spl-llm 3.0.1

SPL — A Declarative Language for Agentic LLM Workflow Orchestration

SPL — Structured Prompt Language

SPL is a declarative language for agentic AI workflows — SQL for LLMs.

Where SQL abstracts over databases, SPL abstracts over language models: the same .spl file runs on Ollama, Claude, OpenAI, Gemini, or a Momagrid compute grid without changing a single line of workflow code.

WORKFLOW self_refine
    INPUT:  @topic TEXT
    OUTPUT: @essay TEXT
DO
    GENERATE writer(@topic) INTO @essay
    CALL critique_workflow(@essay) INTO @feedback
    GENERATE refiner(@essay, @feedback) INTO @essay
    COMMIT @essay
END

spl3 run cookbook/05_self_refine/self_refine.spl --adapter ollama --param topic="the Tao of coding"

Why SPL

Most agentic frameworks require hundreds of lines of imperative Python to wire up prompts, manage context, handle errors, and switch LLM providers. SPL takes a different path: declare what you want, not how to get it.

Concern	Framework approach	SPL approach
Provider switch	Rewrite adapter glue code	--adapter ollama → --adapter momagrid
Retry logic	Try/except boilerplate	RETRY / EXCEPTION WHEN
Sub-agent calls	Async queue + callback hell	CALL workflow_name() INTO @result
Parallel agents	asyncio.gather + coordination	CALL PARALLEL ... END
Token budgets	Manual prompt trimming	OPTIMIZE ... WITHIN 2000 TOKENS

Language Layers

SPL synthesizes three programming paradigms:

Layer	Inspiration	SPL construct
Data	SQL	SELECT, WITH, GENERATE
Logic	Python	CALL, @spl_tool, typed variables
Orchestration	Linux shell	WORKFLOW composition, IMPORT

Version History

Version	Highlights
1.0	SELECT … GENERATE paradigm; single-query PROMPT statements; lexer, parser, executor foundation
2.0	Multi-step WORKFLOW; PROCEDURE; EVALUATE (semantic branching); WHILE; 14 LLM adapters; text2SPL compiler; Momagrid adapter
3.0	Workflow-to-workflow CALL; CALL PARALLEL; IMPORT; Hub registry; Hub-to-Hub peering; multimodal codecs (image / audio / video); splc transpiler (Go, TypeScript, LangGraph)

Quick Start

conda create -n spl123 python=3.11
conda activate spl123        # Python 3.11+

pip install -e ".[dev]"      # install from this repo

# Verify install
spl3 --help

# Run hello world (Ollama)
spl3 run cookbook/01_hello_world/hello.spl --adapter ollama

# Run self-refine with a local model
spl3 run cookbook/05_self_refine/self_refine.spl \
    --adapter ollama \
    --param writer_model=gemma3 \
    --param critic_model=gemma3

# Run all active recipes
python cookbook/run_all.py

# Run a specific subset
python cookbook/run_all.py --ids 01,05,13

# Run multimodal / SPL 3.0 recipes (tier 1 = Ollama only)
python cookbook/run_all.py --tier 1 --category multimodal

CLI Reference

spl3 run   <file.spl> [--adapter NAME] [--model MODEL] [--param KEY=VALUE ...]
spl3 validate  <file.spl>
spl3 explain   <file.spl>
spl3 text2spl  "natural language description" [--mode prompt|workflow|auto]
spl3 code-rag  seed <dir> --catalog <catalog.json>
spl3 code-rag  query "judge-retry loop"
spl3 --hub http://localhost:8080 run <file.spl>       # Hub-backed registry
spl3 --hub http://localhost:8080 register <dir/>       # register workflows on Hub

Adapters

Four adapters form the mandatory baseline that every SPL runtime must support — they cover the full development lifecycle from local prototyping to production grid deployment:

Adapter	Provider	Stage	Notes
ollama	Local models	Local prototyping	Zero cost, zero credentials, works offline — lowest barrier to entry
claude_cli	Claude Code CLI	Best-model validation	Top-tier Anthropic models; subscription billing, no per-call cost
openrouter	200+ models	Broad model coverage	Single key unlocks frontier + open-source models for cross-model testing
momagrid	Decentralized GPU grid	Production grid	SPL workflows as system calls dispatched across owned GPU hardware

Additional adapters available in the Python (spl3) runtime:

Adapter	Provider	Notes
anthropic	Claude (Anthropic API)	ANTHROPIC_API_KEY
openai	GPT / o-series	OPENAI_API_KEY
google	Gemini	GOOGLE_API_KEY
deepseek	DeepSeek	DEEPSEEK_API_KEY
qwen	Qwen (Alibaba)	DASHSCOPE_API_KEY
bedrock	AWS Bedrock	boto3 + AWS credentials
vertex	GCP Vertex AI	GOOGLE_CLOUD_PROJECT + ADC
azure_openai	Azure OpenAI	AZURE_OPENAI_ENDPOINT + key
dd_llm_bridge	Any dd-llm provider	Generic bridge

Porting checklist: Any new SPL runtime port must implement the four mandatory adapters before being considered feature-complete. echo is testing scaffolding only.

Architecture

spl/              SPL 2.0 runtime (lexer, parser, executor, 14 adapters)
  lexer.py          tokenization
  parser.py         recursive-descent parser → AST
  ast_nodes.py      30+ dataclass node types
  executor.py       runtime engine
  analyzer.py       semantic validation
  optimizer.py      token budget allocation
  explain.py        ASCII plan rendering
  ir.py             JSON AST serialization
  text2spl.py       natural language → SPL compiler
  adapters/         LLM backend plugins
  storage/          SQLite memory + vector store (RAG)

spl3/             SPL 3.0 extension layer (inherits from spl/)
  executor.py       SPL3Executor(SPL2Executor) — CALL dispatch, type coercion
  parser.py         SPL3Parser(SPL2Parser) — IMPORT, SET, NONE, CALL PARALLEL
  composer.py       workflow-to-workflow CALL execution
  registry.py       LocalRegistry + FederatedRegistry
  hub_registry.py   REST-backed Hub registry
  event.py          WorkflowInvocationEvent (UUID, lifecycle, Hub serialization)
  status.py         COMMIT status → exception type mapping
  codecs/           image / audio / video codec layer
  splc/             transpilers: Go, TypeScript, LangGraph
  text2spl/         SPL 3.0 text2spl (extends spl/text2spl.py)
  adapters/         SPL 3.0 adapters: multimodal, Liquid, Snap

cookbook/         65 recipes (SPL 2.0: 00–49, SPL 3.0: 50–64)
tests/            unified test suite (300+ tests)

Cookbook

65 recipes spanning beginner to advanced:

Range	Theme
00–09	Basics: hello world, proxy, multilingual, model showdown, self-refine, ReAct
10–19	Patterns: batch test, debate, plan-execute, map-reduce, multi-agent, reflection, tree-of-thought
20–29	Applied: text2SPL, structured output, few-shot, nested procs, A/B test, data extraction
30–39	Applications: code gen, sentiment, Socratic tutor, interview sim, hypothesis tester, tool use
40–49	Advanced: human steering, knowledge synthesis, prompt tuning, adaptive failover, vision, finance
50–64	SPL 3.0: code pipeline, multimodal (image/audio/video), parallel code review, voice dialogue

python cookbook/run_all.py --list           # all 65 recipes
python cookbook/run_all.py --catalog        # full table with tier + category
python cookbook/run_all.py --check          # verify env vars + Ollama models

Momagrid — Decentralized Compute Grid

The momagrid adapter routes SPL inference tasks to a Momagrid Hub, which dispatches them across a LAN grid of GPU nodes.

export MOMAGRID_HUB_URL=http://192.168.1.10:9000

# Single recipe on the grid
spl3 run cookbook/05_self_refine/self_refine.spl --adapter momagrid -m llama3.2

# Full cookbook in parallel (fills the task queue so all nodes get work)
python cookbook/run_all.py --adapter momagrid --workers 5

SPL 3.0 extends Momagrid as a Compute OS: each CALL workflow_name() becomes a Hub-dispatched system call; CALL PARALLEL concurrently routes sub-workflows to different nodes.

Tests

pytest                              # full suite
pytest tests/test_registry.py       # single file
pytest -k test_status_mapping       # single test

Development Workflow

SPL.py is the stable public repo. New features are prototyped in SPL30 and graduate here when they have test coverage and a cookbook recipe.

Future major versions (spl4/, spl5/, ...) will be prototyped in SPL30 and added as new subpackages alongside spl/ and spl3/. The spl3 CLI command is permanent — it always points to the highest available runtime layer.

License

Apache-2.0