Fieldflow
Converts REST APIs into GraphQL-like MCP servers so AI agents fetch only the specific fields they need
Last verified:
What is Fieldflow?
FieldFlow is an open-source tool that turns any REST API described by an OpenAPI specification into a GraphQL-like MCP (Model Context Protocol) server, allowing AI agents to fetch only the specific fields they need. This eliminates the problem of LLMs ingesting raw, noisy API payloads and burning tokens on useless fields. The tool works by point-ing at an OpenAPI 3.x JSON or YAML file, automatically discovering endpoints, methods, and schemas, then generating Pydantic models and FastAPI routes that support selective field filtering.
Key features include OpenAPI native support that automatically discovers endpoints and schemas, typed Pydantic models that preserve aliases and nested references, selective responses where every route accepts an optional fields list to fetch only needed payloads, FastAPI gateway that proxies requests with httpx, spec-first fidelity with recursive schema resolution for nested objects and arrays, and optional MCP tooling to expose filtered endpoints to Model Context Protocol clients like Claude Desktop.
FieldFlow is designed for developers building AI agents and MCP servers, particularly those working with LLMs and AI clients who need to reduce token consumption and improve agent response speed and accuracy. It's ideal for teams who already ship REST APIs with OpenAPI specifications and want to make them more efficient for AI integration without rebuilding everything in GraphQL or making backend changes.
Fieldflow pricing
Pricing model: Freemium
Free and open source. FieldFlow is an open-source tool available on GitHub with no paid tiers or subscription plans. The pricing model is completely free with no Tools/Prompts/Resources limits.
Fieldflow pros
- Up to 80% fewer LLM tokens consumed
- Ready in under two minutes setup time
- No backend changes required
- Works with any REST API you already ship
- Automatic field discovery for agents
- OpenAPI 3.x native support
- Typed Pydantic models with precise validation
- Selective responses prevent over-fetching
- FastAPI gateway with httpx proxy
- Recursive schema resolution for nested objects
- Optional MCP layer for Claude Desktop
- Handles arrays and references automatically
- Preserves field aliases and optional fields
- Open source and free to use
- Single command line setup
- Improves agent response speed by up to 40%
- Boosts overall accuracy by reducing context noise
- No schema definition required
Fieldflow cons
- Requires OpenAPI 3.x specification to exist
- Only supports GraphQL queries, mutations planned
- MCP layer is optional not mandatory
- Limited to REST APIs only
- May need customization for complex auth
- New open source project with smaller community
- FastAPI dependency adds Python runtime requirement
- Proxy layer adds slight latency overhead
Frequently asked questions about Fieldflow
What is FieldFlow?
FieldFlow is an open-source tool that turns any REST API described by an OpenAPI specification into a GraphQL-like MCP server, enabling AI agents to fetch only the specific fields they need and reducing token consumption by up to 80%.
How does FieldFlow reduce LLM token usage?
FieldFlow enables selective field filtering on API responses, so agents fetch only the payload they need instead of raw, noisy API payloads with useless fields, reducing context by up to 85% and consuming up to 75% fewer tokens.
What OpenAPI versions does FieldFlow support?
FieldFlow supports OpenAPI 3.x JSON or YAML files, automatically discovering endpoints, methods, and schemas from the specification.
Do I need to change my backend to use FieldFlow?
No, FieldFlow requires no backend changes. It works as a proxy that points at your existing OpenAPI specification and generates filtered endpoints on top of your current REST API.
How long does setup take?
FieldFlow is ready in under two minutes with just a single command line setup after providing your OpenAPI specification.
What AI clients work with FieldFlow?
FieldFlow works with Model Context Protocol clients like Claude Desktop, as well as any standard HTTP client through its FastAPI gateway.
Does FieldFlow support mutations?
Currently FieldFlow supports GraphQL queries, with mutations support planned for future releases.
How does FieldFlow handle nested API responses?
FieldFlow uses recursive schema resolution that handles nested objects, arrays, and references from your OpenAPI spec, keeping the proxy aligned with the upstream API.
What programming language does FieldFlow use?
FieldFlow is built with Python using FastAPI for the gateway and Pydantic for typed models, with httpx for proxying requests.
Is FieldFlow production-ready?
FieldFlow is an open-source project that has crossed 100 stars on GitHub organically and is designed to ship field-level API tooling from a single OpenAPI specification for production AI agent workflows.