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`
`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`

All 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
npm install @ai-sdk/cloudflare # Workers AI

Workers AI

Use any Workers AI model by prefixing with @cf/:

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

const agent = createAgent({
  name: 'my-agent',
  model: '@cf/meta/llama-3.1-8b-instruct',  // Uses Workers AI
  system: 'You are a helpful assistant.',
});

Workers AI support requires the optional @ai-sdk/cloudflare package:

npm install @ai-sdk/cloudflare

AI Gateway

Route all LLM calls through Cloudflare AI Gateway for logging, rate limiting, and caching at the edge:

const agent = createAgent({
  name: 'my-agent',
  model: 'claude-sonnet-4-5',
  observability: {
    aiGateway: {
      accountId: 'your-account-id',
      gatewayId: 'your-gateway-id',
    },
  },
  system: 'You are a helpful assistant.',
});
// All LLM calls now route through CF AI Gateway for observability

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: {
    logLevel: 'debug',
    onEvent: (event) => {
      // Send to your logging/analytics service
      console.log(event.type, event.durationMs);
    },
    aiGateway: {
      accountId: 'your-cf-account-id',
      gatewayId: 'your-gateway-id',
    },
  },
});

Event Types

Event	Emitted When
`agent.request`	Incoming chat request
`agent.response`	Response stream complete
`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

AI Gateway

Set observability.aiGateway to route LLM calls through Cloudflare AI Gateway for logging, rate limiting, and caching at the edge.

API Reference

`createAgent(config)`

Option	Type	Default	Description
`name`	`string`	—	Agent name
`model`	`string`	—	Model ID (`claude-sonnet-4-5`, `gpt-4o`)
`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

Returns { fetch, DurableObject }.

`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"

Name		Name	Last commit message	Last commit date
Latest commit History 28 Commits
bin		bin
claude-skill		claude-skill
example		example
examples		examples
migrations		migrations
src		src
tests		tests
.gitignore		.gitignore
README.md		README.md
bun.lock		bun.lock
package.json		package.json
tsconfig.json		tsconfig.json

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Honi

Quick Start

Install

Create an Agent

Configure Wrangler

Deploy

Chat with Your Agent

Memory (Phase 2)

Setup

Configure

How it works

Graph memory — writing entities from tools

Using GraphMemory standalone

Core Concepts

Agents

Tools

Memory

Supported Models

Workers AI

AI Gateway

Streaming

Comparison

Examples

Workflows (Phase 3)

CLI (Phase 4)

Observability (Phase 5)

Configuration

Event Types

AI Gateway

API Reference

createAgent(config)

tool(config)

License

Bundle Size

Optimization Tips

MCP Server

Endpoints

Authentication

Example: Connect from Claude Desktop

Multi-Agent Orchestration

wrangler.toml Setup

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