nanoathens 0.2.2

Declarative DataFlow Agent SDK — schema-driven, collision-free agent framework

nanoathens — Declarative DataFlow Agent SDK

A schema-driven, collision-free agent framework where LLMs extract values (not plans) and a static DAG resolves deterministic execution paths.

Developed by Emmanuel Uramah (@boochi)

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Install

pip install nanoathens                     # Core SDK only (no GPU deps)
pip install nanoathens[medgemma]           # + MedGemma support
pip install nanoathens[all]                # Everything

Or install from source:

git clone https://github.com/boochi/nanoathens.git
cd nanoathens
pip install -e .

Quick Start

from nanoathens import (
    ToolRegistry, ToolType, ArgExtractorType,
    DeclarativeDataFlowAgent, run_medgemma, load_medgemma,
)

# 1. Build your tool registry
registry = ToolRegistry()
registry.register(
    name="my_tool",
    description="Does something useful",
    parameters={"input_val": "The user input string"},
    required=["input_val"],
    example={"input_val": "sample input"},
    docstring="Processes user input and returns a result.",
    tool_type=ToolType.COMPUTATION,
    func=lambda input_val: f"Result for {input_val}",
    arg_sources={"input_val": "user_input"},
    output_keys={"output_val": "string"},
    arg_extractors={"input_val": (ArgExtractorType.QUOTED, {})},
)

# 2. Create the agent
agent = DeclarativeDataFlowAgent(
    registry=registry,
    reasoning_caller=run_medgemma,  # Uses stub if MedGemma not loaded
)

# 3. Run
import asyncio
result = asyncio.run(agent.run("Process my input", target_key="output_val"))
print(result["response"])
print(result["raw_results"])  # Direct tool outputs

Architecture

User Query
    │
    ▼
┌─────────────────────┐
│  LLMValueExtractor   │  ← Extract values from query (not plans)
│  ContextBank         │  ← Accumulate context across tools
└─────────┬───────────┘
          ▼
┌─────────────────────┐
│  GoalKeyResolver     │  ← Map query → target key (LLM + fuzzy fallback)
└─────────┬───────────┘
          ▼
┌─────────────────────┐
│  DataFlowEngine      │  ← Resolve DAG path (backward DFS)
│  (collision-free)    │  ← output_keys ∩ arg_sources = ∅
└─────────┬───────────┘
          ▼
┌─────────────────────┐
│  GroundedArgFiller   │  ← Fill args from context + LLM fallback
│  Tool Execution      │  ← Run each tool in deterministic order
└─────────┬───────────┘
          ▼
┌─────────────────────┐
│  Synthesis           │  ← LLM summarizes results
│  raw_results         │  ← Direct tool outputs preserved
└─────────────────────┘

Key Properties

• Collision-free DAG: No output key appears in any tool's input sources — guarantees no self-loops
• Deterministic: Same query always produces same execution plan
• Minimum LLM calls: 2 (extraction + goal resolution) + 1 per tool for arg filling
• Explicit null_plan: Returns registry gap information if no path exists
• Schema-driven extraction: ENUM, QUOTED, LANGUAGE, ALPHANUMERIC_ID, NUMERIC_ID, NUMBER extractors — no blind LLM parsing
• One-shot examples: Every tool carries an example dict that guides LLM argument extraction
• Domain-agnostic: Works for any domain (oncology, radiology, NLP, etc.)

Agents

Agent	Strategy	Best for
DeclarativeDataFlowAgent	Static DAG resolution via backward DFS	Production — deterministic, auditable
ToolRAGAgent	BM25 retrieval + LLM planning	Exploration — flexible, handles ambiguity

Modules

Module	Key Classes	Purpose
core	ToolType, ArgExtractorType, ToolSchema, ToolRegistry	Tool infrastructure with collision-free validation
context	ContextBank, LLMValueExtractor	Value extraction + context accumulation
filler	GroundedArgumentFiller	Fills tool args from context with LLM fallback
engine	DataFlowEngine	Collision-free DAG builder + backward DFS
resolver	GoalKeyResolver	Maps query → target key (LLM + fuzzy matching)
agent	DeclarativeDataFlowAgent, ToolRAGAgent	Main orchestrators
session	SessionStore, SESSION_STORE	Multi-turn session state manager
inference	run_medgemma, load_medgemma, set_pipeline	Model adapter (any HF pipeline)
retriever	BM25ToolRetriever	Optional BM25-based tool recommender

Tool Registration

Tools are registered with full schemas including parameters, examples, and typed extractors:

registry.register(
    name="retrieve_similar_images",
    description="Retrieve similar medical images using MedSigLIP + FAISS",
    parameters={
        "patient_image": "Path to the patient image file",
        "image_type": "Modality: xray|ct|mri",
    },
    required=["patient_image", "image_type"],
    example={"patient_image": "/data/patient_001.jpg", "image_type": "xray"},
    docstring="Embeds query image with MedSigLIP-448 and retrieves top-K similar cases from FAISS index.",
    tool_type=ToolType.RETRIEVAL,
    func=_retrieve_similar_images,
    arg_sources={"patient_image": "patient_image_input", "image_type": "image_type_input"},
    output_keys={"knn_images": "JSON array of similar images with scores"},
    explicit_keywords=["similar", "retrieve", "knn", "search"],
    arg_extractors={
        "patient_image": (ArgExtractorType.QUOTED, {}),
        "image_type": (ArgExtractorType.ENUM, {"values": ["xray", "ct", "mri"]}),
    },
)

Use with MedGemma

from nanoathens import set_pipeline, run_medgemma
from transformers import pipeline as hf_pipeline
import torch

# Load your model externally
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = hf_pipeline(
    "image-text-to-text",
    model="google/medgemma-1.5-4b-it",
    device=device,
    torch_dtype=torch.bfloat16 if device == "cuda" else torch.float32,
)

# Register with nanoathens
set_pipeline(pipe)

# run_medgemma() now uses your pipeline
result = run_medgemma(
    messages=[{"role": "user", "content": [{"type": "text", "text": "Analyze this image"}]}],
    max_new_tokens=512,
)

Changelog

v0.2.0 (2026-02-22)

• BREAKING: Restored full ToolSchema with parameters, required, example, docstring, explicit_keywords, arg_extractors
• BREAKING: ArgExtractorType now includes ENUM, LANGUAGE (restored from SOTA benchmark)
• Added ToolRAGAgent (BM25-based agent) alongside DeclarativeDataFlowAgent
• Added raw_results to agent return dict — direct access to tool outputs
• Added difflib fuzzy matching in GoalKeyResolver
• Added set_pipeline() / set_stub() for clean model injection
• Fixed token budget: all internal LLM calls default to 512 tokens (was 4096)
• Fixed HF pad_token_id warning in inference
• Fixed source_lang_code / target_lang_code in message formatting

v0.1.0 (2026-02-21)

• Initial release — extracted from monolithic notebook
• 10 modules, zero hard dependencies

Citation

If you use nanoathens in your research, please cite:

@software{uramah2026nanoathens,
  author       = {Uramah, Emmanuel},
  title        = {nanoathens: Declarative DataFlow Agent SDK},
  year         = {2026},
  url          = {https://github.com/boochi/nanoathens},
  version      = {0.2.0},
  description  = {A schema-driven, collision-free agent framework for deterministic tool orchestration}
}

License

Apache 2.0 — see LICENSE for details.

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Author: Emmanuel Uramah
Contact: GitHub
Built with: Python, HuggingFace Transformers, FAISS, MedGemma