RLM Runtime v2.0.0

# Standard installation

pip install rlm-runtime

# With Docker support (recommended for isolation)

pip install rlm-runtime[docker]

# With WebAssembly support (browser-safe execution)

pip install rlm-runtime[wasm]

# With MCP server for Claude Desktop/Code

pip install rlm-runtime[mcp]

# With Snipara context optimization

pip install rlm-runtime[snipara]

# With trajectory visualizer

pip install rlm-runtime[visualizer]

# Full installation with all features

pip install rlm-runtime[all]

# Initialize configuration

rlm init

# Run a completion

rlm run "Summarize the authentication flow"

# Run with Docker isolation

rlm run --env docker "Parse and analyze logs"

# Run an autonomous agent

rlm agent "Analyze all CSV files and generate a report"

# Launch the visualization dashboard

rlm visualize

import asyncio

from rlm import RLM

async def main():

    rlm = RLM(

        model="gpt-4o-mini",

        environment="local"

    )

    result = await rlm.completion("Count the lines in data.csv")

    print(result.response)

asyncio.run(main())

from rlm import RLM

rlm = RLM(

    model="claude-sonnet-4-20250514",

    environment="docker",

    # Snipara integration (optional but recommended)

    snipara_api_key="rlm_your_api_key",

    snipara_project_slug="your-project"

)

# LLM can now query your documentation

result = await rlm.completion(

    "Implement a new API endpoint following our coding standards"

)

from rlm.agent import AgentRunner

runner = AgentRunner(

    model="gpt-4o",

    environment="docker",

    max_iterations=10,   # Max observe-think-act cycles (clamped to 50)

    cost_limit=2.0,      # Dollar cap (max $10)

    token_budget=50000,  # Token budget

)

result = await runner.run("Analyze all CSV files and generate a report")

print(result.response)

rlm agent "Analyze all CSV files and generate a report"

rlm agent --max-iterations 20 --cost-limit 5.0 "Complex multi-step task"

rlm = RLM(

    model="gpt-4o",

    environment="docker",

    max_depth=4,  # Controls nesting depth for sub-calls

)

# Sub-calls are automatic - the model decides when to delegate

result = await rlm.completion("Research and compare 3 approaches to caching")

rlm run --sub-calls "Complex task requiring delegation"

rlm run --no-sub-calls "Simple task, no delegation"

rlm run --max-sub-calls 5 "Limited delegation"

{

  "mcpServers": {

    "rlm": {

      "command": "rlm",

      "args": ["mcp-serve"]

    }

  }

}

{

  "mcpServers": {

    "rlm": {

      "command": "rlm",

      "args": ["mcp-serve"]

    },

    "snipara": {

      "command": "snipara-mcp-server"

    }

  }

}

# LLM Provider (choose one)

export OPENAI_API_KEY="sk-..."

export ANTHROPIC_API_KEY="sk-ant-..."

# RLM Settings

export RLM_MODEL="gpt-4o-mini"

export RLM_ENVIRONMENT="docker"

# Snipara (optional)

export SNIPARA_API_KEY="rlm_..."

export SNIPARA_PROJECT_SLUG="my-project"

[rlm]

backend = "litellm"

model = "gpt-4o-mini"

environment = "docker"   # "local", "docker", or "wasm"

max_depth = 4

max_subcalls = 12

token_budget = 8000

verbose = false

# Docker settings

docker_image = "python:3.11-slim"

docker_cpus = 1.0

docker_memory = "512m"

# Snipara (optional)

snipara_api_key = "rlm_..."

snipara_project_slug = "your-project"

# Agent memory (requires Snipara)

memory_enabled = false

from rlm import RLM

rlm = RLM(

    model="claude-sonnet-4-20250514",

    environment="docker",

    snipara_api_key="rlm_...",

    snipara_project_slug="my-app"

)

# The LLM will:

# 1. Call context_query to find auth patterns

# 2. Call shared_context to get coding standards

# 3. Execute code to explore existing files

# 4. Spawn sub-LLM calls for focused sub-tasks

# 5. Generate new code following conventions

result = await rlm.completion("""

    Add a password reset endpoint to our auth system.

    Follow our existing patterns and coding standards.

    Include error handling and tests.

""")

print(result.response)

print(f"Tool calls: {result.tool_calls}")

print(f"Total tokens: {result.total_tokens}")