| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 9 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Applies a multi-intensity rule engine (Lite/Full/Ultra modes) that surgically removes linguistic filler—articles, hedging phrases, pleasantries—while preserving code blocks, technical terminology, and safety-critical information. Uses a single-source-of-truth SKILL.md configuration file that defines transformation rules across all host environments (Claude Code, Codex, Gemini CLI), achieving ~75% token reduction without sacrificing technical accuracy through a 'Smart Caveman' principle that protects machine-critical data.
Unique: Implements a three-tier intensity system (Lite/Full/Ultra) with a 'Smart Caveman' principle that differentiates between human-centric filler and machine-critical data, using a declarative SKILL.md single-source-of-truth that synchronizes behavior across Claude Code, Codex, and Gemini CLI without requiring code changes per platform. This contrasts with generic prompt-injection approaches by maintaining explicit whitelist/blacklist rules for technical terms and safety-critical operations.
vs alternatives: Achieves 75% token savings while maintaining 100% technical accuracy through linguistic rule-based filtering, whereas generic prompt compression (e.g., 'be concise') often loses technical precision or requires manual prompt engineering per use case.
Distributes caveman as a portable 'Skill' artifact across heterogeneous AI agent platforms (Claude Code plugin marketplace, Codex CLI, Gemini CLI) using a unified SKILL.md configuration format. Provides platform-specific installation hooks (shell scripts for macOS/Linux/WSL, PowerShell for Windows) that auto-merge configuration into host environment settings (~/.claude/settings.json, Codex config, etc.), enabling single-source-of-truth behavior across all platforms without duplicating rule definitions.
Unique: Uses a declarative SKILL.md single-source-of-truth that auto-syncs across Claude Code, Codex, and Gemini CLI via GitHub Actions CI/CD pipeline, with platform-specific installation hooks (shell/PowerShell scripts) that auto-merge into native environment configs. This eliminates the need for separate plugin codebases per platform while maintaining platform-native integration patterns.
vs alternatives: Simpler distribution than maintaining separate plugins for each platform (e.g., VS Code extension + CLI tool + web app) because SKILL.md defines behavior once and CI/CD handles platform-specific packaging; faster than manual installation because hooks auto-configure environment settings.
Exposes three discrete compression intensity levels (Lite, Full, Ultra) that users can toggle per session, each applying progressively aggressive linguistic transformation rules. Lite mode removes only obvious filler (articles, some hedging); Full mode aggressively compresses prose while preserving code and technical terms; Ultra mode maximizes compression by removing even more linguistic scaffolding. Implementation uses a rule registry in SKILL.md that maps intensity levels to specific transformation patterns, allowing users to trade off readability vs. token savings without code changes.
Unique: Implements three discrete intensity levels (Lite/Full/Ultra) as first-class configuration options in SKILL.md, allowing users to toggle compression aggressiveness per session without code changes. Each level maps to a specific rule subset, enabling progressive compression that trades readability for token savings in a predictable, testable manner.
vs alternatives: More granular than binary 'on/off' compression (e.g., generic prompt compression) because users can tune intensity to their specific task; more predictable than adaptive compression because rules are explicit and intensity levels are well-defined.
Implements a whitelist-based protection mechanism that exempts code blocks (markdown fences), technical terminology (e.g., useMemo, shallow comparison), and safety-critical operations (e.g., rm -rf) from compression rules. Uses pattern matching and AST-aware detection to identify protected regions, ensuring that compression never degrades technical accuracy or introduces ambiguity in destructive commands. This 'Smart Caveman' principle is enforced via explicit rules in SKILL.md that define protected patterns and categories.
Unique: Implements a 'Smart Caveman' principle via explicit whitelist rules in SKILL.md that protect code blocks (markdown fences), technical terminology, and safety-critical operations from compression. This is more sophisticated than naive compression because it uses pattern matching and category-based rules to distinguish between human-centric filler (safe to compress) and machine-critical data (must preserve).
vs alternatives: Guarantees 100% technical accuracy while achieving 75% token savings, whereas generic compression tools often sacrifice accuracy for brevity; more maintainable than hardcoded protection logic because rules are declarative in SKILL.md.
Provides a Python-based benchmarking suite (benchmarks/run.py) that measures caveman's token savings, compression ratios, generation speed, and technical accuracy across multiple intensity levels and test prompts. Generates quantitative metrics (e.g., ~75% token savings, ~46% input compression, ~3x speed increase) and supports custom benchmark prompts. Results are published to GitHub Pages documentation, enabling transparent performance tracking and user-facing proof of efficiency gains.
Unique: Provides a reproducible, open-source benchmarking suite (benchmarks/run.py) that measures token savings, speed, and accuracy across intensity levels, with results published to GitHub Pages. This enables transparent, user-verifiable performance claims rather than marketing assertions.
vs alternatives: More rigorous than anecdotal claims because benchmarks are reproducible and published; more comprehensive than single-metric reporting because it measures tokens, speed, and accuracy simultaneously.
Automatically generates and publishes comprehensive documentation to GitHub Pages via CI/CD pipeline, including installation guides, intensity level explanations, linguistic rules, trigger/command reference, plugin architecture details, and benchmark results. Documentation is derived from SKILL.md and repository metadata, ensuring single-source-of-truth consistency. Provides both human-readable guides and technical deep-dives for developers integrating caveman into custom workflows.
Unique: Implements automated documentation generation from SKILL.md and repository metadata via GitHub Actions, publishing to GitHub Pages with single-source-of-truth consistency. This eliminates manual wiki maintenance and ensures documentation stays synchronized with code changes.
vs alternatives: More maintainable than manually-edited wikis because documentation is auto-generated from source; more discoverable than README-only documentation because it provides structured, searchable pages.
Provides explicit command-based activation mechanism (e.g., /caveman, /caveman lite, /caveman full, /caveman ultra) that users invoke to enable compression for a specific session. Activation is session-scoped (not persistent across Claude Code instances) and can be toggled on/off mid-conversation. Implementation uses Claude Code's command/trigger system to intercept user input and apply caveman rules to model output, without requiring permanent configuration changes.
Unique: Implements session-scoped, command-based activation (/caveman, /caveman lite, /caveman full, /caveman ultra) that allows users to toggle compression on-demand without persistent configuration. This provides explicit user control and enables A/B testing within single conversations.
vs alternatives: More flexible than always-on compression because users can selectively enable caveman; more discoverable than configuration-file-based activation because commands are explicit and visible in chat history.
Implements a declarative rule registry in SKILL.md that defines linguistic transformation patterns (e.g., 'The reason is' → 'Reason:', delete articles 'a'/'an'/'the', remove hedging phrases). Rules are organized by category (grammar, articles, filler, safety-critical) and intensity level (Lite/Full/Ultra), enabling pattern-based text transformation without hardcoded logic. Uses regex or string-matching patterns to identify and replace linguistic elements, with explicit exceptions for code blocks and technical terms.
Unique: Implements a declarative rule registry in SKILL.md that defines linguistic transformation patterns organized by category and intensity level, enabling non-engineers to understand, audit, and customize compression rules without code changes. This is more transparent than hardcoded compression logic.
vs alternatives: More maintainable than hardcoded transformation logic because rules are declarative and version-controlled; more auditable than black-box compression because rules are explicit and human-readable.
+1 more capabilities
Automatically loads and parses documents from diverse sources (PDFs, Word docs, HTML, Markdown, code files, databases) into a unified in-memory representation using format-specific loaders and node-based document abstractions. Each document is decomposed into Document objects containing metadata, content, and relationships, enabling downstream processing without format-specific handling in application code.
Unique: Provides a unified loader abstraction (BaseReader interface) that normalizes 100+ data source connectors into a single Document/Node API, eliminating format-specific branching logic in application code. Loaders are composable and chainable, allowing sequential transformations (e.g., load → split → extract metadata → embed).
vs alternatives: Broader out-of-the-box loader coverage than LangChain's document loaders and more structured node-based decomposition than raw text splitting, reducing boilerplate for multi-source RAG pipelines.
Splits documents into semantically coherent chunks using multiple strategies (character-based, token-aware, recursive, semantic) with configurable overlap and chunk size. Preserves document hierarchy and metadata through a node tree structure, enabling retrieval systems to maintain context relationships and enable hierarchical re-ranking or parent-document retrieval patterns.
Unique: Implements a node-tree abstraction that preserves document hierarchy and enables parent-document retrieval patterns. Supports multiple splitting strategies (recursive, semantic, code-aware) with pluggable custom splitters, and automatically propagates metadata through the node tree.
vs alternatives: More sophisticated than LangChain's text splitters because it preserves hierarchical relationships and supports semantic splitting; better for complex document structures than simple character-based splitting.
caveman scores higher at 40/100 vs LlamaIndex at 40/100. caveman also has a free tier, making it more accessible.
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Processes documents containing mixed content (text, images, tables, code) by extracting and understanding each modality separately, then synthesizing information across modalities. Uses vision models for image understanding, specialized parsers for tables and code, and integrates results into a unified document representation for retrieval and generation.
Unique: Integrates vision models, table parsers, and code extractors into a unified multi-modal document processing pipeline that synthesizes information across modalities. Preserves modality-specific structure (table schemas, code formatting) while enabling cross-modal retrieval and generation.
vs alternatives: More comprehensive multi-modal support than text-only RAG; built-in vision integration reduces boilerplate for document understanding compared to manual vision API calls.
Enables streaming of LLM responses token-by-token and real-time retrieval updates, allowing applications to display partial results as they become available. Supports streaming from retrieval (progressive document discovery) and generation (token-by-token output) with backpressure handling and cancellation support for responsive user experiences.
Unique: Provides first-class streaming support for both retrieval and generation with automatic backpressure handling and cancellation. Enables progressive result display without custom async/streaming code in application layer.
vs alternatives: More integrated streaming support than manual LLM API streaming; built-in retrieval streaming and backpressure handling reduce complexity compared to custom streaming implementations.
Tracks API costs for LLM calls, embeddings, and other operations with per-query and per-session cost attribution. Provides cost optimization recommendations (e.g., batch processing, model selection, caching) and enables cost-aware query planning to balance quality and expense. Integrates with multiple LLM providers to normalize cost tracking across models.
Unique: Provides automatic cost tracking across multiple LLM providers with per-query attribution and cost optimization recommendations. Integrates with query execution to enable cost-aware planning without manual cost calculation.
vs alternatives: More integrated cost tracking than manual API billing review; built-in optimization recommendations reduce guesswork for cost reduction.
Enables building custom RAG pipelines by composing modular components (retrievers, synthesizers, agents, tools) through a declarative or programmatic API. Supports complex workflows with branching, loops, and conditional logic, with automatic dependency resolution and execution optimization. Pipelines are reusable, testable, and can be deployed as APIs or batch jobs.
Unique: Provides a flexible pipeline composition API supporting both declarative and programmatic definitions, with automatic dependency resolution and execution optimization. Enables complex workflows with branching and conditional logic without custom orchestration code.
vs alternatives: More flexible pipeline composition than fixed RAG architectures; better workflow support than manual component chaining.
Generates embeddings for documents/nodes using pluggable embedding providers (OpenAI, Hugging Face, local models) and stores them in a unified vector store interface that abstracts over multiple backends (Pinecone, Weaviate, Milvus, FAISS, Chroma, etc.). The abstraction layer enables switching vector stores without changing application code, and handles batching, retry logic, and metadata indexing.
Unique: Provides a unified VectorStore interface that abstracts 10+ vector database backends, enabling zero-code switching between providers. Handles embedding batching, retry logic, and metadata propagation automatically. Supports both cloud and local embedding models through a pluggable EmbedModel interface.
vs alternatives: Broader vector store coverage and more seamless provider switching than LangChain's vectorstore integrations; better abstraction consistency across backends than using raw vector store SDKs directly.
Retrieves semantically similar documents from vector stores using embedding-based similarity search, with optional re-ranking, filtering, and fusion strategies (hybrid search combining dense and sparse retrieval). Supports multiple retrieval modes (similarity, MMR, fusion) and enables custom retrieval logic through a pluggable Retriever interface that can combine multiple strategies.
Unique: Implements a pluggable Retriever abstraction supporting multiple retrieval strategies (similarity, MMR, fusion, custom) that can be composed and chained. Built-in support for re-ranking via LLM or cross-encoder, and hybrid search combining dense and sparse retrieval without custom integration code.
vs alternatives: More flexible retrieval composition than LangChain's retrievers; built-in re-ranking and fusion strategies reduce boilerplate for advanced retrieval pipelines.
+6 more capabilities