Faaah (Filesystem as an AI Handler) – Agent Subscription as OpenAI API via Files

Developer Sebastian Carlos released FAAAH, a 308-line, dependency-free Python tool that turns an existing AI coding-agent subscription (such as Claude Code or opencode) into an OpenAI-compatible local server by writing requests to `.txt` files in a queue directory and having the agent write responses back. FAAAH runs on Python's `http.server`, listens on 127.0.0.1:8000 by default, installs via `uv tool install`, and works with any tool expecting the OpenAI API format, including GraphRAG, LangChain, LlamaIndex, LiteLLM and the `openai` SDK. Carlos states it is his understanding that local, non-commercial use does not break any agent provider's terms of service, while warning users against processing massive datasets because some providers may deploy telemetry to detect infractions.

The simplest OpenAI-compatible LLM proxy you will ever need.™️ FAAAH allows you to reuse your AI Agent subscription as a generic OpenAI-compatible local server. FAAAH is dependency-free, implemented as a plain-text file protocol UNIX-philosophy certified : 1. Instead of sending your prompts to cloud LLM APIs, send them to FAAAH, which reads OpenAI-compatible requests and dumps them into a folder as .txt files. 2. Then, tell your existing AI coding agent Claude Code, opencode, etc to read the files and write responses to other .txt files. 3. FAAAH then packages the responses into OpenAI-compatible JSON, and returns them to your app. Because you already pay for an AI coding assistant. Stop paying for API keys just for your weekend side projects faaah It's also an agnostic proxy between any OpenAPI-expecting tool, and any LLM. As an example, check GraphRAG fully driven through FAAAH https://github.com/sebastiancarlos/graphrag-faaah . demo-9mb.mp4 It is my understanding that local, non-commercial use of this tool doesn't break the existing ToS of any AI agent provider. But if any lawyer disagrees, kindly send me a message. I would then introduce you to a friend of mine: Miss Barbra Streisand. Be cautious about using FAAAH to process massive datasets. Some providers you know which ones https://news.ycombinator.com/item?id=47963204 might do some Kafkaesque interpretations of their ambiguous ToS, and deploy Orwellian telemetry to detect infractions hasn't happened to me yet, YOLO - Zero Dependencies: Uses Python's http.server . That's it. - 308 lines of code: Have you seen the bloat of other tools in this space? Yuck. - Unix Philosophy: Everything is a file. Do one thing well. Keep it KISS, ya YAGNI. - Universal Compatibility: If a tool supports the de-facto OpenAI API format GraphRAG, LangChain, LlamaIndex, LiteLLM, the openai SDK , it supports FAAAH. - Agent Agnostic: Due to the agent entrypoint being a prompt , it's not tied to any specific agent provider/version. Future-proof. - Human-in-the-loop Fallback: If the AI agent gets stuck or hits usage limits, you can literally open the current response file say response-0004.txt , type the answer yourself or copy-paste the request to your favorite web chatbot , and hit save. FAAAH will succeed. Install with uv , from inside this repo: uv tool install . installs the faaah command on PATH uv tool install . embeddings with the local embeddings extra see later Or straight from the git repository: uv tool install git+https://github.com/sebastiancarlos/faaah faaah listens on 127.0.0.1:8000, queue ~/.cache/faaah/queue faaah --port 8080 override port faaah --queue /tmp/q override queue directory faaah --log-dir /tmp/logs override session log root see below faaah -c also copy the agent prompt to the clipboard Each run creates a session-log dir ~/.cache/faaah/sessions/<TIMESTAMP -<RANDOM by default that mirrors every queue file written or answered during that run, so you can debug a session even after the queue is wiped on restart: - prompt-<id .txt , response-<id .txt — same files as the queue - request-<id .json , response-<id .json — the raw OpenAI request/response - embed-<id -request.json , embed-<id -response.json — embeddings traffic - session.log — plain-text copy of all log lines Pass --log-dir "" to disable session logging. The agent prompt is printed on startup. To grab it again: faaah --agent-message Paste it into your coding agent, which then starts a FAAAH coordinator loop : - Call faaah --watch to obtain the next request blocks until one exists . - Delegate the request to a worker subagent to prevent accumulating context . - Repeat. If a worker leaves no response file, faaah --watch simply returns the same path again, so the coordinator retries it. Note: FAAAH uses subagents to prevent exhaustion of context on multiple requests. Thereby, your AI Agents must support creation of subagents on request by prompt . You can use curl , for example: curl http://127.0.0.1:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer anything" \ -d '{ "model": "faaah", "messages": {"role": "user", "content": "Write a haiku."} }' Or any OpenAI-API shaped client: python from openai import OpenAI client = OpenAI base url="http://127.0.0.1:8000/v1", api key="anything" response = client.chat.completions.create model="faaah", this field is ignored anyway messages= {"role": "user", "content": "Write a haiku."} , timeout=None, agents can be slow .choices 0 .message.content print response A dependency-free example lives in examples/chat.py . For an advanced usage, GraphRAG fully driven through FAAAH , see graphrag-faaah https://github.com/sebastiancarlos/graphrag-faaah . FAAAH supports the OpenAI tools parameter, including multi-turn tool loops. Send a non-empty tools array and the prompt tells the agent which tools exist and how to request them. Without tools , the classic text-only behavior is unchanged. The agent requests tool calls by committing a JSON object whose only key is tool calls : {"tool calls": {"name": "get weather", "arguments": {"city": "Lisbon"}} } FAAAH mints the call ids, returns finish reason: "tool calls" , and expects the client to execute the tool and send the result back as a tool message. On the next request, tool results are rendered as <tool result blocks and the agent answers in plain text with finish reason: "stop" . Supported tool choice values are auto default , none , required , and a forced function. parallel tool calls: false limits the reply to one call. Malformed tool replies are retried up to twice with the error fed back to the agent; after that FAAAH returns 502 . Streaming is not supported; requests with stream: true get a 400 . A dependency-free example lives in examples/tools.py : ./examples/tools.py "What's the weather in Lisbon?" FAAAH stays dependency-free by default, but can serve local embeddings via the optional embeddings uv extra Installs a spaCy model, en core web md : uv tool install '. embeddings ' global tool with embeddings from inside this repo uv sync --extra embeddings or just the dev environment When installed, POST /v1/embeddings returns OpenAI-shaped vectors computed locally. Without the extra dependencies, the endpoint just returns 503 . curl http://127.0.0.1:8000/v1/embeddings \ -H "Content-Type: application/json" \ -d '{"model": "en core web md", "input": "hello world" }' usage: faaah -h --host HOST --port PORT --queue QUEUE --log-dir LOG DIR --timeout TIMEOUT --agent-message -c -d --watch Filesystem As An AI Handler: an OpenAI-compatible proxy backed by an AI agent working over text files. options: -h, --help show this help message and exit --host HOST Address to bind default: 127.0.0.1 . --port PORT Port to listen on default: 8000 . --queue QUEUE Directory where prompt/response files live default: ~/.cache/faaah/queue . --log-dir LOG DIR Parent dir for per-session logs default: ~/.cache/faaah/sessions . A new <TIMESTAMP -<RANDOM subdir is created each run; pass empty string to disable. --timeout TIMEOUT Abort each call after N seconds. 0 default waits forever. --agent-message Print ONLY the agent prompt and exit it's also printed on launch . -c, --copy Copy the agent prompt to the system clipboard on startup uses wl-copy, xclip, xsel, pbcopy or clip.exe if available . -d, --debug Print the full request/response flow for each call once it completes. --watch Block until a pending prompt exists, print its path. The protocol relies on files on the queue directory ~/.cache/faaah/queue by default . Each request produces a prompt-<id .txt file, where the first one's ID will be 00001 and increase monotonically. FAAAH then expects the agent or anything really to generate a corresponding response-<id .txt . The subagent workers are prompted to write a first pass as response-<id .txt.draft , which they may revise, before renaming it to the final response-<id .txt they consider final . | File | Who writes | Meaning | |---|---|---| | prompt-<id .txt | server | an incoming request for the agent | | response-<id .txt.draft | agent | an in-progress, editable draft | | response-<id .txt | agent | the answer | Retry is automatic: a prompt with no response file is simply re-offered by faaah --watch to the coordinator until one appears. Tool-call validation retries also use a fresh prompt id, with the rejected reply and the reason included in the new prompt. If the client disconnects, or the server-side --timeout fires, faaah removes the prompt so --watch does not keep re-offering work nobody is waiting for. Other projects put an API in front of a CLI subscription: - CLIProxyAPI https://github.com/router-for-me/CLIProxyAPI is a Go proxy that exposes several CLI providers such as Claude Code, Codex, Gemini, and Grok through OpenAI, Gemini, Claude, Codex, and Grok compatible endpoints. It handles OAuth, streaming, multimodal input, tool calling, and multi-account load balancing. - opencode2api https://github.com/jasonxu114514/opencode2api is a Go gateway for OpenCode Zen and Zen Go. Unlike the above, FAAAH doesn't talk to a provider at all: FAAAH tells you how to prompt the model you already have to act as an OpenAI API , so in principle any model that you can prompt locally works. MIT