pyrlm-runtime

Minimal Python runtime for Recursive Language Models (RLMs) — inspired by the MIT CSAIL paper "Recursive Language Models".

RLMs solve the long-context problem: instead of sending huge contexts directly to an LLM (which truncates or degrades), the context lives as environment state in a Python REPL. The LLM writes code to inspect, search, and chunk the data, making recursive subcalls to smaller models when needed. Result: handle arbitrarily large contexts with constant token usage per step.

Installation
Quickstart
Live Rich Trace
Core Concepts
API Reference
- RLM
- Context
- Adapters
- Policy
- Trace
- Cache
- Router
REPL Backends
REPL Functions Available to the LLM
Retrieval Integration
Parallel Subcalls
Multi-Turn Conversation History
Guard Mechanisms & Fallbacks
Configuration
Examples
When to Use RLMs
Benchmark: RLM vs Baseline
Development
References
License

Installation

pip install pyrlm-runtime

Or with uv:

uv add pyrlm-runtime

For live terminal visualization of the REPL loop with rich:

pip install "pyrlm-runtime[rich]"

Requirements: Python 3.12+

Optional: For the secure Monty REPL backend (Rust sandbox):

pip install pydantic-monty

Quickstart

1. Set your API key

export LLM_API_KEY="your-api-key-here"
# Optional: custom endpoint (Ollama, LM Studio, etc.)
# export LLM_BASE_URL="http://localhost:11434/v1"

2. Basic usage

from pyrlm_runtime import RLM, Context
from pyrlm_runtime.adapters import OpenAICompatAdapter

# Create context from your documents
documents = [
    "Document 1: Very long content...",
    "Document 2: More content...",
    # ... could be 100s of documents, millions of tokens
]
context = Context.from_documents(documents)

# Initialize RLM with an adapter
adapter = OpenAICompatAdapter(model="gpt-4")
rlm = RLM(adapter=adapter)

# Ask questions over the entire context
answer, trace = rlm.run("What are the main themes across all documents?", context)
print(answer)

3. Run without external APIs (for testing)

from pyrlm_runtime import RLM, Context
from pyrlm_runtime.adapters import FakeAdapter

adapter = FakeAdapter(script=[
    "snippet = peek(80)\nsummary = llm_query(f'Summarize: {snippet}')\nanswer = f'Summary -> {summary}'",
    "FINAL_VAR: answer",
])
adapter.add_rule("You are a sub-LLM", "[fake] short summary")

context = Context.from_text("RLMs treat long prompts as environment state.")
output, trace = RLM(adapter=adapter).run("Summarize this.", context)
print(output)  # Summary -> [fake] short summary

Live Rich Trace

from rich.console import Console

from pyrlm_runtime import Context, RLM
from pyrlm_runtime.adapters import FakeAdapter
from pyrlm_runtime.rich_trace import RichTraceListener

console = Console()
listener = RichTraceListener(console=console)

adapter = FakeAdapter(
    script=[
        "snippet = peek(40)\nsummary = llm_query(f'Summarize: {snippet}')\nprint(summary)\nanswer = summary",
        "FINAL_VAR: answer",
    ]
)
adapter.add_rule("You are a sub-LLM", "[fake] summary")

output, trace = RLM(adapter=adapter, event_listener=listener).run(
    "Summarize the first chunk.",
    Context.from_text("RLMs treat long prompts as environment state."),
)

With a real Azure OpenAI deployment:

from dotenv import load_dotenv

from pyrlm_runtime import Context, RLM
from pyrlm_runtime.adapters import AzureOpenAIAdapter
from pyrlm_runtime.rich_trace import RichTraceListener

load_dotenv()

adapter = AzureOpenAIAdapter(model="gpt-5.1")
listener = RichTraceListener()

demo_text = "SpaceX Falcon 9 launched on Jan 6 with $50M revenue. ..."

output, trace = RLM(adapter=adapter, event_listener=listener).run(
    "Which launch had the largest revenue?",
    Context.from_text(demo_text),
)

Azure env contract for the live demo:

AZURE_OPENAI_API_KEY="..."
OPENAI_ENDPOINT="https://your-resource.openai.azure.com"
# or: AZURE_ACCOUNT_NAME="your-resource"
AZURE_OPENAI_API_VERSION="2024-10-21"  # optional

uv run python examples/rich_repl_demo.py --model gpt-5.1

Core Concepts

How the RLM loop works

rlm.run(query, context)
  │
  ├── 1. Initialize REPL with context as variables `P` (text) and `ctx` (Context object)
  ├── 2. Build system prompt + user message with context metadata
  │
  └── 3. Loop (until FINAL or max_steps):
        │
        ├── LLM generates Python code (or FINAL answer)
        │
        ├── If code → execute in REPL sandbox
        │   ├── Code can call peek(), ctx.find(), ctx.chunk(), etc.
        │   ├── Code can call llm_query() / ask_chunks() for subcalls
        │   └── REPL output is sent back to LLM as next iteration
        │
        └── If FINAL → return answer
            ├── "FINAL: <answer>"        → inline answer
            ├── "FINAL_VAR: <varname>"   → return REPL variable value
            └── auto_finalize_var        → return when variable is set

Return: (output: str, trace: Trace)

Finalization

The LLM signals completion in three ways:

Method	Example	When to use
`FINAL: <text>`	`FINAL: The answer is 42`	Short inline answers
`FINAL_VAR: <name>`	`FINAL_VAR: result`	Return a REPL variable
`auto_finalize_var`	`RLM(adapter, auto_finalize_var="answer")`	Auto-return when variable is set

API Reference

RLM

The main entry point. Orchestrates the REPL loop, subcalls, and conversation history.

from pyrlm_runtime import RLM

rlm = RLM(
    adapter,                            # Required: LLM adapter (see Adapters)
    policy=None,                        # Resource limits (see Policy)
    cache=None,                         # Subcall cache (see Cache)
    max_tokens=512,                     # Max tokens per LLM call
    system_prompt=BASE_SYSTEM_PROMPT,   # Override system prompt

    # REPL backend
    repl_backend="python",              # "python" (default) or "monty"

    # Conversation history
    conversation_history=True,          # Multi-turn mode (default: True)
    max_history_tokens=0,               # Token budget for history (0=unlimited)

    # Retrieval
    retriever=None,                     # RetrieverProtocol impl (e.g. ElasticsearchRetriever)

    # Subcalls
    subcall_adapter=None,               # Separate (cheaper) adapter for subcalls
    recursive_subcalls=False,           # Subcalls run mini-RLM loops
    max_recursion_depth=2,              # Max recursion depth
    parallel_subcalls=False,            # Run subcalls in parallel

    # Guards & fallbacks
    require_repl_before_final=False,    # Enforce ≥1 REPL execution
    require_subcall_before_final=False, # Enforce ≥1 subcall
    invalid_response_limit=None,        # Max retries on non-code responses
    fallback_code=None,                 # Emergency code if LLM stalls
)

output, trace = rlm.run(query="Your question", context=context)

Context

Wraps your data and provides safe inspection methods for the REPL.

from pyrlm_runtime import Context

# From a single text
context = Context.from_text("Your long text here...")

# From multiple documents (separated by markers)
context = Context.from_documents([
    "Document 1 content...",
    "Document 2 content...",
    "Document 3 content...",
], separator="\n---DOC_BOUNDARY---\n")

# Available methods (used by the LLM inside the REPL):
context.len_chars()                    # Total character count
context.num_documents()                # Number of documents
context.get_document(index)            # Get a specific document
context.document_lengths()             # List of document lengths
context.slice(start, end)             # Safe substring
context.find(pattern, regex=False)    # Search with optional regex
context.chunk(size, overlap=0)        # Split into chunks
context.chunk_documents(docs_per_chunk=10)  # Group documents into chunks
context.metadata()                    # Summary dict for system prompts

Adapters

Adapters connect pyrlm-runtime to any LLM provider.

OpenAICompatAdapter

Works with OpenAI, Anthropic (via proxy), Ollama, LM Studio, vLLM, and any OpenAI-compatible API.

from pyrlm_runtime.adapters import OpenAICompatAdapter

# OpenAI
adapter = OpenAICompatAdapter(model="gpt-4")

# Ollama (local)
adapter = OpenAICompatAdapter(
    model="llama3",
    base_url="http://localhost:11434/v1",
)

# Any OpenAI-compatible endpoint
adapter = OpenAICompatAdapter(
    model="my-model",
    base_url="https://my-endpoint.com/v1",
)

Uses environment variables: LLM_API_KEY (or OPENAI_API_KEY), LLM_BASE_URL.

GenericChatAdapter

For non-standard APIs with custom request/response formats.

from pyrlm_runtime.adapters import GenericChatAdapter

adapter = GenericChatAdapter(
    base_url="https://custom-api.com",
    path="/chat/completions",
    model="custom-model",
    api_key="your-key",
    payload_builder=my_custom_builder,    # Custom request format
    response_parser=my_custom_parser,     # Custom response format
    timeout=60.0,
    max_retries=3,
)

Auto-retries on 429, 500, 502, 503, 504 with exponential backoff. Supports context manager (with GenericChatAdapter(...) as adapter:).

FakeAdapter

Deterministic adapter for testing. No external API needed.

from pyrlm_runtime.adapters import FakeAdapter

adapter = FakeAdapter(
    script=["code step 1", "code step 2", "FINAL_VAR: result"]
)
# Pattern-based rules for subcall responses
adapter.add_rule(pattern="Summarize", response="This is a summary")
adapter.add_rule(pattern=r"find.*key", response="key_term", regex=True)

Custom adapters

Implement the ModelAdapter protocol:

from pyrlm_runtime.adapters import ModelAdapter, ModelResponse

class MyAdapter:
    def complete(
        self,
        messages: list[dict[str, str]],
        max_tokens: int = 512,
        temperature: float = 0.0,
    ) -> ModelResponse:
        # Call your LLM and return a ModelResponse
        ...

Policy

Controls resource limits to prevent runaway execution.

from pyrlm_runtime import Policy

policy = Policy(
    max_steps=40,              # Max RLM loop iterations
    max_subcalls=200,          # Max total subcalls
    max_recursion_depth=1,     # Max subcall nesting depth
    max_total_tokens=200_000,  # Token budget (root + subcalls)
    max_subcall_tokens=None,   # Token budget for subcalls only
)

rlm = RLM(adapter=adapter, policy=policy)

Raises specific exceptions when limits are exceeded: MaxStepsExceeded, MaxSubcallsExceeded, MaxRecursionExceeded, MaxTokensExceeded.

Trace

Records every step of the RLM execution for debugging and analysis.

output, trace = rlm.run(query, context)

# Inspect steps
for step in trace.steps:
    print(f"Step {step.step_id}: {step.kind}")
    if step.code:
        print(f"  Code: {step.code[:100]}")
    if step.stdout:
        print(f"  Output: {step.stdout[:100]}")
    if step.error:
        print(f"  Error: {step.error}")

# Serialize
json_str = trace.to_json()
trace_restored = Trace.from_json(json_str)

Step kinds: root_call, repl_exec, subcall, recursive_subcall, sub_root_call, sub_repl_exec, sub_subcall.

Cache

File-based cache for subcall results. Avoids repeating identical LLM calls.

from pyrlm_runtime import FileCache

cache = FileCache(root="./cache")
rlm = RLM(adapter=adapter, cache=cache)

Router

Automatically selects between baseline (direct LLM call) and RLM based on context size.

from pyrlm_runtime import SmartRouter, RouterConfig, ExecutionProfile

router = SmartRouter(
    adapter,
    config=RouterConfig(baseline_threshold=8000),  # chars
)

result = router.run(query, context, profile=ExecutionProfile.DETERMINISTIC_FIRST)
print(f"Method: {result.method}")   # "baseline" or "rlm"
print(f"Answer: {result.output}")
print(f"Tokens: {result.tokens_used}")

Execution profiles:

Profile	Strategy
`DETERMINISTIC_FIRST`	Try regex/`extract_after` first, minimal subcalls
`SEMANTIC_BATCHES`	Parallel subcalls for classification tasks
`HYBRID`	Deterministic first, fall back to semantic
`VERIFY`	Double-check with recursive subcalls

REPL Backends

pyrlm-runtime ships with two interchangeable REPL backends:

PythonREPL (default)

Uses exec() with a whitelist sandbox. Allowed modules: re, math, json, textwrap. Stdout capped at 4000 chars.

rlm = RLM(adapter=adapter, repl_backend="python")

MontyREPL (secure sandbox)

Uses pydantic-monty, a Rust-based Python interpreter with compile-time safety. Enforces resource limits: 5s duration, 128MB memory, 1M allocations.

# Requires: pip install pydantic-monty
rlm = RLM(adapter=adapter, repl_backend="monty")

How MontyREPL handles complex objects: Python objects like Context can't run natively in the Rust sandbox. MontyREPL uses an object proxy system — methods are registered as external functions with {name}__{method} naming, and AST rewrites transform ctx.method() calls into ctx__method() calls transparently.

Variable persistence: MontyREPL uses AST-based detection of assignments, appending a capture dict to extract variable state from each execution.

Both backends implement the same REPLProtocol interface: exec(code) -> ExecResult, get(name), set(name, value).

REPL Functions Available to the LLM

When the LLM generates code during the RLM loop, these functions are available in the REPL:

Context inspection

P                              # The full context text (str)
ctx                            # The Context object

peek(n=2000)                   # First n chars of context
tail(n=2000)                   # Last n chars of context
lenP()                         # Total character count

ctx.slice(start, end)          # Safe substring
ctx.find(pattern, regex=False) # Search (returns list of matches)
ctx.chunk(size, overlap=0)     # Split into char-based chunks
ctx.chunk_documents(docs_per_chunk=10)  # Group documents
ctx.num_documents()            # Document count
ctx.get_document(index)        # Get specific document
ctx.document_lengths()         # List of doc lengths

Subcalls (call sub-LLMs)

llm_query(text, model=None, max_tokens=None)
    # Single subcall to a sub-LLM
    # max_tokens defaults to subcall_max_tokens (256) at runtime

llm_batch(prompts, model=None, max_tokens=None)
    # Process multiple prompts in parallel (always parallel, uses ThreadPoolExecutor)
    # max_tokens defaults to subcall_max_tokens (256) at runtime
    # → Use this for independent batch operations
    # Example: llm_batch(["prompt1", "prompt2", "prompt3"])

llm_query_batch(chunks, model=None, max_tokens=None, parallel=None)
    # Batch subcall over multiple chunks
    # max_tokens defaults to subcall_max_tokens (256) at runtime
    # → Parallel if parallel_subcalls=True or parallel=True (default: sequential)

ask(question, text, max_tokens=None)
    # Convenience: ask a question about a text snippet

ask_chunks(question, chunks, max_tokens=None, parallel=None)
    # Ask the same question over multiple chunks
    # → Parallel if parallel_subcalls=True or parallel=True (default: sequential)

ask_chunks_first(question, chunks, ...)
    # Return first valid (non-empty) answer from chunks (always sequential)

pick_first_answer(answers)
    # Filter and return first non-empty answer from a list

Parallelization note:

llm_batch() always runs in parallel via ThreadPoolExecutor
ask_chunks() and llm_query_batch() run:
- Sequential by default (unless RLM(parallel_subcalls=True) or ask_chunks(..., parallel=True))
- Parallel when enabled (limited to max_concurrent_subcalls, default 10 workers)

Retrieval (when retriever is configured)

es_search(query, top_k=10, filters=None)
    # BM25 full-text search → list of {doc_id, preview, score, metadata}

es_vector_search(query, top_k=10, filters=None)
    # Semantic similarity search → list of {doc_id, preview, score, metadata}

es_hybrid_search(query, top_k=10, filters=None)
    # Combined BM25 + semantic (recommended) → list of {doc_id, preview, score, metadata}

es_get(doc_id)
    # Fetch full document → {doc_id, content, metadata}

Deterministic extraction

extract_after(marker, max_len=128)
    # Extract text after a marker without using a subcall (fast, 0 tokens)

Retrieval Integration

For large corpora that don't fit in memory, the RLM can search external document indexes directly from the REPL loop. See the detailed architecture guide: docs/RETRIEVAL.md

Quick Setup

First, install the optional Elasticsearch extra:

pip install "pyrlm-runtime[elasticsearch]"

from pyrlm_runtime import RLM
from pyrlm_runtime.adapters import OpenAICompatAdapter
from pyrlm_runtime.retrieval import ElasticsearchRetriever

retriever = ElasticsearchRetriever(
    host="https://my-cluster.es.cloud.com",
    api_key="xxx",
    index="pdf_corpus",
    embedding_model="text-embedding-3-small",
)

rlm = RLM(adapter=OpenAICompatAdapter(model="gpt-5"), retriever=retriever)
answer, trace = rlm.run("Who signed document X?")  # No context needed

When a retriever is configured, four functions become available in the REPL:

es_search(query, top_k=10, filters=None)        # BM25 keyword search
es_vector_search(query, top_k=10, filters=None)  # Semantic similarity
es_hybrid_search(query, top_k=10, filters=None)  # Combined (recommended)
es_get(doc_id)                                    # Fetch full document

The retrieval layer is backend-agnostic: any object implementing the RetrieverProtocol (with search, vector_search, hybrid_search, get methods) works as a drop-in backend.

Parallel Subcalls

See the detailed architecture guide: docs/PARALLEL_SUBCALLS.md

Quick Summary

pyrlm-runtime supports three ways to parallelize LLM subcalls:

llm_batch(prompts) — Always parallel, best for independent prompts:

results = llm_batch(["Q1?", "Q2?", "Q3?"])  # All 3 run in parallel

ask_chunks(..., parallel=True) — Opt-in per-call:

answers = ask_chunks("Q?", chunks, parallel=True)  # Chunks processed in parallel

RLM(..., parallel_subcalls=True) — Global flag:

rlm = RLM(adapter, parallel_subcalls=True)  # All ask_chunks calls are parallel

Why parallel? LLM API calls are I/O-bound. Making 10 requests sequentially takes ~20s; in parallel, ~2s.

Thread safety: All parallel execution is protected by locks on Policy, Trace, and step ID counters.

Limits: Default 10 concurrent workers (max_concurrent_subcalls); adjust per your API's rate limits.

Multi-Turn Conversation History

By default (conversation_history=True), the LLM sees its previous code attempts and REPL outputs across iterations. This enables self-correction.

rlm = RLM(
    adapter=adapter,
    conversation_history=True,      # Default
    max_history_tokens=4000,        # Optional token budget (0=unlimited)
)

How it works:

The initial message contains full query + context metadata
Each iteration appends a lightweight message with REPL results
Token trimming (if configured) always preserves system + initial user message, dropping oldest middle turns

Guard Mechanisms & Fallbacks

For robustness, RLM supports several guard mechanisms:

rlm = RLM(
    adapter=adapter,

    # Require at least 1 REPL execution before accepting FINAL
    require_repl_before_final=True,

    # Require at least 1 subcall before accepting FINAL
    require_subcall_before_final=True,

    # Max non-code responses before giving up
    invalid_response_limit=5,

    # Emergency code to run if LLM stalls
    fallback_code="answer = pick_first_answer(ask_chunks('answer?', ctx))",
)

Configuration

Environment variables

# API key (checked in order)
LLM_API_KEY="your-key"        # Primary
OPENAI_API_KEY="your-key"     # Fallback

# Azure OpenAI
AZURE_OPENAI_API_KEY="your-key"
OPENAI_ENDPOINT="https://your-resource.openai.azure.com"
# or: AZURE_ACCOUNT_NAME="your-resource"
AZURE_OPENAI_API_VERSION="2024-10-21"  # optional

# Custom endpoint (optional)
LLM_BASE_URL="https://..."

# For local models (no auth needed)
LLM_BASE_URL="http://localhost:11434/v1"  # Ollama

Common configurations by use case

Use case	Configuration
Small context (<8K chars)	Use `SmartRouter` — it will pick baseline automatically
Large corpus (10K+ docs)	`RLM(adapter, retriever=ElasticsearchRetriever(...))` — search on demand
Large context (>100K chars)	`RLM(adapter, conversation_history=True, parallel_subcalls=True)`
Batch many independent prompts	Use `llm_batch(prompts)` — always parallel, no config needed
Cost-sensitive	Use a cheaper `subcall_adapter` for subcalls
Safety-critical code execution	`repl_backend="monty"`
Deterministic extraction	`SmartRouter` with `DETERMINISTIC_FIRST` profile
Complex multi-hop reasoning	`recursive_subcalls=True, max_recursion_depth=2`

Supported providers

Provider	Setup
Azure	`AzureOpenAIAdapter(model="gpt-5.1")` + `AZURE_OPENAI_API_KEY` + endpoint
OpenAI	`OpenAICompatAdapter(model="gpt-4")` + `LLM_API_KEY`
Anthropic	Via OpenAI-compatible proxy
Ollama	`OpenAICompatAdapter(model="llama3", base_url="http://localhost:11434/v1")`
LM Studio	`OpenAICompatAdapter(model="...", base_url="http://localhost:1234/v1")`
vLLM	`OpenAICompatAdapter(model="...", base_url="http://localhost:8000/v1")`
Custom	`GenericChatAdapter(...)` or implement `ModelAdapter`

Examples

Example	Description	Requires API?
`minimal.py`	Basic RLM flow with FakeAdapter	No
`rlm_vs_baseline.py`	Needle-in-haystack benchmark (MIT paper Figure 1)	Yes
`smart_router_demo.py`	SmartRouter auto-selecting baseline vs RLM by context size	Yes
`bench_repl_python_vs_monty.py`	Raw REPL performance: PythonREPL vs MontyREPL (no LLM calls)	No
`bench_rlm_repl_backends.py`	Full RLM loop benchmark with both REPL backends (FakeAdapter)	No

Run any example:

uv run python examples/minimal.py

When to Use RLMs

Use RLM when:

Context size exceeds the model's window (>100K tokens)
Information is scattered across the entire context
The task requires examining most or all of the input
Accuracy matters more than latency
Context doesn't fit the RAG chunk paradigm

Don't use RLM when:

Context always fits in the model window (<50K tokens)
Simple keyword search would work
Information is localized (RAG is faster)
Real-time response is required (milliseconds)

Benchmark: RLM vs Baseline

The rlm_vs_baseline.py example reproduces the key finding from the MIT paper (Figure 1): RLMs maintain accuracy as context grows, while baseline approaches degrade due to truncation.

Figure 1: RLM accuracy remains high as distractor documents increase, while baseline accuracy drops.

Running the benchmark

# Quick demo
RLM_CONTEXT_SIZES=5,30 uv run python examples/rlm_vs_baseline.py

# Full benchmark
RLM_CONTEXT_SIZES=5,20,50,120 uv run python examples/rlm_vs_baseline.py

# With detailed execution trajectory
SHOW_TRAJECTORY=1 RLM_CONTEXT_SIZES=5,30 uv run python examples/rlm_vs_baseline.py

The crossover point

Around ~50 documents (~100K+ characters), the context exceeds the LLM's window and baseline accuracy drops to 0%. RLM maintains near-perfect accuracy by inspecting the context via code instead of sending it all as input.

Development

# Install dependencies
uv sync

# Run tests
uv run pytest

# Lint and format
uv run ruff check src/ tests/
uv run ruff format src/ tests/

References

MIT CSAIL Paper: Recursive Language Models — Zhou, et al.
This implementation is not affiliated with MIT.

License

MIT License — see LICENSE for details.

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pyrlm-runtime

Table of Contents

Installation

Quickstart

1. Set your API key

2. Basic usage

3. Run without external APIs (for testing)

Live Rich Trace

Core Concepts

How the RLM loop works

Finalization

API Reference

RLM

Context

Adapters

OpenAICompatAdapter

GenericChatAdapter

FakeAdapter

Custom adapters

Policy

Trace

Cache

Router

REPL Backends

PythonREPL (default)

MontyREPL (secure sandbox)

REPL Functions Available to the LLM

Context inspection

Subcalls (call sub-LLMs)

Retrieval (when retriever is configured)

Deterministic extraction

Retrieval Integration

Quick Setup

Parallel Subcalls

Quick Summary

Multi-Turn Conversation History

Guard Mechanisms & Fallbacks

Configuration

Environment variables

Common configurations by use case

Supported providers

Examples

When to Use RLMs

Benchmark: RLM vs Baseline

Running the benchmark

The crossover point

Development

References

License

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