fastspec

Install

pip install fastspec

Quick Start

fastspec turns any OpenAPI (or Google Discovery) spec into a fully async Python client. Load a spec, create a client, and call any endpoint with attribute chaining.

Loading Specs

fastspec supports both OpenAPI (JSON/YAML) and Google Discovery specs:

import yaml

specs_path = Path('../specs/')

# OpenAPI specs (Anthropic, OpenAI, GitHub, Stripe)
ant_spec  = SpecParser.from_openapi(dict2obj(yaml.safe_load((specs_path/'anthropic.yml').read_text())))
oai_spec  = SpecParser.from_openapi(dict2obj(yaml.safe_load((specs_path/'openai.with-code-samples.yml').read_text())))
gh_spec   = SpecParser.from_openapi(dict2obj(json.loads((specs_path/'github.json').read_text())))

# Google Discovery spec (Gemini)
gem_spec  = SpecParser.from_discovery(dict2obj(json.loads((specs_path/'gemini.json').read_text())))

ant_spec, oai_spec, gh_spec, gem_spec

(SpecParser(base_url='https://api.anthropic.com', ops=47),
 SpecParser(base_url='https://api.openai.com/v1', ops=241),
 SpecParser(base_url='https://api.github.com', ops=1112),
 SpecParser(base_url='https://generativelanguage.googleapis.com/', ops=79))

Creating Clients

Pass a parsed spec and any required auth headers to OpenAPIClient:

ant_cli = OpenAPIClient(ant_spec, headers={"x-api-key": os.environ["ANTHROPIC_API_KEY"], "anthropic-version": "2023-06-01"})
oai_cli = OpenAPIClient(oai_spec, headers={"Authorization": f"Bearer {os.environ['OPENAI_API_KEY']}"})
gh_cli  = OpenAPIClient(gh_spec,  headers={"Authorization": f"token {os.environ['GITHUB_TOKEN']}"})

Exploring Operations

Every client organizes endpoints into groups. Use doc() to browse what’s available:

ant_cli.messages

• messages.messages_post(model, messages, max_tokens, cache_control, container, inference_geo, metadata, output_config, service_tier, stop_sequences, stream, system, temperature, thinking, tool_choice, tools, top_k, top_p): Create a Message
• messages.message_batches_post(requests): Create a Message Batch
• messages.message_batches_list(before_id, after_id, limit): List Message Batches
• messages.message_batches_retrieve(message_batch_id): Retrieve a Message Batch
• messages.message_batches_delete(message_batch_id): Delete a Message Batch
• messages.message_batches_cancel(message_batch_id): Cancel a Message Batch
• messages.message_batches_results(message_batch_id): Retrieve Message Batch results
• messages.messages_count_tokens_post(messages, model, cache_control, output_config, system, thinking, tool_choice, tools): Count tokens in a Message
• messages.beta_message_batches_post(requests): Create a Message Batch
• messages.beta_message_batches_list(before_id, after_id, limit): List Message Batches
• messages.beta_message_batches_retrieve(message_batch_id): Retrieve a Message Batch
• messages.beta_message_batches_delete(message_batch_id): Delete a Message Batch
• messages.beta_message_batches_cancel(message_batch_id): Cancel a Message Batch
• messages.beta_message_batches_results(message_batch_id): Retrieve Message Batch results
• messages.beta_messages_count_tokens_post(messages, model, cache_control, context_management, mcp_servers, output_config, output_format, speed, system, thinking, tool_choice, tools): Count tokens in a Message

Drill into any operation to see its full signature and parameter docs:

ant_cli.models.models_get

Get a Model

Parameters:

• model_id (str, required): Model identifier or alias.

Anthropic

A simple message request:

resp = await ant_cli.messages.messages_post(
    model="claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "What is FastSpec?"}],
    max_tokens=64,)
resp['content'][0]['text']

"FastSpec could refer to a few different things depending on the context. Here are the most likely meanings:\n\n## 1. **Testing Framework**\nFastSpec is a testing framework, particularly associated with Scala development. It's designed to provide:\n- Fast test execution\n- Clear, readable test syntax"

With streaming — just pass stream=True and iterate:

resp = await ant_cli.messages.messages_post(
    model="claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Say hello in 3 languages."}],
    max_tokens=128, stream=True)
async for ev in resp: 
    if ct:= nested_idx(ev,'delta','text'): print(ct, end=' ')

Hello! Here are gr eetings in 3 languages:

1. **English**: Hello!
2. **Spanish**: ¡Hola!
3 . **French**: Bonjour! 

OpenAI

Chat Completion

resp = await oai_cli.chat.create_chat_completion(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "What is fastspec?"}],
    max_tokens=64)
resp['choices'][0]['message']['content']

'As of my last update in October 2023, "fastspec" is not widely known or associated with a specific standalone technology, product, or concept in public discourse. However, the term could refer to various topics depending on the context, such as:\n\n1. **Software or Libraries**: It might denote a'

Text-to-Speech (file output)

resp = await oai_cli.audio.create_speech(model="tts-1", input="Hello from fastspec!", voice="alloy")
Path("hello.mp3").write_bytes(resp)
print(f"Saved {len(resp)} bytes to hello.mp3")

Saved 26400 bytes to hello.mp3

Transcription (file upload + streaming)

resp = await oai_cli.audio.create_transcription(
    file=open("hello.mp3", "rb"), model="gpt-4o-transcribe", stream=True)
async for ev in resp: print(ev.get('delta', ''), end='')

Hello from Fastbec.

Gemini

Google Discovery specs use nested resource groups with attribute chaining:

gem_cli = OpenAPIClient(gem_spec, headers={"x-goog-api-key": os.environ["GEMINI_API_KEY"]})
str(gem_cli.models)[:500]

'- models.generate_content(model, contents, system_instruction, tools, tool_config, safety_settings, generation_config, cached_content, service_tier, store): *Generates a model response given an input `GenerateContentRequest`. Refer to the [text generation guide](https://ai.google.dev/gemini-api/docs/text-generation) for detailed usage information. Input capabilities differ between models, including tuned models. Refer to the [model guide](https://ai.google.dev/gemini-api/docs/models/gemini) and '

resp = await gem_cli.models.generate_content(
    model="models/gemini-2.5-flash",
    contents=[{"parts": [{"text": "What is fastspec?"}]}])
resp['candidates'][0]['content']['parts'][0]['text'][:200]

'**FastSpec** is a Ruby Gem designed to significantly speed up your local RSpec test suite execution by intelligently identifying and running only the specs relevant to your recent code changes.\n\n### T'

Nested resource groups are accessed with attribute chaining:

gem_cli.tuned_models.permissions.create

Create a permission to a specific resource.

Parameters:

• parent (str, required): Required. The parent resource of the Permission. Formats: tunedModels/{tuned_model} corpora/{corpus}
• role (str, required): Required. The role granted by this permission.
• name (str, optional): Output only. Identifier. The permission name. A unique name will be generated on create. Examples: tunedModels/{tuned_model}/permissions/{permission} corpora/{corpus}/permissions/{permission} Output only.
• grantee_type (str, optional): Optional. Immutable. The type of the grantee.
• email_address (str, optional): Optional. Immutable. The email address of the user of group which this permission refers. Field is not set when permission’s grantee type is EVERYONE.

GitHub

Route parameters (like {owner} and {repo}) are passed as regular function arguments:

resp = await gh_cli.repos.get(owner="AnswerDotAI", repo="fastcore")
resp['full_name'], resp['description'], resp['stargazers_count']

('AnswerDotAI/fastcore', 'Python supercharged for the fastai library', 1101)

gh_cli.repos.get

Get a repository

Docs: https://docs.github.com/rest/repos/repos#get-a-repository

Parameters:

• owner (str, required): The account owner of the repository. The name is not case sensitive.
• repo (str, required): The name of the repository without the .git extension. The name is not case sensitive.

AI Tool Integration (python)

fastspec clients can be made available to AI assistants via solveit’s python sandbox using allow(). Registering an op lets the sandboxed code call it (including the network access it needs); everything else stays blocked. Four levels of access are supported:

Single method access — lock down to specific operations:

allow(oai_cli.images.create_image)

Single group access — only specific groups:

allow(oai_cli.chat)

Single client access — all groups on one specific client:

allow(oai_cli)

Full API access — every operation on every client (any OpFunc may run):

allow({OpFunc: ['__call__']})