fleet-rlm 0.1.0

Python package + Typer CLI implementation of the RLM with Modal notebook

fleet-rlm

A Python package implementing Recursive Language Models (RLM) with DSPy and Modal for secure, cloud-based code execution. This project demonstrates how LLMs can treat long contexts as external environments, using programmatic code exploration in sandboxed environments.

Reference: Recursive Language Models (Zhang, Kraska, Khattab, 2025)

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Overview

Recursive Language Models (RLM) represent an inference strategy where:

• LLMs treat long contexts as an external environment rather than direct input
• The model writes Python code to programmatically explore data
• Code executes in a sandboxed environment (Modal cloud)
• Only relevant snippets are sent to sub-LLMs for semantic analysis

This package provides both a comprehensive Jupyter notebook and a Typer CLI for running RLM workflows.

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Features

• Secure Cloud Execution: Code runs in Modal's isolated sandbox environment
• DSPy Integration: Built on DSPy 3.1.3 with custom signatures for RLM tasks
• CLI Interface: Typer-based CLI with multiple demo commands
• Extensible Tools: Support for custom tools that bridge sandbox and host
• Secret Management: Secure handling of API keys via Modal secrets

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Technology Stack

Component	Technology
Language	Python >= 3.10
Package Manager	uv (modern Python package manager)
Core Framework	DSPy 3.1.3
Cloud Sandbox	Modal
CLI Framework	Typer >= 0.12
Testing	pytest >= 8.2
Linting/Formatting	ruff >= 0.8

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Installation

# Clone the repository
git clone https://github.com/qredence/fleet-rlm.git
cd fleet-rlm

# Install dependencies with uv
uv sync

# For development (includes test tools)
uv sync --extra dev

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Quick Start

1. Configure Environment

Create a .env file in the repository root:

# Required
DSPY_LM_MODEL=openai/gemini-3-flash-preview
DSPY_LLM_API_KEY=sk-...

# Optional
DSPY_LM_API_BASE=https://your-litellm-proxy.com
DSPY_LM_MAX_TOKENS=65536

2. Setup Modal

# Authenticate with Modal
uv run modal setup

# Create a Modal volume for data
uv run modal volume create rlm-volume-dspy

# Create Modal secret for API keys
uv run modal secret create LITELLM \
  DSPY_LM_MODEL=... \
  DSPY_LM_API_BASE=... \
  DSPY_LLM_API_KEY=... \
  DSPY_LM_MAX_TOKENS=...

3. Run CLI Commands

# Show all available commands
uv run fleet-rlm --help

# Run a basic demo
uv run fleet-rlm run-basic --question "What are the first 12 Fibonacci numbers?"

# Doc-analysis commands require --docs-path
# Extract architecture from documentation
uv run fleet-rlm run-architecture \
    --docs-path rlm_content/dspy-knowledge/dspy-doc.txt \
    --query "Extract all modules and optimizers"

# Extract API endpoints
uv run fleet-rlm run-api-endpoints --docs-path rlm_content/dspy-knowledge/dspy-doc.txt

# Find error patterns
uv run fleet-rlm run-error-patterns --docs-path rlm_content/dspy-knowledge/dspy-doc.txt

# Inspect trajectory on a document sample
uv run fleet-rlm run-trajectory \
    --docs-path rlm_content/dspy-knowledge/dspy-doc.txt \
    --chars 5000

# Use custom regex tool
uv run fleet-rlm run-custom-tool \
    --docs-path rlm_content/dspy-knowledge/dspy-doc.txt \
    --chars 5000

# Check Modal secrets are configured
uv run fleet-rlm check-secret

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CLI Commands

Command	Description
run-basic	Basic code generation (Fibonacci example)
run-architecture	Extract DSPy architecture from documentation
run-api-endpoints	Extract API endpoints using batched queries
run-error-patterns	Find and categorize error patterns in docs
run-trajectory	Examine RLM execution trajectory
run-custom-tool	Demo with custom regex tool
check-secret	Verify Modal secret presence
check-secret-key	Inspect specific secret key

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Architecture

┌─────────────────────────────────────────────────────────────┐
│ LOCAL (Jupyter/CLI)                                         │
│  ┌─────────────┐  ┌──────────────┐  ┌──────────────────┐   │
│  │ Planner LM  │  │ RLM Module   │  │ ModalInterpreter │   │
│  │ (decides    │→ │ (builds      │→ │ (manages sandbox │   │
│  │  what code  │  │  signatures) │  │  lifecycle)      │   │
│  │  to write)  │  │              │  │                  │   │
│  └─────────────┘  └──────────────┘  └──────────────────┘   │
│           │                │                  │             │
│           │                │ JSON stdin       │ gRPC        │
│           │                ↓                  ↓             │
└───────────┼────────────────┼──────────────────┼─────────────┘
            │                │                  │
            │                │                  ▼
            │                │     ┌──────────────────────┐
            │                │     │ MODAL CLOUD          │
            │                │     │  ┌────────────────┐  │
            │                └────→│  │ Sandbox        │  │
            │                      │  │ - Python 3.12  │  │
            │                      │  │ - Volume /data │  │
            │                      │  │ - Secrets      │  │
            │                      │  └────────────────┘  │
            │                      │           │          │
            │                      │           ▼          │
            │                      │  ┌────────────────┐  │
            │                      │  │ Driver Process │  │
            │                      │  │ - exec() code  │  │
            │                      │  │ - tool bridging│  │
            │                      │  └────────────────┘  │
            │                      └──────────────────────┘
            │                                │
            └────────────────────────────────┘
                        tool_call requests
                        (llm_query, etc.)

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Package Structure

src/fleet_rlm/
├── __init__.py      # Package exports
├── cli.py           # Typer CLI interface
├── config.py        # Environment configuration
├── driver.py        # Sandbox protocol driver
├── interpreter.py   # ModalInterpreter implementation
├── runners.py       # High-level RLM demo runners
├── signatures.py    # DSPy signatures for RLM tasks
└── tools.py         # Custom RLM tools

Module Descriptions

• config.py: Loads environment variables, configures DSPy's planner LM, guards against Modal package shadowing
• cli.py: Typer CLI with commands for running demos and checking secrets
• driver.py: Runs inside Modal's sandbox as a long-lived JSON protocol driver
• interpreter.py: DSPy-compatible CodeInterpreter managing Modal sandbox lifecycle
• runners.py: High-level functions orchestrating complete RLM workflows
• signatures.py: RLM task signatures (ExtractArchitecture, ExtractAPIEndpoints, etc.)
• tools.py: Custom tools like regex_extract() for RLM use

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RLM Patterns

Pattern 1: Navigate → Query → Synthesize

1. Code searches for headers in documentation
2. llm_query() extracts info from relevant sections
3. SUBMIT(modules=list, optimizers=list, principles=str) returns structured output

Pattern 2: Parallel Chunk Processing

1. Split documents into chunks by headers
2. llm_query_batched([chunk1, chunk2, ...]) executes in parallel
3. Aggregate results into final output

Pattern 3: Stateful Multi-Step

1. Search for keywords in documentation
2. Save matches to variable (persists across iterations)
3. Query LLM to categorize findings
4. Iterate with refined queries

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Testing

# Run all tests
uv run pytest

# Or via Make
make test

Test File	Purpose
test_cli_smoke.py	CLI help display, command discovery, error handling
test_config.py	Environment loading, quoted values, fallback API keys
test_driver_protocol.py	SUBMIT output mapping, tool call round-trips
test_tools.py	Regex extraction, groups, flags

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Development

# Install dev dependencies
make sync-dev

# Run linting
make lint

# Format code
make format

# Run all checks
make check

# Run release validation (lint, tests, build, twine check)
make release-check

# Install pre-commit hooks
make precommit-install
make precommit-run

Release process documentation is in RELEASING.md, including the TestPyPI-first workflow.

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Jupyter Notebook

The original implementation is available as a Jupyter notebook:

# Launch Jupyter Lab
uv run jupyter lab notebooks/rlm-dspy-modal.ipynb

# Execute headlessly (for CI/validation)
uv run jupyter nbconvert \
  --to notebook \
  --execute \
  --inplace \
  --ExecutePreprocessor.timeout=3600 \
  notebooks/rlm-dspy-modal.ipynb

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Security

• Secrets Management: All credentials stored in Modal secrets, never in code
• Sandbox Isolation: Code executes in Modal's isolated sandbox environment
• Local .env: Contains API keys - is gitignored and should never be committed
• Shadow Protection: config.py guards against modal.py naming conflicts

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Troubleshooting

"Planner LM not configured"

Set DSPY_LM_MODEL and DSPY_LLM_API_KEY in .env, then restart your shell or kernel.

"Modal sandbox process exited unexpectedly"

uv run modal token set
uv run modal volume list

"No module named 'modal'"

uv sync

Modal package shadowing

Remove any modal.py file or __pycache__/modal.*.pyc in the working directory.

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References

• RLM Paper: Recursive Language Models
• DSPy Docs: https://dspy-docs.vercel.app/
• Modal Docs: https://modal.com/docs
• UV Docs: https://docs.astral.sh/uv/

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License

This project is licensed under the MIT License.

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Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

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Acknowledgments

This project is based on research from the Recursive Language Models paper by Zhang, Kraska, and Khattab (2025).

Built with:

• DSPy - Framework for programming with language models
• Modal - Serverless computing for AI
• Typer - CLI framework