honidev

Honi

Edge-first AI agents for Cloudflare Workers.

The Mastra for developers who deploy on Cloudflare.

Honi is a lightweight framework for building agentic AI applications on Cloudflare Workers. It combines Hono for routing, Durable Objects for persistent memory, and the Vercel AI SDK for multi-provider LLM support — all in a single createAgent() call.

Quick Start

Install

bun add honidev

Create an Agent

// src/index.ts
import { createAgent, tool, z } from 'honidev';

const searchCRM = tool({
  name: 'search_crm',
  description: 'Search HubSpot for deal info',
  input: z.object({ query: z.string() }),
  handler: async ({ query }) => {
    // Your CRM logic here
    return { results: [] };
  },
});

const salesCoach = createAgent({
  name: 'sales-coach',
  model: 'claude-sonnet-4-5',
  memory: { enabled: true },
  tools: [searchCRM],
  system: 'You are a real-time sales coach.',
  binding: 'SALES_COACH_DO',
});

export default { fetch: salesCoach.fetch };
export const SalesCoachDO = salesCoach.DurableObject;

Configure Wrangler

# wrangler.toml
name = "sales-coach-agent"
main = "src/index.ts"
compatibility_date = "2024-12-01"

[[durable_objects.bindings]]
name = "SALES_COACH_DO"
class_name = "SalesCoachDO"

[[migrations]]
tag = "v1"
new_classes = ["SalesCoachDO"]

Deploy

wrangler deploy

Chat with Your Agent

curl -X POST https://your-worker.workers.dev/chat \
  -H "Content-Type: application/json" \
  -H "X-Thread-Id: thread-123" \
  -d '{"message": "What deals are closing this week?"}'

Memory (Phase 2)

Honi supports four tiers of memory, all opt-in:

Tier	Backing	Survives DO eviction?	Queryable across threads?
Working	Durable Object storage	No	No
Episodic	D1	Yes	Yes
Semantic	Vectorize + Workers AI	Yes	Yes (similarity search)
Graph	edgraph (DO)	Yes	Yes (entity/relationship traversal)

Setup

Add bindings to your wrangler.toml:

[[d1_databases]]
binding = "DB"
database_name = "honi-memory"
database_id = "YOUR_DB_ID"

[[vectorize]]
binding = "VECTORIZE"
index_name = "honi-semantic"
dimensions = 768
metric = "cosine"

[ai]
binding = "AI"

# Graph memory — deploy edgraph and add a service binding (or use urlEnvVar for HTTP)
[[services]]
binding = "EDGRAPH"
service = "edgraph"

Run the D1 migration:

wrangler d1 migrations apply honi-memory

Configure

const agent = createAgent({
  name: 'my-agent',
  model: 'claude-sonnet-4-5',
  memory: {
    enabled: true,
    episodic: { enabled: true },
    semantic: { enabled: true, topK: 3 },
    graph: {
      enabled: true,
      graphId: 'my-knowledge-base',
      binding: 'EDGRAPH',       // CF service binding (preferred)
      // urlEnvVar: 'EDGRAPH_URL', // or HTTP URL via env var
      apiKeyEnvVar: 'EDGRAPH_API_KEY',
      contextDepth: 1,          // hop depth for context expansion
    },
  },
  system: 'You are a helpful assistant.',
});

All tiers are fully opt-in. Missing bindings log a warning and fall back gracefully.

How it works

• On each request: past messages load from D1 (episodic), the user message is embedded and searched against Vectorize (semantic), and any entity IDs found in semantic results are expanded via graph traversal. All context is prepended to the system prompt.
• After each response: the conversation turn is saved to D1 and both user + assistant messages are embedded and upserted to Vectorize for future retrieval.

Graph memory — writing entities from tools

Graph memory is most powerful when tools write entities as they discover them:

import { tool, GraphMemory } from 'honidev';

const myTool = tool({
  name: 'lookup_customer',
  description: 'Look up a customer by ID',
  input: z.object({ customerId: z.string() }),
  handler: async (input, ctx) => {
    const customer = await db.getCustomer(input.customerId);

    // Write to graph — ctx.graph is the live GraphMemory instance
    if (ctx?.graph && customer) {
      await ctx.graph.upsertNode(customer.id, 'Customer', {
        name: customer.name,
        plan: customer.plan,
      });
      if (customer.accountManagerId) {
        await ctx.graph.upsertEdge(
          customer.id,
          customer.accountManagerId,
          'managed_by',
        );
      }
    }

    return customer;
  },
});

ctx.graph is the live GraphMemory instance bound to the current agent. Entities written here are immediately available for future context retrieval.

Using GraphMemory standalone

GraphMemory can also be used outside of an agent — as a shared knowledge base across multiple services:

import { GraphMemory } from 'honidev';

const graph = new GraphMemory({
  graphId: 'crm',
  url: 'https://edgraph.myapp.workers.dev',
  apiKey: process.env.EDGRAPH_API_KEY,
});

await graph.upsertNode('alice', 'Person', { role: 'CTO', company: 'ACME' });
await graph.upsertNode('acme', 'Company', { industry: 'SaaS' });
await graph.upsertEdge('alice', 'acme', 'works_at');

// Get context block for LLM injection
const context = await graph.toContext(['alice'], 2);
// "[Knowledge graph context:]
//  - (Person:alice) {role="CTO", company="ACME"}
//    → [works_at] → (Company:acme)
//  [End graph context]"

// Traversal
const path = await graph.shortestPath('alice', 'bob');
const neighbours = await graph.getNeighbours('alice', 'out', ['manages']);

Core Concepts

Agents

An agent is created with createAgent() and bundles:

• A Hono-powered HTTP API with /chat, /history routes
• A Durable Object class for persistent, per-thread state
• An LLM connection with automatic tool-calling loops and streaming

Tools

Define tools with Zod schemas. Honi auto-generates the JSON schema for the LLM and validates inputs at runtime:

const weatherTool = tool({
  name: 'get_weather',
  description: 'Get current weather for a city',
  input: z.object({
    city: z.string().describe('City name'),
    units: z.enum(['celsius', 'fahrenheit']).default('celsius'),
  }),
  handler: async ({ city, units }) => {
    return { temp: 22, city, units };
  },
});

Memory

When memory: { enabled: true }, conversation history is persisted in Durable Object storage. Each thread (identified by X-Thread-Id header) gets its own isolated memory.

• POST /chat — send a message, get a streaming response
• GET /history?threadId=xxx — retrieve conversation history
• DELETE /history?threadId=xxx — clear a thread's history

Supported Models

Honi uses the Vercel AI SDK under the hood. Model routing is automatic based on the model ID prefix:

Model prefix	Provider	Env var / binding	Example model
claude-*	Anthropic	ANTHROPIC_API_KEY	claude-sonnet-4-5
bedrock/*	AWS Bedrock (Anthropic models)	AWS_BEARER_TOKEN_BEDROCK + AWS_BEDROCK_REGION	bedrock/anthropic.claude-haiku-4-5
gpt-*, o1, o3-*	OpenAI	OPENAI_API_KEY	gpt-4o, o3-mini
gemini-*	Google	GOOGLE_AI_API_KEY	gemini-2.5-flash-preview
groq/*	Groq	GROQ_API_KEY	groq/llama-3.3-70b-versatile
deepseek-*	DeepSeek	DEEPSEEK_API_KEY	deepseek-chat, deepseek-reasoner
mistral-*, codestral-*, pixtral-*	Mistral	MISTRAL_API_KEY	mistral-large-latest
grok-*	xAI	XAI_API_KEY	grok-3, grok-3-mini
sonar*, perplexity/*	Perplexity	PERPLEXITY_API_KEY	sonar-pro, sonar-reasoning
together/*	Together AI	TOGETHER_API_KEY	together/meta-llama/Llama-3.3-70B-Instruct-Turbo
command-*	Cohere	COHERE_API_KEY	command-r-plus, command-a-03-2025
azure/*	Azure OpenAI	AZURE_OPENAI_API_KEY + AZURE_OPENAI_ENDPOINT	azure/gpt-4o
@cf/*	Workers AI	AI binding in wrangler.toml	@cf/meta/llama-3.1-8b-instruct

Workers AI (@cf/*) and AI Gateway support are built in — no extra packages, and no API keys needed (see below). Other non-core providers use optional peer dependencies — zero bundle cost unless installed. Set API keys as Cloudflare Worker secrets:

wrangler secret put ANTHROPIC_API_KEY
wrangler secret put GOOGLE_AI_API_KEY
# etc.

Non-Anthropic/OpenAI/Google providers require their AI SDK package:

npm install @ai-sdk/groq       # Groq
npm install @ai-sdk/deepseek   # DeepSeek
npm install @ai-sdk/mistral    # Mistral
npm install @ai-sdk/xai        # xAI
npm install @ai-sdk/perplexity # Perplexity
npm install @ai-sdk/togetherai # Together AI
npm install @ai-sdk/cohere     # Cohere
npm install @ai-sdk/azure      # Azure OpenAI

Workers AI — no API key needed

Use any Workers AI model by prefixing with @cf/. Inference runs on your Cloudflare account via the AI binding — no API key, no extra packages:

# wrangler.toml
[ai]
binding = "AI"

const agent = createAgent({
  name: 'my-agent',
  model: '@cf/meta/llama-3.3-70b-instruct-fp8-fast',  // Uses Workers AI
  system: 'You are a helpful assistant.',
});

AI Gateway — hosted models without BYOK

Route all LLM calls through Cloudflare AI Gateway for logging, rate limiting, and caching at the edge. When your gateway holds provider credentials — via BYOK stored keys or Unified Billing — your Worker needs no provider API keys at all:

const agent = createAgent({
  name: 'my-agent',
  model: 'claude-sonnet-4-5',
  aiGateway: {
    gatewayId: 'your-gateway-id',
  },
  system: 'You are a helpful assistant.',
});
// All LLM calls route through your AI Gateway. With the AI binding
// ([ai] binding = "AI" in wrangler.toml) authentication is automatic —
// no account ID, no tokens, no provider keys.

Gateway authentication resolves in this order:

• AI binding (recommended): with [ai] binding = "AI" in wrangler.toml, Honi calls the gateway through env.AI.gateway() — fully keyless from the Worker.
• Gateway token: without the binding, set accountId and store an AI Gateway token as a secret (wrangler secret put CF_AIG_TOKEN). Honi sends it as cf-aig-authorization:

aiGateway: {
  accountId: 'your-account-id',
  gatewayId: 'your-gateway-id',
  tokenEnvVar: 'CF_AIG_TOKEN', // default
},

Provider API keys set in the Worker still work and are passed through — the gateway's stored keys simply take precedence when configured. Gateway routing covers Anthropic, OpenAI, Google, Groq, DeepSeek, Mistral, xAI, Perplexity, and Azure OpenAI; Together AI and Cohere always connect directly. Workers AI models (@cf/*) log through the gateway natively via the binding.

The legacy observability.aiGateway config is still honored, but prefer top-level aiGateway.

AWS Bedrock — Claude on AWS infrastructure

Run Anthropic models on AWS-operated serving infrastructure with the bedrock/ prefix. Honi targets Bedrock's mantle endpoint, which speaks the Anthropic Messages API natively with plain bearer-token auth — no SigV4 signing and no AWS SDK in your Worker:

const agent = createAgent({
  name: 'my-agent',
  model: 'bedrock/anthropic.claude-haiku-4-5',  // Bedrock's vendor-prefixed model id
  system: 'You are a helpful assistant.',
});

wrangler secret put AWS_BEARER_TOKEN_BEDROCK   # a long-term Amazon Bedrock API key
wrangler secret put AWS_BEDROCK_REGION         # e.g. eu-west-1

Notes:

• The model id after bedrock/ is passed to Bedrock verbatim and uses Bedrock's naming (anthropic.claude-haiku-4-5), not Anthropic's.
• Pick the region deliberately — it decides where prompts are processed. If you have data-residency requirements, choose a region (and account-level inference profile) that guarantees them.
• Bedrock is an independent failure domain from api.anthropic.com running the same models, which makes a claude-* ⇄ bedrock/* pair a natural primary/failover setup.
• bedrock/* cannot be combined with aiGateway — the gateway's Bedrock support uses its own endpoint scheme. Honi throws rather than silently going direct.

Gateway request options — caching, retries, privacy

aiGateway.options passes per-request gateway options through to Cloudflare (they become cf-aig-* headers), on both the binding and token auth paths:

aiGateway: {
  gatewayId: 'your-gateway-id',
  options: {
    collectLog: false,                    // don't store prompt/response bodies in gateway logs
    metadata: { app: 'my-app' },          // filterable in gateway analytics (max 5 keys)
    cacheTtl: 3600,
    retries: { maxAttempts: 3, backoff: 'exponential' },
    requestTimeoutMs: 30_000,
  },
},

Privacy note: the gateway stores full request and response bodies by default, and there is no gateway-level setting to turn that off. If your prompts carry user data, set collectLog: false — token counts, model, cost and latency are still logged; the payloads are not.

Streaming

All responses stream via the AI SDK data protocol, compatible with the useChat() hook from ai/react:

import { useChat } from 'ai/react';

const { messages, input, handleSubmit } = useChat({
  api: 'https://your-worker.workers.dev/chat',
  headers: { 'X-Thread-Id': 'thread-123' },
});

Comparison

Feature	Honi	Mastra	LangChain
Runtime	Cloudflare Workers	Node.js	Node.js
Memory	Durable Objects	Postgres / Redis	Various
Streaming	SSE (AI SDK)	SSE	Callbacks
Deploy	wrangler deploy	Self-host	Self-host
Cold start	~0 ms (edge)	Seconds	Seconds
Bundle size	Minimal	Heavy	Heavy
Tool system	Zod-native	Zod-native	Mixed
Framework	Hono	Express / Hono	Custom

Examples

Ready-to-deploy example projects in the examples/ directory:

Example	Description
01-simple-agent	Minimal hello-world agent with Durable Object memory
02-rag-agent	Full tiered memory — episodic (D1) + semantic RAG (Vectorize)
03-research-workflow	Multi-step research pipeline using Cloudflare Workflows

Each example includes a wrangler.toml, package.json, and README with setup instructions.

Workflows (Phase 3)

Honi wraps Cloudflare Workflows with a simple workflow() + step() API for durable, multi-step agent pipelines.

import { workflow, step } from 'honidev';

const IngestWorkflow = workflow({
  steps: [
    step({ name: 'fetch-data', retries: { limit: 3, backoff: 'exponential' } }, async (input, step) => {
      const res = await fetch(input.url);
      return res.json();
    }),
    step({ name: 'process', timeout: '60s' }, async (data, step) => {
      return { processed: true, items: data.length };
    }),
  ],
  onComplete: async (result, env) => {
    console.log('Pipeline complete:', result);
  },
  onError: async (error, env) => {
    console.error('Pipeline failed:', error.message);
  },
});

export { IngestWorkflow };

Add to wrangler.toml:

[[workflows]]
name = "ingest-workflow"
binding = "INGEST_WORKFLOW"
class_name = "IngestWorkflow"

CLI (Phase 4)

Honi includes a CLI for scaffolding and managing projects.

# Install globally
npm install -g honidev

# Create a new project
honi new my-sales-coach

# Start local dev server
honi dev

# Deploy to Cloudflare Workers
honi deploy

honi new generates a ready-to-run project with src/index.ts, wrangler.toml, tsconfig.json, and package.json.

Observability (Phase 5)

Honi emits structured events for every agent request, tool call, memory operation, and workflow step.

Configuration

const agent = createAgent({
  name: 'my-agent',
  model: 'claude-sonnet-4-5',
  observability: {
    // Recommended for long-lived isolates when onEvent exports every event.
    captureEvents: false,
    logLevel: 'debug',
    onEvent: (event) => {
      // Send to your logging/analytics service
      console.log(event.type, event.durationMs);
    },
  },
});

Event Types

Event	Emitted When
agent.request	Incoming chat request
agent.phase	Model resolution, tool setup, prompt construction, and optional memory-context phases finish
agent.stream.first_chunk	First provider output arrives; durationMs is provider time-to-first-output
agent.step	One AI SDK model/tool-loop step finishes
agent.response	Response stream complete — metadata carries model, usage, finishReason, providerMetadata
agent.turn.complete	Response and all configured persistence have finished
tool.call	Tool execution starts
tool.result	Tool execution finishes
memory.load	Memory loaded from storage
memory.save	Memory persisted
workflow.start	Workflow begins
workflow.step	Workflow step executes
workflow.complete	Workflow finishes
workflow.error	Workflow errors

Phase, memory, and tool events are content-free: they carry timings, bounded counts, model/tool names, outcomes, and error classes, never prompts, model output, tool arguments, tool results, or raw error messages.

Token usage & cost telemetry

agent.response includes token counts in metadata, so consumers can attribute LLM spend per call without proxying anything:

observability: {
  onEvent: (event) => {
    if (event.type === 'agent.response') {
      const { model, usage, providerMetadata } = event.metadata ?? {};
      // usage: { promptTokens, completionTokens, totalTokens }
      // providerMetadata.anthropic: cacheCreationInputTokens / cacheReadInputTokens
      // (the prompt-cache buckets, billed at different rates to fresh input)
      recordSpend(model, usage, providerMetadata);
    }
  },
},

AI Gateway

Set top-level aiGateway to route LLM calls through Cloudflare AI Gateway for logging, rate limiting, and caching at the edge — see AI Gateway — hosted models without BYOK. The legacy observability.aiGateway shape is still honored.

API Reference

createAgent(config)

Option	Type	Default	Description
name	string	—	Agent name
model	string	—	Model ID (claude-sonnet-4-5, gpt-4o)
modelSettings	ModelSettings	—	AI SDK generation + provider controls
system	string	—	System prompt
memory	MemoryConfig	{}	Memory configuration (see Phase 2)
tools	ToolDefinition[]	[]	Agent tools
binding	string	"AGENT"	Durable Object binding name
maxSteps	number	10	Max tool-calling loop iterations
aiGateway	AiGatewayConfig	—	Route LLM calls through CF AI Gateway
cache	boolean | CacheConfig	false	Anthropic prompt caching (see below)

Returns { fetch, DurableObject }.

Model controls (AI SDK)

modelSettings passes the AI SDK's common generation controls to every step: maxTokens, temperature, topP, topK, presence/frequency penalties, stop sequences, seed, and retry count. Provider-specific capabilities go under the AI SDK's providerOptions namespace and are passed through unchanged:

createAgent({
  name: 'voice-agent',
  model: 'claude-haiku-4-5',
  modelSettings: {
    temperature: 0,
    maxTokens: 512,
    providerOptions: {
      anthropic: { thinking: { type: 'disabled' } },
      // Gemini alternative:
      // google: { thinkingConfig: { thinkingBudget: 0, includeThoughts: false } },
      // OpenAI alternative:
      // openai: { reasoningEffort: 'low' },
    },
  },
});

Honi deliberately does not invent a cross-provider thinking abstraction: providers expose different semantics, and the provider adapter remains the authority. Unsupported options are handled according to that adapter/model's normal AI SDK behavior.

Prompt caching (Anthropic)

A large system prompt re-prefilled on every turn is usually the dominant term in an agent's time-to-first-token. cache: true places two Anthropic cache breakpoints so that work happens once:

createAgent({
  name: 'support',
  model: 'claude-haiku-4-5',
  system: BIG_SYSTEM_PROMPT,
  tools,
  memory: { enabled: true },
  cache: true,
});

Breakpoint	Covers	Why
cache.system	tools + system prompt	Anthropic serialises tools ahead of system, so one marker covers both — and both are byte-stable across every turn of every thread.
cache.history	the conversation prefix, re-anchored each turn	Each turn reads the cache the previous turn wrote, so a growing thread stays roughly O(1) to prefill instead of O(n).

Both default to on when cache is set; opt out individually with cache: { history: false }.

Caching is off by default because turning it on changes how the prompt is assembled. The Anthropic provider reads cache control off a message, and a top-level system string carries no providerOptions — so the system prompt has to move into messages to be markable at all. With cache unset the prompt is assembled exactly as before.

Worth knowing:

• Anthropic ignores cache control below a minimum cacheable prefix — 1024 tokens for Opus/Sonnet, 2048 for Haiku — silently, with no error. A small-prompt agent will see no effect and no warning.
• The default TTL is 5 minutes, refreshed on every read. Turns seconds apart keep it warm; a long gap costs one cold prefill.
• A cache write is 1.25x base input, a read 0.1x — so with a large stable prefix this is a cost saving as well as a latency one.
• Anthropic allows 4 breakpoints; this uses 2.
• The anthropic namespace is ignored by other providers, but the assembly change applies to all of them. The AI SDK normalises both forms to the same internal prompt, so it should be equivalent — the opt-in default is there so nobody finds out the hard way.
• To confirm it is working, read the cache buckets off providerMetadata on the agent.response observability event: cacheReadInputTokens should climb and inputTokens collapse from the second turn on.

tool(config)

Option	Type	Description
name	string	Tool name (sent to LLM)
description	string	What the tool does
input	ZodType	Zod schema for tool input
handler	(input) => Promise<unknown>	Tool implementation

License

MIT

Bundle Size

Honi is designed to fit comfortably within Cloudflare Workers' limits:

Component	Size
Honi library (dist/)	~30 KB
Full demo app (with AI SDK)	691 KB uncompressed
Gzip compressed	123 KB
CF Workers limit	1 MB compressed

You're using ~12% of the limit with a full-featured agent including all AI SDK providers.

Optimization Tips

• Only import the providers you need (@ai-sdk/anthropic OR @ai-sdk/openai, not both)
• Use Workers AI (@cf/ models) to skip external provider SDKs entirely
• Tree-shaking works — unused features don't add to bundle size

MCP Server

Honi agents can expose their tools as MCP endpoints, allowing connection from Claude Desktop, Cursor, and other MCP-compatible clients.

Endpoints

Endpoint	Method	Description
/mcp	POST	JSON-RPC 2.0 MCP endpoint
/mcp/tools	GET	List available tools (convenience)

Authentication

For remote connections (Cursor, custom clients, any non-local use) you should lock down /mcp with a Bearer token. Set mcp.secretEnvVar in your agent config:

const agent = createAgent({
  name: 'my-agent',
  model: 'claude-sonnet-4-5',
  tools: [searchDocs],
  mcp: { secretEnvVar: 'MCP_SECRET' },
})

Then set the secret via Wrangler:

wrangler secret put MCP_SECRET

Clients send Authorization: Bearer <secret> on every request. If secretEnvVar is not set, /mcp is unauthenticated — fine for local Claude Desktop (stdio transport), but don't expose it publicly without this.

Example: Connect from Claude Desktop

Add to your Claude Desktop MCP config:

{
  "mcpServers": {
    "my-honi-agent": {
      "url": "https://my-agent.workers.dev/mcp"
    }
  }
}

For authenticated remote connections:

{
  "mcpServers": {
    "my-honi-agent": {
      "url": "https://my-agent.workers.dev/mcp",
      "headers": {
        "Authorization": "Bearer your-secret-here"
      }
    }
  }
}

Multi-Agent Orchestration

Honi supports agent-to-agent communication for building complex agentic workflows.

import { routeToAgent, callAgentTool, listAgentTools } from 'honidev';

// Send a message to another agent
const response = await routeToAgent(env, { binding: 'OTHER_AGENT' }, 'Hello!');

// Call a specific tool on another agent
const result = await callAgentTool(env, { binding: 'OTHER_AGENT' }, 'search', { query: 'test' });

// List available tools from another agent
const tools = await listAgentTools(env, { binding: 'OTHER_AGENT' });

wrangler.toml Setup

[[durable_objects.bindings]]
name = "MY_AGENT"
class_name = "MyAgentDO"

[[durable_objects.bindings]]
name = "OTHER_AGENT"
class_name = "OtherAgentDO"

Recursive Memory — RLM tier (Phase 6)

Recursive memory implements the Recursive Language Model pattern from Zhang, Kraska & Khattab (MIT CSAIL, 2025). Instead of one-shot RAG retrieval, the model iteratively queries a document store — deciding what to read at each step based on what it has already learned.

Tier	Backing	Survives DO eviction?	Use case
Recursive	Durable Object storage	No (session-scoped)	Structured KB, support docs, product manuals

How it differs from Semantic (RAG)

	Semantic (RAG)	Recursive (RLM)
Retrieval	Embedding similarity — a guess before reasoning	Model decides what to read during reasoning
Cross-references	Misses joins — top-k doesn't follow links	Iterative — reads lead to further reads
Structured data	Flattens tables/matrices into embeddings	Queries structure directly
Token cost	One large context per call	Many small calls — reads only what's needed
Best for	Unstructured text, past conversations	Product docs, error codes, KB articles, version matrices

Configure

No extra bindings needed — recursive memory uses the agent's existing Durable Object storage.

const agent = createAgent({
  name: 'support-agent',
  model: 'claude-sonnet-4-5',
  memory: {
    recursive: {
      enabled: true,
      maxDepth: 10,      // max REPL iterations (default: 10)
      timeoutMs: 30_000, // loop timeout in ms (default: 30s)
      chunkSize: 800,    // chars per chunk (default: 800)
    },
  },
});

For voice agents where latency is critical:

memory: {
  recursive: { enabled: true, maxDepth: 5, timeoutMs: 5_000 },
}

Loading documents

Load KB documents from a tool, a Worker startup handler, or a DO alarm:

const loadKb = tool({
  name: 'load_kb',
  description: 'Load a KB article into the document store',
  input: z.object({ id: z.string(), content: z.string(), title: z.string().optional() }),
  handler: async ({ id, content, title }, ctx) => {
    await ctx.recursive!.loadDocument(id, content, title);
    return { ok: true };
  },
});

Or directly on agent startup:

// In your Worker fetch handler, before routing to the agent:
const agentId = env.AGENT.idFromName('support');
const agentStub = env.AGENT.get(agentId);

// Call a custom /load-kb route on the AgentDO (add it in a subclass)
await agentStub.fetch(new Request('https://do/load-kb', {
  method: 'POST',
  body: JSON.stringify({ id: 'bridge-upgrade', content: kbArticle }),
}));

How the loop works

• User message arrives.
• runLoop() fires before streamText — the model calls search(), read_chunks(), and get_index() iteratively via generateText with maxSteps.
• Each tool call executes against DO storage — sub-millisecond, no network hop.
• When the model returns text instead of a tool call, the loop ends.
• The research result is injected into the system prompt.
• streamText produces the final streamed response to the client using the enriched context.

RecursiveMemory API

For direct use outside createAgent():

import { RecursiveMemory } from 'honidev';

const mem = new RecursiveMemory(doStorage, { enabled: true, chunkSize: 800 });

await mem.loadDocument('doc-id', content, 'Optional Title');
const hits = await mem.search('activation error arm mac');
const chunks = await mem.readChunks([0, 1, 2]);
const index = await mem.getIndex();
const result = await mem.runLoop(userMessage, model, systemPrompt);
// result: { answer: string, iterations: number, chunksRead: number[] }