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bridge for recursive-llm: Recursive Language Models for unbounded context processing with structured outputs","maintainers":[{"name":"jacobbeck_dev","email":"jacobbeck.dev@gmail.com"}],"readme":"# recursive-llm-ts\n\nTypeScript/JavaScript package for [Recursive Language Models (RLM)](https://github.com/alexzhang13/rlm) - process unbounded context lengths with LLMs.\n\n**Based on the paper**: [Recursive Language Models](https://alexzhang13.github.io/blog/2025/rlm/) by Alex Zhang and Omar Khattab (MIT, 2025)\n\n## Features\n\n**Core**\n- **Unbounded Context** - Process 10M+ tokens without degradation via recursive decomposition\n- **Structured Outputs** - Extract typed data with Zod schemas, parallel execution, and instructor-style retry\n- **Streaming** - Progressive text output and partial structured objects via async iterables\n- **Batch Operations** - Process multiple queries in parallel with concurrency control\n\n**Performance & Resilience**\n- **Pure Go Backend** - 50x faster startup, 3x less memory vs Python\n- **Context Overflow Recovery** - Automatic detection and 6 reduction strategies (mapreduce, truncate, chunked, tfidf, textrank, refine)\n- **Lossless Context Management** - Episodic memory, 5-level summarization escalation, SQLite persistence with FTS5 search\n- **BPE Token Counting** - Accurate model-specific tokenization via tiktoken (o200k, cl100k) with cached counting\n- **Caching** - Exact-match caching with in-memory and file-based backends\n- **Retry & Fallback** - Exponential backoff, jitter, and multi-provider fallback chains\n- **AbortController** - Cancel any operation mid-flight\n\n**Developer Experience**\n- **Typed Errors** - Rich error hierarchy with codes, retryable flags, and suggestions\n- **Event System** - Monitor LLM calls, cache hits, retries, and errors in real-time\n- **Builder API** - Fluent configuration with full IDE discoverability\n- **Factory Methods** - `RLM.fromEnv()`, `RLM.withDebug()`, `RLM.forAzure()`\n- **Config Validation** - Catches typos and invalid settings at construction time\n- **Result Formatters** - `prettyStats()`, `toJSON()`, `toMarkdown()`\n\n**Ecosystem**\n- **Provider Agnostic** - Works with OpenAI, Anthropic, Azure, Bedrock, local models\n- **Meta-Agent Mode** - Automatically optimize queries for better results\n- **Observability** - OpenTelemetry tracing, Langfuse integration, and debug logging\n- **File Storage** - Process local directories or S3/MinIO/LocalStack buckets as LLM context\n- **200+ Tests** - Comprehensive Vitest + Go test suites\n\n## Installation\n\n```bash\nnpm install recursive-llm-ts\n```\n\n### Prerequisites\n\n- **Node.js 16+**\n- **Go 1.25+** (only if you need to build the Go binary from source)\n\n### Go Binary Resolution\n\nOn supported platforms, npm installs a matching pre-built binary package automatically:\n\n- `@recursive-llm/darwin-arm64`\n- `@recursive-llm/darwin-x64`\n- `@recursive-llm/linux-x64`\n- `@recursive-llm/linux-arm64`\n- `@recursive-llm/win32-x64`\n\nThis is the default path for most Linux containers, so Go is usually not required in container images.\n\nThe bridge resolves the binary in this order:\n\n1. `config.go_binary_path`\n2. `RLM_GO_BINARY`\n3. Matching `@recursive-llm/<platform>` package\n4. Local `bin/rlm-go` or `go/rlm-go`\n\n### Go Binary (Automatic Fallback)\n\nWhen no matching pre-built binary is available, the `postinstall` script attempts to build the Go binary during installation. If Go is not available, the script will warn but not fail.\n\nIf you need to build manually:\n\n```bash\n# From the package directory\ncd node_modules/recursive-llm-ts\nnode scripts/build-go-binary.js\n\n# Or directly with Go\ncd go && go build -o ../bin/rlm-go ./cmd/rlm\n```\n\nOverride the binary path if needed:\n\n```bash\nexport RLM_GO_BINARY=/custom/path/to/rlm-go\n```\n\n## Usage\n\n### Quick Start\n```typescript\nimport { RLM } from 'recursive-llm-ts';\n\n// Initialize RLM with a model\nconst rlm = new RLM('gpt-4o-mini', {\n  max_iterations: 15,\n  api_key: process.env.OPENAI_API_KEY\n});\n\n// Process a query with unbounded context\nconst result = await rlm.completion(\n  'What are the key points in this document?',\n  longDocument\n);\n\nconsole.log(result.result);\nconsole.log('Stats:', result.stats);\n```\n\n### Structured Outputs with Zod Schemas\n\nExtract structured, typed data from any context using Zod schemas. Supports complex nested objects, arrays, enums, and automatic parallel execution for performance.\n\n```typescript\nimport { RLM } from 'recursive-llm-ts';\nimport { z } from 'zod';\n\nconst rlm = new RLM('gpt-4o-mini', {\n  api_key: process.env.OPENAI_API_KEY\n});\n\n// Define your schema\nconst sentimentSchema = z.object({\n  sentimentValue: z.number().min(1).max(5),\n  sentimentExplanation: z.string(),\n  keyPhrases: z.array(z.object({\n    phrase: z.string(),\n    sentiment: z.number()\n  })),\n  topics: z.array(z.enum(['pricing', 'features', 'support', 'competition']))\n});\n\n// Extract structured data\nconst result = await rlm.structuredCompletion(\n  'Analyze the sentiment and extract key information',\n  callTranscript,\n  sentimentSchema\n);\n\n// result.result is fully typed!\nconsole.log(result.result.sentimentValue); // number\nconsole.log(result.result.keyPhrases); // Array<{phrase: string, sentiment: number}>\n```\n\n**Key Benefits:**\n- ✅ **Type-safe** - Full TypeScript types from your Zod schema\n- ✅ **Automatic validation** - Retries with error feedback if schema doesn't match\n- ✅ **Parallel execution** - Complex schemas processed in parallel with goroutines (3-5x faster)\n- ✅ **Deep nesting** - Supports arbitrarily nested objects and arrays\n- ✅ **Enum support** - Validates enum values automatically\n\n**Performance Options:**\n```typescript\n// Enable/disable parallel execution\nconst result = await rlm.structuredCompletion(\n  query,\n  context,\n  schema,\n  { \n    parallelExecution: true,  // default: true for complex schemas\n    maxRetries: 3              // default: 3\n  }\n);\n```\n\n### Streaming\n\nGet progressive output with async iterables and AbortController support:\n\n```typescript\n// Stream text output\nconst stream = rlm.streamCompletion('Summarize', longDocument);\nfor await (const chunk of stream) {\n  if (chunk.type === 'text') process.stdout.write(chunk.text);\n}\n\n// Collect all text\nconst text = await rlm.streamCompletion('Summarize', doc).toText();\n\n// Cancel mid-stream\nconst controller = new AbortController();\nconst stream = rlm.streamCompletion('Summarize', doc, { signal: controller.signal });\nsetTimeout(() => controller.abort(), 5000);\n```\n\n### Caching\n\nAvoid redundant API calls with exact-match caching:\n\n```typescript\nconst rlm = new RLM('gpt-4o-mini', {\n  api_key: process.env.OPENAI_API_KEY,\n  cache: {\n    enabled: true,\n    strategy: 'exact',   // 'exact' | 'none'\n    maxEntries: 1000,\n    ttl: 3600,            // seconds\n    storage: 'memory',    // 'memory' | 'file'\n  }\n});\n\nconst r1 = await rlm.completion('Summarize', doc); // API call\nconst r2 = await rlm.completion('Summarize', doc); // Cache hit!\nconsole.log(r2.cached); // true\nconsole.log(rlm.getCacheStats()); // { hits: 1, misses: 1, hitRate: 0.5, ... }\n```\n\n### Retry & Resilience\n\nAutomatic retry with exponential backoff and provider fallback:\n\n```typescript\nconst rlm = new RLM('gpt-4o-mini', {\n  api_key: process.env.OPENAI_API_KEY,\n  retry: {\n    maxRetries: 3,\n    backoff: 'exponential', // 1s, 2s, 4s with jitter\n    onRetry: (attempt, error, delay) => {\n      console.log(`Retry ${attempt} after ${delay}ms: ${error.message}`);\n    },\n  },\n});\n\n// Or use standalone retry/fallback utilities:\nimport { withRetry, withFallback } from 'recursive-llm-ts';\n\nconst result = await withFallback(\n  (model) => rlm.completion(query, context),\n  { models: ['gpt-4o', 'claude-sonnet-4-20250514', 'gemini-2.0-flash'] }\n);\n```\n\n### Event System\n\nMonitor operations in real-time:\n\n```typescript\nconst rlm = new RLM('gpt-4o-mini', { api_key: process.env.OPENAI_API_KEY });\n\nrlm.on('llm_call', (e) => console.log(`Calling ${e.model}...`));\nrlm.on('llm_response', (e) => console.log(`Response in ${e.duration}ms`));\nrlm.on('cache', (e) => console.log(`Cache ${e.action}`));\nrlm.on('error', (e) => reportToSentry(e.error));\nrlm.on('completion_start', (e) => showSpinner());\nrlm.on('completion_end', (e) => hideSpinner());\n```\n\n### Builder API\n\nFluent configuration with full IDE discoverability:\n\n```typescript\nconst rlm = RLM.builder('gpt-4o-mini')\n  .apiKey(process.env.OPENAI_API_KEY!)\n  .maxDepth(10)\n  .maxIterations(30)\n  .withMetaAgent({ model: 'gpt-4o' })\n  .withDebug()\n  .withCache({ strategy: 'exact' })\n  .withRetry({ maxRetries: 3 })\n  .withFallback(['gpt-4o', 'claude-sonnet-4-20250514'])\n  .build();\n```\n\n### Factory Methods\n\nQuick setup for common configurations:\n\n```typescript\n// From environment variables\nconst rlm = RLM.fromEnv('gpt-4o-mini');\n\n// Debug mode\nconst rlm = RLM.withDebug('gpt-4o-mini');\n\n// Azure OpenAI\nconst rlm = RLM.forAzure('my-deployment', {\n  apiBase: 'https://myresource.openai.azure.com',\n  apiVersion: '2024-02-15-preview',\n});\n```\n\n### Batch Operations\n\nProcess multiple queries in parallel:\n\n```typescript\nconst results = await rlm.batchCompletion([\n  { query: 'Summarize chapter 1', context: ch1 },\n  { query: 'Summarize chapter 2', context: ch2 },\n  { query: 'Summarize chapter 3', context: ch3 },\n], { concurrency: 2 });\n```\n\n### Error Handling\n\nRich error hierarchy with actionable information:\n\n```typescript\nimport {\n  RLMRateLimitError, RLMValidationError,\n  RLMTimeoutError, RLMContextOverflowError\n} from 'recursive-llm-ts';\n\ntry {\n  const result = await rlm.completion(query, context);\n} catch (err) {\n  if (err instanceof RLMContextOverflowError) {\n    console.log(`Context overflow: ${err.requestTokens} tokens > ${err.modelLimit} limit`);\n    // Enable context_overflow config to auto-recover from this\n  } else if (err instanceof RLMRateLimitError) {\n    console.log(`Rate limited. Retry after: ${err.retryAfter}s`);\n  } else if (err instanceof RLMValidationError) {\n    console.log(`Schema mismatch:`, err.zodErrors);\n  } else if (err instanceof RLMTimeoutError) {\n    console.log(`Timed out after ${err.elapsed}ms`);\n  }\n  // All RLM errors have: err.code, err.retryable, err.suggestion\n}\n```\n\n### Context Overflow Handling\n\nAutomatically detect and recover from context window overflows. When your input exceeds the model's token limit, RLM catches the error and applies a reduction strategy to fit the context within bounds.\n\n```typescript\nconst rlm = new RLM('gpt-4o-mini', {\n  api_key: process.env.OPENAI_API_KEY,\n  context_overflow: {\n    enabled: true,           // Enable overflow recovery (default: true)\n    strategy: 'tfidf',       // Reduction strategy (see table below)\n    max_model_tokens: 32768, // Override auto-detected limit (optional)\n    safety_margin: 0.15,     // Reserve 15% for prompts/overhead (default: 0.15)\n    max_reduction_attempts: 3, // Max retry attempts (default: 3)\n  }\n});\n\n// Process a document that may exceed the model's context window\nconst result = await rlm.completion(\n  'Summarize the key findings',\n  veryLargeDocument  // If too large, auto-reduces and retries\n);\n```\n\n**Builder API:**\n```typescript\nconst rlm = RLM.builder('gpt-4o-mini')\n  .apiKey(process.env.OPENAI_API_KEY!)\n  .withContextOverflow({ strategy: 'textrank', max_model_tokens: 32768 })\n  .build();\n```\n\n**Strategy Comparison:**\n\n| Strategy | API Calls | Speed | Quality | Best For |\n|----------|-----------|-------|---------|----------|\n| `mapreduce` | Many (parallel) | Medium | High | General-purpose, large documents |\n| `truncate` | 0 | Fastest | Low | Quick-and-dirty, when beginning of doc matters |\n| `chunked` | Many (sequential) | Slow | High | Detailed extraction from specific sections |\n| `tfidf` | 0 | Fast | Medium | Fast first pass, keyword-rich documents |\n| `textrank` | 0 | Fast | Medium-High | Documents with clear sentence structure |\n| `refine` | Many (sequential) | Slow | Highest | When quality matters most, iterative refinement |\n\n**Strategy Details:**\n\n- **`mapreduce`** (default) - Splits context into chunks, summarizes each in parallel via LLM calls, then merges summaries. Good balance of quality and speed.\n- **`truncate`** - Drops tokens from the end to fit the budget. Zero API calls, but loses information. Best when the beginning of the document is most important.\n- **`chunked`** - Processes chunks sequentially, extracting relevant content from each. Higher quality than mapreduce for targeted extraction.\n- **`tfidf`** - Pure Go, zero API calls. Uses TF-IDF scoring to select the most informative sentences. Preserves original document order. Great for a fast, no-cost first pass.\n- **`textrank`** - Pure Go, zero API calls. Graph-based sentence ranking using PageRank over cosine-similarity of TF-IDF vectors. Better at identifying structurally important sentences than plain TF-IDF.\n- **`refine`** - Sequential iterative refinement. Processes chunks one at a time, building and refining an answer progressively. Highest quality but slowest, as each chunk sees the accumulated context.\n\n### Config Validation\n\nCatch configuration issues at construction time:\n\n```typescript\nconst rlm = new RLM('gpt-4o-mini', { max_detph: 5 }); // typo!\nconst result = rlm.validate();\n// result.issues: [{ level: 'warning', field: 'max_detph', message: 'Unknown config key...' }]\n```\n\n### Result Formatting\n\n```typescript\nconst result = await rlm.completion(query, context);\nconst formatted = rlm.formatResult(result);\n\nconsole.log(formatted.prettyStats());\n// \"LLM Calls: 3 | Iterations: 12 | Depth: 2\"\n\nconsole.log(formatted.toMarkdown());\n// Full markdown-formatted result with stats table\n```\n\n### Agent Coordinator (Advanced)\n\nFor complex multi-field schemas, use the coordinator API:\n\n```typescript\nimport { RLMAgentCoordinator } from 'recursive-llm-ts';\n\nconst coordinator = new RLMAgentCoordinator(\n  'gpt-4o-mini',\n  { api_key: process.env.OPENAI_API_KEY },\n  'auto',\n  { parallelExecution: true }\n);\n\nconst result = await coordinator.processComplex(\n  'Extract comprehensive call analysis',\n  transcript,\n  complexSchema\n);\n```\n\n### Bridge Selection\n\nThe package automatically uses the Go binary by default (if available). You can explicitly specify a bridge if needed:\n\n```typescript\nimport { RLM } from 'recursive-llm-ts';\n\n// Default: Auto-detection (prefers Go if available)\nconst rlm = new RLM('gpt-4o-mini', {\n  max_iterations: 15,\n  api_key: process.env.OPENAI_API_KEY\n});\n\n// Explicit: Force Go binary\nconst rlmGo = new RLM('gpt-4o-mini', {\n  max_iterations: 15,\n  api_key: process.env.OPENAI_API_KEY\n}, 'go');\n\n```\n\n\n### Meta-Agent Mode\n\nThe meta-agent automatically optimizes queries before they are processed by the RLM engine. This is useful when queries are vague, non-specific, or not optimized for recursive processing.\n\n```typescript\nimport { RLM } from 'recursive-llm-ts';\n\nconst rlm = new RLM('gpt-4o-mini', {\n  api_key: process.env.OPENAI_API_KEY,\n  meta_agent: {\n    enabled: true,\n    model: 'gpt-4o',           // Optional: model for optimization (defaults to main model)\n    max_optimize_len: 10000    // Optional: skip optimization for short contexts\n  }\n});\n\n// The meta-agent will automatically optimize this vague query\nconst result = await rlm.completion(\n  'what happened?',\n  longCallTranscript\n);\n\n// Also works with structured completions\nconst structured = await rlm.structuredCompletion(\n  'analyze this',\n  callTranscript,\n  sentimentSchema\n);\n```\n\nThe meta-agent:\n- Rewrites vague queries to be specific and actionable\n- Adds format specifications and extraction hints\n- Optimizes for recursive decomposition patterns\n- Falls back to the original query if optimization fails\n\n### Observability and Debugging\n\nBuilt-in support for OpenTelemetry tracing, Langfuse integration, and debug logging.\n\n#### Debug Mode\n\nEnable verbose logging of all internal operations:\n\n```typescript\nconst rlm = new RLM('gpt-4o-mini', {\n  api_key: process.env.OPENAI_API_KEY,\n  debug: true  // Shorthand for observability.debug\n});\n\n// Or with full config:\nconst rlm2 = new RLM('gpt-4o-mini', {\n  api_key: process.env.OPENAI_API_KEY,\n  observability: {\n    debug: true,\n    log_output: 'stderr'  // \"stderr\" (default), \"stdout\", or a file path\n  }\n});\n```\n\n#### OpenTelemetry Tracing\n\n```typescript\nconst rlm = new RLM('gpt-4o-mini', {\n  api_key: process.env.OPENAI_API_KEY,\n  observability: {\n    trace_enabled: true,\n    trace_endpoint: 'localhost:4317',  // OTLP endpoint\n    service_name: 'my-rlm-service'\n  }\n});\n\nconst result = await rlm.completion('Summarize', document);\n\n// Access trace events programmatically\nconst events = rlm.getTraceEvents();\nconsole.log('Trace events:', events);\n```\n\n#### Langfuse Integration\n\n```typescript\nconst rlm = new RLM('gpt-4o-mini', {\n  api_key: process.env.OPENAI_API_KEY,\n  observability: {\n    langfuse_enabled: true,\n    langfuse_public_key: process.env.LANGFUSE_PUBLIC_KEY,\n    langfuse_secret_key: process.env.LANGFUSE_SECRET_KEY,\n    langfuse_host: 'https://cloud.langfuse.com'  // Optional, defaults to cloud\n  }\n});\n```\n\n#### Environment Variable Configuration\n\nObservability can also be configured via environment variables:\n\n```bash\nRLM_DEBUG=1                           # Enable debug mode\nOTEL_EXPORTER_OTLP_ENDPOINT=...      # Auto-enable OTEL tracing\nLANGFUSE_PUBLIC_KEY=pk-...            # Auto-enable Langfuse\nLANGFUSE_SECRET_KEY=sk-...\nLANGFUSE_HOST=https://cloud.langfuse.com\n```\n\n### File Storage Context\n\nProcess files from local directories or S3-compatible storage as LLM context. Supports recursive directory traversal, filtering, and automatic context formatting.\n\n#### Local Files\n\n```typescript\nimport { RLM } from 'recursive-llm-ts';\n\nconst rlm = new RLM('gpt-4o-mini', {\n  api_key: process.env.OPENAI_API_KEY\n});\n\n// Process all TypeScript files in a directory\nconst result = await rlm.completionFromFiles(\n  'Summarize the architecture of this codebase',\n  {\n    type: 'local',\n    path: '/path/to/project/src',\n    extensions: ['.ts', '.tsx'],\n    excludePatterns: ['*.test.ts', '*.spec.ts'],\n    maxFileSize: 100_000,  // Skip files over 100KB\n  }\n);\n\nconsole.log(result.result);\nconsole.log(`Files processed: ${result.fileStorage?.files.length}`);\n```\n\n#### S3 / Object Storage\n\nWorks with AWS S3, MinIO, LocalStack, DigitalOcean Spaces, and Backblaze B2.\n\n```typescript\n// AWS S3 with explicit credentials\nconst result = await rlm.completionFromFiles(\n  'What are the key findings in these reports?',\n  {\n    type: 's3',\n    path: 'my-bucket',          // Bucket name\n    prefix: 'reports/2024/',    // Folder prefix\n    extensions: ['.md', '.txt'],\n    credentials: {\n      accessKeyId: 'AKIA...',\n      secretAccessKey: '...',\n    },\n    region: 'us-west-2',\n  }\n);\n\n// S3 with environment variable credentials\n// Uses AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_SESSION_TOKEN\nconst result2 = await rlm.completionFromFiles(\n  'Summarize these documents',\n  { type: 's3', path: 'my-bucket', prefix: 'docs/' }\n);\n\n// MinIO / LocalStack\nconst result3 = await rlm.completionFromFiles(\n  'Analyze the data',\n  {\n    type: 's3',\n    path: 'local-bucket',\n    endpoint: 'http://localhost:9000',  // MinIO\n    credentials: { accessKeyId: 'minioadmin', secretAccessKey: 'minioadmin' },\n  }\n);\n```\n\n**Credential Resolution Order:**\n1. Explicit `credentials` in config\n2. Environment variables: `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `AWS_SESSION_TOKEN`\n3. AWS SDK default credential chain (IAM roles, `~/.aws/credentials`, ECS task role, etc.)\n\n#### Structured Extraction from Files\n\n```typescript\nimport { z } from 'zod';\n\nconst schema = z.object({\n  summary: z.string(),\n  mainTopics: z.array(z.string()),\n  sentiment: z.enum(['positive', 'negative', 'neutral']),\n});\n\nconst result = await rlm.structuredCompletionFromFiles(\n  'Extract a summary, main topics, and overall sentiment',\n  { type: 'local', path: './docs', extensions: ['.md'] },\n  schema\n);\n\nconsole.log(result.result.mainTopics); // string[]\n```\n\n#### Preview Files Before Processing\n\n```typescript\nimport { FileContextBuilder } from 'recursive-llm-ts';\n\nconst builder = new FileContextBuilder({\n  type: 'local',\n  path: './src',\n  extensions: ['.ts'],\n  excludePatterns: ['node_modules/**'],\n});\n\n// List matching files without reading content\nconst files = await builder.listMatchingFiles();\nconsole.log('Would process:', files);\n\n// Build full context\nconst ctx = await builder.buildContext();\nconsole.log(`Total size: ${ctx.totalSize} bytes`);\nconsole.log(`Files included: ${ctx.files.length}`);\nconsole.log(`Files skipped: ${ctx.skipped.length}`);\n```\n\n## API\n\n### `RLM`\n\nMain class for recursive language model completions.\n\n**Constructor:**\n```typescript\nconstructor(model: string, rlmConfig?: RLMConfig)\n```\n\n- `model`: The LLM model to use (e.g., 'gpt-4o-mini', 'claude-3-sonnet')\n- `rlmConfig`: Optional configuration object\n\n**Methods:**\n\n#### `completion(query: string, context: string): Promise<RLMResult>`\n\nProcess a query with the given context using recursive language models.\n\n**Parameters:**\n- `query`: The question or task to perform\n- `context`: The context/document to process (can be arbitrarily large)\n\n**Returns:**\n- `Promise<RLMResult>`: Result containing the answer and statistics\n\n#### `structuredCompletion<T>(query: string, context: string, schema: ZodSchema<T>, options?): Promise<StructuredRLMResult<T>>`\n\nExtract structured, typed data from context using a Zod schema.\n\n**Parameters:**\n- `query`: The extraction task to perform\n- `context`: The context/document to process\n- `schema`: Zod schema defining the output structure\n- `options`: Optional configuration\n  - `parallelExecution?: boolean` - Enable parallel processing (default: true)\n  - `maxRetries?: number` - Max validation retries (default: 3)\n\n**Returns:**\n- `Promise<StructuredRLMResult<T>>`: Typed result matching your schema\n\n**Example:**\n```typescript\nconst schema = z.object({ score: z.number(), summary: z.string() });\nconst result = await rlm.structuredCompletion('Analyze', doc, schema);\n// result.result is typed as { score: number, summary: string }\n```\n\n#### `completionFromFiles(query: string, fileConfig: FileStorageConfig): Promise<RLMResult & { fileStorage?: FileStorageResult }>`\n\nProcess a query using files from local or S3 storage as context.\n\n**Parameters:**\n- `query`: The question or task to perform\n- `fileConfig`: File storage configuration (local path or S3 bucket)\n\n**Returns:**\n- Result with `fileStorage` metadata (files included, skipped, total size)\n\n#### `structuredCompletionFromFiles<T>(query: string, fileConfig: FileStorageConfig, schema: ZodSchema<T>, options?): Promise<StructuredRLMResult<T> & { fileStorage?: FileStorageResult }>`\n\nExtract structured data from file-based context.\n\n#### `streamCompletion(query, context, options?): RLMStream`\n\nStream a completion with progressive text output. Returns an async iterable.\n\n#### `streamStructuredCompletion<T>(query, context, schema, options?): RLMStream<T>`\n\nStream a structured completion with partial object updates.\n\n#### `batchCompletion(queries, options?): Promise<Array<RLMCompletionResult | Error>>`\n\nExecute multiple completions in parallel with concurrency control.\n\n#### `batchStructuredCompletion<T>(queries, options?): Promise<Array<StructuredRLMResult<T> | Error>>`\n\nExecute multiple structured completions in parallel.\n\n#### `validate(): ValidationResult`\n\nValidate the current configuration without making API calls.\n\n#### `getCacheStats(): CacheStats`\n\nGet cache performance statistics (hits, misses, hit rate).\n\n#### `clearCache(): void`\n\nClear the completion cache.\n\n#### `formatResult(result): RLMResultFormatter`\n\nCreate a formatted result with `prettyStats()`, `toJSON()`, `toMarkdown()`.\n\n#### `on(event, listener) / off(event, listener) / once(event, listener)`\n\nRegister/remove event listeners. Events: `llm_call`, `llm_response`, `error`, `cache`, `completion_start`, `completion_end`, `retry`, `validation_retry`, `meta_agent`, `recursion`.\n\n#### `cleanup(): Promise<void>`\n\nClean up the bridge and free resources.\n\n```typescript\nawait rlm.cleanup();\n```\n\n#### `getTraceEvents(): TraceEvent[]`\n\nReturns trace events from the last operation (when observability is enabled).\n\n```typescript\nconst events = rlm.getTraceEvents();\nfor (const event of events) {\n  console.log(`${event.type}: ${event.name}`, event.attributes);\n}\n```\n\n### Types\n\n```typescript\ninterface RLMConfig {\n  // Model configuration\n  recursive_model?: string;      // Model to use for recursive calls (defaults to main model)\n\n  // API configuration\n  api_base?: string;             // Custom API base URL (for Azure, Bedrock, etc.)\n  api_key?: string;              // API key for authentication\n\n  // Execution limits\n  max_depth?: number;            // Maximum recursion depth (default: 5)\n  max_iterations?: number;       // Maximum REPL iterations per call (default: 30)\n  go_binary_path?: string;       // Override path for Go binary (optional)\n\n  // Meta-agent configuration\n  meta_agent?: MetaAgentConfig;\n\n  // Observability configuration\n  observability?: ObservabilityConfig;\n\n  // Shorthand for observability.debug\n  debug?: boolean;\n\n  // LiteLLM parameters\n  api_version?: string;          // API version (e.g., for Azure)\n  timeout?: number;              // Request timeout in seconds\n  temperature?: number;          // Sampling temperature\n  max_tokens?: number;           // Maximum tokens in response\n\n  // Context overflow recovery\n  context_overflow?: ContextOverflowConfig;\n\n  // Caching, retry, fallback\n  cache?: CacheConfig;           // Cache configuration\n  retry?: RetryConfig;           // Retry configuration\n  fallback?: FallbackConfig;     // Fallback model configuration\n}\n\ninterface CacheConfig {\n  enabled?: boolean;             // Enable caching (default: false)\n  strategy?: 'exact' | 'none';  // Cache strategy (default: 'exact')\n  maxEntries?: number;           // Max cached entries (default: 1000)\n  ttl?: number;                  // Time-to-live in seconds (default: 3600)\n  storage?: 'memory' | 'file';  // Storage backend (default: 'memory')\n  cacheDir?: string;             // Dir for file cache (default: '.rlm-cache')\n}\n\ninterface RetryConfig {\n  maxRetries?: number;           // Max retries (default: 3)\n  backoff?: 'exponential' | 'linear' | 'fixed';\n  baseDelay?: number;            // Base delay ms (default: 1000)\n  maxDelay?: number;             // Max delay ms (default: 30000)\n  jitter?: boolean;              // Add jitter (default: true)\n  onRetry?: (attempt: number, error: Error, delay: number) => void;\n}\n\ninterface FallbackConfig {\n  models?: string[];             // Ordered fallback models\n  strategy?: 'sequential';      // Fallback strategy\n}\n\ninterface ContextOverflowConfig {\n  enabled?: boolean;             // Enable overflow recovery (default: true)\n  max_model_tokens?: number;     // Override auto-detected model limit (0 = auto-detect)\n  strategy?: 'mapreduce' | 'truncate' | 'chunked' | 'tfidf' | 'textrank' | 'refine';\n  safety_margin?: number;        // Fraction to reserve for overhead (default: 0.15)\n  max_reduction_attempts?: number; // Max reduction retries (default: 3)\n}\n\ninterface MetaAgentConfig {\n  enabled: boolean;              // Enable the meta-agent\n  model?: string;                // Model for query optimization (defaults to main model)\n  max_optimize_len?: number;     // Max context length before optimization (0 = always)\n}\n\ninterface ObservabilityConfig {\n  debug?: boolean;               // Enable verbose debug logging\n  trace_enabled?: boolean;       // Enable OpenTelemetry tracing\n  trace_endpoint?: string;       // OTLP endpoint (e.g., \"localhost:4317\")\n  service_name?: string;         // Service name for traces (default: \"rlm\")\n  log_output?: string;           // Log destination: \"stderr\", \"stdout\", or file path\n  langfuse_enabled?: boolean;    // Enable Langfuse integration\n  langfuse_public_key?: string;  // Langfuse public key\n  langfuse_secret_key?: string;  // Langfuse secret key\n  langfuse_host?: string;        // Langfuse API host\n}\n\ninterface RLMResult {\n  result: string;\n  stats: RLMStats;\n  trace_events?: TraceEvent[];   // Observability events (when enabled)\n}\n\ninterface RLMStats {\n  llm_calls: number;\n  iterations: number;\n  depth: number;\n  parsing_retries?: number;\n}\n\ninterface TraceEvent {\n  timestamp: string;\n  type: string;                  // \"span_start\", \"span_end\", \"llm_call\", \"error\", \"event\"\n  name: string;\n  attributes: Record<string, string>;\n  duration?: number;\n  trace_id?: string;\n  span_id?: string;\n}\n\ninterface FileStorageConfig {\n  type: 'local' | 's3';         // Storage provider type\n  path: string;                  // Local directory path or S3 bucket name\n  prefix?: string;               // S3 key prefix (folder path)\n  region?: string;               // AWS region (default: AWS_REGION env or 'us-east-1')\n  credentials?: {                // S3 explicit credentials (optional)\n    accessKeyId: string;\n    secretAccessKey: string;\n    sessionToken?: string;\n  };\n  endpoint?: string;             // Custom S3 endpoint (MinIO, LocalStack, etc.)\n  forcePathStyle?: boolean;      // Force path-style S3 URLs (auto-enabled with endpoint)\n  extensions?: string[];         // File extensions to include (e.g., ['.ts', '.md'])\n  includePatterns?: string[];    // Glob patterns to include\n  excludePatterns?: string[];    // Glob patterns to exclude\n  maxFileSize?: number;          // Max individual file size in bytes (default: 1MB)\n  maxTotalSize?: number;         // Max total context size in bytes (default: 10MB)\n  maxFiles?: number;             // Max number of files (default: 1000)\n}\n\ninterface FileStorageResult {\n  context: string;               // Built context string with file contents\n  files: Array<{ relativePath: string; size: number }>;\n  totalSize: number;\n  skipped: Array<{ relativePath: string; reason: string }>;\n}\n\n// Error hierarchy - all extend RLMError\nclass RLMError extends Error {\n  code: string;                  // Machine-readable: \"RATE_LIMIT\", \"VALIDATION\", etc.\n  retryable: boolean;            // Whether caller should retry\n  suggestion?: string;           // Human-readable fix suggestion\n}\nclass RLMValidationError extends RLMError { expected; received; zodErrors; }\nclass RLMRateLimitError extends RLMError { retryAfter?: number; }\nclass RLMTimeoutError extends RLMError { elapsed; limit; }\nclass RLMProviderError extends RLMError { statusCode; provider; }\nclass RLMBinaryError extends RLMError { binaryPath; }\nclass RLMConfigError extends RLMError { field; value; }\nclass RLMContextOverflowError extends RLMError { modelLimit; requestTokens; }\nclass RLMSchemaError extends RLMError { path; constraint; }\nclass RLMAbortError extends RLMError {}\n```\n\n## Environment Variables\n\nSet your API key as an environment variable:\n\n```bash\nexport OPENAI_API_KEY='your-api-key-here'\n```\n\nOr pass it in the configuration:\n\n```typescript\nconst rlm = new RLM('gpt-4o-mini', {\n  api_key: 'your-api-key-here'\n});\n```\n\n### S3 File Storage\n\nWhen using S3 file storage without explicit credentials, these environment variables are checked:\n\n```bash\nexport AWS_ACCESS_KEY_ID='your-access-key'\nexport AWS_SECRET_ACCESS_KEY='your-secret-key'\nexport AWS_SESSION_TOKEN='optional-session-token'\nexport AWS_REGION='us-east-1'\n```\n\n## Custom Providers\n\nThe Go binary uses an **OpenAI-compatible chat completion API** and works seamlessly with\n[LiteLLM proxy](https://docs.litellm.ai/docs/simple_proxy) or any provider that supports the\nOpenAI `/chat/completions` schema. This keeps the implementation provider-agnostic.\n\n### Quick Reference\n\nThe Go binary speaks the **OpenAI chat completion schema**, so you can:\n\n- Use OpenAI directly with `api_key`\n- Use an OpenAI-compatible endpoint (Azure OpenAI, vLLM, Ollama)\n- Use a LiteLLM proxy to reach providers like Anthropic, Bedrock, or Cohere\n\n### Amazon Bedrock (via LiteLLM proxy)\n\n```typescript\nimport { RLM } from 'recursive-llm-ts';\n\nconst rlm = new RLM('bedrock/anthropic.claude-3-sonnet-20240229-v1:0', {\n  api_base: 'http://localhost:4000', // LiteLLM proxy URL\n  api_key: process.env.LITELLM_API_KEY,\n  max_iterations: 15\n});\n```\n\n### Azure OpenAI\n\n```typescript\nconst rlm = new RLM('gpt-4o', {\n  api_base: 'https://your-resource.openai.azure.com/openai/deployments/your-deployment',\n  api_key: process.env.AZURE_API_KEY,\n  api_version: '2024-02-15-preview' // Passed through to the OpenAI-compatible API\n});\n```\n\n### Custom OpenAI-Compatible APIs\n\nFor providers with OpenAI-compatible APIs (e.g., local models, vLLM, Ollama):\n\n```typescript\nconst rlm = new RLM('openai/your-model', {\n  api_base: 'https://your-custom-endpoint.com/v1',\n  api_key: 'your-key-here'\n});\n```\n\n### Long-Running Processes\n\nFor large documents or queue-based processing that may take longer than the default 100s timeout:\n\n```typescript\nconst rlm = new RLM('gpt-4o-mini', {\n  max_iterations: 50,           // Allow more iterations for complex processing\n  timeout: 300                  // 5 minutes timeout for LLM API calls\n});\n\n// Process very large document\nconst result = await rlm.completion(\n  'Summarize all key points from this document',\n  veryLargeDocument\n);\n```\n\n### Other Providers\n\nSee the [LiteLLM documentation](https://docs.litellm.ai/docs/providers) for the complete list of supported providers and their configuration.\n\n## Docker Deployment\n\n### Option 1: Use the Published Linux Binary Package\n\nOn `linux-x64` and `linux-arm64`, `npm ci --omit=dev` installs the matching `@recursive-llm/linux-*` package automatically. This is the simplest container setup and does not require Go or `RLM_GO_BINARY`.\n\n```dockerfile\nFROM node:22-alpine\nWORKDIR /app\n\nCOPY package*.json ./\nRUN npm ci --omit=dev\n\nCOPY . .\n\nENV OPENAI_API_KEY=\"\"\nENV NODE_ENV=production\n\nCMD [\"node\", \"your-app.js\"]\n```\n\n### Option 2: Copy or Mount a Custom Binary\n\nUse this when you build the Go binary elsewhere and want to point the package at an explicit path.\n\n```dockerfile\nFROM node:22-alpine\nWORKDIR /app\n\nCOPY package*.json ./\nRUN npm ci --omit=dev --ignore-scripts\n\nCOPY . .\nCOPY ./bin/rlm-go /app/bin/rlm-go\nRUN chmod +x /app/bin/rlm-go\n\nENV OPENAI_API_KEY=\"\"\nENV NODE_ENV=production\nENV RLM_GO_BINARY=/app/bin/rlm-go\n\nCMD [\"node\", \"your-app.js\"]\n```\n\n### Option 3: Build from Source in a Multi-Stage Image\n\nUse this for unsupported targets or when you need a custom-compiled binary.\n\n```dockerfile\n# Stage 1: Build the Go binary\nFROM golang:1.25-alpine AS go-builder\nWORKDIR /build\nCOPY go/go.mod go/go.sum ./\nRUN go mod download\nCOPY go/ ./\nRUN CGO_ENABLED=0 GOOS=linux go build -ldflags=\"-s -w\" -o rlm-go ./cmd/rlm\n\n# Stage 2: Build Node.js dependencies\nFROM node:22-alpine AS node-builder\nWORKDIR /app\nCOPY package*.json ./\nRUN npm ci --omit=dev --ignore-scripts\n\n# Stage 3: Final runtime image\nFROM node:22-alpine\nWORKDIR /app\n\nCOPY --from=node-builder /app/node_modules ./node_modules\nCOPY --from=go-builder /build/rlm-go ./bin/rlm-go\nRUN chmod +x ./bin/rlm-go\n\nCOPY . .\n\nENV NODE_ENV=production\nENV RLM_GO_BINARY=/app/bin/rlm-go\nENV OPENAI_API_KEY=\"\"\n\nCMD [\"node\", \"your-app.js\"]\n```\n\n**Benefits:** Small runtime image, deterministic binary path, and no Go toolchain in the final image.\n\n### Docker Compose\n\n```yaml\nversion: '3.8'\nservices:\n  app:\n    build: .\n    environment:\n      - OPENAI_API_KEY=${OPENAI_API_KEY}\n      - NODE_ENV=production\n    ports:\n      - \"3000:3000\"\n```\n\n### Installing Go in Different Base Images\n\n```dockerfile\n# Alpine\nRUN apk add --no-cache go\n\n# Debian/Ubuntu\nRUN apt-get update && apt-get install -y golang-1.25\n\n# Or use the published platform package (no Go required)\n# npm ci --omit=dev installs @recursive-llm/linux-x64 or @recursive-llm/linux-arm64 automatically\n\n# Or use a release binary explicitly\n# Download from GitHub Releases and set RLM_GO_BINARY=/app/bin/rlm-go\n```\n\n## Using the Go Module Directly\n\nThe Go implementation can be used as a standalone library in Go projects.\n\n### Installation\n\n```bash\ngo get github.com/howlerops/recursive-llm-ts/go\n```\n\n### Usage\n\n```go\npackage main\n\nimport (\n    \"fmt\"\n    \"os\"\n\n    \"github.com/howlerops/recursive-llm-ts/go/rlm\"\n)\n\nfunc main() {\n    config := rlm.Config{\n        MaxDepth:      5,\n        MaxIterations: 30,\n        APIKey:        os.Getenv(\"OPENAI_API_KEY\"),\n    }\n\n    engine := rlm.New(\"gpt-4o-mini\", config)\n\n    answer, stats, err := engine.Completion(\n        \"What are the key points?\",\n        \"Your long document here...\",\n    )\n    if err != nil {\n        fmt.Fprintf(os.Stderr, \"Error: %v\\n\", err)\n        os.Exit(1)\n    }\n\n    fmt.Printf(\"Answer: %s\\n\", answer)\n    fmt.Printf(\"Stats: %d LLM calls, %d iterations\\n\",\n        stats.LlmCalls, stats.Iterations)\n}\n```\n\n### Structured Output\n\n```go\nschema := &rlm.JSONSchema{\n    Type: \"object\",\n    Properties: map[string]*rlm.JSONSchema{\n        \"summary\": {Type: \"string\"},\n        \"score\":   {Type: \"number\"},\n    },\n    Required: []string{\"summary\", \"score\"},\n}\n\nconfig := &rlm.StructuredConfig{\n    Schema:     schema,\n    MaxRetries: 3,\n}\n\nresult, stats, err := engine.StructuredCompletion(\n    \"Summarize and score\",\n    document,\n    config,\n)\n```\n\n### Building from Source\n\n```bash\ncd go\n\n# Standard build\ngo build -o rlm-go ./cmd/rlm\n\n# Optimized (smaller binary)\ngo build -ldflags=\"-s -w\" -o rlm-go ./cmd/rlm\n\n# Cross-compile\nGOOS=linux GOARCH=amd64 go build -o rlm-linux-amd64 ./cmd/rlm\nGOOS=darwin GOARCH=arm64 go build -o rlm-darwin-arm64 ./cmd/rlm\n```\n\n### Running Tests\n\n```bash\ncd go\ngo test -v ./rlm/...\n```\n\n## How It Works\n\nThis package provides a TypeScript wrapper around a Go implementation of Recursive-LLM, enabling seamless integration into Node.js/TypeScript applications without Python dependencies. The Go binary is built locally (or supplied via `RLM_GO_BINARY`) and invoked for completions.\n\nThe recursive-llm approach breaks down large contexts into manageable chunks and processes them recursively, allowing you to work with documents of any size without hitting token limits.\n\n### Key Features\n\n- ✅ **No Python dependency** - Go binary handles the full recursive loop\n- ✅ **Provider-agnostic** - Works with OpenAI-compatible APIs or LiteLLM proxy\n- ✅ **Type-safe** - Full TypeScript type definitions\n- ✅ **Simple API** - Just `npm install` and start using\n\n## Testing\n\n```bash\n# Run all tests (Vitest)\nnpm test\n\n# Watch mode\nnpm run test:watch\n\n# Coverage\nnpm run test:coverage\n\n# Type-check\nnpm run typecheck\n\n# Go tests\ncd go && go test ./rlm/... -v\n```\n\n## Documentation\n\n- [Quick Start Guide](docs/QUICKSTART.md)\n- [Architecture Overview](docs/ARCHITECTURE.md)\n- [Contributing Guide](CONTRIBUTING.md)\n- [UX/DX Gap Analysis](docs/UX-DX-GAP-ANALYSIS.md)\n\n## Publishing\n\nThis package uses automated GitHub Actions workflows to publish to npm. See [RELEASE.md](RELEASE.md) for detailed instructions on publishing new versions.\n\n**Quick start:**\n```bash\nnpm version patch  # Bump version\ngit push origin main --tags  # Push tag\n# Then create a GitHub release to trigger automatic npm publish\n```\n\n## License\n\nMIT\n","readmeFilename":"README.md"}