name	agent-squad-typescript
description	Use when building or modifying a Node.js / TypeScript app that uses the agent-squad npm package — multi-agent orchestration: orchestrator, agents (all built-in types + GroundedAgent), classifier routing (Bedrock / Anthropic / OpenAI), storage (in-memory / DynamoDB / SQL), retrievers (Amazon KB / Dakera), and tools (AgentTools + MCPToolProvider).

agent-squad TypeScript — assistant guide

Node.js / TypeScript multi-agent orchestration framework (npm package agent-squad). All public symbols are exported from a single barrel typescript/src/index.ts. This file is guidance and a map — not an API reference. Read exact signatures from typescript/src/ and worked recipes from docs/src/content/docs/; this file tells you what to use, when, and what to watch out for.

When to use what

One assistant → a single Agent subclass + AgentSquad with no routing. Or skip the orchestrator entirely and call agent.processRequest(...) directly.
Several specialists → multiple agents registered with orchestrator.addAgent(agent), a classifier routes each turn.
Answers must not drift from data (prices, balances, live lookups) → GroundedAgent: a gatherer LLM calls tools, an isolated presenter LLM speaks only from the curated results.
Fixed pipeline → ChainAgent: each agent's output is the next agent's input.
One lead LLM coordinating a team → SupervisorAgent: the lead calls sub-agents as tools.
External tools via MCP → MCPToolProvider (async factory pattern, optional peer dep).
RAG context → attach a Retriever to any agent that supports retriever? in its options.

How to install

npm install agent-squad

Optional peer dependencies — install only what you use:

Package	Used by
`@aws-sdk/client-bedrock-runtime`	`BedrockLLMAgent`, `BedrockClassifier` (already a hard dep in current releases)
`@anthropic-ai/sdk`	`AnthropicAgent`, `AnthropicClassifier` (already a hard dep)
`openai`	`OpenAIAgent`, `OpenAIClassifier` (already a hard dep)
`@modelcontextprotocol/sdk`	`MCPToolProvider` — lazy `await import()` at connect time
`@dakera-ai/dakera`	`DakeraRetriever` — lazy `require()` at construction time

@modelcontextprotocol/sdk and @dakera-ai/dakera are the only two true optional peer deps; everything else ships as a hard dependency at the moment.

How a turn works

routeRequest is the single entry point. It classifies the input, dispatches to the selected agent, saves the exchange, and returns an AgentResponse. The response is either a plain string or a Node.js Transform stream:

import { AgentSquad, BedrockLLMAgent, BedrockClassifier } from 'agent-squad';

const orchestrator = new AgentSquad({
  classifier: new BedrockClassifier(),   // default when omitted
  // storage: new DynamoDbChatStorage(...),
  // config: { LOG_AGENT_CHAT: true, MAX_MESSAGE_PAIRS_PER_AGENT: 50 },
});

orchestrator.addAgent(new BedrockLLMAgent({
  name: 'Tech Support',
  description: 'Handles technical questions about software and hardware',
  streaming: true,
}));

const response = await orchestrator.routeRequest(
  userInput,
  userId,
  sessionId,
  additionalParams   // optional Record<string, any>
);

if (response.streaming) {
  // response.output is an AccumulatorTransform (Node.js Transform)
  for await (const chunk of response.output) {
    process.stdout.write(chunk);
  }
} else {
  // response.output is a string
  console.log(response.output);
  // response.thinking? is set when the agent used extended thinking
}

// response.metadata: { agentId, agentName, userId, sessionId, userInput, additionalParams }

routeRequest never throws — it catches all errors and returns them as a non-streaming AgentResponse with the error string in output (configurable via GENERAL_ROUTING_ERROR_MSG_MESSAGE).

The pieces

Orchestrator: `AgentSquad`

new AgentSquad(options?: OrchestratorOptions)

Key OrchestratorOptions fields:

Field	Default	Notes
`classifier`	`new BedrockClassifier()`	Any `Classifier` subclass
`storage`	`new InMemoryChatStorage()`	Any `ChatStorage` subclass
`defaultAgent`	`undefined`	Used when classifier returns no match and `USE_DEFAULT_AGENT_IF_NONE_IDENTIFIED` is true
`config.USE_DEFAULT_AGENT_IF_NONE_IDENTIFIED`	`true`	Fall back to `defaultAgent` or return `NO_SELECTED_AGENT_MESSAGE`
`config.MAX_MESSAGE_PAIRS_PER_AGENT`	`100`	Per-agent history cap (pairs = user+assistant)
`config.MAX_RETRIES`	`3`	Classifier retries on bad XML response
`config.LOG_AGENT_CHAT`	`false`

Useful methods: addAgent(agent), setDefaultAgent(agent), getDefaultAgent(), getAllAgents(), analyzeAgentOverlap(), classifyRequest(...), agentProcessRequest(...).

The classifier is exposed as a public field (orchestrator.classifier) so its system prompt can be overridden after construction.

Agents

All agents extend Agent and require at minimum { name, description } in their options.

agent.id is derived automatically from name: non-alphanumeric stripped, spaces → hyphens, lowercased. "Tech Support" → "tech-support". This is the key used for storage and classifier matching — it must be stable across restarts.

Class	Options type	Notes
`BedrockLLMAgent`	`BedrockLLMAgentOptions`	Bedrock Converse API; supports `streaming`, `modelId`, `inferenceConfig`, `guardrailConfig`, `reasoningConfig`, `retriever`, `toolConfig`, `customSystemPrompt`, `client`, `callbacks`
`AnthropicAgent`	`AnthropicAgentOptions`	Direct Anthropic SDK; similar options shape
`OpenAIAgent`	`OpenAIAgentOptions`	OpenAI Chat Completions
`AmazonBedrockAgent`	`AmazonBedrockAgentOptions`	Amazon Bedrock Agents (pre-built agents, not Converse)
`BedrockInlineAgent`	`BedrockInlineAgentOptions`	Bedrock inline agents
`BedrockFlowsAgent`	`BedrockFlowsAgentOptions`	Bedrock Flows
`LambdaAgent`	`LambdaAgentOptions`	Invokes a Lambda function as an agent
`LexBotAgent`	`LexBotAgentOptions`	Amazon Lex V2 bot
`ChainAgent`	`ChainAgentOptions`	Fixed pipeline; `agents: Agent[]`, `defaultOutput?`
`SupervisorAgent`	`SupervisorAgentOptions`	Lead + team; `leadAgent` must be `BedrockLLMAgent` or `AnthropicAgent`; lead must have no `toolConfig` (SupervisorAgent manages tools)
`GroundedAgent`	`GroundedAgentOptions`	2-LLM anti-hallucination; `gatherer`, `presenter`, `tools`, `curator?`, `presenterPrompt?`

AgentOptions base fields: name (required), description (required), saveChat? (default true), logger?, LOG_AGENT_DEBUG_TRACE?.

BedrockLLMAgent toolConfig shape:

toolConfig: {
  tool: AgentTools | Tool[],   // AgentTools instance or raw Bedrock Tool array
  useToolHandler: (response: any, conversation: ConversationMessage[]) => any,
  toolMaxRecursions?: number,
}

When using MCPToolProvider, pass it as toolConfig.tool and omit useToolHandler — the provider overrides toolHandler internally.

GroundedAgent

Two-LLM anti-hallucination pattern. The gatherer calls tools; the presenter receives only the curated facts (never raw tool output, never chat history from the gatherer):

import {
  GroundedAgent, DataBlockCurator, PerToolCurator, PresenterPrompt,
  BedrockLLMAgent, AgentTools, AgentTool,
} from 'agent-squad';

const tools = new AgentTools([
  new AgentTool({ name: 'get_price', description: '...', func: async ({ sku }) => fetchPrice(sku) }),
]);

const gatherer = new BedrockLLMAgent({ name: 'Gatherer', description: '...', toolConfig: { tool: tools, useToolHandler: ... } });
const presenter = new BedrockLLMAgent({ name: 'Presenter', description: '...' });

const agent = new GroundedAgent({
  name: 'Price Agent',
  description: 'Answers pricing questions grounded in live data',
  gatherer,
  presenter,
  tools,
  curator: new DataBlockCurator(),          // default; or PerToolCurator for per-tool formatting
  presenterPrompt: PresenterPrompt.default(), // generic grounding prompt; or per-tool map
});

A no-tool turn (chit-chat) is answered by the gatherer directly, skipping the presenter.

Classifiers

Class	Options type	Notes
`BedrockClassifier`	`BedrockClassifierOptions`	Default when no classifier is passed to `AgentSquad`
`AnthropicClassifier`	`AnthropicClassifierOptions`
`OpenAIClassifier`	`OpenAIClassifierOptions`

All classifiers support setSystemPrompt(template?, variables?) to override the routing prompt. Template variables use {{VAR_NAME}} syntax; AGENT_DESCRIPTIONS and HISTORY are always injected automatically.

Storage

Class	Notes
`InMemoryChatStorage`	Default; non-persistent; fine for dev and tests
`DynamoDbChatStorage`	Requires `@aws-sdk/client-dynamodb` and `@aws-sdk/lib-dynamodb` (hard deps)
`SqlChatStorage`	Requires `@libsql/client` (hard dep); works with Turso or local libsql
`SummarizingChatStorage`	Wraps any storage; compresses history via a user-supplied `ChatSummarizer` callable when `fetchChat` returns more than `triggerAt * 2` messages; cache-based save-back

Storage is keyed by (userId, sessionId, agentId). fetchAllChats(userId, sessionId) is used by the classifier to get cross-agent history for context.

Retrievers

Class	Options type	Notes
`AmazonKnowledgeBasesRetriever`	`AmazonKnowledgeBasesRetrieverOptions`	Amazon Bedrock Knowledge Bases
`DakeraRetriever`	`DakeraRetrieverOptions`	Dakera memory server; optional peer dep `@dakera-ai/dakera`

DakeraRetrieverOptions: namespace (required), apiKey? (falls back to DAKERA_API_KEY env), url? (falls back to DAKERA_URL then http://localhost:3000), topK? (default 10), filter?.

Attach to a BedrockLLMAgent via retriever: option. The agent calls retriever.retrieveAndCombineResults(inputText) to augment its system prompt.

DakeraRetriever.retrieveAndGenerate() always throws — Dakera is retrieval-only.

Tools: `AgentTools` and `AgentTool`

import { AgentTools, AgentTool } from 'agent-squad';

const myTools = new AgentTools([
  new AgentTool({
    name: 'search_web',
    description: 'Search the web for current information',
    properties: {
      query: { type: 'string', description: 'The search query' },
    },
    required: ['query'],
    func: async ({ query }) => webSearch(query),
  }),
]);

AgentTool constructor will auto-extract parameter names from func if properties is omitted — but this is fragile with minification. Always pass explicit properties and required.

MCPToolProvider

MCPToolProvider extends AgentTools. Always use the async factory — never new MCPToolProvider(...) directly — so that tool definitions are fetched before the agent makes its first API call:

import { MCPToolProvider } from 'agent-squad';

const provider = await MCPToolProvider.create([
  { type: 'stdio', command: 'uvx', args: ['my-mcp-server'] },
  { type: 'sse', url: 'http://localhost:3000/sse', headers: { Authorization: 'Bearer tok' } },
]);

const agent = new BedrockLLMAgent({
  name: 'MCP Agent',
  description: 'Agent with MCP tools',
  toolConfig: { tool: provider },
});

// Clean up when done (closes stdio processes and SSE connections)
await provider.disconnect();

MCPServerConfig.type is "stdio" or "sse". For stdio: command is required, args? and env? are optional. For sse: url is required, headers? is optional.

MCPToolProvider overrides toBedrockFormat(), toAnthropicFormat(), and toOpenAIFormat() to pass MCP inputSchema through unchanged rather than re-serializing it.

Requires npm install @modelcontextprotocol/sdk. The SDK is imported lazily via await import() inside ensureConnected() — installing agent-squad without the SDK is safe as long as you don't instantiate MCPToolProvider.

Custom implementations

Extend the abstract base class and pass your type where the built-in goes.

Seam	Base class	Method to implement	Source
Agent	`Agent`	`processRequest(inputText, userId, sessionId, chatHistory, additionalParams?)` returns `Promise<ConversationMessage \| AsyncIterable<any>>`	`typescript/src/agents/agent.ts`
Classifier	`Classifier`	`processRequest(inputText, chatHistory)` returns `Promise<ClassifierResult>`	`typescript/src/classifiers/classifier.ts`
Storage	`ChatStorage`	`saveChatMessage`, `fetchChat`, `fetchAllChats`	`typescript/src/storage/chatStorage.ts`
Retriever	`Retriever`	`retrieve`, `retrieveAndCombineResults`, `retrieveAndGenerate`	`typescript/src/retrievers/retriever.ts`

ClassifierResult shape: { selectedAgent: Agent | null, confidence: number }.

Classifier base class provides setAgents, setHistory, setSystemPrompt, and getAgentById(agentId) — use getAgentById in your processRequest to look up the selected agent from the classifier's registered map.

Gotchas

agentId is derived from name at construction time: non-alphanumeric stripped, spaces replaced with -, lowercased. Changing an agent's name changes its id, which breaks chat history lookups in storage. Keep names stable across deployments.
Streaming response is a Node.js Transform stream, not an async generator. Check response.streaming before iterating. The transform accumulates the full response internally; for await (const chunk of response.output) works because Transform implements AsyncIterable. Do not call response.output.read() manually.
routeRequest never throws. Errors are returned as non-streaming AgentResponse with the error string in output. If you need to distinguish errors from real responses, check response.metadata.errorType === 'classification_failed' or inspect metadata.agentId.
MCPToolProvider.create(...) must be awaited before the agent is used. The constructor alone does not connect; calling processRequest before create resolves means tool definitions are empty and the agent will behave as if it has no tools.
BedrockClassifier is the default. If boto3/AWS credentials are not configured and you don't pass an explicit classifier, AgentSquad will construct a BedrockClassifier that will fail at runtime. Pass classifier: new AnthropicClassifier(...) or new OpenAIClassifier(...) if you're not on AWS.
Optional peer deps use lazy import/require. MCPToolProvider uses await import(...) inside ensureConnected(); DakeraRetriever uses require(...) inside the constructor. Neither adds a top-level import, so a missing peer dep is only discovered at instantiation time — not at module load. Throw the missing-dep error early, before user input arrives.
SupervisorAgent restrictions: leadAgent must be BedrockLLMAgent or AnthropicAgent; the lead agent must have no toolConfig set (SupervisorAgent wires its own tool loop). Pass additional native tools via extraTools.
saveChat defaults to true. Every agent persists both sides of each exchange after the turn completes. Set saveChat: false on agents that should not write to storage (e.g. a presenter inside a GroundedAgent that is managed externally).
additionalParams flows through routeRequest → dispatchToAgent → agent.processRequest. Use it to pass per-request context (tenant ID, request ID, feature flags) without touching agent options. The values end up in response.metadata.additionalParams.
AgentTools auto-extracts parameter names from func via .toString(). This breaks with minification and TypeScript arrow functions with destructured arguments. Always supply explicit properties and required arrays to AgentTool.
ThinkingResponse: when a BedrockLLMAgent is configured with reasoningConfig, the non-streaming path may return response.thinking (a string) alongside response.output. The streaming path does not surface thinking tokens separately.

Go deeper

Prose & recipes — docs/src/content/docs/ (run the site from docs/ with npm run dev): orchestrator/overview, agents/built-in/bedrock-llm-agent, agents/built-in/grounded-agent, classifiers/overview, storage/overview, retrievers/overview, tools/mcp.
Exact signatures — typescript/src/ (orchestrator.ts, agents/, classifiers/, storage/, retrievers/, tools/mcpToolProvider.ts, utils/tool.ts, types/index.ts).
Tests — typescript/tests/ for usage patterns and mock strategies (virtual mocks for optional peer deps via jest.mock(..., { virtual: true })).
Barrel — typescript/src/index.ts is the definitive list of every public symbol.

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agent-squad TypeScript — assistant guide

When to use what

How to install

How a turn works

The pieces

Orchestrator: `AgentSquad`

Agents

GroundedAgent

Classifiers

Storage

Retrievers

Tools: `AgentTools` and `AgentTool`

MCPToolProvider

Custom implementations

Gotchas

Go deeper

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agent-squad TypeScript — assistant guide

When to use what

How to install

How a turn works

The pieces

Orchestrator: AgentSquad

Agents

GroundedAgent

Classifiers

Storage

Retrievers

Tools: AgentTools and AgentTool

MCPToolProvider

Custom implementations

Gotchas

Go deeper

Orchestrator: `AgentSquad`

Tools: `AgentTools` and `AgentTool`