents-acp README](libs/acp/README.md) and the [ACP server example](examples/acp-server/) for full documentation and advanced configuration.\n","readmeFilename":"README.md"} ![\"Deep](\"https://raw.githubusercontent.com/langchain-ai/deepagentsjs/refs/heads/main/.github/images/logo-dark.svg\"){kind=logo}
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