What can you do with the Fireworks AI integration?

LiveContext exposes 35 operations on Fireworks AI, covering dataset, deployment, batch inference job and response. Common uses include feature verification, code completion and building chatbots.

LiveContext

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Fireworks AI integration

Fireworks AI inference and model platform. Run chat and text completions, an Anthropic-compatible messages call and the stateful Responses API against open-source models, create embeddings and rerank documents, then manage the account side: models, dedicated deployments and their scale, datasets, supervised fine-tuning jobs, batch inference jobs, evaluation jobs and billing usage. Serverless inference and the control plane share one host but not one path prefix, so every route below is expressed against https://api.fireworks.ai. Authentication is a bearer API key.

35 toolsAPI keyAPI docs

Connect Fireworks AI How integrations work

Common Fireworks AI use cases

feature verification
code completion
building chatbots
discovering models
finding dataset IDs
monitoring training
domain-specific models
semantic search systems
uploading training data
checking model capabilities

What you can do with Fireworks AI

Each of these is one operation on one endpoint, available both as a workflow step and as a tool an AI agent can call on its own. Nothing bundles several operations into one action, so what a run did stays readable afterwards.

Most endpoints run on the free plan. A few, mainly the ones that publish on your behalf, need a paid plan.

Dataset, 5 operations

POSTcreate_dataset
Create Dataset.
DELETEdelete_dataset
Delete Dataset.
GETget_dataset
Get Dataset.
GETlist_datasets
Lists all datasets for the account. Returns dataset metadata including IDs, states, sizes, and creation times.
POSTupload_dataset_file
Uploads dataset files to Fireworks AI for fine-tuning. Returns file metadata for use in subsequent API calls.

Deployment, 5 operations

POSTcreate_deployment
Create Deployment.
DELETEdelete_deployment
Delete Deployment.
GETget_deployment
Get Deployment.
GETlist_deployments
List Deployments.
PATCHscale_deployment
Scale Deployment to a specific number of replicas or to zero.

Batch inference job, 4 operations

POSTcreate_batch_inference_job
Create Batch Inference Job.
DELETEdelete_batch_inference_job
Delete Batch Inference Job.
GETget_batch_inference_job
Get Batch Inference Job.
GETlist_batch_inference_jobs
List Batch Inference Jobs.

Response, 4 operations

POSTcreate_response
Create Response.
DELETEdelete_response
Delete Response.
GETget_response
Get Response.
GETlist_responses
List Responses.

Evaluation job, 3 operations

POSTcreate_evaluation_job
Create Evaluation Job.
GETget_evaluation_job
Get Evaluation Job.
GETlist_evaluation_jobs
List Evaluation Jobs.

Fine tuning job, 3 operations

POSTcreate_fine_tuning_job
Creates a supervised fine-tuning job to customize a Fireworks AI model on your data. Supports efficient LoRA fine-tuning with configurable hyperparameters.
GETget_fine_tuning_job
Retrieves detailed information about a supervised fine-tuning job. Returns full status, metrics, configuration, and result model when complete.
GETlist_fine_tuning_jobs
Lists all supervised fine-tuning jobs for the account. Returns job metadata including status, model, and training progress.

Account, 2 operations

GETget_account
Get Account.
GETlist_accounts
List Accounts.

Model, 2 operations

GETget_model
Retrieves detailed information about a specific Fireworks AI model. Returns full specifications including context length, state, kind, and supported precisions.
GETlist_models
Lists all available Fireworks AI models with capabilities and specifications. Returns model IDs, context lengths, supported features, and deployment status.

Billing usage, 1 operation

GETget_billing_usage
Get Account Usage.

Chat, 1 operation

POSTchat_completions
Creates chat completions using Fireworks AI models. Supports multi-turn conversations, function calling, JSON mode, reasoning, and streaming with OpenAI-compatible API format.

Completion, 1 operation

POSTcompletions
Creates text completions from prompts using Fireworks AI models. Legacy endpoint for base models - use chat_completions for instruction-tuned models.

Embedding, 1 operation

POSTembeddings
Creates vector embeddings from text using Fireworks AI embedding models. Converts text into numerical vectors for semantic search, similarity comparison, and clustering.

Message, 1 operation

POSTcreate_anthropic_message
Create a Message.

Rerank, 1 operation

POSTrerank_documents
Rerank documents.

Supervised fine tuning job, 1 operation

DELETEdelete_supervised_fine_tuning_job
Delete Supervised Fine-tuning Job.

Use Fireworks AI from Claude Code, Cursor or Codex

Connect Fireworks AI in LiveContext, create an API key in Settings, then add the LiveContext MCP server to your client. Its Fireworks AI operations become tools the client can call, next to every other integration you connected.

Claude Code

claude mcp add --transport http livecontext https://livecontext.ai/mcp --header "X-API-Key: YOUR_API_KEY"

Codex (~/.codex/config.toml)

[mcp_servers.livecontext]
url = "https://livecontext.ai/mcp"

[mcp_servers.livecontext.http_headers]
"X-API-Key" = "YOUR_API_KEY"

Cursor, Claude Desktop and other JSON configs

{
  "mcpServers": {
    "livecontext": {
      "type": "http",
      "url": "https://livecontext.ai/mcp",
      "headers": {
        "X-API-Key": "YOUR_API_KEY"
      }
    }
  }
}

Set up the MCP server step by step

How to connect Fireworks AI

Create your workspace
Sign up free. Nothing to install.
Connect Fireworks AI once
Add your Fireworks AI connection in Settings. Every tool from this integration reuses it, and secrets are encrypted at rest and visible only to you.
Describe the job in chat
The workflow builds itself in front of you and picks the endpoints it needs. Drag the steps yourself on the canvas whenever you would rather be explicit.

Fireworks AI integration FAQ

What can you do with the Fireworks AI integration?: LiveContext exposes 35 operations on Fireworks AI, covering dataset, deployment, batch inference job and response. Common uses include feature verification, code completion and building chatbots.