Configuring Cloudflare

Conductor runs on Cloudflare’s edge platform. Here’s how to set up everything you need. This guide covers: Workers AI, KV, D1, R2, Vectorize, Durable Objects, and Queues.

Workers AI

AI models running at the edge. No API keys, no external dependencies.

Setup

Add to wrangler.toml:

[ai]
binding = "AI"

That’s it. No additional configuration.

Available Models

Text Generation:

@cf/meta/llama-3.1-8b-instruct - Fast, good quality (recommended)
@cf/meta/llama-3-8b-instruct - Previous generation
@cf/mistral/mistral-7b-instruct-v0.1 - Fast, creative

Embeddings:

@cf/baai/bge-base-en-v1.5 - 768 dimensions (recommended)
@cf/baai/bge-small-en-v1.5 - 384 dimensions (faster)
@cf/baai/bge-large-en-v1.5 - 1024 dimensions (best quality)

Image:

@cf/stabilityai/stable-diffusion-xl-base-1.0

Full list: https://developers.cloudflare.com/workers-ai/models/

Usage in Ensembles

agents:
  - name: generate
    operation: think
    config:
      provider: cloudflare
      model: '@cf/meta/llama-3.1-8b-instruct'
      prompt: ${input.text}

Limits

Free: 10,000 neurons/day (~100 requests for Llama 3.1)
Paid: Unlimited, pay per neuron

KV (Key-Value Storage)

Fast, globally replicated key-value store. Perfect for caching.

Create Namespace

# Create production namespace
wrangler kv:namespace create CACHE

# Create preview namespace (for local dev)
wrangler kv:namespace create CACHE --preview

Output:

{ binding = "CACHE", id = "abc123..." }
{ binding = "CACHE", preview_id = "xyz789..." }

Configure

Add to wrangler.toml:

[[kv_namespaces]]
binding = "CACHE"
id = "abc123..."
preview_id = "xyz789..."

Usage in Ensembles

agents:
  # Read from KV
  - name: get-cache
    operation: storage
    config:
      type: kv
      action: get
      key: result-${input.query}

  # Write to KV
  - name: set-cache
    operation: storage
    config:
      type: kv
      action: put
      key: result-${input.query}
      value: ${generate.output}
      expirationTtl: 3600  # 1 hour

KV Operations

Get:

config:
  type: kv
  action: get
  key: my-key
  default: null  # Return if key doesn't exist

Put:

config:
  type: kv
  action: put
  key: my-key
  value: ${data}
  expirationTtl: 3600  # Optional: expire after 1 hour

Delete:

config:
  type: kv
  action: delete
  key: my-key

List:

config:
  type: kv
  action: list
  prefix: user-  # List all keys starting with "user-"
  limit: 100

Limits

Free: 100,000 reads/day, 1,000 writes/day
Paid: Unlimited, $0.50/million reads,$ 5/million writes
Key size: Max 512 bytes
Value size: Max 25MB
Latency: <10ms globally

D1 (SQL Database)

SQLite at the edge. Perfect for structured data.

Create Database

wrangler d1 create production-db

Output:

database_name = "production-db"
database_id = "abc123-def456-ghi789"

Configure

Add to wrangler.toml:

[[d1_databases]]
binding = "DB"
database_name = "production-db"
database_id = "abc123-def456-ghi789"

Create Schema

# Create migration
wrangler d1 migrations create production-db initial_schema

Edit migrations/0001_initial_schema.sql:

CREATE TABLE IF NOT EXISTS users (
  id TEXT PRIMARY KEY,
  email TEXT UNIQUE NOT NULL,
  name TEXT NOT NULL,
  created_at INTEGER NOT NULL
);

CREATE INDEX idx_users_email ON users(email);
CREATE INDEX idx_users_created ON users(created_at);

CREATE TABLE IF NOT EXISTS documents (
  id TEXT PRIMARY KEY,
  user_id TEXT NOT NULL,
  content TEXT NOT NULL,
  embedding BLOB,
  created_at INTEGER NOT NULL,
  FOREIGN KEY (user_id) REFERENCES users(id)
);

CREATE INDEX idx_documents_user ON documents(user_id);

Apply migration:

# Apply to local dev database
wrangler d1 migrations apply production-db --local

# Apply to remote production database
wrangler d1 migrations apply production-db --remote

Usage in Ensembles

Query (SELECT):

agents:
  - name: get-user
    operation: storage
    config:
      type: d1
      query: |
        SELECT id, email, name FROM users
        WHERE email = ?
      params:
        - ${input.email}

Insert:

agents:
  - name: create-user
    operation: storage
    config:
      type: d1
      query: |
        INSERT INTO users (id, email, name, created_at)
        VALUES (?, ?, ?, ?)
      params:
        - ${input.id}
        - ${input.email}
        - ${input.name}
        - ${Date.now()}

Update:

agents:
  - name: update-user
    operation: storage
    config:
      type: d1
      query: |
        UPDATE users SET name = ?
        WHERE id = ?
      params:
        - ${input.name}
        - ${input.id}

Batch Insert:

agents:
  - name: batch-insert
    operation: storage
    config:
      type: d1
      batch:
        - query: INSERT INTO users (id, email) VALUES (?, ?)
          params: ['1', 'user1@example.com']
        - query: INSERT INTO users (id, email) VALUES (?, ?)
          params: ['2', 'user2@example.com']

D1 CLI Commands

# Execute query
wrangler d1 execute production-db --command="SELECT * FROM users LIMIT 10"

# Execute from file
wrangler d1 execute production-db --file=query.sql

# Backup database
wrangler d1 export production-db --output=backup.sql

# Restore from backup
wrangler d1 execute production-db --file=backup.sql

Limits

Free: 5M rows read/day, 100k rows written/day
Paid: Unlimited, $0.001/1k rows read,$ 1/1M rows written
Database size: 10GB (can request increase)
Query time: 30 seconds max

R2 (Object Storage)

S3-compatible object storage. No egress fees.

Create Bucket

wrangler r2 bucket create conductor-assets

Configure

Add to wrangler.toml:

[[r2_buckets]]
binding = "ASSETS"
bucket_name = "conductor-assets"

Usage in Ensembles

Upload:

agents:
  - name: upload-file
    operation: storage
    config:
      type: r2
      action: put
      key: documents/${input.filename}
      value: ${input.content}
      contentType: application/pdf

Download:

agents:
  - name: download-file
    operation: storage
    config:
      type: r2
      action: get
      key: documents/${input.filename}

Delete:

agents:
  - name: delete-file
    operation: storage
    config:
      type: r2
      action: delete
      key: documents/${input.filename}

List:

agents:
  - name: list-files
    operation: storage
    config:
      type: r2
      action: list
      prefix: documents/
      limit: 1000

R2 CLI Commands

# Upload file
wrangler r2 object put conductor-assets/path/to/file.pdf --file=local-file.pdf

# Download file
wrangler r2 object get conductor-assets/path/to/file.pdf

# Delete file
wrangler r2 object delete conductor-assets/path/to/file.pdf

# List objects
wrangler r2 object list conductor-assets --prefix=documents/

Limits

Storage: 10GB free, then $0.015/GB/month
Class A operations (writes): 1M free/month, then $4.50/million
Class B operations (reads): 10M free/month, then $0.36/million
Egress: FREE (no egress fees!)
Max object size: 5TB

Vectorize (Vector Database)

Vector similarity search. Perfect for RAG and semantic search.

Create Index

wrangler vectorize create embeddings \
  --dimensions=768 \
  --metric=cosine

Dimension options:

384: @cf/baai/bge-small-en-v1.5
768: @cf/baai/bge-base-en-v1.5 (recommended)
1024: @cf/baai/bge-large-en-v1.5
1536: OpenAI text-embedding-3-small

Metric options:

cosine: Best for normalized vectors (recommended)
euclidean: L2 distance
dot-product: Inner product

Configure

Add to wrangler.toml:

[[vectorize]]
binding = "VECTORIZE"
index_name = "embeddings"

Usage in Ensembles

Insert Vectors:

agents:
  # Generate embedding
  - name: embed
    operation: think
    config:
      provider: cloudflare
      model: '@cf/baai/bge-base-en-v1.5'
      input: ${input.text}

  # Insert into Vectorize
  - name: insert-vector
    operation: storage
    config:
      type: vectorize
      action: insert
      id: ${input.document_id}
      vector: ${embed.output.data[0]}
      metadata:
        text: ${input.text}
        timestamp: ${Date.now()}

Search Vectors:

agents:
  # Generate query embedding
  - name: embed-query
    operation: think
    config:
      provider: cloudflare
      model: '@cf/baai/bge-base-en-v1.5'
      input: ${input.question}

  # Search similar vectors
  - name: search
    operation: storage
    config:
      type: vectorize
      action: query
      vector: ${embed-query.output.data[0]}
      topK: 5
      filter:
        timestamp: { $gte: ${Date.now() - 86400000} }  # Last 24 hours

Delete Vectors:

agents:
  - name: delete-vector
    operation: storage
    config:
      type: vectorize
      action: delete
      ids:
        - ${input.document_id}

Vectorize CLI Commands

# Get index info
wrangler vectorize get embeddings

# Insert vectors (from file)
wrangler vectorize insert embeddings --file=vectors.ndjson

# Delete index
wrangler vectorize delete embeddings

Limits

Free: Included in Workers plan
Max vectors: 5M (can request increase)
Max dimensions: 1536
Query latency: ~50ms

Durable Objects

Stateful edge workers. Perfect for real-time features.

Configure

Add to wrangler.toml:

[[durable_objects.bindings]]
name = "EXECUTION_STATE"
class_name = "ExecutionState"
script_name = "my-conductor-app"

[[durable_objects.bindings]]
name = "HITL_STATE"
class_name = "HITLState"
script_name = "my-conductor-app"

[[migrations]]
tag = "v1"
new_classes = ["ExecutionState", "HITLState"]

Export Classes

In src/index.ts:

export { ExecutionState } from '@ensemble-edge/conductor/durable-objects';
export { HITLState } from '@ensemble-edge/conductor/durable-objects';

Usage

Conductor uses Durable Objects internally for:

HITL (Human-in-the-Loop): Approval workflows
Long-running executions: State persistence

You typically don’t interact with them directly.

Queues

Asynchronous task processing.

Create Queue

wrangler queues create conductor-tasks

Configure

Add to wrangler.toml:

# Producer (send messages)
[[queues.producers]]
binding = "TASK_QUEUE"
queue = "conductor-tasks"

# Consumer (receive messages)
[[queues.consumers]]
queue = "conductor-tasks"
max_batch_size = 10
max_batch_timeout = 30
max_retries = 3
dead_letter_queue = "conductor-tasks-dlq"

Usage in Ensembles

Send Message:

agents:
  - name: queue-task
    operation: storage
    config:
      type: queue
      action: send
      queue: TASK_QUEUE
      body:
        task_type: process-document
        document_id: ${input.document_id}

Consumer (in src/index.ts):

export default {
  async queue(batch: MessageBatch, env: Env): Promise<void> {
    for (const message of batch.messages) {
      const { task_type, document_id } = message.body;

      if (task_type === 'process-document') {
        const conductor = new Conductor({ env });
        await conductor.execute('process-document', { document_id });
      }

      message.ack();
    }
  }
};

Complete Configuration Example

Here’s a full wrangler.toml with all services:

name = "my-conductor-app"
main = "src/index.ts"
compatibility_date = "2024-01-01"
node_compat = true

# Workers AI
[ai]
binding = "AI"

# KV Namespaces
[[kv_namespaces]]
binding = "CACHE"
id = "abc123..."
preview_id = "xyz789..."

[[kv_namespaces]]
binding = "API_KEYS"
id = "def456..."

[[kv_namespaces]]
binding = "SESSIONS"
id = "ghi789..."

# D1 Databases
[[d1_databases]]
binding = "DB"
database_name = "production-db"
database_id = "jkl012..."

# R2 Buckets
[[r2_buckets]]
binding = "ASSETS"
bucket_name = "conductor-assets"

# Vectorize
[[vectorize]]
binding = "VECTORIZE"
index_name = "embeddings"

# Durable Objects
[[durable_objects.bindings]]
name = "EXECUTION_STATE"
class_name = "ExecutionState"
script_name = "my-conductor-app"

[[durable_objects.bindings]]
name = "HITL_STATE"
class_name = "HITLState"
script_name = "my-conductor-app"

[[migrations]]
tag = "v1"
new_classes = ["ExecutionState", "HITLState"]

# Queues
[[queues.producers]]
binding = "TASK_QUEUE"
queue = "conductor-tasks"

[[queues.consumers]]
queue = "conductor-tasks"
max_batch_size = 10
max_batch_timeout = 30

# Environment Variables
[vars]
ENVIRONMENT = "production"
LOG_LEVEL = "info"

# Production Environment
[env.production]
name = "my-conductor-app-prod"
vars = { ENVIRONMENT = "production" }

# Staging Environment
[env.staging]
name = "my-conductor-app-staging"
vars = { ENVIRONMENT = "staging" }

Best Practices

Use KV for caching - Fast and cheap
Use D1 for structured data - When you need SQL
Use R2 for large files - No egress fees
Use Vectorize for semantic search - Native vector support
Use Queues for async work - Don’t block requests
Separate environments - dev, staging, production
Monitor limits - Check Cloudflare dashboard regularly
Optimize queries - Add indexes to D1 tables

Cost Optimization

Stay in Free Tier

Workers: 100k requests/day
KV: 100k reads, 1k writes/day
D1: 5M reads, 100k writes/day
R2: 10GB storage, 1M writes, 10M reads/month
Workers AI: 10k neurons/day

Tips to Reduce Costs

Cache aggressively - Use KV to cache AI/API results
Use Cloudflare AI - Cheaper than external providers
Batch D1 operations - Reduce write counts
Use R2 for storage - No egress fees
Monitor usage - Set up billing alerts

Next Steps

Operations Reference

Learn all 12 operations

Core Concepts

Deep dive into concepts

Pre-built Agents

Use ready-made agents

Playbooks

Real-world examples

Conductor

Getting Started

Core Concepts

Building

Operations Reference

Pre-built Agents Reference

Playbooks

Reference

​Configuring Cloudflare

​Workers AI

​Setup

​Available Models

​Usage in Ensembles

​Limits

​KV (Key-Value Storage)

​Create Namespace

​Configure

​Usage in Ensembles

​KV Operations

​Limits

​D1 (SQL Database)

​Create Database

​Configure

​Create Schema

​Usage in Ensembles

​D1 CLI Commands

​Limits

​R2 (Object Storage)

​Create Bucket

​Configure

​Usage in Ensembles

​R2 CLI Commands

​Limits

​Vectorize (Vector Database)

​Create Index

​Configure

​Usage in Ensembles

​Vectorize CLI Commands

​Limits

​Durable Objects

​Configure

​Export Classes

​Usage

​Queues

​Create Queue

​Configure

​Usage in Ensembles

​Complete Configuration Example

​Best Practices

​Cost Optimization

​Stay in Free Tier

​Tips to Reduce Costs

​Next Steps

Operations Reference

Core Concepts

Pre-built Agents

Playbooks

Configuring Cloudflare

Workers AI

Setup

Available Models

Usage in Ensembles

Limits

KV (Key-Value Storage)

Create Namespace

Configure

Usage in Ensembles

KV Operations

Limits

D1 (SQL Database)

Create Database

Configure

Create Schema

Usage in Ensembles

D1 CLI Commands

Limits

R2 (Object Storage)

Create Bucket

Configure

Usage in Ensembles

R2 CLI Commands

Limits

Vectorize (Vector Database)

Create Index

Configure

Usage in Ensembles

Vectorize CLI Commands

Limits

Durable Objects

Configure

Export Classes

Usage

Queues

Create Queue

Configure

Usage in Ensembles

Complete Configuration Example

Best Practices

Cost Optimization

Stay in Free Tier

Tips to Reduce Costs

Next Steps