agentic-compiler

agentic-compiler

Runtime-adaptive compilation for Python. Automatically profiles hot functions, compiles them to optimized backends (Numba, Rust, CUDA), and hot-swaps them at runtime — no human intervention required.

The Pitch

You write plain Python. The compiler watches. After 100+ calls it notices your function is slow, compiles it with Numba, verifies the output is identical, measures the speedup, and silently replaces the slow version — all while your program is still running. If the compiled version is ever wrong, it rolls back in one call.

Installation

pip install agentic-compiler

With Numba support (recommended):

pip install agentic-compiler[numba]

Quickstart

from agentic_compiler import Compiler
import numpy as np

compiler = Compiler()
compiler.install()  # monkey-patch all hot paths

# Your normal code — the compiler is watching
def slow_sum(arr):
    total = 0.0
    for i in range(len(arr)):
        total += arr[i] * arr[i]
    return total

# Run it enough times to trigger compilation
for _ in range(200):
    slow_sum(np.random.randn(1000))

# The compiler will auto-compile and hot-swap if speedup > 2x
print(compiler.profiler.report())

Decorator-style profiling

from agentic_compiler import Profiler

profiler = Profiler(sample_rate=0.05)

@profiler.watch
def my_hot_function(x):
    return np.sum(x ** 2)

Manual hot-swap

from agentic_compiler import Compiler

compiler = Compiler()

result = compiler.hot_swap(my_slow_function)
print(f"Speedup: {result.speedup:.1f}x, Validated: {result.validated}")

# Rollback if something goes wrong
compiler.restore()

One-shot compilation

from agentic_compiler import ast_to_numba

kernel = ast_to_numba(my_function)
if kernel.ready:
    print(f"Compiled to {kernel.backend} in {kernel.compile_time_ms:.1f}ms")

Grid-Aware Backend Selection

The GridBackendSelector automatically picks the right backend based on workload size:

from agentic_compiler import GridBackendSelector

backend = GridBackendSelector.select(n_rooms=750)
print(GridBackendSelector.report())

API Reference

Compiler

• install(module_name=None) — monkey-patch functions for profiling
• uninstall() — restore originals
• compile_hotspots(top_n=5) — auto-compile top-N hot functions
• compile_function(func, module, attr_name) — manual compile + swap
• hot_swap(func, module, attr_name, backend) — compile + swap in one call
• restore(key=None) — rollback to original function

Profiler

• watch(func) — decorator to profile a function
• get_hotspots(top_n=10) — ranked list by optimization potential
• report() — human-readable profiling summary

CodeGenerator

• compile(func) — compile to best available backend
• validate(kernel, original, test_args) — A/B test for correctness
• measure_speedup(kernel, original, test_args) — benchmark speedup
• deploy(kernel, module, attr_name) — hot-swap into module

GeneratedKernel

• ready — bool, True if compilation succeeded
• compiled — the compiled callable
• backend — "numba", "rust", or "python"
• compile_time_ms — how long compilation took
• speedup — measured speedup vs original

Architecture

Profiler → Analyzer → CodeGenerator → Validator → Deployer
 (watch)   (rank)    (compile)     (A/B test)  (hot-swap)

1. Profiler samples 5% of calls, tracks timing and input shapes
2. Analyzer scores functions for Numba vs Rust suitability via AST
3. CodeGenerator compiles to best backend, with fallback to identity
4. Validator runs A/B tests to verify correctness
5. Deployer hot-swaps if speedup exceeds 2× threshold

Testing

pip install -e ".[numba]"
pytest

Tests cover profiler instrumentation, backend selection, hot-swap correctness, and Numba integration (mocked when unavailable).

Related Repos

• forgemaster — Forge orchestration (uses agentic compilation for swarm deliberation)
• cocapn-plato — Fleet PLATO integration
• tensor-spline — Compressed neural network layers

License

MIT