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| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
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
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
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
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
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)
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
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
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
Manages conversation history as immutable thread objects stored server-side, where each message appends to a thread rather than requiring clients to maintain conversation state. Threads persist across API calls and sessions, enabling stateless client implementations. The architecture decouples conversation management from model invocation, allowing assistants to be reused across multiple independent threads without state collision.
Unique: Server-side thread abstraction eliminates client-side conversation state management; threads are first-class API objects with immutable append-only semantics, not just message arrays. This differs from stateless LLM APIs where clients must manage context windows and history truncation.
vs alternatives: Eliminates context window management burden compared to raw LLM APIs (e.g., Claude API, GPT-4 completions), but adds latency and cost overhead vs. in-memory conversation state in frameworks like LangChain
Provides a managed Python 3.11 execution environment accessible via the Code Interpreter tool, where assistants can write and execute arbitrary Python code with access to common libraries (pandas, numpy, matplotlib, scikit-learn). Code runs in isolated sandboxes with file I/O, plotting, and data visualization capabilities. Execution results (stdout, stderr, generated files) are returned to the assistant for further processing.
Unique: Managed Python sandbox integrated directly into the agent loop — assistants can iteratively write, execute, and refine code without external compute provisioning. Execution results feed back into the LLM context, enabling self-correcting workflows. Differs from Replit or Jupyter APIs which require explicit session management.
vs alternatives: Simpler than provisioning Jupyter kernels or Lambda functions for code execution, but slower and less flexible than local Python execution; better for lightweight analysis than heavy ML workloads
OpenAI Assistants scores higher at 76/100 vs Khoj at 58/100. However, Khoj offers a free tier which may be better for getting started.
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When an assistant calls a tool, the run enters a 'requires_action' state. Clients must submit tool call results via the submit_tool_outputs API, which resumes the run with the tool results injected into context. This enables iterative workflows where assistants can call tools, receive results, and refine responses based on results. Tool results are stored in the thread and visible to subsequent runs, enabling multi-turn tool-assisted reasoning.
Unique: Tool results are submitted explicitly via API, not returned in-band — enables clients to process, validate, or transform results before injection. Runs pause in 'requires_action' state, giving clients full control over tool execution and result handling.
vs alternatives: More flexible than automatic tool execution (clients can implement custom logic), but requires more client-side code than frameworks like LangChain where tool execution is automatic; enables external tool integration without modifying assistant code
Assistants can be created from scratch or cloned from existing assistants, copying all configuration (instructions, tools, model, file attachments). Cloning enables template-based assistant creation where a base assistant is configured once and then cloned for different use cases or users. Cloned assistants are independent — changes to one don't affect others. This reduces setup overhead for creating similar assistants.
Unique: Assistants are cloneable objects — configuration can be copied to create new assistants without manual setup. Enables template-based assistant creation and multi-tenant provisioning patterns.
vs alternatives: Simpler than manually creating assistants with identical configuration, but less flexible than parameterized templates; no built-in versioning or rollback compared to infrastructure-as-code approaches
Files uploaded to assistants are stored in OpenAI's managed file storage and associated with assistants or threads. Files can be deleted explicitly via API, and OpenAI automatically cleans up files after 30 days of inactivity. File storage is charged per file per assistant; deleting unused files reduces costs. Files can be reused across multiple assistants and threads, but each association incurs a separate storage charge.
Unique: Files are managed server-side with automatic cleanup after 30 days — no manual file system management required. Files are associated with assistants and charged per association, enabling cost tracking at the file level.
vs alternatives: Simpler than managing files in external storage (S3, GCS), but less flexible and more expensive for high-volume file usage; automatic cleanup reduces manual maintenance but limits retention control
The File Search tool indexes uploaded files (PDFs, text, code) using OpenAI's embedding model and enables assistants to retrieve relevant passages via semantic search. Files are chunked, embedded, and stored in a managed vector index. When an assistant queries the index, it retrieves the most relevant chunks based on cosine similarity, then includes them in the prompt context. This enables RAG-style retrieval without managing embeddings or vector databases.
Unique: Fully managed vector indexing and retrieval without exposing embedding or vector database layers — files are indexed automatically on upload, and search is invoked implicitly when assistants reference file_search tool. Abstracts away Pinecone/Weaviate setup but sacrifices control over chunking and embedding strategies.
vs alternatives: Faster to implement than building custom RAG with LangChain + Pinecone, but less flexible; no control over chunk size, embedding model, or retrieval parameters compared to self-managed vector databases
Assistants can invoke multiple tools (Code Interpreter, File Search, custom functions) in parallel or sequence based on task requirements. Tool calls are defined via JSON schema (OpenAI function calling format), and the assistant decides which tools to invoke and in what order. Results from tool calls are fed back into the assistant's context, enabling iterative refinement. Supports both parallel execution (multiple tools called simultaneously) and sequential chaining (tool output feeds into next tool's input).
Unique: Tool invocation is driven by the LLM's reasoning — the assistant decides which tools to call, in what order, and with what parameters based on task context. Supports both parallel and sequential execution patterns. Differs from static tool pipelines (e.g., Zapier) where execution order is pre-defined.
vs alternatives: More flexible than hardcoded tool chains, but less predictable than explicit DAGs; requires careful prompt engineering to ensure correct tool selection vs. frameworks like LangChain where tool routing can be more explicit
Assistants can receive file attachments (PDFs, images, code, data files) within messages, which are automatically indexed and made available for retrieval or analysis. Files are stored in OpenAI's managed file storage and can be referenced by subsequent messages in the thread. The assistant can analyze file content via Code Interpreter, search file content via File Search, or reference files in function calls. Files persist within a thread and are accessible across multiple turns.
Unique: Files are first-class message attachments with automatic indexing and managed storage — no separate file management API required. Files persist in thread context and are automatically made available to all tools (Code Interpreter, File Search, function calls) without explicit routing.
vs alternatives: Simpler than managing files separately and passing file paths to tools; automatic indexing reduces setup vs. manual chunking and embedding, but less control over file processing compared to custom pipelines
+5 more capabilities