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| Pricing | Free | Paid |
| Capabilities | 17 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Coordinates a specialized 10-agent pipeline (Radar, Planner, Composer, Architect, Writer, Continuity Auditor, Reviser, and others) where each agent handles specific creative and logical tasks in sequence. Agents communicate through a shared Truth Files state system that maintains canonical world state, character matrices, and plot hooks across the entire generation process. The pipeline enforces human review gates between critical stages, allowing writers to approve or reject agent outputs before proceeding to the next phase.
Unique: Uses a persistent Truth Files system (7 canonical markdown/JSON documents) as the single source of truth for world state, character matrices, and plot hooks, enabling agents to maintain narrative consistency across 100+ chapters without context window degradation. Each agent reads/writes to Truth Files rather than relying on conversation history, making the system scalable to novel-length outputs.
vs alternatives: Unlike ChatGPT or Claude plugins that lose context after ~50k tokens, InkOS maintains explicit state artifacts that agents can reference indefinitely, preventing character amnesia and narrative drift in long-form fiction.
Maintains 7 canonical markdown and JSON files that serve as the persistent knowledge base for the entire novel generation pipeline: World Bible (setting/lore), Character Matrix (detailed character profiles with relationships), Plot Hooks (story beats and foreshadowing), Chapter Snapshots (summaries of each completed chapter), Writing Rules (genre-specific and universal constraints), and Continuity Log (detected inconsistencies and resolutions). Agents read from and write to these files via a State Manager API that enforces schema validation using Zod and TypeBox, preventing malformed state updates.
Unique: Implements a schema-driven state system where Truth Files are validated against Zod/TypeBox schemas before agents can write updates, preventing invalid state mutations. Agents cannot directly modify files; all writes go through a State Manager API that enforces type safety and logs all mutations for audit trails.
vs alternatives: Unlike RAG systems that retrieve relevant context on-demand, InkOS maintains explicit, mutable state that agents can both read and update, enabling bidirectional information flow (agents learn from Truth Files and update them with new discoveries).
Allows users to provide existing source material (published novels, fanfic, or original works) as reference context, enabling agents to generate spinoffs, prequels, or alternate universe stories that maintain consistency with the source. The system extracts key characters, plot points, and world details from the source material and populates the Truth Files automatically, reducing manual setup. Agents are instructed to respect source canon while exploring new story directions.
Unique: Automatically extracts characters, plot points, and world details from source material and populates Truth Files, reducing manual setup for spinoffs. Agents are instructed to respect source canon while exploring new story directions, with the Auditor validating consistency against the source.
vs alternatives: Unlike generic novel generation, fanfic mode is specifically designed to maintain consistency with existing source material, enabling derivative works that feel authentic to the original universe.
A specialized agent that generates detailed plot outlines and story structures based on a high-level premise. The Architect reads the genre profile and writing rules, then produces a hierarchical outline (acts, chapters, scenes) with plot beats, character arcs, and foreshadowing notes. The output is stored in Truth Files (Plot Hooks) and used by downstream agents (Planner, Writer) to maintain narrative coherence. The Architect can be invoked standalone to generate outlines for human review before any writing begins.
Unique: Generates hierarchical plot outlines (acts → chapters → scenes) with explicit plot beats, character arcs, and foreshadowing notes. The output is structured as JSON for machine-readability, enabling downstream agents to reference specific plot beats and ensure consistency.
vs alternatives: Unlike generic outline generators, the Architect agent understands genre conventions and writing rules, producing outlines that respect the target style and constraints.
The Planner agent breaks down the overall plot outline into individual chapter plans, specifying which plot beats, characters, and scenes should appear in each chapter. The Composer agent then structures the chapter plan into a detailed scene-by-scene breakdown with dialogue notes, pacing guidance, and emotional beats. Both agents read from Truth Files (Plot Hooks, Character Matrix) and write their outputs back to Truth Files for the Writer agent to consume. This two-stage planning ensures chapters are coherent and aligned with the overall story structure.
Unique: Implements a two-stage planning process where the Planner breaks the overall outline into chapter plans, and the Composer structures each chapter into scenes with dialogue notes and pacing guidance. Both stages write to Truth Files, creating a detailed roadmap for the Writer agent.
vs alternatives: Unlike single-stage planning, the two-stage approach (Planner → Composer) produces more detailed and coherent chapter structures, reducing the likelihood of Writer agent deviations.
The core writing agent that generates chapter text based on chapter plans from the Composer agent. The Writer reads from Truth Files (Character Matrix, World Bible, Plot Hooks, Chapter Snapshots) to maintain consistency with established facts and previous chapters. It generates prose in the target style (learned from Style Profile) and respects writing rules (genre-specific and universal). The Writer produces raw chapter text that is then passed to the Auditor for continuity checking and the Reviser for style refinement.
Unique: Reads from multiple Truth Files (Character Matrix, World Bible, Plot Hooks, Chapter Snapshots) to maintain consistency with established facts and previous chapters. Uses a learned Style Profile to match the target author's voice and respects genre-specific writing rules during generation.
vs alternatives: Unlike generic LLM prompts that lose context after 50k tokens, the Writer agent maintains explicit state in Truth Files, enabling consistent writing across 100+ chapters without context degradation.
A specialized agent that audits generated chapters for continuity errors, character inconsistencies, plot contradictions, and AIGC markers. The Auditor reads the chapter text and Truth Files (Character Matrix, World Bible, Chapter Snapshots) and produces a detailed audit report listing detected issues with severity levels (critical, warning, info). Critical issues (e.g., character name misspelling, contradicted plot point) trigger automatic re-generation; warnings (e.g., character behavior inconsistency) are flagged for human review. The Auditor also updates the Continuity Log in Truth Files with detected issues and resolutions.
Unique: Produces a structured audit report with severity levels (critical, warning, info) and automatically triggers re-generation for critical issues. Maintains a Continuity Log that tracks all detected issues and resolutions, enabling writers to understand what errors were found and how they were fixed.
vs alternatives: Unlike manual continuity checking which is time-consuming and error-prone, the Auditor agent automatically detects and flags issues, enabling scalable quality assurance for long-form fiction.
A specialized agent that refines chapter prose to improve readability, remove AIGC markers, and match the target style. The Reviser reads flagged passages from the Auditor (high AIGC probability, style inconsistencies) and rewrites them to be more natural and authentic. It uses the learned Style Profile to guide rewrites and respects writing rules during revision. The Reviser can also perform broader revisions (e.g., 'make this dialogue more natural', 'reduce adjective usage') based on human feedback.
Unique: Combines AIGC detection (flagging passages with high AI probability) with targeted revision that rewrites only flagged passages rather than the entire chapter. Uses the learned Style Profile to guide rewrites, ensuring revisions match the target author's voice.
vs alternatives: Unlike full-chapter rewrites that are time-consuming and may introduce new errors, the Reviser agent performs targeted revisions of flagged passages, minimizing disruption while improving authenticity.
+9 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.
inkos scores higher at 40/100 vs LlamaIndex at 40/100. inkos 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