Metadata-Version: 2.4
Name: manhattan-reasoning-gym
Version: 0.1.2
Summary: SDK + CLI (mrg) for local synth/PnR feedback, sandboxed agents, and running FPGA designs on real silicon.
Project-URL: Homepage, https://manhattanreasoning.com
Project-URL: Repository, https://github.com/ManhattanReasoning/manhattan-reasoning-gym
Project-URL: Issues, https://github.com/ManhattanReasoning/manhattan-reasoning-gym/issues
Author: Barnard PL Labs
License: MIT License
        
        Copyright (c) 2026 Barnard PL Labs
        
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License-File: LICENSE
Keywords: amaranth,ecp5,fpga,hardware,place-and-route,reinforcement-learning,rl,synthesis
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.12
Requires-Dist: requests>=2.31
Provides-Extra: dev
Requires-Dist: pytest>=8.0; extra == 'dev'
Requires-Dist: ruff>=0.4; extra == 'dev'
Description-Content-Type: text/markdown

# Manhattan Reasoning Gym

Python SDK and CLI (`mrg`) for hardware design: iterate **locally** with fast
synthesis / place-and-route feedback, run untrusted agents in a **sandbox**, and
program **real cloud FPGAs** — all from one package.

| Surface | What it does | Needs |
| --- | --- | --- |
| `mrg.build` | local synth / place-and-route reports (no board) | Docker |
| `mrg.Sandbox` | run an agent in a locked container that promotes to silicon | Docker |
| `mrg.cloud` | program + drive a real ECP5 over the cloud | API key |

> Cloud FPGA access requires an **allowlisted API key** (`mrg login`). Local
> synth/PnR and sandboxing need only **Docker** — no toolchain to install.

## Install

```bash
pip install manhattan-reasoning-gym
```

For local synth/PnR and sandboxing, pull the pinned toolchain image once (the
SDK runs it for you — you never install yosys/nextpnr/LiteX):

```bash
docker pull ghcr.io/manhattanreasoning/mrg-sandbox:latest
```

If your system Python is broken or externally managed, the robust path is
`uv venv && uv pip install manhattan-reasoning-gym`.

## Agent quickstart (headless)

Copy-paste path for coding agents and CI — no browser, no prompts:

```bash
export MRG_API_KEY=...        # or: mrg login --github-token <no-scope PAT>
mrg synth examples/design.py  # JSON report on stdout, ~seconds
mrg pnr   examples/design.py  # JSON report (fmax_mhz, timing_met), ~tens of seconds
mrg run   examples/app.py     # programs a real FPGA, ~2-3 min first time
mrg reset 0                   # free the board when done (async, ~1 min)
```

- **All bus addresses are byte addresses**: word *n* in your design's register
  map is byte address `4*n` for `app.read`/`app.write` and `mrg read`/`mrg write`.
- **After `mrg run` the board stays reserved to you** — reuse it with
  `mrg run --no-program` / `mrg read` / `mrg write`, and release it with
  `mrg reset <fpga_id>` when done. Reset is an async queued job that reflashes
  the base SoC (~1 min); poll `mrg status --json` rather than expecting
  instant `idle`.
- `mrg synth` / `mrg pnr` print machine-readable JSON on stdout and exit
  non-zero when the build fails; `mrg status --json` and `mrg job --json`
  emit the raw orchestrator response — parse JSON, don't scrape tables.
- Rough time budget: `synth` seconds · `pnr` tens of seconds · `mrg run`
  ~2-3 min for the first program · `--no-program` seconds · `reset` ~1 min.
- `mrg login` with no flags is an interactive GitHub device flow. Headless,
  prefer `$MRG_API_KEY`, or `mrg login --github-token <PAT>` (also reads
  `$GITHUB_TOKEN`) with a **no-scope** PAT — it's only used to read your
  username. If you must run the device flow from a non-tty context, relay the
  printed URL + code to a human.

## Local feedback — `mrg.build`

```python
import manhattan_reasoning_gym as mrg

rep = mrg.build.synth("examples/design.py")   # fast: resource utilization
rep = mrg.build.pnr("examples/design.py")     # full SoC: real Fmax + timing
print(rep.fmax_mhz, rep.timing_met, rep.util.dsp.used)
```

CLI: `mrg synth design.py` · `mrg pnr design.py [--target-mhz N]`.

`synth` is the cheap "is it getting bigger?" signal (core-only util); `pnr`
places & routes the full SoC for the truthful system-clock Fmax and timing.

## Sandboxed agents — `mrg.Sandbox`

Run untrusted agent code in a locked container (`--network none`, no key) that
iterates locally and promotes vetted candidates to silicon:

```python
import manhattan_reasoning_gym as mrg

result = mrg.Sandbox(files=["examples/design.py", "examples/agent.py"]).run("agent.py")
for promo in result.promotions:
    print(promo)
```

The container never holds a key or touches the network; the promote is a file
handoff the trusted host brokers to silicon. **No promote gating by default** —
the agent gates itself; pass `guard=` for an opt-in host-side check. With no API
key, silicon resolves to a no-op backend; set one (and `silicon="cloud"`) for
real hardware.

## Real silicon — `mrg.cloud`

```python
import manhattan_reasoning_gym as mrg

app = mrg.cloud.App("my_design", design="examples/design.py")
with app:                    # programs the FPGA on first use, releases on exit
    app.write(0x0, 3)        # byte address: word n in the design => 4*n
    print("read:", app.read(0x8))
```

Or drive it from the CLI with a file that defines an `App` and an
`@app.local_entrypoint()` — see [`examples/app.py`](https://github.com/ManhattanReasoning/manhattan-reasoning-gym/blob/main/examples/app.py), which
exercises the example MAC design end-to-end:

```bash
mrg run examples/app.py
```

Name your registers by subclassing `mrg.RegisterMap` (byte addresses).

**Board lifecycle:** the `with app:` form releases the board on exit, but
`mrg run` does **not** — the reservation persists so you can keep iterating
with `mrg run --no-program`, `mrg read`, and `mrg write`. Free the board with
`mrg reset <fpga_id>` (an async job that reflashes the base SoC, ~1 min — poll
`mrg status`).

## Authentication (cloud only)

API requests authenticate with an opaque API key sent as `X-API-Key`. You obtain
one by exchanging a GitHub identity — the orchestrator verifies you against its
allowlist and mints a key.

```bash
mrg login       # GitHub device flow (or paste a no-scope PAT), then you're set
```

The key is saved to `~/.config/mrg/credentials.json` (mode `0600`, keyed by
orchestrator URL). Every command resolves the key in this order:

1. `--api-key` flag
2. `$MRG_API_KEY`
3. the stored login (`mrg login`)

`mrg logout` revokes the key on the server and clears it locally.

## Tutorials

Two runnable notebooks in [`examples/notebooks/`](https://github.com/ManhattanReasoning/manhattan-reasoning-gym/tree/main/examples/notebooks):

- **`01_build_and_run`** — local synth/pnr feedback → program a real FPGA.
- **`02_sandboxing`** — run an agent in a locked sandbox that promotes to silicon.

The shared `examples/design.py` is a tiny tunable multiply-accumulate; tweak its
`WIDTH` and watch DSP/Fmax move. `examples/agent.py` is the sandboxed agent, and
`examples/app.py` is the `mrg run` app that drives the MAC on real silicon.

## CLI reference

```
mrg login | logout
mrg synth <design.py> [--target-mhz N]      # local synthesis report
mrg pnr   <design.py> [--target-mhz N]      # local full-SoC place & route
mrg run   <file.py> [--fpga-id N] [--no-program]
mrg status [fpga_id] [--json]
mrg job <fpga_id> <job_id> [--json]
mrg logs | cancel <fpga_id> <job_id>
mrg reset <fpga_id>
mrg read  <fpga_id> <addr> [--count N]
mrg write <fpga_id> <addr> <value>
```

## Development

```bash
pip install -e ".[dev]"
ruff check . && pytest -q
```

## License

MIT — see [LICENSE](https://github.com/ManhattanReasoning/manhattan-reasoning-gym/blob/main/LICENSE).
