multi-source document and note indexing with semantic search
Khoj indexes local documents, notes, and files into a searchable knowledge base using semantic embeddings, enabling retrieval of contextually relevant information across heterogeneous sources (markdown, PDFs, text files, etc.). The system maintains a local or cloud-hosted vector index that maps document chunks to embeddings, allowing natural language queries to surface relevant context without keyword matching. This indexed knowledge is then injected into the agent's context window for grounded responses.
Unique: Supports self-hosted deployment with local vector indexing, giving users full control over data privacy and index management without relying on third-party vector databases; integrates directly with personal note-taking systems (Obsidian, Logseq, etc.) for automatic knowledge base construction
vs alternatives: Offers local-first indexing unlike cloud-dependent RAG systems (Pinecone, Weaviate SaaS), reducing latency and eliminating data transmission concerns for privacy-sensitive use cases
web search and online content retrieval with agent integration
Khoj enables the agent to search the web in real-time and retrieve current information from online sources, augmenting local knowledge with live data. The agent can invoke web search as a tool during reasoning, fetching and parsing search results to answer questions about current events, recent publications, or information not present in local documents. Search results are ranked and summarized before injection into the LLM context.
Unique: Integrates web search as a native agent tool that can be invoked during multi-step reasoning, allowing the agent to decide when to search the web vs. rely on local knowledge, rather than treating web search as a separate query mode
vs alternatives: Combines local document search and web search in a unified agent loop, unlike siloed tools (ChatGPT's web search, Perplexity) that treat web and local knowledge separately
structured data extraction from documents and web content
Khoj can extract structured information (entities, relationships, tables, metadata) from documents and web content using LLM-based extraction with optional schema guidance. Extracted data can be formatted as JSON, CSV, or other structured formats, enabling integration with downstream systems. The extraction process can be applied to individual documents or batched across large collections.
Unique: Applies LLM-based extraction to both indexed documents and web search results, enabling structured data extraction from heterogeneous sources in a unified workflow
vs alternatives: Combines document extraction with web search capabilities, unlike specialized extraction tools (Docparser, Zapier) that focus on single document sources
model configuration and parameter tuning
Allows users to configure LLM parameters (temperature, top-p, max tokens, etc.) and embedding model selection to tune assistant behavior and performance. Provides configuration interfaces for adjusting generation quality, response length, and semantic search sensitivity without code changes.
Unique: User-configurable LLM parameters and embedding model selection, enabling fine-grained control over generation behavior and search sensitivity without code modifications
vs alternatives: More flexible than fixed-behavior assistants (ChatGPT) by exposing parameter tuning, though less automated than systems with built-in parameter optimization
multi-model llm abstraction with provider-agnostic agent configuration
Khoj abstracts away LLM provider differences through a unified interface, allowing users to configure any supported model (OpenAI, Anthropic, Ollama, local models, etc.) as the agent backbone. The system handles prompt formatting, token counting, and API calls transparently, enabling users to swap models without changing agent logic or tool definitions. This abstraction supports both cloud-hosted and self-hosted model deployment.
Unique: Provides a unified configuration layer that treats local models (Ollama, vLLM) and cloud APIs (OpenAI, Anthropic) as interchangeable, enabling seamless switching between self-hosted and cloud deployment without code changes
vs alternatives: Offers broader model support and local-first options compared to frameworks tied to single providers (LangChain's default OpenAI bias, Vercel AI SDK's limited local model support)
conversational context management with multi-turn memory
Khoj maintains conversation history across multiple turns, managing context windows and token budgets to keep relevant prior exchanges accessible to the agent while respecting model token limits. The system implements context compression or summarization strategies to preserve conversation coherence without exceeding token budgets. Memory can be persisted across sessions for long-term conversation continuity.
Unique: Integrates conversation memory with document indexing, allowing the agent to reference both prior conversation turns and indexed documents in a unified context window, creating a hybrid memory system
vs alternatives: Combines conversation memory with RAG-based document retrieval in a single context, unlike chat systems that treat conversation history and knowledge base as separate concerns
content generation and writing assistance with template support
Khoj can generate written content (emails, blog posts, summaries, etc.) using the configured LLM, optionally grounded in indexed documents or web search results. The system supports templates and structured prompts to guide content generation toward specific formats or styles. Generated content can be edited, refined, and exported in multiple formats.
Unique: Grounds content generation in indexed personal documents and web search results, enabling the agent to generate contextually relevant content that cites sources rather than producing generic outputs
vs alternatives: Combines content generation with RAG grounding, unlike general-purpose writing assistants (ChatGPT, Grammarly) that lack access to user-specific knowledge bases
task automation and scheduling with local execution
Khoj (via the Pipali product) can schedule and execute automated tasks on a local machine, such as periodic research, document processing, or data collection. Tasks run 'safely on your computer' with defined execution schedules and can integrate with local tools and scripts. The system manages task state, logging, and error handling for autonomous execution.
Unique: Executes tasks locally on the user's machine rather than in cloud infrastructure, providing full control over execution environment and data handling while maintaining autonomous scheduling capabilities
vs alternatives: Offers local-first task automation unlike cloud-based workflow platforms (Zapier, Make), eliminating data transmission and enabling integration with local-only tools
+4 more capabilities