RLM - Recursive Language Models

Python implementation of Recursive Language Models for processing arbitrarily long contexts.

What is RLM?

RLM is an inference strategy that lets language models recursively call themselves to handle unlimited input context length. Instead of feeding long text directly to the LLM (causing "context rot"), RLM stores context as a variable in a Python REPL environment.

Traditional LLM: Context -> LLM -> Answer (quality degrades with length)
RLM: LLM <-> REPL Environment
|-- context (variable)
|-- llm_query() (recursive calls)
|-- exec() (run code)
v
FINAL(answer)

Key Concepts

• Context as Environment: Long text stored in REPL variable, not in LLM prompt
• Code as Tool: LLM writes Python to explore context (context[:1000], re.findall(), etc.)
• Recursive Decomposition: Complex problems split via llm_query() sub-calls
• Self-Correction: LLM sees execution errors and fixes its own code
• Output Offloading: Large outputs auto-stored as variables to prevent context rot

Installation

# Clone the repository
git clone https://github.com/Ray0907/rlm.git
cd rlm
# Install with uv
uv sync
# Set up API key
cp .env.example .env
# Edit .env with your API key (OPENAI_API_KEY or ANTHROPIC_API_KEY)

Usage

importrlmresult=rlm.run(
query="What is the total salary of all Engineering employees?",
context=""" Employee Records: - Alice: Engineering, $75,000 - Bob: Marketing, $65,000 - Charlie: Engineering, $85,000 """,
model="anthropic/claude-sonnet-4-5-20250929", # or "gpt-4o-mini"
)
print(result.answer) # $160,000print(result.iterations) # Number of execute-observe cycles

How It Works

1. Query sent to LLM with system prompt explaining REPL environment
2. LLM writes code in ```repl blocks to explore context
3. Code executed, output returned to LLM (large outputs auto-offloaded)
4. LLM iterates until it finds the answer
5. FINAL(answer) signals completion

Output Offloading (Context Rot Prevention)

RLM automatically offloads large outputs to variables to prevent context rot:

Without Offloading:
Iteration 1: output (2000 chars) -> history grows
Iteration 2: output (1500 chars) -> history grows more
Iteration N: history bloated -> quality degrades
With Offloading:
Iteration 1: output -> stored as _result_0, preview (200 chars) in history
Iteration 2: output -> stored as _result_1, preview in history
Iteration N: history stays small -> quality maintained

Benchmark Results (50 employee records, Haiku 3.5):

Metric	With Offload	Without Offload	Improvement
Input Tokens	8,980	10,748	-16.4%
History Size	2,154 chars	5,636 chars	-61.8%
Avg Output	718 chars	1,878 chars	-62%

Data still accessible via print(_result_0). Run benchmark: uv run python examples/offload_benchmark.py

REPL Functions

The LLM has access to these memory management functions:

# Check what's stored in memorymemory_status() # -> {'context': 10000, '_result_0': 2000, ...}# Clean up variables no longer neededforget('_result_0', 'temp_var') # -> "Cleared: _result_0, temp_var"

RLM vs Traditional Agents

Aspect	Traditional Agent	RLM
Problem Decomposition	Human predefines workflow	Model decides how to decompose
Tool Usage	Fixed tool set	Code as universal tool
Control Flow	Human-designed (ReAct)	Model-driven iteration
Flexibility	Limited to designed tools	Unlimited (any valid Python)

Supported Models

Any model supported by LiteLLM:

# Anthropic (Recommended)
LITELLM_MODEL=anthropic/claude-sonnet-4-5-20250929 uv run python examples/simple_qa.py
# OpenAI
LITELLM_MODEL=gpt-4o-mini uv run python examples/simple_qa.py
# Local (Ollama)
LITELLM_MODEL=ollama/llama2 uv run python examples/simple_qa.py

Examples

# Simple Q&A
uv run python examples/simple_qa.py
# Long document processing
uv run python examples/long_document.py
# Output offloading benchmark (compare with/without)
uv run python examples/offload_benchmark.py

Benchmark Results

Weak Model + RLM vs Strong Model Direct

Comparing Haiku 4.5 + RLM vs Opus 4.5 Direct on employee salary aggregation task.

Records	Context	Expected	RLM Answer	Direct Answer	RLM Cost	Direct Cost	RLM Time	Direct Time	Winner
100	24,390 chars	$1,105,000	$1,105,000	$145,000	$0.0437	$0.0410	43.9s	3.3s	RLM
200	48,835 chars	$2,822,000	$2,822,000	$145,000	$0.0469	$0.0791	53.8s	3.8s	RLM

Key Findings:

• RLM with weak model (Haiku 4.5) achieves correct answers while strong model (Opus 4.5) fails on longer contexts
• Cost is comparable or lower with RLM
• Trade-off: RLM takes longer due to multiple iterations

API Reference

rlm.run()

defrun(
query: str, # Question to answercontext: str|list, # Data to process (stored in REPL, not sent to LLM)model: str="gpt-4o-mini",
max_iterations: int=20,
max_output_chars: int=30000, # Max chars before truncationverbose: bool=False,
) ->RLMResult

rlm.RLM() (Advanced)

rlm_instance=rlm.RLM(
model: str="gpt-4o-mini",
sub_model: str=None, # Model for llm_query() callsmax_iterations: int=20,
max_output_chars: int=30000, # Max chars before truncationverbose: bool=False,
)

RLMResult

@dataclassclassRLMResult:
answer: str# Final answeriterations: int# Number of iterationstotal_input_tokens: int# Total input tokens usedtotal_output_tokens: int# Total output tokens usedcode_executions: int# Number of code blocks executedsub_calls: int# Number of llm_query() callshistory: list[dict] # Full execution history

References

1. Paper: Recursive Language Models - arXiv:2512.24601
2. Blog: RLM: Scalable LLM Inference - Author's implementation notes

License

MIT