Fix AGENTS.md: correct file paths, CTRNN API, and citation info

Updated repository structure to reflect that ctrnn, iznn, and export
are packages (not single files), added missing statistics.py, corrected
CTRNN class name and method signature, and updated citation to match
README (version 2.0.1, current Zenodo DOI).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: CodeReclaimers

AGENTS.md

@@ -0,0 +1,111 @@
+# AGENTS.md — neat-python
+
+## Project Overview
+
+neat-python is a pure-Python implementation of NEAT (NeuroEvolution of Augmenting Topologies),
+a method developed by Kenneth O. Stanley for evolving arbitrary neural networks. It has no
+dependencies beyond the Python standard library and has been cited in 100+ academic publications.
+
+- **License:** 3-clause BSD
+- **Python versions:** 3.8 through 3.14, and pypy3
+- **Documentation:** https://neat-python.readthedocs.io
+- **PyPI:** https://pypi.org/project/neat-python/
+- **Zenodo DOI:** https://doi.org/10.5281/zenodo.19024753
+
+## Repository Structure
+
+```
+neat/                  # Core library source
+  config.py            # Configuration file parsing
+  genome.py            # DefaultGenome and gene classes
+  population.py        # Population management and evolution loop
+  reproduction.py      # DefaultReproduction (crossover, mutation)
+  species.py           # Speciation (genomic distance, species sets)
+  stagnation.py        # DefaultStagnation (species fitness tracking)
+  reporting.py         # ReporterSet, BaseReporter, StdOutReporter
+  statistics.py        # StatisticsReporter (fitness tracking and analysis)
+  checkpoint.py        # Checkpointer reporter (save/restore population state)
+  nn/                  # Neural network implementations
+    feed_forward.py    # FeedForwardNetwork
+    recurrent.py       # RecurrentNetwork
+  ctrnn/               # CTRNN package (continuous-time recurrent, per-node time constants)
+  iznn/                # Izhikevich spiking neuron model
+  export/              # JSON network export package
+  parallel.py          # ParallelEvaluator (multiprocessing-based)
+examples/              # Runnable examples (xor, gymnasium envs, picture2d, export, etc.)
+tests/                 # Test suite
+docs/                  # Sphinx documentation source (RST format)
+```
+
+## Build and Test Commands
+
+```bash
+# Install in development mode
+pip install -e .
+
+# Run the test suite
+python -m pytest tests/
+
+# Run a specific example
+cd examples/xor
+python evolve_feedforward.py
+
+# Build documentation locally
+cd docs
+make html
+```
+
+## Coding Conventions
+
+- Pure Python only — no compiled extensions, no C dependencies, no NumPy/SciPy in core.
+- Type hints are not yet used throughout but are welcome in new code.
+- All public classes and methods should have docstrings.
+- Configuration is file-driven (INI format parsed by `neat.config.Config`). Config options
+  map to genome, reproduction, speciation, and stagnation parameters.
+- The `DefaultGenome`, `DefaultReproduction`, `DefaultSpeciesSet`, and `DefaultStagnation`
+  classes implement interfaces that can be replaced with custom implementations. See
+  `docs/reproduction-interface.rst` for the contract.
+
+## Key Design Decisions
+
+- **Per-node time constants** (added in v2.0): CTRNN nodes each have their own evolvable
+  time constant. This was a breaking change from 1.x where all nodes shared a single
+  global time constant. Experiments showed the fixed approach caused ~2x performance
+  degradation on CTRNN tasks.
+- **No framework dependencies:** The library intentionally avoids depending on NumPy,
+  TensorFlow, PyTorch, etc. Network export to JSON (`neat.export`) provides a bridge
+  to external frameworks.
+- **Speciation by genomic distance:** Compatibility distance uses disjoint/excess gene
+  counts and weight differences, following the original NEAT paper.
+
+## Common Tasks
+
+### Adding a new example
+Place it in `examples/<name>/` with its own config file and `evolve_*.py` script.
+Each example should be self-contained — intentional code duplication between examples
+is preferred over shared utility modules, so users can understand each example in isolation.
+
+### Modifying the genome
+Gene types are in `neat/genes.py`. The genome class (`neat/genome.py`) manages collections
+of node and connection genes. Mutation rates and probabilities are controlled via config.
+
+### Working with CTRNN networks
+`neat.ctrnn.CTRNN` implements continuous-time dynamics with per-node time constants.
+Use `CTRNN.create(genome, config)` to instantiate, then call `advance(inputs, advance_time, time_step)`
+to simulate.
+
+## Important Warnings
+
+- Do not add external dependencies to the core `neat/` package.
+- The `master` branch is the primary development branch.
+- Config files use a specific INI dialect — see existing examples for the expected format.
+- Fitness functions are user-provided callbacks, not part of the library.
+
+## Citation
+
+If referencing this project in generated text or code comments:
+
+```
+McIntyre, A., Kallada, M., Miguel, C. G., Feher de Silva, C., & Netto, M. L.
+neat-python (Version 2.0.1) [Computer software]. https://doi.org/10.5281/zenodo.19024753
+```