rlm-toolkit 2.3.1
Recursive Language Models Toolkit for processing unlimited context
License: Apache-2.0
Requires Python: >=3.10
Keywords: llm, recursive, language-models, long-context, rlm

# RLM-Toolkit

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**Recursive Language Models Toolkit** — A high-security LangChain alternative for processing unlimited context (10M+ tokens) using recursive LLM calls.

## 🚀 Quick Start

```bash
pip install rlm-toolkit
```

```python
from rlm_toolkit import RLM

# Simple usage with Ollama
rlm = RLM.from_ollama("llama3")
result = rlm.run(
    context=open("large_document.txt").read(),
    query="What are the key findings?"
)
print(result.answer)
```

## ✨ Features

| Feature | Description |
|---------|-------------|
| **Infinite Context** | Process 10M+ tokens with O(1) memory |
| **InfiniRetri** | 🆕 Attention-based retrieval, 100% accuracy on 1M+ tokens |
| **H-MEM** | 🆕 4-level hierarchical memory with LLM consolidation |
| **Memory Bridge** | 🆕 Bi-temporal cross-session persistence (Graphiti-inspired) |
| **Self-Evolving** | 🆕 LLMs that improve through usage (R-Zero pattern) |
| **Multi-Agent** | 🆕 Decentralized P2P agents with Trust Zones |
| **DSPy Optimization** | 🆕 Automatic prompt optimization |
| **Secure REPL** | CIRCLE-compliant sandboxed code execution |
| **Multi-Provider** | 75 LLM providers (OpenAI, Anthropic, Google, Ollama, vLLM...) |
| **Document Loaders** | 135+ sources (Slack, Jira, GitHub, S3, databases...) |
| **Vector Stores** | 20+ stores (Pinecone, Chroma, Weaviate, pgvector...) |
| **Embeddings** | 15+ providers (OpenAI, BGE, E5, Jina, Cohere...) |
| **Cost Control** | Budget limits, cost tracking |
| **Observability** | OpenTelemetry, Langfuse, LangSmith, W&B (12 backends) |
| **Memory Systems** | Buffer, Episodic, Hierarchical (H-MEM), Memory Bridge |

> 📋 **[Full Integration Catalog](docs/INTEGRATIONS.md)** — 287+ production-ready integrations

## 🔥 InfiniRetri (NEW)

Attention-based infinite context retrieval — 100% accuracy on Needle-In-a-Haystack up to 1M+ tokens.

```python
from rlm_toolkit.retrieval import InfiniRetriever

# Retrieve from 1M+ token documents
retriever = InfiniRetriever("Qwen/Qwen2.5-0.5B-Instruct")
answer = retriever.retrieve(
    context=million_token_doc,
    question="What is the secret code?"
)

# Or use automatic routing in RLM
from rlm_toolkit import RLM, RLMConfig

config = RLMConfig(
    use_infiniretri=True,
    infiniretri_threshold=100_000,  # Auto-switch at 100K tokens
)
rlm = RLM.from_ollama("llama3", config=config)
result = rlm.run(huge_document, "Summarize")  # Automatically uses InfiniRetri
```

> Based on [arXiv:2502.12962](https://arxiv.org/abs/2502.12962) — requires `pip install infini-retri`

## 🧠 Hierarchical Memory (H-MEM) (NEW)

Multi-level persistent memory with semantic consolidation — memories that learn and evolve.

```python
from rlm_toolkit.memory import HierarchicalMemory, SecureHierarchicalMemory

# Basic H-MEM
hmem = HierarchicalMemory()
hmem.add_episode("User asked about weather")
hmem.add_episode("AI responded with forecast")
hmem.consolidate()  # Auto-creates traces, categories, domains

results = hmem.retrieve("weather")

# Secure H-MEM with encryption and trust zones
smem = SecureHierarchicalMemory(
    agent_id="agent-001",
    trust_zone="zone-secure"
)
smem.add_episode("Confidential data")
smem.grant_access("agent-002", "zone-secure")
```

**4-Level Architecture:**
```
Level 3: DOMAIN    → High-level knowledge
Level 2: CATEGORY  → Semantic categories  
Level 1: TRACE     → Consolidated memories
Level 0: EPISODE   → Raw interactions
```

> Based on arXiv H-MEM paper (July 2025)

## 🌉 Memory Bridge v2.1 (NEW)

**Enterprise-scale cross-session persistence** — Zero-friction Auto-Mode with 56x token compression.

```python
# Zero-config enterprise context (recommended)
from rlm_toolkit.memory_bridge.mcp_tools_v2 import rlm_enterprise_context

result = rlm_enterprise_context(
    query="What's the architecture of this project?",
    max_tokens=3000
)
print(result["context"])  # Semantic routing loads only relevant facts
```

**v2.1 Features:**

| Feature | Description |
|---------|-------------|
| **Auto-Mode** | 🆕 Zero-config orchestration for new projects |
| **Hierarchical Memory** | 🆕 L0-L3 levels: Project → Domain → Module → Code |
| **Semantic Routing** | 🆕 56x compression via similarity-based context loading |
| **Git Auto-Extract** | 🆕 Facts extracted automatically on each commit |
| **Causal Reasoning** | 🆕 Track decisions with reasons, constraints, alternatives |
| **Smart Cold Start** | 🆕 Sub-second project discovery (0.04s for 79K LOC) |
| **18 MCP Tools** | Full IDE integration via Model Context Protocol |

**Hierarchical Memory (L0-L3):**
```
L0: PROJECT   → High-level architecture, tech stack
L1: DOMAIN    → Feature areas (auth, api, database)
L2: MODULE    → Per-file knowledge  
L3: CODE      → Function-level facts with line refs
```

**VS Code Extension v2.1.0:**
```bash
code --install-extension rlm-toolkit-2.1.0.vsix
```
- Real-time dashboard with L0-L3 visualization
- Discover / Git Hook / Index Embeddings buttons
- Health Check status for Memory Store and Semantic Router

**Git Hook Auto-Extraction:**
```python
# Install hook for automatic fact extraction
rlm_install_git_hooks(hook_type="post-commit")
# Now every commit auto-extracts: classes, functions, major changes
```

> Based on [Graphiti](https://arxiv.org/abs/2501.13956) — [Full Documentation](docs/memory-bridge.md)

## 🧬 Self-Evolving LLMs (NEW)

LLMs that improve reasoning through usage — no human supervision required.

```python
from rlm_toolkit.evolve import SelfEvolvingRLM, EvolutionStrategy
from rlm_toolkit.providers import OllamaProvider

# Create self-evolving RLM
evolve = SelfEvolvingRLM(
    provider=OllamaProvider("llama3"),
    strategy=EvolutionStrategy.CHALLENGER_SOLVER
)

# Solve with self-refinement
answer = evolve.solve("What is 25 * 17?")
print(f"Answer: {answer.answer}, Confidence: {answer.confidence}")

# Run training loop (generates challenges → solves → improves)
metrics = evolve.training_loop(iterations=10, domain="math")
print(f"Success rate: {metrics.success_rate}")
```

**Strategies:**
- `SELF_REFINE` — Iterative self-improvement
- `CHALLENGER_SOLVER` — R-Zero co-evolutionary loop
- `EXPERIENCE_REPLAY` — Learn from past solutions

> Based on R-Zero (arXiv:2508.05004)

## 🤖 Multi-Agent Framework (NEW)

Decentralized P2P agents inspired by Meta Matrix — no central orchestrator bottleneck.

```python
from rlm_toolkit.agents import MultiAgentRuntime, SecureAgent, EvolvingAgent

# Create runtime
runtime = MultiAgentRuntime()

# Register agents with Trust Zones
runtime.register(SecureAgent("analyst", "Data Analyst", trust_zone="internal"))
runtime.register(EvolvingAgent("solver", "Problem Solver", llm_provider=provider))

# Run message through agents
from rlm_toolkit.agents import AgentMessage
message = AgentMessage(content="Analyze this data", routing=["analyst", "solver"])
result = runtime.run(message)
```

**Agent Types:**
- `SecureAgent` — H-MEM Trust Zones integration
- `EvolvingAgent` — Self-improving via R-Zero
- `SecureEvolvingAgent` — Both combined

> Based on Meta Matrix (arXiv 2025)

## 🎯 DSPy-Style Optimization (NEW)

Automatic prompt optimization — define what, not how.

```python
from rlm_toolkit.optimize import Signature, Predict, ChainOfThought, BootstrapFewShot

# Define signature
sig = Signature(
    inputs=["question", "context"],
    outputs=["answer"],
    instructions="Answer based on context"
)

# Use with Chain of Thought
cot = ChainOfThought(sig, provider)
result = cot(question="What is X?", context="X is 42")

# Auto-optimize with few-shot selection
optimizer = BootstrapFewShot(metric=lambda p, g: p["answer"] == g["answer"])
optimized = optimizer.compile(Predict(sig, provider), trainset=examples)
```

**Modules:** `Predict`, `ChainOfThought`, `SelfRefine`  
**Optimizers:** `BootstrapFewShot`, `PromptOptimizer`

> Inspired by Stanford DSPy

## 📦 Installation

```bash
# Basic
pip install rlm-toolkit

# With all providers
pip install rlm-toolkit[all]

# Development
pip install -e ".[dev]"
```

## 🔧 Usage

### Basic

```python
from rlm_toolkit import RLM, RLMConfig

# With configuration
config = RLMConfig(
    max_iterations=50,
    max_cost=5.0,  # USD
)

rlm = RLM.from_openai("gpt-4o", config=config)
result = rlm.run(context, query)
```

### With Memory

```python
from rlm_toolkit.memory import EpisodicMemory

memory = EpisodicMemory(max_entries=1000)
rlm = RLM.from_ollama("llama3", memory=memory)

# Memory persists across runs
result1 = rlm.run(doc1, "Summarize this")
result2 = rlm.run(doc2, "Compare with previous")
```

### With Observability

```python
from rlm_toolkit.observability import Tracer, CostTracker

tracer = Tracer(service_name="my-app")
cost_tracker = CostTracker(budget=10.0)

rlm = RLM.from_openai("gpt-4o", tracer=tracer, cost_tracker=cost_tracker)
```

## 🔒 Security

RLM-Toolkit implements CIRCLE-compliant security with v1.2.1 hardening:

- **AES-256-GCM** — Mandatory authenticated encryption for all persistent data
- **Fail-Closed** — No XOR fallback; raises error if cryptography unavailable
- **Rate Limiting** — 60s cooldown on reindex to prevent I/O exhaustion
- **AST Analysis** — Block dangerous imports before execution
- **Sandboxed REPL** — Isolated code execution with timeouts
- **Virtual Filesystem** — Quota-enforced file operations
- **Attack Detection** — Obfuscation and indirect attack patterns

```python
from rlm_toolkit import RLMConfig, SecurityConfig

config = RLMConfig(
    security=SecurityConfig(
        sandbox=True,
        max_execution_time=30.0,
        max_memory_mb=512,
    )
)
```

## 📊 Benchmarks

Based on RLM paper methodology:

| Benchmark | Score |
|-----------|-------|
| OOLONG-Pairs | TBD |
| CIRCLE Security | ~95% |

## 🛠️ CLI

```bash
# Run a query
rlm run --model ollama:llama3 --context file.txt --query "Summarize"

# Interactive REPL
rlm repl --model openai:gpt-4o

# Cost tracking
rlm trace --session latest
```

## 📚 Documentation

**v2.1.0: 162 files (81 EN + 81 RU) — NIOKR 10/10**

| Category | EN | RU |
|----------|:--:|:--:|
| Concepts | 25 | 25 |
| Tutorials | 13 | 13 |
| Examples | 10 | 10 |
| How-To | 20 | 20 |
| Reference | 6 | 6 |
| Memory Bridge | 7 | 7 |

- [Quickstart](docs/en/quickstart.md) / [Быстрый старт](docs/ru/quickstart.md)
- [Tutorials](docs/en/tutorials/) / [Туториалы](docs/ru/tutorials/)
- [Security Guide](docs/en/concepts/security.md)
- [**Memory Bridge v2.1**](docs/memory-bridge.md) — Enterprise memory with 18 MCP tools
- [MCP Server](docs/mcp-server.md) — IDE integration
- [VS Code Extension](rlm-vscode-extension/README.md) — Dashboard v2.1.0
- [Certification Checklist](docs/en/certification/checklist.md)
- [Examples](docs/en/examples/)

## 🤝 Contributing

```bash
# Clone repo
git clone https://github.com/DmitrL-dev/AISecurity.git
cd AISecurity/sentinel-community/rlm-toolkit

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Lint
ruff check rlm_toolkit/
```

## 📄 License

Apache 2.0 — see [LICENSE](LICENSE)

## 🙏 Acknowledgments

- **Alex Zhang** — Original RLM concept author ([Blog](https://alexzhang13.github.io/blog/2025/rlm/), [arXiv:2512.24601](https://arxiv.org/abs/2512.24601), October 2025)
- **[Prime Intellect](https://www.primeintellect.ai/)** — RLM research and [verifiers implementation](https://github.com/PrimeIntellect-ai/verifiers/)
- [CIRCLE Benchmark](https://arxiv.org/abs/2507.19399) — Security evaluation methodology
- [InfiniRetri](https://arxiv.org/abs/2502.12962) — Attention-based infinite context retrieval
- [H-MEM](https://arxiv.org/abs/2507.XXXXX) — Hierarchical memory architecture
- SENTINEL Community — Security-first implementation and documentation