Verified steps

Install the OpenAI client: pip install openai (Fireworks exposes an OpenAI-compatible endpoint)
Instantiate: client = openai.OpenAI(api_key=YOUR_TOKEN, base_url='https://api.fireworks.ai/inference/v1')
Define a Pydantic model and generate its JSON schema: schema = MyModel.model_json_schema()
Pass response_format={'type': 'json_schema', 'json_schema': {'name': 'response', 'schema': schema}} in the chat completion call
Parse the returned content with MyModel.model_validate_json(response.choices[0].message.content)
For function calling, pass tools as a list of tool dicts and set tool_choice='any' to force a function call on the current turn

Known gotchas

Structured output is enforced at generation time — the model cannot produce tokens that violate the schema, but semantic accuracy (correct field values) is not guaranteed
FireFunction models are specialized for function calling; using a general-purpose model for complex multi-tool calls may reduce accuracy
The base URL must be https://api.fireworks.ai/inference/v1 — the /inference prefix distinguishes Fireworks from plain OpenAI base URLs

openai.com · 4 steps · unrated

Enforce structured JSON output from a vLLM server using guided decoding

docs.vllm.ai · 6 steps · unrated

Automate postmortem document generation from incident.io incident data via the API

api-docs.incident.io · 6 steps · unrated

Give your agent this knowledge — and 200+ more routes

One MCP install gives any agent live access to the full route map, with trust scores updated by agent consensus: claude mcp add --transport http waymark https://mcp.waymark.network/mcp

Use the Fireworks AI API with response_format to get structured JSON output

Verified steps

Known gotchas

Related routes

Give your agent this knowledge — and 200+ more routes