fleet-rlm 0.4.3

Recursive Language Models with DSPy + Modal for secure long-context code execution

fleet-rlm

Secure, cloud-sandboxed Recursive Language Models (RLM) with DSPy and Modal.

fleet-rlm provides a production-ready implementation of Recursive Language Modeling aligned with the DSPy RLM API. It gives your AI agent a secure "computer" in the cloud to read, search, and analyze massive datasets without local resource constraints.

Paper | Contributing | Docs

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Architecture

User ─── CLI / API / WebSocket ─── RLMReActChatAgent (dspy.Module)
                                        │
                          ┌──────────────┼──────────────┐
                          │              │              │
                     load_document  rlm_query      edit_file
                     list_files     (recursive)    search_code
                     read_file_slice ...            ...
                          │              │
                          ▼              ▼
                   ModalInterpreter ── dspy.RLM
                          │              │
                          ▼              ▼
                   Modal Sandbox (isolated Python REPL)
                   ├── Persistent Volume (/data/)
                   ├── Execution Profiles (ROOT / DELEGATE / MAINTENANCE)
                   └── Session State (per workspace/user)

Features

• Interactive Agent: RLMReActChatAgent (a dspy.Module) combines fast, interactive chat with deep, recursive task execution via rlm_query.
• DSPy Aligned: Implements dspy.RLM, dspy.Module, and dspy.Tool interfaces — compatible with DSPy optimizers (BootstrapFewShot, MIPROv2).
• Secure Sandbox: Code runs in isolated Modal containers with persistent storage volumes, execution profiles, and sensitive data redaction.
• Recursive Delegation: Large tasks are broken down via rlm_query sub-agents with depth enforcement to prevent infinite recursion.
• PDF Ingestion: Native document loading via MarkItDown with pypdf fallback; OCR guidance for scanned PDFs.
• Session State: Per-workspace, per-user session persistence with manifests stored on Modal volumes.
• MCP Server: Expose fleet-rlm capabilities as an MCP tool server via serve-mcp.
• Observability: Real-time streaming of thoughts, tool execution, trajectory normalization, and structured logging.

Quick Start

1. Install

pip install fleet-rlm

Optional extras for server and MCP support:

pip install fleet-rlm[server]   # FastAPI server + WebSocket
pip install fleet-rlm[mcp]      # MCP server
pip install fleet-rlm[full]     # All extras

2. Configure

Set up your Modal and LLM credentials:

modal setup
modal volume create rlm-volume-dspy
modal secret create LITELLM DSPY_LM_MODEL=openai/gpt-4o DSPY_LLM_API_KEY=sk-...

3. Run

# Interactive chat (requires OpenTUI / Bun)
fleet-rlm code-chat --opentui

# One-shot task
fleet-rlm run-basic --question "What are the first 12 Fibonacci numbers?"

# Document analysis
fleet-rlm run-architecture --docs-path docs/architecture.md --query "Extract all components"

# API server (FastAPI + WebSocket)
fleet-rlm serve-api --port 8000

# MCP server
fleet-rlm serve-mcp --transport stdio

Development Setup

# Clone and install
git clone https://github.com/qredence/fleet-rlm.git
cd fleet-rlm
uv sync --extra dev

# With server/MCP support
uv sync --extra dev --extra server --extra mcp

# Copy environment template
cp .env.example .env

# Quality gate
uv run ruff check src tests && uv run ty check src && uv run pytest -q

Documentation

• Concepts — Core architecture (Agent, RLM, Sandbox)
• User Flows — Interaction diagrams (Chat, Tools, Delegation)
• Architecture — System components and hierarchy
• Tutorials — Step-by-step lessons
• How-To Guides — Installation, deployment, troubleshooting
• CLI Reference — Full CLI command reference
• HTTP API Reference — Server endpoints and WebSocket protocol
• Source Layout — Package structure guide

Contributing

We welcome contributions! Please see our Contribution Guide and run the quality gate before submitting:

uv run ruff check src tests && uv run ty check src && uv run pytest -q

License

MIT License — see LICENSE.

Based on Recursive Language Modeling research by Alex L. Zhang (MIT CSAIL), Omar Khattab (Stanford), and Tim Kraska (MIT).