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Language Model - Process documents larger than LLM context windows","maintainers":[{"name":"yogthos","email":"dmitri.sotnikov@gmail.com"}],"readme":"# Matryoshka\n\n[![Tests](https://github.com/yogthos/Matryoshka/actions/workflows/test.yml/badge.svg)](https://github.com/yogthos/Matryoshka/actions/workflows/test.yml)\n[![SafeSkill 88/100](https://img.shields.io/badge/SafeSkill-88%2F100_Passes%20with%20Notes-yellow)](https://safeskill.dev/scan/yogthos-matryoshka)\n\nProcess documents 100x larger than your LLM's context window—without vector databases or chunking heuristics.\n\n## The Problem\n\nLLMs 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.\n\nBased on the [Recursive Language Models paper](https://arxiv.org/abs/2512.24601).\n\n## How It Works\n\nUnlike traditional approaches where an LLM writes arbitrary code, RLM uses **[Nucleus](https://github.com/michaelwhitford/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.\n\n```\n┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐\n│   User Query    │────▶│   LLM Reasons   │────▶│ Nucleus Command │\n│ \"total sales?\"  │     │  about intent   │     │  (sum RESULTS)  │\n└─────────────────┘     └─────────────────┘     └────────┬────────┘\n                                                         │\n┌─────────────────┐     ┌─────────────────┐     ┌────────▼────────┐\n│  Final Answer   │◀────│ Lattice Engine  │◀────│     Parser      │\n│   13,000,000    │     │    Executes     │     │    Validates    │\n└─────────────────┘     └─────────────────┘     └─────────────────┘\n```\n\n**Why this works better than code generation:**\n\n1. **Reduced entropy** - Nucleus has a rigid grammar with fewer valid outputs than JavaScript\n2. **Fail-fast validation** - Parser rejects malformed commands before execution\n3. **Safe execution** - Lattice only executes known operations, no arbitrary code\n4. **Small model friendly** - 7B models handle symbolic grammars better than freeform code\n\n## Architecture\n\n### The Nucleus DSL\n\nThe LLM outputs commands in the Nucleus DSL—an S-expression language designed for document analysis:\n\n```scheme\n; Search for patterns\n(grep \"ERROR\")\n\n; Filter results\n(filter RESULTS (lambda x (match x \"timeout\" 0)))\n\n; Aggregate\n(sum RESULTS)    ; Auto-extracts numbers from lines\n(count RESULTS)  ; Count matching items\n\n; Final answer\n<<<FINAL>>>13000000<<<END>>>\n```\n\n#### Feature availability by execution path\n\nMatryoshka has two execution paths and not every primitive works in both:\n\n| Feature | `runRLM` (CLI / programmatic) | `lattice-mcp` (MCP server) |\n|---|---|---|\n| `(grep …)`, `(filter …)`, `(map …)`, etc. | ✅ | ✅ |\n| `(llm_query …)`, `(llm_batch …)` | ✅ | ✅ via MCP sampling protocol |\n| `(rlm_query …)`, `(rlm_batch …)` | ✅ (concurrent rlm_batch) | ✅ — child Nucleus session spawns via the same MCP sampling bridge; M suspensions per `rlm_query` call. `rlm_batch` runs **sequentially** (children one at a time) because the multi-turn suspension protocol only carries one pending request at a time — concurrent children would lose suspensions. Round-trip count is the same; wall-clock is N×slower for non-sampling clients. |\n| `(context N)` selector | ✅ (multi-doc via `runRLMFromContent(query, string[])`) | partial — `(context 0)` works; multi-doc loading not exposed via `lattice_load` |\n| `(grep \"X\" haystack)` | ✅ | ✅ |\n| `(show_vars)` | ✅ | ✅ (internal `_<name>` bindings filtered out) |\n| `FINAL_VAR(name)` resolution | ✅ | N/A — MCP returns query results directly |\n| `maxTimeoutMs` / `maxChars` / `maxErrors` | ✅ | ❌ — MCP has its own session timeout |\n| `compactionThresholdChars` | ✅ | ❌ — MCP doesn't have a multi-turn FSM history |\n\nThe resource-limit features remain `runRLM`-only. The recursive primitives (`rlm_query`/`rlm_batch`) work in both paths — the MCP path spawns a child `runRLMFromContent` whose `llmClient` is the same sampling bridge as the parent, so each child turn flows through the existing MCP suspension/sampling protocol.\n\n#### Recursive Primitives\n\n`rlm_query` spawns a child Nucleus session with its own FSM loop. The child runs to FINAL and returns a string — useful when a sub-task needs multi-turn reasoning over a structured handle:\n\n```scheme\n; Child sees the resolved handle as its working document, NOT a\n; JSON-stringified prompt blob. Lets the child use grep/lines/\n; chunk_by_lines over arrays without JSON-syntax noise.\n(rlm_query \"extract dates\" (context RESULTS))\n\n; No (context …) → child's document is the prompt itself.\n(rlm_query \"summarize each error type\")\n```\n\n`rlm_batch` runs the same per-item recursion across a collection. Each item produces one entry in the returned array, in input order. Per-item failures surface as `\"Error: rlm_batch item N failed — …\"` strings without aborting the rest of the batch:\n\n```scheme\n(rlm_batch (chunk_by_lines 100)\n  (lambda c (rlm_query \"extract metrics\" (context c))))\n```\n\n- **`runRLM`**: children fan out concurrently via a worker pool capped at `maxConcurrentSubcalls` (default 4).\n- **`lattice-mcp`**: children run **sequentially** because the multi-turn suspension protocol can carry only one pending request at a time. Round-trip count is identical to the concurrent path (N children × M turns each); only wall-clock differs.\n\n#### Multi-Context Loading\n\nPass `string[]` to `runRLMFromContent` to load multiple documents. Address them via `(context N)`; index 0 is the default for primitives that don't specify a haystack:\n\n```scheme\n(grep \"DEPLOY\" (context 0))   ; deploy.log\n(grep \"OUTAGE\" (context 2))   ; comms.log\n\n; (context N) is just a term — pipe it anywhere a string is expected\n(rlm_query \"scan\" (context (context 1)))   ; child sees doc 1\n```\n\nPer-doc line numbers come back, so the LLM can cite \"doc 0 line 4, doc 2 line 2\" with confidence rather than inventing absolute offsets across a concatenation.\n\n#### Introspection\n\n```scheme\n(show_vars)   ; Returns a string summary of every binding currently\n              ; in scope. Useful before a (filter RESULTS …) or a\n              ; FINAL_VAR(name) reference when the LLM lost track of\n              ; what's bound. Same surface as the `lattice_bindings`\n              ; MCP tool but reachable from inside a query.\n```\n\nUnknown FINAL_VAR markers surface a clear error rather than passing the literal text through:\n\n```\n<<<FINAL>>>FINAL_VAR(_99)<<<END>>>\n→ \"[FINAL_VAR error: unknown binding \"_99\". Available: _1, RESULTS]\"\n```\n\n#### Resource Limits\n\nAll optional. With none set, behavior is unchanged:\n\n```typescript\nrunRLM(query, file, {\n  maxTimeoutMs: 30_000,    // wall-clock cap, propagates to children\n  maxChars: 100_000,       // cumulative chars sent + received\n  maxErrors: 5,            // consecutive parse/execution errors\n  compactionThresholdChars: 50_000,  // summarize history when prompt grows past this\n})\n```\n\nWhen a limit hits, the run terminates cleanly with a string of the form:\n\n```\n[aborted: timeout 32100ms of 30000ms]\n\nBest partial answer:\n<the most recent meaningful solver result>\n```\n\nThe partial answer is always preserved when present — completed work is never silently lost on abort.\n\n### The Lattice Engine\n\nThe Lattice engine (`src/logic/`) processes Nucleus commands:\n\n1. **Parser** (`lc-parser.ts`) - Parses S-expressions into an AST\n2. **Type Inference** (`type-inference.ts`) - Validates types before execution\n3. **Constraint Resolver** (`constraint-resolver.ts`) - Handles symbolic constraints like `[Σ⚡μ]`\n4. **Solver** (`lc-solver.ts`) - Executes commands against the document\n\nLattice uses **miniKanren** (a relational programming engine) for pattern classification and filtering operations.\n\n### In-Memory Handle Storage\n\nFor large result sets, RLM uses a handle-based architecture with in-memory SQLite (`src/persistence/`) that achieves **97%+ token savings**:\n\n```\nTraditional:  LLM sees full array    [15,000 tokens for 1000 results]\nHandle-based: LLM sees stub          [50 tokens: \"$grep_error: Array(1000) [preview...]\"]\n```\n\n**How it works:**\n1. Results are stored in SQLite with FTS5 full-text indexing\n2. LLM receives descriptive handle references derived from the command (e.g., `$grep_error`, `$bm25_timeout`, `$filter_status`)\n3. Operations execute server-side, returning new handles\n4. Full data is only materialized when needed\n\nHandle 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`).\n\n### Memory Pad\n\nThe 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:\n\n```\nAgent reads file, summarizes → lattice_memo \"auth architecture\"\n                              → $memo_auth_architecture: \"auth architecture\" (2.1KB, 50 lines)\n\n20 messages later, needs it  → lattice_expand $memo_auth_architecture\n                              → Full 50-line summary\n```\n\n**Token math** (30-message session, 3 source files stashed):\n- Traditional roundtripping: **836K tokens**\n- Memo-based (stubs + 6 expands): **57K tokens** — **93% savings**\n\nMemos 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.\n\n### The Role of the LLM\n\nThe LLM does **reasoning**, not code generation:\n\n1. **Understands intent** - Interprets \"total of north sales\" as needing grep + filter + sum\n2. **Chooses operations** - Decides which Nucleus commands achieve the goal\n3. **Verifies results** - Checks if the current results answer the query\n4. **Iterates** - Refines search if results are too broad or narrow\n\nThe LLM never writes JavaScript. It outputs Nucleus commands that Lattice executes safely.\n\n## Installation\n\nInstall from npm:\n\n```bash\npnpm add -g matryoshka-rlm\n```\n\nOr run without installing:\n\n```bash\nnpx matryoshka-rlm \"How many ERROR entries are there?\" ./server.log\n```\n\n### Included Tools\n\nThe package provides several CLI tools:\n\n| Command | Description |\n|---------|-------------|\n| `rlm` | Main CLI for document analysis with LLM reasoning |\n| `rlm-mcp` | MCP server with full RLM + LLM orchestration (`analyze_document` tool) |\n| `lattice-mcp` | MCP server exposing direct Nucleus commands (no LLM required) |\n| `lattice-repl` | Interactive REPL for Nucleus commands |\n| `lattice-http` | HTTP server for Nucleus queries |\n| `lattice-pipe` | Pipe adapter for programmatic access |\n| `lattice-setup` | Setup script for Claude Code integration |\n\n### From Source\n\n```bash\ngit clone https://github.com/yogthos/Matryoshka.git\ncd Matryoshka\npnpm install\npnpm run build\n```\n\n## Configuration\n\nCopy `config.example.json` to `~/.config/matryoshka/config.json`:\n\n```json\n{\n  \"llm\": {\n    \"provider\": \"ollama\"\n  },\n  \"providers\": {\n    \"ollama\": {\n      \"url\": \"http://localhost:11434/api/generate\",\n      \"model\": \"qwen3-coder:30b\",\n      \"options\": { \"temperature\": 0.2, \"num_ctx\": 8192 }\n    },\n    \"deepseek\": {\n      \"url\": \"https://api.deepseek.com/chat/completions\",\n      \"apiKey\": \"${DEEPSEEK_API_KEY}\",\n      \"model\": \"deepseek-chat\",\n      \"options\": { \"temperature\": 0.2 }\n    },\n    \"glm\": {\n      \"url\": \"https://open.bigmodel.cn/api/paas/v4/chat/completions\",\n      \"apiKey\": \"${ZHIPU_API_KEY}\",\n      \"model\": \"glm-4-plus\",\n      \"options\": { \"temperature\": 0.2 }\n    }\n  },\n  \"rlm\": {\n    \"maxTurns\": 10\n  },\n  \"grammars\": {\n    \"ocaml\": {\n      \"package\": \"tree-sitter-ocaml\",\n      \"extensions\": [\".ml\", \".mli\"],\n      \"moduleExport\": \"ocaml\",\n      \"symbols\": {\n        \"value_definition\": \"function\",\n        \"type_definition\": \"type\",\n        \"module_definition\": \"module\"\n      }\n    }\n  }\n}\n```\n\n- **`llm` / `providers` / `rlm`** — LLM provider selection and RLM tuning (shown above with example providers). Each provider takes a full `url` (the complete API endpoint), an optional `apiKey` (supports `${ENV_VAR}` interpolation), a `model` name, and `options`.\n- **`grammars`** — custom tree-sitter language mappings for symbol extraction (see [Adding Language Support](#adding-language-support) for the full list of built-in languages). Use the [tree-sitter playground](https://tree-sitter.github.io/tree-sitter/playground) to explore node types for your language.\n\n## Usage\n\n### CLI\n\n```bash\n# Basic usage\nrlm \"How many ERROR entries are there?\" ./server.log\n\n# With options\nrlm \"Count all ERROR entries\" ./server.log --max-turns 15 --verbose\n\n# See all options\nrlm --help\n```\n\n### MCP Integration\n\nRLM 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.\n\nThe 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.\n\n#### Available Tools\n\n| Tool | Description |\n|------|-------------|\n| `lattice_load` | Load a document for analysis |\n| `lattice_query` | Execute Nucleus commands on the loaded document |\n| `lattice_expand` | Expand a handle to see full data (with optional limit/offset) |\n| `lattice_memo` | Store arbitrary context as a memo handle (no document required) |\n| `lattice_memo_delete` | Delete a stale memo to free memory |\n| `lattice_close` | Close the session and free memory |\n| `lattice_status` | Get session status, document info, and memo usage |\n| `lattice_bindings` | Show current variable bindings and memo labels |\n| `lattice_reset` | Reset all bindings and memos but keep document loaded |\n| `lattice_llm_respond` | Respond to a pending `(llm_query ...)` suspension |\n| `lattice_llm_batch_respond` | Respond to a pending `(llm_batch ...)` suspension with all N responses |\n| `lattice_help` | Get Nucleus command reference |\n\n#### Example MCP config\n\n```json\n{\n  \"mcp\": {\n    \"lattice\": {\n      \"type\": \"stdio\",\n      \"command\": \"lattice-mcp\"\n    }\n  }\n}\n```\n\n#### Efficient Usage Pattern\n\n```\n1. lattice_load(\"/path/to/large-file.txt\")   # Load document (use for >500 lines)\n2. lattice_query('(grep \"ERROR\")')           # Search → $grep_error: Array(500) [preview]\n3. lattice_query('(filter RESULTS ...)')     # Narrow → $filter_timeout: Array(50) [preview]\n4. lattice_query('(count RESULTS)')          # Count without seeing data → 50\n5. lattice_expand(\"$filter_timeout\", limit=10) # Expand only what you need to see\n6. lattice_close()                           # Free memory when done\n```\n\n**Token efficiency tips:**\n- Query results return descriptive handle stubs, not full data\n- Use `lattice_expand` with `limit` to see only what you need\n- Chain `grep → filter → count/sum` to refine progressively\n- Use `RESULTS` in queries (always points to last result)\n- Use descriptive handle names (e.g., `$grep_error`) with `lattice_expand` to inspect specific results\n\n#### Chunking and Sub-LLM Recursion\n\nTwo primitive families power the paper's `Ω(|P|²)` semantic-horizon pattern:\n\n**Chunking** — pre-slice a document that's too big to map over directly:\n\n```scheme\n(chunk_by_size 2000)                ; 2000-character slices\n(chunk_by_lines 100)                ; 100-line slices\n(chunk_by_regex \"\\\\n\\\\n\")           ; Split on blank lines; capture groups ignored\n```\n\n**Sub-LLM calls** — `(llm_query ...)` invokes a sub-LLM with an\ninterpolated prompt. Works at the top level and nested inside\n`map` / `filter` / `reduce` lambdas:\n\n```scheme\n(llm_query \"Summarize this\")                                         ; bare\n(llm_query \"Classify: {items}\" (items RESULTS))                      ; with binding\n(map (chunk_by_lines 100)\n     (lambda c (llm_query \"summarize: {chunk}\" (chunk c))))           ; OOLONG\n(filter RESULTS (lambda x (match (llm_query \"keep?: {item}\" (item x)) \"keep\" 0)))\n```\n\nThe last two patterns fire **one sub-LLM call per item** — classification\nor summarization over an entire document, one chunk at a time, without\npulling any of it into the root model's context.\n\n**Batched sub-LLM** — when per-item calls are independent, `llm_batch`\ncollapses N serial suspensions into one:\n\n```scheme\n(llm_batch RESULTS (lambda x (llm_query \"tag: {item}\" (item x))))\n```\n\nSame surface syntax as `map` + `llm_query`, but fires a single\n`[LLM_BATCH_REQUEST id=... count=N]` suspension. The client replies once\nwith a JSON array of N responses via `lattice_llm_batch_respond`.\n~92% round-trip reduction on N=12, ~99% on N=100.\n\n**Constrain responses with `(one_of ...)`** for classification tasks:\n\n```scheme\n(llm_batch RESULTS\n  (lambda x (llm_query \"Rate: {item}\" (item x)\n                       (one_of \"low\" \"medium\" \"high\"))))\n```\n\nValidates responses case-insensitively against the allowed values,\nmaking downstream `(filter ...)` / `(count ...)` reliable without\nre-normalizing free-text output.\n\n**Add `(calibrate)` for subjective-judgment tasks:**\n\n```scheme\n(llm_batch RESULTS\n  (lambda x (llm_query \"Rate: {item}\" (item x)\n                       (one_of \"low\" \"medium\" \"high\")\n                       (calibrate))))\n```\n\nAsks the model to scan all N prompts and establish a consistent relative\nscale before answering any. Useful when ratings depend on the distribution\nof the corpus rather than being absolute.\n\n**Multi-turn suspension protocol (works with any MCP client):**\n\nWhen `(llm_query ...)` is evaluated, execution suspends and returns a\n`[LLM_QUERY_REQUEST id=...]` message. The MCP client responds via\n`lattice_llm_respond` to resume execution. For queries with multiple\n`llm_query` calls (e.g., inside `map`), each item triggers one\nsuspension — respond to each in turn until the final handle stub or\nscalar is returned. No special client capabilities (like `sampling`)\nare required.\n\nFor the native recursive sub-RLM implementation,\nuse `runRLMFromContent(query, content, { subRLMMaxDepth: 1 })` directly\nfrom the programmatic API — see the Programmatic section below.\n\n#### Memory Pad Usage\n\n```\n1. lattice_memo(content=\"<file summary>\", label=\"auth module\")  → $memo_auth_module stub\n2. lattice_memo(content=\"<analysis>\", label=\"perf bottlenecks\") → $memo_perf_bottlenecks stub\n3. # ... many turns later, need the auth context ...\n4. lattice_expand(\"$memo_auth_module\")                          → Full summary\n5. lattice_memo_delete(\"$memo_auth_module\")                     → Drop when stale\n```\n\nMemos don't require a loaded document — they create a session automatically.\nLimits: 100 memos, 10MB total. Oldest evicted when exceeded.\n\n### Programmatic\n\n```typescript\nimport { runRLM } from \"matryoshka-rlm/rlm\";\nimport { createLLMClient } from \"matryoshka-rlm\";\n\nconst llmClient = createLLMClient(\"ollama\", {\n  url: \"http://localhost:11434/api/generate\",\n  model: \"qwen3-coder:30b\",\n  options: { temperature: 0.2 }\n});\n\nconst result = await runRLM(\"How many ERROR entries are there?\", \"./server.log\", {\n  llmClient,\n  maxTurns: 10,\n  turnTimeoutMs: 30000,\n});\n```\n\n## Nucleus DSL Reference\n\n### Search Commands\n\n```scheme\n(grep \"pattern\")              ; Regex search, returns matches with line numbers\n(fuzzy_search \"query\" 10)     ; Fuzzy search, returns top N matches with scores\n(bm25 \"query terms\" 10)      ; BM25 ranked keyword search (TF-IDF scoring)\n(semantic \"query terms\" 10)   ; TF-IDF cosine similarity search\n(text_stats)                  ; Document metadata (length, line count, samples)\n(lines 10 20)                 ; Get specific line range (1-indexed)\n```\n\n### Multi-Signal Fusion & Ranking\n\nCombine results from multiple search operations for better relevance:\n\n```scheme\n;; Reciprocal Rank Fusion — merge results from different search signals\n(fuse (grep \"ERROR\") (bm25 \"error handling\") (semantic \"failure\"))\n\n;; Gravity dampening — halve scores for false positives lacking query term overlap\n(dampen (bm25 \"database error\") \"database error\")\n\n;; Q-value reranking — learns which lines are useful across turns\n(rerank (fuse (grep \"ERROR\") (bm25 \"error\")))\n\n;; Full pipeline: fuse → dampen → rerank\n(rerank (dampen (fuse (grep \"ERROR\") (bm25 \"error\") (semantic \"failure\")) \"error\"))\n```\n\n### Symbol Operations (Code Files)\n\nFor code files, Lattice uses tree-sitter to extract structural symbols. This enables code-aware queries that understand functions, classes, methods, and other language constructs.\n\n**Built-in languages (packages included):**\n- TypeScript (.ts, .tsx), JavaScript (.js, .jsx), Python (.py), Go (.go)\n- HTML (.html), CSS (.css), JSON (.json)\n\n**Additional languages (install package to enable):**\n- Rust, C, C++, Java, Ruby, PHP, C#, Kotlin, Swift, Scala, Lua, Haskell, Bash, SQL, and more\n\n```scheme\n(list_symbols)                ; List all symbols (functions, classes, methods, etc.)\n(list_symbols \"function\")     ; Filter by kind: \"function\", \"class\", \"method\", \"interface\", \"type\", \"struct\"\n(get_symbol_body \"myFunc\")    ; Get source code body for a symbol by name\n(get_symbol_body RESULTS)     ; Get body for symbol from previous query result\n(find_references \"myFunc\")    ; Find all references to an identifier\n```\n\nSymbols include metadata like name, kind, start/end lines, and parent relationships (e.g., methods within classes).\n\n### Knowledge Graph (Code Structure)\n\nWhen 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.\n\n```scheme\n(callers \"funcName\")            ; Who calls this function?\n(callees \"funcName\")            ; What does this function call?\n(ancestors \"ClassName\")         ; Inheritance chain (extends)\n(descendants \"ClassName\")       ; All subclasses (transitive)\n(implementations \"IFace\")       ; Classes implementing this interface\n(dependents \"name\")             ; All transitive dependents\n(dependents \"name\" 2)           ; Dependents within depth limit\n(symbol_graph \"name\" 1)         ; Neighborhood subgraph around symbol\n```\n\nThe 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.\n\n#### Graph Analysis\n\nCommunity detection and structural insights help you understand codebase architecture:\n\n```scheme\n(communities)                   ; Detect communities with cohesion scores\n(community_of \"name\")           ; Which community does this symbol belong to?\n(god_nodes)                     ; Top 10 most-connected nodes (hubs)\n(god_nodes 5)                   ; Top N most-connected nodes\n(surprising_connections)         ; Cross-community or low-confidence edges\n(bridge_nodes)                  ; Nodes bridging different communities\n(suggest_questions)             ; Questions the graph can answer\n(graph_report)                  ; Full analysis (all of the above)\n```\n\n#### Adding Language Support\n\nMatryoshka includes built-in symbol mappings for 20+ languages. To enable a language, install its tree-sitter grammar package:\n\n```bash\n# Enable Rust support\npnpm add tree-sitter-rust\n\n# Enable Java support\npnpm add tree-sitter-java\n\n# Enable Ruby support\npnpm add tree-sitter-ruby\n\n# pnpm 10+ blocks install scripts by default — the grammar packages\n# need their native bindings built, so approve them after install:\npnpm approve-builds\n```\n\n**Languages with built-in mappings:**\n- TypeScript, JavaScript, Python, Go, Rust, C, C++, Java\n- Ruby, PHP, C#, Kotlin, Swift, Scala, Lua, Haskell, Elixir\n- HTML, CSS, JSON, YAML, TOML, Markdown, SQL, Bash\n\nOnce a package is installed, the language is automatically available for symbol extraction.\n\n#### Custom Language Configuration\n\nFor languages without built-in mappings, add a `grammars` entry to your config — see the [Configuration](#configuration) section for the full example and details.\n\n### Control Flow\n\n```scheme\n(if (count RESULTS) (sum RESULTS) 0)  ; Conditional: if/then/else\n(add 10 20)                           ; Arithmetic addition\n```\n\n### Collection Operations\n\n```scheme\n(filter RESULTS (lambda x (match x \"pattern\" 0)))  ; Filter by regex\n(map RESULTS (lambda x (match x \"(\\\\d+)\" 1)))      ; Extract from each\n(sum RESULTS)                                       ; Sum numbers in results\n(count RESULTS)                                     ; Count items\n```\n\n### String Operations\n\n```scheme\n(match str \"pattern\" 0)       ; Regex match, return group N\n(replace str \"from\" \"to\")     ; String replacement\n(split str \",\" 0)             ; Split and get index\n(parseInt str)                ; Parse integer\n(parseFloat str)              ; Parse float\n```\n\n### Type Coercion\n\n```scheme\n(parseDate \"Jan 15, 2024\")           ; -> \"2024-01-15\"\n(parseDate \"01/15/2024\" \"US\")        ; -> \"2024-01-15\" (MM/DD/YYYY)\n(parseCurrency \"$1,234.56\")          ; -> 1234.56\n(parseNumber \"1,234,567\")            ; -> 1234567\n(coerce value \"date\")                ; General coercion (date/currency/number/boolean/string)\n(extract str \"\\\\$[\\\\d,]+\" 0 \"currency\")  ; Extract and coerce in one step\n```\n\n### Program Synthesis\n\nThe model provides constraints (input/output examples), not code — the synthesizer builds programs automatically using Barliman-style relational synthesis with miniKanren.\n\n```scheme\n; Synthesize from input/output pairs\n(synthesize\n  (\"$100\" 100)\n  (\"$1,234\" 1234)\n  (\"$50,000\" 50000))\n\n; Named functions — synthesize once, apply many times\n(define-fn \"parse_price\" ((\"$100\" 100) (\"$1,234\" 1234)))\n(apply-fn \"parse_price\" \"$50,000\")    ; -> 50000\n\n; Boolean classifiers from examples\n(predicate \"is_error\" ((\"ERROR: timeout\" true) (\"INFO: ok\" false)))\n```\n\n### Cross-Turn State\n\nResults from previous turns are available:\n- `RESULTS` - Latest array result (updated by grep, filter)\n- `_1`, `_2`, `_3`, ... - Results from specific turns (1-indexed)\n\n### Final Answer\n\n```scheme\n<<<FINAL>>>your answer here<<<END>>>\n```\n\n## Development\n\n```bash\npnpm test                              # Run tests\npnpm test -- --coverage                # With coverage\nRUN_E2E=1 pnpm test -- tests/e2e.test.ts  # E2E tests (requires Ollama)\npnpm run build                         # Build\npnpm run typecheck                     # Type check\n```\n\n## Acknowledgements\n\nThis project incorporates ideas and code from:\n\n- **[Ori-Mnemos](https://github.com/aayoawoyemi/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.\n- **[Nucleus](https://github.com/michaelwhitford/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.\n- **[ramo](https://github.com/wjlewis/ramo)** - A miniKanren implementation in TypeScript by Will Lewis. Used for constraint-based program synthesis.\n- **[Barliman](https://github.com/webyrd/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.\n- **[tree-sitter](https://tree-sitter.github.io/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.\n\n## License\n\nApache-2.0\n\n## References\n\n- [RLM Paper](https://arxiv.org/abs/2512.24601)\n- [Original Implementation](https://github.com/alexzhang13/rlm)\n- [Model Context Protocol](https://modelcontextprotocol.io/)\n- [miniKanren](http://minikanren.org/)\n","readmeFilename":"README.md"}