Matryoshka

Process documents 100x larger than your LLM's context window—without vector databases or chunking heuristics.

LLMs have fixed context windows. Traditional solutions (RAG, chunking) lose information or miss connections across chunks. RLM takes a different approach: the model reasons about your query and outputs symbolic commands that a logic engine executes against the document.

Based on the Recursive Language Models paper.

Unlike traditional approaches where an LLM writes arbitrary code, RLM uses Nucleus—a constrained symbolic language based on S-expressions. The LLM outputs Nucleus commands, which are parsed, type-checked, and executed by Lattice, our logic engine.

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   User Query    │────▶│   LLM Reasons   │────▶│ Nucleus Command │
│ "total sales?"  │     │  about intent   │     │  (sum RESULTS)  │
└─────────────────┘     └─────────────────┘     └────────┬────────┘
                                                         │
┌─────────────────┐     ┌─────────────────┐     ┌────────▼────────┐
│  Final Answer   │◀────│ Lattice Engine  │◀────│     Parser      │
│   13,000,000    │     │    Executes     │     │    Validates    │
└─────────────────┘     └─────────────────┘     └─────────────────┘

Why this works better than code generation:

1. Reduced entropy - Nucleus has a rigid grammar with fewer valid outputs than JavaScript
2. Fail-fast validation - Parser rejects malformed commands before execution
3. Safe execution - Lattice only executes known operations, no arbitrary code
4. Small model friendly - 7B models handle symbolic grammars better than freeform code

The LLM outputs commands in the Nucleus DSL—an S-expression language designed for document analysis:

; Search for patterns
(grep "ERROR")

; Filter results
(filter RESULTS (lambda x (match x "timeout" 0)))

; Aggregate
(sum RESULTS)    ; Auto-extracts numbers from lines
(count RESULTS)  ; Count matching items

; Final answer
<<<FINAL>>>13000000<<<END>>>

The Lattice engine (src/logic/) processes Nucleus commands:

1. Parser (lc-parser.ts) - Parses S-expressions into an AST
2. Type Inference (type-inference.ts) - Validates types before execution
3. Constraint Resolver (constraint-resolver.ts) - Handles symbolic constraints like [Σ⚡μ]
4. Solver (lc-solver.ts) - Executes commands against the document

Lattice uses miniKanren (a relational programming engine) for pattern classification and filtering operations.

For large result sets, RLM uses a handle-based architecture with in-memory SQLite (src/persistence/) that achieves 97%+ token savings:

Traditional:  LLM sees full array    [15,000 tokens for 1000 results]
Handle-based: LLM sees stub          [50 tokens: "$grep_error: Array(1000) [preview...]"]

How it works:

1. Results are stored in SQLite with FTS5 full-text indexing
2. LLM receives descriptive handle references derived from the command (e.g., $grep_error, $bm25_timeout, $filter_status)
3. Operations execute server-side, returning new handles
4. Full data is only materialized when needed

Handle names are auto-generated from the Nucleus command: (grep "ERROR") produces $grep_error, (list_symbols "function") produces $list_symbols_function. Repeated commands get a numeric suffix ($grep_error_2, $grep_error_3).

The Lattice engine doubles as a context memory for LLM agents. Instead of roundtripping large text blobs in every message, agents stash context server-side and carry only compact handle stubs:

Agent reads file, summarizes → lattice_memo "auth architecture"
                              → $memo_auth_architecture: "auth architecture" (2.1KB, 50 lines)

20 messages later, needs it  → lattice_expand $memo_auth_architecture
                              → Full 50-line summary

Token math (30-message session, 3 source files stashed):

• Traditional roundtripping: 836K tokens
• Memo-based (stubs + 6 expands): 57K tokens — 93% savings

Memos persist across document loads (lattice_load clears query handles but keeps memos), support LRU eviction (100 memo cap, 10MB budget), and can be explicitly deleted when stale. No document needs to be loaded to use memos.

The LLM does reasoning, not code generation:

1. Understands intent - Interprets "total of north sales" as needing grep + filter + sum
2. Chooses operations - Decides which Nucleus commands achieve the goal
3. Verifies results - Checks if the current results answer the query
4. Iterates - Refines search if results are too broad or narrow

The LLM never writes JavaScript. It outputs Nucleus commands that Lattice executes safely.

Install from npm:

npm install -g matryoshka-rlm

Or run without installing:

npx matryoshka-rlm "How many ERROR entries are there?" ./server.log

The package provides several CLI tools:

git clone https://github.com/yogthos/Matryoshka.git
cd Matryoshka
npm install
npm run build

Copy config.example.json to config.json and configure your LLM provider:

{
  "llm": {
    "provider": "ollama"
  },
  "providers": {
    "ollama": {
      "baseUrl": "http://localhost:11434",
      "model": "qwen3-coder:30b",
      "options": { "temperature": 0.2, "num_ctx": 8192 }
    },
    "deepseek": {
      "baseUrl": "https://api.deepseek.com",
      "apiKey": "${DEEPSEEK_API_KEY}",
      "model": "deepseek-chat",
      "options": { "temperature": 0.2 }
    }
  },
  "rlm": {
    "maxTurns": 10
  }
}

# Basic usage
rlm "How many ERROR entries are there?" ./server.log

# With options
rlm "Count all ERROR entries" ./server.log --max-turns 15 --verbose

# See all options
rlm --help

RLM includes lattice-mcp, an MCP (Model Context Protocol) server for direct access to the Nucleus engine. This allows coding agents to analyze documents with 80%+ token savings compared to reading files directly.

The key advantage is handle-based results: query results are stored server-side in SQLite, and the agent receives compact stubs like $grep_error: Array(1000) [preview...] instead of full data. Handle names are derived from the command for easy identification. Operations chain server-side without roundtripping data.

{
  "mcp": {
    "lattice": {
      "type": "stdio",
      "command": "lattice-mcp"
    }
  }
}

1. lattice_load("/path/to/large-file.txt")   # Load document (use for >500 lines)
2. lattice_query('(grep "ERROR")')           # Search → $grep_error: Array(500) [preview]
3. lattice_query('(filter RESULTS ...)')     # Narrow → $filter_timeout: Array(50) [preview]
4. lattice_query('(count RESULTS)')          # Count without seeing data → 50
5. lattice_expand("$filter_timeout", limit=10) # Expand only what you need to see
6. lattice_close()                           # Free memory when done

Token efficiency tips:

• Query results return descriptive handle stubs, not full data
• Use lattice_expand with limit to see only what you need
• Chain grep → filter → count/sum to refine progressively
• Use RESULTS in queries (always points to last result)
• Use descriptive handle names (e.g., $grep_error) with lattice_expand to inspect specific results

Two primitive families power the paper's Ω(|P|²) semantic-horizon pattern:

Chunking — pre-slice a document that's too big to map over directly:

(chunk_by_size 2000)                ; 2000-character slices
(chunk_by_lines 100)                ; 100-line slices
(chunk_by_regex "\\n\\n")           ; Split on blank lines; capture groups ignored

Sub-LLM calls — (llm_query ...) invokes a sub-LLM with an interpolated prompt. Works at the top level and nested inside map / filter / reduce lambdas:

(llm_query "Summarize this")                                         ; bare
(llm_query "Classify: {items}" (items RESULTS))                      ; with binding
(map (chunk_by_lines 100)
     (lambda c (llm_query "summarize: {chunk}" (chunk c))))           ; OOLONG
(filter RESULTS (lambda x (match (llm_query "keep?: {item}" (item x)) "keep" 0)))

The last two patterns fire one sub-LLM call per item — classification or summarization over an entire document, one chunk at a time, without pulling any of it into the root model's context.

Batched sub-LLM — when per-item calls are independent, llm_batch collapses N serial suspensions into one:

(llm_batch RESULTS (lambda x (llm_query "tag: {item}" (item x))))

Same surface syntax as map + llm_query, but fires a single [LLM_BATCH_REQUEST id=... count=N] suspension. The client replies once with a JSON array of N responses via lattice_llm_batch_respond. ~92% round-trip reduction on N=12, ~99% on N=100.

Constrain responses with (one_of ...) for classification tasks:

(llm_batch RESULTS
  (lambda x (llm_query "Rate: {item}" (item x)
                       (one_of "low" "medium" "high"))))

Validates responses case-insensitively against the allowed values, making downstream (filter ...) / (count ...) reliable without re-normalizing free-text output.

Add (calibrate) for subjective-judgment tasks:

(llm_batch RESULTS
  (lambda x (llm_query "Rate: {item}" (item x)
                       (one_of "low" "medium" "high")
                       (calibrate))))

Asks the model to scan all N prompts and establish a consistent relative scale before answering any. Useful when ratings depend on the distribution of the corpus rather than being absolute.

Multi-turn suspension protocol (works with any MCP client):

When (llm_query ...) is evaluated, execution suspends and returns a [LLM_QUERY_REQUEST id=...] message. The MCP client responds via lattice_llm_respond to resume execution. For queries with multiple llm_query calls (e.g., inside map), each item triggers one suspension — respond to each in turn until the final handle stub or scalar is returned. No special client capabilities (like sampling) are required.

For the native recursive sub-RLM implementation, use runRLMFromContent(query, content, { subRLMMaxDepth: 1 }) directly from the programmatic API — see the Programmatic section below.

1. lattice_memo(content="<file summary>", label="auth module")  → $memo_auth_module stub
2. lattice_memo(content="<analysis>", label="perf bottlenecks") → $memo_perf_bottlenecks stub
3. # ... many turns later, need the auth context ...
4. lattice_expand("$memo_auth_module")                          → Full summary
5. lattice_memo_delete("$memo_auth_module")                     → Drop when stale

Memos don't require a loaded document — they create a session automatically. Limits: 100 memos, 10MB total. Oldest evicted when exceeded.

import { runRLM } from "matryoshka-rlm/rlm";
import { createLLMClient } from "matryoshka-rlm";

const llmClient = createLLMClient("ollama", {
  baseUrl: "http://localhost:11434",
  model: "qwen3-coder:30b",
  options: { temperature: 0.2 }
});

const result = await runRLM("How many ERROR entries are there?", "./server.log", {
  llmClient,
  maxTurns: 10,
  turnTimeoutMs: 30000,
});

(grep "pattern")              ; Regex search, returns matches with line numbers
(fuzzy_search "query" 10)     ; Fuzzy search, returns top N matches with scores
(bm25 "query terms" 10)      ; BM25 ranked keyword search (TF-IDF scoring)
(semantic "query terms" 10)   ; TF-IDF cosine similarity search
(text_stats)                  ; Document metadata (length, line count, samples)
(lines 10 20)                 ; Get specific line range (1-indexed)

Combine results from multiple search operations for better relevance:

;; Reciprocal Rank Fusion — merge results from different search signals
(fuse (grep "ERROR") (bm25 "error handling") (semantic "failure"))

;; Gravity dampening — halve scores for false positives lacking query term overlap
(dampen (bm25 "database error") "database error")

;; Q-value reranking — learns which lines are useful across turns
(rerank (fuse (grep "ERROR") (bm25 "error")))

;; Full pipeline: fuse → dampen → rerank
(rerank (dampen (fuse (grep "ERROR") (bm25 "error") (semantic "failure")) "error"))

For code files, Lattice uses tree-sitter to extract structural symbols. This enables code-aware queries that understand functions, classes, methods, and other language constructs.

Built-in languages (packages included):

• TypeScript (.ts, .tsx), JavaScript (.js, .jsx), Python (.py), Go (.go)
• HTML (.html), CSS (.css), JSON (.json)

Additional languages (install package to enable):

• Rust, C, C++, Java, Ruby, PHP, C#, Kotlin, Swift, Scala, Lua, Haskell, Bash, SQL, and more

(list_symbols)                ; List all symbols (functions, classes, methods, etc.)
(list_symbols "function")     ; Filter by kind: "function", "class", "method", "interface", "type", "struct"
(get_symbol_body "myFunc")    ; Get source code body for a symbol by name
(get_symbol_body RESULTS)     ; Get body for symbol from previous query result
(find_references "myFunc")    ; Find all references to an identifier

Symbols include metadata like name, kind, start/end lines, and parent relationships (e.g., methods within classes).

When a code file is loaded, Lattice automatically builds an in-memory knowledge graph that tracks call relationships, inheritance, and interface implementations. This enables structural queries beyond simple text search.

(callers "funcName")            ; Who calls this function?
(callees "funcName")            ; What does this function call?
(ancestors "ClassName")         ; Inheritance chain (extends)
(descendants "ClassName")       ; All subclasses (transitive)
(implementations "IFace")       ; Classes implementing this interface
(dependents "name")             ; All transitive dependents
(dependents "name" 2)           ; Dependents within depth limit
(symbol_graph "name" 1)         ; Neighborhood subgraph around symbol

The graph is built using line-based heuristics (word-boundary matching for calls, syntax pattern matching for extends/implements), so it produces approximate but useful results without requiring a full language server.

Community detection and structural insights help you understand codebase architecture:

(communities)                   ; Detect communities with cohesion scores
(community_of "name")           ; Which community does this symbol belong to?
(god_nodes)                     ; Top 10 most-connected nodes (hubs)
(god_nodes 5)                   ; Top N most-connected nodes
(surprising_connections)         ; Cross-community or low-confidence edges
(bridge_nodes)                  ; Nodes bridging different communities
(suggest_questions)             ; Questions the graph can answer
(graph_report)                  ; Full analysis (all of the above)

Matryoshka includes built-in symbol mappings for 20+ languages. To enable a language, install its tree-sitter grammar package:

# Enable Rust support
npm install tree-sitter-rust

# Enable Java support
npm install tree-sitter-java

# Enable Ruby support
npm install tree-sitter-ruby

Languages with built-in mappings:

• TypeScript, JavaScript, Python, Go, Rust, C, C++, Java
• Ruby, PHP, C#, Kotlin, Swift, Scala, Lua, Haskell, Elixir
• HTML, CSS, JSON, YAML, TOML, Markdown, SQL, Bash

Once a package is installed, the language is automatically available for symbol extraction.

For languages without built-in mappings, create ~/.matryoshka/config.json mapping tree-sitter node types to symbol kinds (function, method, class, interface, type, struct, enum, trait, module, variable, constant, property):

{
  "grammars": {
    "ocaml": {
      "package": "tree-sitter-ocaml",
      "extensions": [".ml", ".mli"],
      "moduleExport": "ocaml",
      "symbols": {
        "value_definition": "function",
        "type_definition": "type",
        "module_definition": "module"
      }
    }
  }
}

Use the tree-sitter playground to explore node types for your language.

(if (count RESULTS) (sum RESULTS) 0)  ; Conditional: if/then/else
(add 10 20)                           ; Arithmetic addition

(filter RESULTS (lambda x (match x "pattern" 0)))  ; Filter by regex
(map RESULTS (lambda x (match x "(\\d+)" 1)))      ; Extract from each
(sum RESULTS)                                       ; Sum numbers in results
(count RESULTS)                                     ; Count items

(match str "pattern" 0)       ; Regex match, return group N
(replace str "from" "to")     ; String replacement
(split str "," 0)             ; Split and get index
(parseInt str)                ; Parse integer
(parseFloat str)              ; Parse float

(parseDate "Jan 15, 2024")           ; -> "2024-01-15"
(parseDate "01/15/2024" "US")        ; -> "2024-01-15" (MM/DD/YYYY)
(parseCurrency "$1,234.56")          ; -> 1234.56
(parseNumber "1,234,567")            ; -> 1234567
(coerce value "date")                ; General coercion (date/currency/number/boolean/string)
(extract str "\\$[\\d,]+" 0 "currency")  ; Extract and coerce in one step

The model provides constraints (input/output examples), not code — the synthesizer builds programs automatically using Barliman-style relational synthesis with miniKanren.

; Synthesize from input/output pairs
(synthesize
  ("$100" 100)
  ("$1,234" 1234)
  ("$50,000" 50000))

; Named functions — synthesize once, apply many times
(define-fn "parse_price" (("$100" 100) ("$1,234" 1234)))
(apply-fn "parse_price" "$50,000")    ; -> 50000

; Boolean classifiers from examples
(predicate "is_error" (("ERROR: timeout" true) ("INFO: ok" false)))

Results from previous turns are available:

• RESULTS - Latest array result (updated by grep, filter)
• _1, _2, _3, ... - Results from specific turns (1-indexed)

<<<FINAL>>>your answer here<<<END>>>

npm test                              # Run tests
npm test -- --coverage                # With coverage
RUN_E2E=1 npm test -- tests/e2e.test.ts  # E2E tests (requires Ollama)
npm run build                         # Build
npm run typecheck                     # Type check

This project incorporates ideas and code from:

• Ori-Mnemos - A persistent memory infrastructure for AI agents implementing the Recursive Memory Harness framework. BM25 search, Reciprocal Rank Fusion, gravity dampening, and Q-value learning reranking were ported from Ori-Mnemos and adapted for line-based document analysis.
• Nucleus - A symbolic S-expression language by Michael Whitford. RLM uses Nucleus syntax for the constrained DSL that the LLM outputs, providing a rigid grammar that reduces model errors.
• ramo - A miniKanren implementation in TypeScript by Will Lewis. Used for constraint-based program synthesis.
• Barliman - A prototype smart editor by William Byrd and Greg Rosenblatt that uses program synthesis to assist programmers. The Barliman-style approach of providing input/output constraints instead of code inspired the synthesis workflow.
• tree-sitter - A parser generator tool and incremental parsing library. Used for extracting structural symbols (functions, classes, methods) from code files to enable code-aware queries.

Apache-2.0

• RLM Paper
• Original Implementation
• Model Context Protocol
• miniKanren