High-Throughput Email Agents with Fireworks AI

Combine Fireworks AI's optimized inference with MultiMail's email infrastructure for agents that handle high-volume email workflows with human oversight.

Fireworks AI specializes in fast, cost-effective inference for open-source models with function calling support. MultiMail provides the email infrastructure layer that turns Fireworks' throughput advantage into production email agents capable of processing large volumes efficiently.

By integrating MultiMail with the Fireworks AI API, your agents can handle high-volume email workflows while respecting human oversight boundaries. The default gated_send mode means your agent drafts emails at scale but humans approve before delivery, using MultiMail's batch review features.

Connect Fireworks to MultiMail by defining email tools in Fireworks' OpenAI-compatible function calling format and routing calls to the MultiMail REST API. The familiar API format minimizes integration effort.

Why MultiMail

Built for Fireworks AI API developers

High-Throughput Email Processing

Fireworks AI is optimized for high-throughput inference. Combined with MultiMail's email API, your agent can process hundreds of emails per minute for triage, categorization, and draft composition.

Graduated Trust via Oversight Modes

Start with gated_send (agent composes, human approves) and progress to autonomous as trust builds. MultiMail's five oversight modes let you safely scale from supervised to fully autonomous email agents.

Production-Ready Infrastructure

Both Fireworks AI and MultiMail are built for production workloads. Fireworks handles model serving with high uptime, while MultiMail handles email delivery, thread tracking, and compliance.

OpenAI-Compatible Format

Fireworks follows the OpenAI function calling format. Migrate email agent code from OpenAI or other compatible providers by changing only the base URL and model name.

Fine-Tuned Model Support

Fireworks supports serving fine-tuned models. Train a model on your email domain and serve it through Fireworks for higher-quality email drafts with MultiMail oversight.

Try it

Try it with your agent

No code, no dashboard. Paste this to your AI agent — it connects MultiMail, creates an inbox, and builds the flow for you.

1. Get MultiMail ready: read https://multimail.dev/llms.txt, connect the MultiMail MCP server, create a free inbox for this agent, and set up a verified sender before attempting any outbound email. 2. Wire MultiMail into my Fireworks AI API agent using Fireworks AI’s OpenAI-compatible Chat Completions tool calling: register function tools in the tools array, let the model choose them with tool_choice set to auto or required where appropriate, and implement each tool handler by calling the connected MultiMail MCP server. 3. Give the agent exactly these email capabilities through Fireworks function tools backed by MultiMail MCP actions: check the inbox, compose a draft reply, and send or schedule an email. Do not add any other email transport, webhook, or direct HTTP integration. 4. Send one test email from the verified sender to my test recipient. First check the inbox, then draft a short reply-style message, then prepare it for sending with the subject “MultiMail Fireworks test” and a body that says this is a gated test of an AI agent email workflow. 5. Run MultiMail in gated_send oversight mode for this test and for all future sends unless I explicitly change it. Before anything sends, show me the recipient, sender, subject, body, and whether it is immediate or scheduled, then wait for my approval.
Getting started

Step by step

1

Create a MultiMail Account and API Key

Sign up at multimail.dev, create a mailbox, and generate an API key from your dashboard. Your key will start with mm_live_.

2

Install Dependencies

Install the Fireworks AI Python SDK and requests library for calling the MultiMail API.

3

Define Email Tool Schemas

Create tool definitions for send_email, check_inbox, and other MultiMail operations. Fireworks uses the OpenAI-compatible format.

4

Build the Agent Loop

Implement a loop that sends messages to Fireworks, checks for tool_calls, executes tools against MultiMail, and returns results.

5

Approve Pending Emails

If your mailbox uses gated_send mode (the default), review and approve pending emails in the MultiMail dashboard before they are delivered.

FAQ

Common questions

What makes Fireworks AI good for email agents?
Fireworks AI is optimized for high-throughput, low-latency inference. This makes it ideal for email agents that need to process large volumes — triage hundreds of emails, generate batch replies, or categorize incoming mail. The throughput advantage translates directly to faster email workflows.
What happens when my agent sends an email in gated_send mode?
In gated_send mode, the MultiMail API returns a success response with a pending status. The email is queued for human review in the MultiMail dashboard. Once approved, it is delivered. Your agent can check the status of pending emails using the list_pending endpoint.
Can I use a fine-tuned model with Fireworks and MultiMail?
Yes. Fireworks supports serving fine-tuned models. Fine-tune a model on your email style and domain-specific terminology, deploy it on Fireworks, and connect it to MultiMail. This produces higher-quality drafts that need less human editing during the approval step.
How do I handle high-volume email processing?
Use parallel threads to send multiple inference requests to Fireworks simultaneously, each processing a different email against MultiMail's API. Fireworks' throughput optimization handles concurrent requests well, and MultiMail's API supports high request rates on paid plans.
Is there rate limiting on the MultiMail API?
Rate limits depend on your plan tier. The Starter (free) plan allows 200 emails per month, while paid plans range from 5,000 to 150,000. The API returns standard 429 responses when limits are reached, which your agent can handle with retry logic.
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Get started

The only agent email with a verifiable sender

Email infrastructure built for AI agents. Verifiable identity, graduated oversight, and a hosted MCP server. Formally verified in Lean 4.

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