predict-rlm 0.7.3

Production-grade RLMs (Recursive Language Models) with tool use, built on DSPy

[!NOTE] Read the launch post for our optimized SpreadsheetBench skill.

predict-rlm

Many LLM workflows are too complex for one prompt and too adaptive for a fixed chain.

predict-rlm gives models a runtime for those workflows: inspect files, keep state, branch, call focused sub-models, use tools, manage large context through code, and return typed output.

You define the inputs, outputs, tools, and operating procedure. The model writes and executes Python in a sandboxed REPL, adapting as it discovers evidence.

Use it when you know the outcome you want, but not the exact path.

Based on the Recursive Language Models paper by Alex L. Zhang, Tim Kraska, and Omar Khattab from MIT CSAIL.

crafted with ♥ in MTL · NYC · FLP
by Trampoline AI

When to use it

predict-rlm is a good fit when the model needs to explore, reason, and adapt before it can produce the final output:

• codebase analysis and investigations
• document review, redaction, extraction, and comparison
• log analysis and incident-style evidence gathering
• spreadsheet and financial-model workflows
• audits, compliance review, and messy data transformation
• multi-file synthesis with typed outputs and readable traces

It is probably not the right tool for simple chat completions, one-shot classification, deterministic ETL, or tiny prompts where a direct LLM call is already enough.

Installation

uv add predict-rlm

Optional extras are available for adjacent tooling:

# GEPA optimization support
uv add "predict-rlm[gepa]"

# Codex-backed DSPy LM and the `codex-lm` CLI
uv add "predict-rlm[codex-lm]"

# Docker Sandboxes backend support
uv add "predict-rlm[sbx]"

With the Codex LM extra installed, import CodexLM or use the script. The vendored backend supports the GPT-5.6 family: gpt-5.6-sol, gpt-5.6-terra, and gpt-5.6-luna.

from dspy_codex_lm import CodexLM

codex-lm auth list
codex-lm usage

Why RLMs?

• Avoid context rot — The outer LM works through files, variables, and tool calls instead of trying to keep every detail in one prompt. Large inputs stay as file paths and metadata until the runtime needs to inspect them.
• Adaptive execution inside a defined procedure — A signature gives the workflow a contract, while the REPL lets the model branch, retry, verify, and accumulate state across iterations.
• Focused sub-model calls — predict() lets the runtime spin up typed DSPy signatures for narrow perception and extraction tasks, including multimodal calls with dspy.Image.
• Readable trajectories — Every run records generated code, output, tool calls, predict() subcalls, timings, token usage, errors, and final SUBMIT payloads, so you can inspect what happened instead of guessing.
• Optimization-ready traces — The same traces that help humans debug a run can feed tools like GEPA to improve RLM strategies from scored examples.

Features

• Multimodal — process images and rendered document pages through sub-LM calls using native provider multimodal APIs.
• Async tool calling — native RLM async support in the WASM sandbox, enabling concurrent sub-LM invocations and tool calls.
• Skills & tools — bundle domain instructions, PyPI packages, sandbox modules, and host-side tools for reusable task capabilities.
• Simple file I/O — pass local files and mutable workspaces as typed inputs, and return generated artifacts through File outputs.
• Structured sub-LM calls — native Pydantic and DSPy signature support for type-safe sub-LM invocations with structured outputs.

Demos

Description	Input / Output	Preview
Document Analysis — Analyze documents and extract key dates, entities, and financial information into a structured report	Input: PDFs Output: Structured briefing report (example output)
Document Redaction — Redact PII from PDFs based on a policy, then verify the redactions visually	Input: PDFs Output: Redacted PDFs (example output)
Invoice Processing — Extract vendor info, line items, and totals from PDF invoices into a consolidated Excel spreadsheet	Input: PDF invoices Output: Excel spreadsheet (example output)
Contract Comparison — Compare two contract versions and produce a structured diff report with per-section analysis	Input: 2 PDF contracts Output: Structured diff report (example output)

Quick start

With your coding agent

Install the predict-rlm skill in Claude Code, Codex, Cursor, or any compatible coding agent:

npx skills add Trampoline-AI/predict-rlm

Then ask your agent to build an RLM:

❯ /rlm build an RLM that extracts line items from PDF invoices into a spreadsheet

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)

Observability

Every PredictRLM call returns a structured prediction.trace with iterations, code, output, tool calls, predict() subcalls, timings, token usage, and errors. Human-readable colored trace blocks are printed to stderr by default; pass verbose=False for quiet execution. Use debug=True for timestamped RLM and sandbox lifecycle diagnostics; error-like debug records are colored red.

Optional: Docker Sandboxes backend

JSPI/Deno/Pyodide remains the default sandbox. After installing predict-rlm[sbx], 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.

Using the spreadsheet skill

The optimized spreadsheet skill is built in. Import it and pass it through skills=[spreadsheet] so the RLM gets the spreadsheet-specific instructions, openpyxl, pandas, formulas, and the formula_eval verification module inside its sandbox.

import dspy

from predict_rlm import File, PredictRLM
from predict_rlm.skills import spreadsheet


class UpdateWorkbook(dspy.Signature):
    """Update the workbook following the request.

    Use openpyxl for workbook edits, Excel formulas for derived values, and
    verify formulas before returning the final .xlsx file.
    """

    workbook: File = dspy.InputField(desc="Input .xlsx workbook")
    request: str = dspy.InputField(desc="Requested spreadsheet changes")
    updated_workbook: File = dspy.OutputField(desc="Updated .xlsx workbook")


rlm = PredictRLM(
    UpdateWorkbook,
    lm="openai/gpt-5.4",
    sub_lm="openai/gpt-5.1",
    skills=[spreadsheet],
)

result = rlm(
    workbook=File(path="model.xlsx"),
    request="Add a Summary sheet with revenue by quarter and formulas for totals.",
)

print(result.updated_workbook.path)

For workflows that combine source documents and spreadsheets, compose skills:

from predict_rlm.skills import pdf, spreadsheet

rlm = PredictRLM(ProcessInvoices, skills=[pdf, spreadsheet])

Lifecycle callbacks

Hook into the RLM iteration loop to broadcast progress to a UI, write structured logs, or feed an observability pipeline. PredictRLM extends DSPy's existing callback contract (dspy.utils.callback.BaseCallback) with two RLM-specific handlers:

Handler	Fires	Receives
on_rlm_iteration_start	Before the action LM is called for iteration N	call_id, instance, iteration, max_iterations
on_rlm_iteration_end	After iteration N finishes (code executed, IterationStep built — or an error was raised)	call_id, instance, iteration, step: IterationStep | None, is_final: bool, exception: Exception | None

call_id matches the parent module's on_module_start/end ID, so events correlate cleanly with DSPy's own callback events when you invoke the RLM through DSPy's public module call path (rlm(...) or await rlm.acall(...)). Existing BaseCallback subclasses keep working unchanged — handlers we call are opt-in via getattr.

Async-aware. If you use await rlm.acall(...) your handlers may be coroutines and they will be awaited. Sync rlm(...) calls sync handlers; if it encounters an async handler the coroutine is closed and a warning is logged.

Failure-isolated. Handler exceptions are logged and swallowed — a broken callback can never break the run.

Broadcasting a "loading" status to a websocket

import json
from dspy.utils.callback import BaseCallback
from predict_rlm import IterationStep, PredictRLM

class ProgressBroadcaster(BaseCallback):
    def __init__(self, websocket):
        self.ws = websocket

    async def on_rlm_iteration_start(self, *, iteration, max_iterations, **_):
        await self.ws.send_json({
            "type": "iteration_start",
            "iteration": iteration,
            "max_iterations": max_iterations,
        })

    async def on_rlm_iteration_end(
        self, *, iteration, step: IterationStep | None, is_final, exception, **_
    ):
        await self.ws.send_json({
            "type": "iteration_end",
            "iteration": iteration,
            "is_final": is_final,
            "step": step.model_dump(mode="json") if step else None,
            "error": str(exception) if exception else None,
        })

rlm = PredictRLM("query -> answer")
rlm.callbacks = [ProgressBroadcaster(ws)]
result = await rlm.acall(query="...")

Register globally instead with dspy.configure(callbacks=[...]) and the same handlers fire for every PredictRLM instance.

Next steps

• How it works — understand the sandbox, REPL loop, signatures, and file I/O
• Architecture — compare backend component and state/recovery semantics
• API reference — constructor params for PredictRLM, File, and Skill
• Skills — define, compose, and mount custom skills
• RLM-GEPA — optimize RLM skills from traces and configure AgentSpec
• Examples — end-to-end demos with setup instructions