stapel-agent 0.2.11

LLM facade Django app for the Stapel framework

stapel-agent

LLM facade — JSON completion, translation, transcription and summarization in front of swappable model/STT providers, with a prompt cache and a token ledger

Part of the Stapel framework — composable Django apps for building production-grade platforms.

Python port of a prior NestJS service. Same HTTP paths and contracts (stapel-translate's AgentProvider keeps working unchanged), plus a comm surface so monolith deployments call it in-process without HTTP.

Installation

pip install stapel-agent            # core
pip install stapel-agent[anthropic] # + the Anthropic SDK for the default provider

Quick start

# settings.py
INSTALLED_APPS = [
    ...
    'stapel_agent',
]

STAPEL_AGENT = {
    "ANTHROPIC_API_KEY": "sk-ant-...",
}

# urls.py — mount the app under agent/
urlpatterns = [
    ...
    path("agent/", include("stapel_agent.urls")),
]

Two surfaces, same contracts:

Surface	HTTP	comm Function	Does
Complete	POST /agent/api/llm/complete	llm.complete	JSON completion: {"prompt", "model": "small|medium|large", "system_prompt"?, "provider"?, "images"?} → parsed JSON in result, prose in comment. images (vision) entries are {url} or {data_b64, mime?} — OCR, screenshots, photo moderation
Translate	POST /agent/api/llm/translate	llm.translate	{"from"/"from_lang", "to", "entries": {key: text}} → {key: translated} (cached by prompt)
Transcribe	POST /agent/api/llm/transcribe	llm.transcribe	{"audio_url", "language"?, "diarization"?, "provider"?, "timeout_seconds"?} → a normalized transcript (words, utterances, speakers, timings) via the STT router
Summarize	POST /agent/api/llm/summarize	llm.summarize	exactly one of text / transcript (+ language?, model?, provider?) → Markdown summary + aggregated usage; long inputs are map-reduced
Generate image	POST /agent/api/llm/generate-image	llm.generate_image	{"prompt", "size"?, "n"? (1-10), "provider"?} → raw provider results [{url? | data_b64?, mime}] — storing them in a CDN/asset library is the caller's job

# HTTP (service-to-service: X-API-KEY, or a staff session)
POST /agent/api/llm/complete   {"prompt": "...", "model": "small|medium|large",
                                "provider"?: "...", "system_prompt"?: "...",
                                "images"?: [{"url": "https://..."} | {"data_b64": "...", "mime"?: "image/webp"}]}
POST /agent/api/llm/translate  {"from": "auto", "to": "de", "entries": {"key": "text"}}
POST /agent/api/llm/transcribe {"audio_url": "https://...presigned...", "language"?: "en",
                                "diarization"?: true, "provider"?: "elevenlabs"}
POST /agent/api/llm/summarize  {"text": "..."} | {"transcript": {...llm.transcribe output...}}
POST /agent/api/llm/generate-image {"prompt": "a cat", "size"?: "1024x1024", "n"?: 2}

# comm (in-process in a monolith, transport chosen by STAPEL_COMM)
from stapel_core.comm import call

call("llm.complete", {"prompt": "...", "model": "small"})
call("llm.complete", {"prompt": "what is on this?", "model": "small",
                      "images": [{"url": "https://..."}]})  # url or data_b64 — never raw bytes
call("llm.translate", {"from_lang": "auto", "to": "de", "entries": {...}})
call("llm.transcribe", {"audio_url": "https://..."})  # URLs only — never raw bytes
call("llm.summarize", {"transcript": result["transcript"]})
call("llm.generate_image", {"prompt": "a cat", "n": 2})

LLM failures are HTTP 200 with {"status": "failure", "reason": ...} — 4xx/5xx are reserved for request validation and auth. Successful completions return the parsed JSON in result, prose around it in comment, and snake_case usage (input_tokens / output_tokens).

Every provider call writes a PromptLog row: model, size, source, status, duration and the full token ledger (input / output / thinking / cache-read / cache-write) — per-user and per-source cost accounting needs no other table. Transcriptions land there too (source=transcribe, model = STT provider name, token columns NULL, the fallback walk in metadata.attempts); each summarize pass is a normal LLM row (source=summarize); image generations log as source=generate_image with {count, mimes, bytes_total} in metadata — image bytes never touch the ledger, and multimodal completions never collide with the text-keyed prompt cache.

Transcription routes through an STT provider chain — explicit provider in the request beats STT_LANGUAGE_ROUTES[lang], which beats DEFAULT_STT_PROVIDER + STT_FALLBACK_CHAIN — and falls back only on transient failures (429/5xx/timeouts); bad audio or auth errors never retry on another provider.

Settings — STAPEL_AGENT

Key	Default	Meaning
MODELS	{"small": "claude-haiku-4-5-20251001", "medium": "claude-sonnet-5", "large": "claude-opus-4-8"}	Size → model-name map
PROVIDERS	{}	Overlay merged over the built-in registry (anthropic / openai-compat / claude-code) — add/override entries, None removes one; resolved lazily per request
DEFAULT_PROVIDER	"anthropic"	Provider used when a request names none
ANTHROPIC_API_KEY	""	Key for the Anthropic SDK provider (read lazily)
OPENAI_COMPAT_BASE_URL	""	Base URL of any OpenAI-compatible endpoint
OPENAI_COMPAT_API_KEY	""	Bearer token for that endpoint
OPENAI_COMPAT_MODELS	{}	Optional size → model map for openai-compat (missing sizes fall back to MODELS)
CLI_BINARY	"claude"	Claude Code CLI binary (opt-in provider only)
CLI_TIMEOUT	120	Provider timeout, seconds
MAX_TOKENS	4096	Completion token cap
STT_PROVIDERS	{}	Overlay merged over the built-in STT registry (whisper-http / elevenlabs / assemblyai) — same semantics as PROVIDERS
DEFAULT_STT_PROVIDER	"whisper-http"	STT provider used when a request pins none and no language route matches
STT_FALLBACK_CHAIN	[]	STT providers tried in order after the default — on transient failure only
STT_LANGUAGE_ROUTES	{}	{"ru": ["gigaam", "whisper-http"], ...} language matrix (beats the default chain)
STT_TIMEOUT	1800	Hard cap (seconds) on one STT provider's submit+poll cycle
WHISPER_BASE_URL / WHISPER_API_KEY / WHISPER_MODEL	"" / "" / "whisper-1"	OpenAI-compatible Whisper endpoint — the OpenAI API or self-hosted faster-whisper (key optional)
ELEVENLABS_API_KEY / ELEVENLABS_STT_URL / ELEVENLABS_STT_MODEL	"" / Scribe URL / "scribe_v2"	ElevenLabs Scribe
ASSEMBLYAI_API_KEY / ASSEMBLYAI_BASE_URL / ASSEMBLYAI_MODEL	"" / "https://api.assemblyai.com" / "universal"	AssemblyAI (async submit+poll)
IMAGE_PROVIDERS	{}	Overlay merged over the built-in image registry (openai-images) — same semantics as PROVIDERS
DEFAULT_IMAGE_PROVIDER	"openai-images"	Image provider used when a request pins none
IMAGES_BASE_URL / IMAGES_API_KEY	"" / ""	OpenAI-compatible /images/generations endpoint + key; both fall back to the OPENAI_COMPAT_* pair
IMAGES_MODEL	""	Optional model name (gpt-image-1, ...); empty = omitted from the request
CACHE_LOOKUP	{"llm_facade": False, "translate": True, "summarize": False}	Per-source cache-by-prompt toggle (used by the default cache policy)
CACHE_TTL	604800	Cache window in seconds (7 days); older rows are ignored (default policy)
CACHE_POLICY	"stapel_agent.cache.PromptLogCachePolicy"	Dotted path to a CachePolicy subclass — swap the prompt cache (Redis, no-op, ...) without forking

Provider matrix

Name	Class	Backend	Needs
anthropic (default)	providers.anthropic.AnthropicProvider	Anthropic SDK	anthropic extra + ANTHROPIC_API_KEY
openai-compat	providers.openai_compat.OpenAICompatProvider	Any /chat/completions dialect: OpenAI, DeepSeek, MiMo, GLM, Kimi	OPENAI_COMPAT_BASE_URL (+ key)
claude-code	providers.claude_cli.ClaudeCodeCLIProvider	Spawns claude -p ... --output-format json	The CLI in the host image

No CLI in any default path. claude-code is strictly opt-in: it exists for hosts that ship the Claude Code CLI in their image and want the CLI to handle its own authentication (provider: "claude-code" per request, or DEFAULT_PROVIDER override). There is no OAuth credential reading and no background token-refresh — that plumbing was deliberately dropped.

Adding, overriding and removing providers (merge semantics)

STAPEL_AGENT["PROVIDERS"] is an overlay merged over the built-ins, not a replacement dict — adding your provider never requires restating the three shipped ones, and setting a name to None removes it:

# settings.py — one line per change, built-ins stay available
STAPEL_AGENT = {
    "PROVIDERS": {
        "acme": "myproject.llm.AcmeProvider",  # add a custom backend
        "claude-code": None,                    # remove a built-in
    },
    "DEFAULT_PROVIDER": "acme",
}

Or register at runtime from your app's AppConfig.ready() (highest precedence):

from stapel_agent import register_provider

register_provider("acme", AcmeProvider)  # class or dotted path

A custom backend is just a stapel_agent.LlmProvider subclass returning a ProviderResult. Django system checks (stapel_agent.E001/W001/W002) flag a DEFAULT_PROVIDER that is not in the effective registry, unimportable dotted paths and non-LlmProvider entries at startup. See MODULE.md for the full extension-point map.

STT provider matrix

Name	Class	Backend	Needs
whisper-http (default)	stt.providers.whisper_http.WhisperHttpProvider	OpenAI Whisper API or any self-hosted server speaking the same dialect (faster-whisper, whisper.cpp); accepts url/path/bytes refs	WHISPER_BASE_URL (+ key for OpenAI)
elevenlabs	stt.providers.elevenlabs.ElevenLabsProvider	ElevenLabs Scribe (synchronous multipart), diarization	ELEVENLABS_API_KEY; URL refs only
assemblyai	stt.providers.assemblyai.AssemblyAIProvider	AssemblyAI async submit+poll, diarization, 99-language universal model	ASSEMBLYAI_API_KEY; URL refs only

The STT registry has the same merge semantics as the LLM one (STAPEL_AGENT["STT_PROVIDERS"] overlay, None removes, or register_stt_provider() at runtime), and its own startup checks (stapel_agent.W003/W004). A custom engine is a stapel_agent.SttProvider subclass returning a NormalizedTranscript — MODULE.md walks through a self-hosted GigaAM adapter as the worked example.

Vision & image generation

The two vision-capable LLM providers map images into their dialects automatically — anthropic to image content blocks (URL or base64 source), openai-compat to image_url parts (URL or data URI). claude-code has no vision: an image request through it fails fast with status: "failure".

Image generation ships one built-in backend, openai-images — anything speaking the OpenAI POST {base}/images/generations dialect (OpenAI, Together, self-hosted). It is the third instance of the merge-registry pattern (STAPEL_AGENT["IMAGE_PROVIDERS"] overlay, None removes, register_image_provider() at runtime; startup checks stapel_agent.W005/W006). Vendors with their own protocols (Stability, Ideogram, ...) are an app-layer stapel_agent.ImageGenProvider subclass — recipe in MODULE.md. The agent returns raw results and writes the ledger; storage/placement belongs to the calling tier.

License

MIT — see LICENSE