预测RLM

Installation

uv add predict-rlm

Optional extras are available for adjacent tooling:

```bash

Optional: Docker Sandboxes backend

JSPI/Deno/Pyodide remains the default sandbox. Use Docker Sandboxes (sbx) when you want an explicit opt-in Linux Python runner:

brew install docker/tap/sbx
sbx login

from predict_rlm import PredictRLM, SbxConfig, SbxPool

rlm = PredictRLM(
    "question -> answer",
    sandbox_backend="sbx",
    sbx_config=SbxConfig(name="my-predict-rlm-sbx"),
)

By default, SbxConfig passes the explicit non-Docker shell template docker.io/docker/sandbox-templates:shell to sbx create. Pass a custom template="..." to override it, or template=None to omit --template and use Docker's CLI default behavior.

For throughput-sensitive evals or optimization loops, create a pool of prewarmed runners and pass it explicitly:

with SbxPool(size=4, config=SbxConfig()) as pool:
    rlm = PredictRLM(
        "question -> answer",
        sandbox_backend="sbx",
        sbx_pool=pool,
    )

The backend mounts only a per-run staging directory under .predict_rlm_sbx/ by default, preserving model-facing paths such as /sandbox/input/... and /sandbox/output/... without exposing the rest of the repo workspace. Use SbxConfig(extra_workspaces=[...]) only when the sandbox needs explicit additional host mounts. Real sbx integration tests are skipped by default; run them with PREDICT_RLM_RUN_SBX_TESTS=1 uv run pytest -m sbx after the CLI is installed and logged in.

The native execution stack uses a persistent supervisor plus a persistent Python kernel so successful iterations preserve full REPL state. Per-iteration timeouts are recoverable when the backend can interrupt execution cleanly; native hard-kill fallback restores only a pre-timeout pickleable snapshot and tells the RLM which globals / imports were lost.

See predict-rlm Architecture for the component model, timeout behavior, and shared backend contracts.

Quick start

Quick Example

import dspy
from predict_rlm import File, PredictRLM

class AnalyzeImages(dspy.Signature):
    """Analyze images and answer the query. Load each image as a base64 data
    URI and use predict() with dspy.Image to extract visual information."""
    images: list[File] = dspy.InputField()
    query: str = dspy.InputField()
    answer: str = dspy.OutputField()

rlm = PredictRLM(
    AnalyzeImages,
    lm="openai/gpt-5.4",
    sub_lm="openai/gpt-5.1",
)

result = rlm(
    images=[File(path="page.png")],
    query="Extract all visible text, then count each letter A-Z (case-insensitive).",
)

print(result.answer)

Demos