# Fireworks AI

```json
{
  "name": "Fireworks AI",
  "slug": "fireworks_ai",
  "url": "https://composio.dev/toolkits/fireworks_ai",
  "markdown_url": "https://composio.dev/toolkits/fireworks_ai.md",
  "logo_url": "https://logos.composio.dev/api/fireworks_ai",
  "categories": [
    "ai & machine learning"
  ],
  "is_composio_managed": false,
  "updated_at": "2026-10-04T15:14:15.799Z"
}
```

![Fireworks AI logo](https://logos.composio.dev/api/fireworks_ai)

## Description

Securely connect your AI agents and chatbots (Claude, ChatGPT, Cursor, etc) with Fireworks AI MCP or direct API to run model inference, manage deployments, create datasets, launch fine-tuning jobs, and review evaluations through natural language.

## Summary

Fireworks AI is a serverless AI platform for model inference, fine-tuning, deployments, datasets, and evaluations.
Use it to run fast open-model workflows without managing GPU infrastructure.

## Categories

- ai & machine learning

## Toolkit Details

- Tools: 9

## Images

- Logo: https://logos.composio.dev/api/fireworks_ai

## Authentication

- **Api Key**
  - Type: `api_key`
  - Description: Api Key authentication for Fireworks AI.
  - Setup:
    - Configure Api Key credentials for Fireworks AI.
    - Use the credentials when creating an auth config in Composio.

## Suggested Prompts

- List active Fireworks AI deployments
- Run Llama inference on customer text
- Create dataset for fine tuning

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `FIREWORKS_AI_CREATE_CHAT_COMPLETION` | Create Chat Completion | Generate one non-streaming OpenAI-compatible chat completion from role-based messages using a caller-supplied Fireworks model or deployment ID. |
| `FIREWORKS_AI_CREATE_EMBEDDINGS` | Create Embeddings | Create embedding vectors for one or more caller-provided inputs using a caller-supplied Fireworks embedding model. |
| `FIREWORKS_AI_GENERATE_MODEL_RESPONSE` | Generate Model Response | Generate one non-streaming Responses API result, optionally continuing a stored response or using function, MCP, or SSE tools; storage is off by default. |
| `FIREWORKS_AI_GET_DEPLOYMENT` | Get Deployment | Return configuration and serving status for one Fireworks deployment without scaling or modifying it. |
| `FIREWORKS_AI_GET_MODEL` | Get Model | Return metadata and status for one model resource in a Fireworks account; this does not test whether the model can serve inference. |
| `FIREWORKS_AI_LIST_ACCOUNTS` | List Accounts | Return one page of Fireworks accounts accessible to the API key so an agent can obtain account IDs for model and deployment discovery. |
| `FIREWORKS_AI_LIST_DEPLOYMENTS` | List Deployments | Return one page of deployment resources in a Fireworks account for deployment ID discovery and serving-capacity and status inspection. |
| `FIREWORKS_AI_LIST_MODELS` | List Models | Return one page of model resources in a Fireworks account for model-ID discovery and status inspection; availability for inference is not guaranteed. |
| `FIREWORKS_AI_RERANK_DOCUMENTS` | Rerank Documents | Rank a provided document list by relevance to a query using an optional caller-supplied Fireworks reranker model. |

## Supported Triggers

None listed.

## Installation and MCP Setup

### Path 1: SDK Installation

#### Path 1, Step 1: Install Composio

Install the Composio SDK, the OpenAI provider, and the OpenAI SDK
```python
pip install composio composio_openai openai
```

```typescript
npm install @composio/core @composio/openai openai
```

#### Path 1, Step 2: Create a Composio session

Initialize Composio with the OpenAI Responses provider and create a session scoped to Fireworks AI
```python
import json
from openai import OpenAI
from composio import Composio
from composio_openai import OpenAIResponsesProvider

composio = Composio(provider=OpenAIResponsesProvider())
client = OpenAI()

session = composio.create(user_id="your-user-id", toolkits=["fireworks_ai"])
tools = session.tools()
```

```typescript
import OpenAI from "openai";
import { Composio } from "@composio/core";
import { OpenAIResponsesProvider } from "@composio/openai";

const composio = new Composio({ provider: new OpenAIResponsesProvider() });
const client = new OpenAI();

const session = await composio.create("your-user-id", { toolkits: ["fireworks_ai"] });
const tools = await session.tools();
```

#### Path 1, Step 3: Run Fireworks AI tools with your agent

Send a request, execute the Fireworks AI tool calls through the session, and print the final answer
```python
response = client.responses.create(
    model="gpt-5.6-sol",
    tools=tools,
    input=[{"role": "user", "content": "List active Fireworks AI deployments"}],
)

while True:
    tool_calls = [o for o in response.output if o.type == "function_call"]
    if not tool_calls:
        break
    results = composio.provider.handle_tool_calls(response=response, session=session)
    response = client.responses.create(
        model="gpt-5.6-sol",
        tools=tools,
        previous_response_id=response.id,
        input=[
            {"type": "function_call_output", "call_id": call.call_id, "output": json.dumps(results[i])}
            for i, call in enumerate(tool_calls)
        ],
    )

print(response.output_text)
```

```typescript
let response = await client.responses.create({
  model: "gpt-5.6-sol",
  tools,
  input: [{ role: "user", content: "List active Fireworks AI deployments" }],
});

while (response.output.some((o) => o.type === "function_call")) {
  const outputs = await composio.provider.handleToolCalls(session, response.output);
  response = await client.responses.create({
    model: "gpt-5.6-sol",
    tools,
    previous_response_id: response.id,
    input: outputs,
  });
}

console.log(response.output_text);
```

### Path 2: MCP Server Setup

#### Path 2, Step 1: Install Composio

Install the Composio SDK and your agent framework
```python
pip install composio claude-agent-sdk
```

```typescript
npm install @composio/core ai @ai-sdk/mcp @ai-sdk/openai
```

#### Path 2, Step 2: Create a session with MCP enabled

Create a session scoped to Fireworks AI and read its MCP URL and headers
```python
from composio import Composio

composio = Composio()
session = composio.create(user_id="your-user-id", toolkits=["fireworks_ai"], mcp=True)
```

```typescript
import { Composio } from "@composio/core";

const composio = new Composio();
const { mcp } = await composio.create("your-user-id", {
  toolkits: ["fireworks_ai"],
  mcp: true,
});
```

#### Path 2, Step 3: Connect your agent to the MCP server

Pass the session's MCP URL and headers to your agent and run a Fireworks AI request
```python
import asyncio
from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions, AssistantMessage, TextBlock

options = ClaudeAgentOptions(
    mcp_servers={
        "composio": {
            "type": "http",
            "url": session.mcp.url,
            "headers": session.mcp.headers,
        }
    },
    system_prompt="You are a helpful assistant with access to Fireworks AI tools.",
    tools=[],
    allowed_tools=["mcp__composio__*"],
)

async def main():
    async with ClaudeSDKClient(options=options) as client:
        await client.query("List active Fireworks AI deployments")
        async for message in client.receive_response():
            if isinstance(message, AssistantMessage):
                for block in message.content:
                    if isinstance(block, TextBlock):
                        print(block.text)

asyncio.run(main())
```

```typescript
import { createMCPClient } from "@ai-sdk/mcp";
import { openai } from "@ai-sdk/openai";
import { generateText, stepCountIs } from "ai";

const client = await createMCPClient({
  transport: { type: "http", url: mcp.url, headers: mcp.headers },
});

const { text } = await generateText({
  model: openai("gpt-5.6-sol"),
  tools: await client.tools(),
  prompt: "List active Fireworks AI deployments",
  stopWhen: stepCountIs(10),
});

console.log(text);
await client.close();
```

## Why Use Composio?

### 1. AI Native Fireworks AI Integration

- Supports both Fireworks AI MCP and direct API based integrations
- Structured, LLM-friendly schemas for reliable model, dataset, deployment, and evaluation actions
- Rich coverage for reading, writing, and querying Fireworks AI resources without bespoke integration code

### 2. Managed Auth

- Secure API key handling so your agents don't need hard-coded Fireworks AI credentials
- Central place to manage, scope, and revoke Fireworks AI access
- Per user and per environment credentials for safer development and production use

### 3. Agent Optimized Design

- Tools are designed so language models can reliably understand Fireworks AI actions and inputs
- Execution logs show what your agent ran, when it ran, and which Fireworks AI account it used
- Cleaner error handling helps agents recover when an inference, dataset, or deployment action needs adjustment

### 4. Enterprise Grade Security

- Fine-grained RBAC so you control which agents and users can access Fireworks AI
- Scoped, least privilege access to Fireworks AI resources like models, deployments, datasets, and evaluations
- Full audit trail of agent actions to support review, debugging, and compliance

## Use Fireworks AI with any AI Agent Framework

Choose a framework you want to connect Fireworks AI with:

None listed.

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## Frequently Asked Questions

### Do I need my own developer credentials to use Fireworks AI with Composio?

Yes, Fireworks AI requires you to configure your own API key. Once set up, Composio handles secure credential storage and API request handling for you.

### Can I use multiple toolkits together?

Yes! Composio's Tool Router enables agents to use multiple toolkits. [Learn more](https://docs.composio.dev/tool-router/overview).

### Is Composio secure?

Composio is SOC 2 and ISO 27001 compliant with all data encrypted in transit and at rest. [Learn more](https://trust.composio.dev).

### What if the API changes?

Composio maintains and updates all toolkit integrations automatically, so your agents always work with the latest API versions.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)