tinydsl 0.0.5

A modular agent-ready framework for exploring domain-specific languages (DSLs)

🧩 TinyDSL

TinyDSL is a modular, agent-ready framework for exploring and testing domain-specific languages (DSLs).
It currently supports two DSLs:

• 🎨 Gli — a graphics DSL for procedural image generation
• 🗣️ Lexi — a text DSL for structured, expressive text generation and reasoning

Both are served via a unified FastAPI backend and are designed to be invoked by LLM agents or external REST clients.

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⚙️ Get Started

Use uv for lightweight environment management:

uv venv
uv sync
python -m api.main

Then open http://localhost:8008/docs for the interactive API UI.

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🧠 Example Usage

Lexi (Text DSL)

set mood happy
say "Hello!"
repeat 2 { say "Have a wonderful day!" }

Lexi supports memory persistence:

remember name = "John Arthur"
recall name

Gli (Graphics DSL)

set color orange
repeat 10 {
  set size 3+$i
  draw circle x=cos($i*20)*$i*10 y=sin($i*20)*$i*10
}

Images are saved to /output with timestamped filenames.

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🚀 Highlights

• Unified API for multiple DSLs (/api/gli, /api/lexi)
• Persistent, session-safe memory for Lexi (/api/lexi/memory)
• Built-in benchmark tasks and evaluation metrics
• Lightweight agent tool (TinyDSLTool) for integration with LangChain, Autogen, or OpenAI agents
• Simple modular structure to add new DSLs (e.g., music, logic, or code)

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🤖 Agent Integration

Agents or Python scripts can call TinyDSL directly using the included tool:

from tinydsl_tool import TinyDSLTool

tool = TinyDSLTool(base_url="http://localhost:8008/api")

# Run Lexi DSL
lexi_code = 'set mood happy\nsay "Hello there!"'
print(tool.run_lexi(lexi_code)["output"])

# Run a benchmark task
print(tool.run_lexi_task("005"))

# View and clear Lexi memory
print(tool.get_memory())
tool.clear_memory()

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📚 API Overview

DSL	Endpoint	Method	Purpose
Gli	/api/gli/run	POST	Run graphics DSL code
Gli	/api/gli/run_example/{id}	GET	Execute stored example
Lexi	/api/lexi/run	POST	Execute Lexi DSL code
Lexi	/api/lexi/task	POST	Run a predefined benchmark task
Lexi	/api/lexi/eval	POST	Evaluate multiple outputs
Lexi	/api/lexi/memory	GET	View persistent memory
Lexi	/api/lexi/memory/clear	POST	Clear memory
Lexi	/api/lexi/memory/set	POST	Set key-value in memory

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🧩 Extend

Add a new DSL by creating:

• An interpreter (<dsl_name>.py)
• An API router (routes_<dsl_name>.py)
• Example data or evaluation JSONs (optional)

Then register it in api/main.py. TinyDSL’s modular design supports quick experimentation for acquire-and-apply learning across multiple DSLs.

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🧭 Agent Workflow Example

TinyDSL is built for agent-based experiments testing acquisition, consolidation, and transfer of new skills. A typical loop looks like this:

1. Discover Tasks The agent fetches all available benchmark tasks:

   tasks = [tool.run_lexi_task(tid) for tid in ["001", "002"]]
   

2. Execute & Learn The agent runs Lexi or Gli code, storing intermediate results in memory:

   tool.run_lexi('remember mood = "happy"')
   tool.run_lexi('if mood is happy { say "Training complete!" }')
   

3. Evaluate After training or inference, results are scored automatically:

   results = [{"task_id": "002", "output": "You look great today!"}]
   print(tool.eval_lexi_outputs(results))
   

4. Persist State Memory is persisted across sessions to simulate skill retention:

   print(tool.get_memory())  # See consolidated knowledge
   

5. Transfer Test Clear memory and test new unseen compositions:

   tool.clear_memory()
   tool.run_lexi_task("020")  # Multi-step reasoning
   

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With this flow, LLM agents can learn a new DSL in one session and apply it in another, making TinyDSL a foundation for studying continual learning, compositional reasoning, and symbolic generalization.

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