minrlm

minRLM is a token-efficient implementation of Recursive Language Models. The data never enters the prompt. The cost stays flat regardless of context size. Every step is Python code you can read, rerun, and debug.

Read the full blog post - 12 tasks, 3 models, 4,800 evaluations, all the details.

Results

	minRLM	Vanilla	Official RLM
Accuracy	72.7%	69.5%	69.7%
Tokens/query	8,151	20,967	29,327
Cost (600 evals)	$2.86	$4.74	$7.92

_{GPT-5-mini, 1,800 evaluations, 12 tasks, 50 runs each. Full per-task breakdown in eval/README.md.}

Model scaling

Model	minRLM	Vanilla	Delta	Tasks won
GPT-5-nano	53.7%	63.2%	-9.5	4/12
GPT-5-mini	72.7%	69.5%	+3.2	7/12
GPT-5.2	78.2%	48.2%	+30.0	11/12

The advantage grows with model capability. Details in the blog.

---

Quick start

pip install minrlm   # or: uv add minrlm
export OPENAI_API_KEY="sk-..."

CLI (zero-install)

# Just a task
uvx minrlm "What is the sum of the first 100 primes?"

# Task + file as context
uvx minrlm "How many ERROR lines in the last hour?" ./server.log

# Pipe context from stdin
cat huge_dataset.csv | uvx minrlm "Which product had the highest return rate?"

# Show generated code (-s) and token stats (-v)
uvx minrlm -sv "Return the sum of all primes up to 1,000,000."
# -> Sieve of Eratosthenes in 6,215 tokens, 1 iteration
# -> Answer: 37550402023

uvx minrlm -sv "Return all primes up to 1,000,000, reversed. Return a list of numbers."
# -> 999983, 999979, 999961, 999959, 999953, ...
# -> Tokens: 6,258 | Output: 616,964 chars (~154K tokens) | 25x savings

Visualizer

git clone https://github.com/avilum/minrlm && cd minrlm
uv sync --extra visualizer
uv run python examples/visualizer.py   # http://localhost:7860

Python

from minrlm import RLM

client = RLM(model="gpt-5-mini")

# Large context - data never enters the prompt
answer = client.completion(
    task="Which product had the highest return rate in Q3?",
    context=open("q3_returns.csv").read()  # could be 50MB
)

# No context - the REPL computes via code
result = client.completion(
    "Return all prime numbers up to 1,000,000, reversed. Return a list of numbers."
)
# Output: 999983, 999979, 999961, 999959, 999953, ...
# Tokens used: 6,258 | Output chars: 616,964 (~154K tokens) | Savings: 25x

REPL tools

Function	What it does
input_0	Your context data (string)
search(text, pattern)	Substring search with context windows
sub_llm(task, context)	Recursive LLM call on a sub-chunk
FINAL(answer)	Return answer and stop

Custom endpoints

client = RLM(model="llama-3.1-70b", base_url="http://localhost:8000/v1")

---

What's in this repo

Component	Location	Description
Client	minrlm/	RLM class - the LLM <-> REPL loop
DockerREPL	minrlm/docker_repl.py	Sandboxed execution via Docker + seccomp
Evals	eval/	12-task benchmark framework, 3 model sizes
Examples	examples/	Quickstart, proxy server, Gradio UI

DockerREPL

LLM-generated code runs in isolated Docker containers. Docker is auto-detected. No network, read-only filesystem, memory-capped, seccomp-filtered.

client = RLM(model="gpt-5-mini", use_docker=True, docker_memory="256m")

Evals

git clone https://github.com/avilum/minrlm && cd minrlm
uv sync --extra eval

# Smoke test
uv run python eval/quickstart.py

# Full benchmark (reproduces the table above)
uv run python eval/run.py \
    --tasks all \
    --runners minrlm-reasoning,vanilla,official \
    --runs 50 --parallel 12 --task-parallel 12 \
    --output-dir logs/my_eval

Full results, per-task breakdowns, reproduction steps: eval/README.md

Examples

uv run python examples/minimal.py              # vanilla vs RLM side-by-side
uv run python examples/advanced_usage.py        # search, sub_llm, callbacks
uv run python examples/visualizer.py            # Gradio UI (uv sync --extra visualizer)
uv run uvicorn examples.proxy:app --port 8000   # OpenAI-compatible proxy (uv sync --extra proxy)

---

Credits

Built by Avi Lumelsky. Independent implementation - not a fork. The RLM concept comes from Zhang, Kraska, and Khattab (2025). Official implementation: github.com/alexzhang13/rlm.

@misc{zhang2026recursivelanguagemodels,
      title={Recursive Language Models},
      author={Alex L. Zhang and Tim Kraska and Omar Khattab},
      year={2026},
      eprint={2512.24601},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2512.24601},
}

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License

MIT