| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $25/mo |
| Capabilities | 9 decomposed | 15 decomposed |
| Times Matched | 0 | 0 |
Implements a distributed semantic memory layer using Qdrant vector database as the backend storage, enabling Claude Code agents to persist and retrieve embeddings across sessions. The system stores embeddings generated from code snippets, documentation, and conversation context in a vector index, allowing agents to maintain long-term semantic understanding without re-embedding identical content. Uses MCP protocol to expose memory operations as standardized tools that Claude can invoke during code generation and reasoning tasks.
Unique: Bridges Claude Code agents with Qdrant via MCP protocol, enabling agents to treat distributed vector memory as a first-class tool rather than requiring custom API wrappers. Uses MCP's standardized tool schema to expose memory operations (store, retrieve, search) as native Claude capabilities.
vs alternatives: Unlike generic RAG libraries that require custom integration code, local-rag exposes memory as MCP tools that Claude understands natively, eliminating integration boilerplate and enabling agents to autonomously decide when to use memory.
Provides semantic search over codebases by generating embeddings that incorporate code structure awareness, not just raw text similarity. The system can index code files, extract meaningful code units (functions, classes, modules), and generate embeddings that capture both semantic meaning and syntactic context. Search queries return ranked code snippets with relevance scores, enabling Claude agents to find relevant code patterns and implementations without keyword matching.
Unique: Integrates code structure awareness into embeddings by leveraging language-specific parsing (likely tree-sitter or similar), enabling semantic search that understands code intent rather than treating code as plain text. Exposes search as MCP tools that Claude can invoke during code generation.
vs alternatives: Outperforms keyword-based code search (grep, ripgrep) by understanding semantic similarity, and requires less manual prompt engineering than generic RAG systems because it's specifically tuned for code semantics.
Wraps all RAG and memory operations as MCP (Model Context Protocol) tools that Claude Code agents can invoke directly, using MCP's standardized tool schema and request/response format. The system registers tools for memory operations (store, retrieve, search, delete) and exposes them through the MCP server interface, allowing Claude to autonomously decide when to access memory without requiring custom prompt engineering or wrapper code.
Unique: Uses MCP protocol as the integration layer rather than custom REST APIs or SDK wrappers, enabling Claude to treat RAG operations as first-class tools with standardized schemas. Eliminates the need for custom prompt engineering to teach Claude about tool availability.
vs alternatives: Cleaner than custom API wrappers because MCP provides standardized tool schemas that Claude understands natively, and more maintainable than prompt-based tool discovery because tool definitions are declarative and version-controlled.
Integrates with Ollama to generate embeddings locally without external API calls, using open-source embedding models (e.g., nomic-embed-text, all-minilm). The system can invoke Ollama's embedding endpoint to convert code snippets and search queries into vector representations, enabling fully local RAG pipelines without dependency on commercial embedding APIs. Supports fallback to external embedding APIs if Ollama is unavailable.
Unique: Provides local embedding generation as a first-class option in the RAG pipeline, with graceful fallback to external APIs. Uses Ollama's standardized embedding endpoint, enabling users to swap embedding models without code changes.
vs alternatives: Enables fully local RAG without cloud dependencies, unlike systems that require API keys for embeddings. Trades embedding quality for privacy and cost savings, making it ideal for sensitive codebases.
Supports indexing and semantic search across multiple programming languages (JavaScript, TypeScript, Python, Go, Rust, etc.) by using language-agnostic embedding generation and optional language-specific parsing for code structure awareness. The system can index mixed-language codebases, maintain separate vector indices per language if needed, and retrieve relevant code regardless of language boundaries. Enables cross-language code pattern discovery and reuse.
Unique: Handles multi-language codebases without requiring separate indexing pipelines per language, using language-agnostic embeddings while optionally leveraging language-specific parsing for enhanced structure awareness. Exposes unified search interface regardless of language composition.
vs alternatives: More flexible than language-specific code search tools (which only work for one language) and simpler than building separate RAG pipelines per language. Enables cross-language pattern discovery that single-language systems cannot provide.
Stores embeddings with rich metadata (file paths, function signatures, timestamps, code language, author, etc.) and enables filtering/retrieval based on metadata predicates, not just semantic similarity. The system can retrieve embeddings matching specific criteria (e.g., 'all Python functions modified in last week', 'all code in src/utils directory') and combine metadata filtering with semantic search for precise context retrieval. Metadata is stored alongside vectors in Qdrant using payload filtering.
Unique: Leverages Qdrant's payload filtering to enable metadata-aware retrieval, combining semantic search with structured filtering in a single query. Enables agents to respect code organization and ownership boundaries without separate filtering logic.
vs alternatives: More powerful than pure semantic search because it can enforce organizational constraints (e.g., 'only search in my team's code'). More efficient than post-filtering results because metadata filtering happens at the database level.
Provides memory isolation mechanisms that allow different Claude Code agents or sessions to maintain separate memory spaces, preventing cross-contamination of context. The system can scope memory operations to specific sessions, users, or projects using namespace/partition strategies in Qdrant, enabling multiple agents to operate independently while sharing the same vector database infrastructure. Supports both isolated and shared memory modes depending on use case.
Unique: Implements session-scoped memory isolation using Qdrant's partitioning capabilities, enabling multiple agents to share infrastructure while maintaining independent memory spaces. Provides both isolated and shared memory modes for flexibility.
vs alternatives: More efficient than running separate vector databases per agent because it shares infrastructure while maintaining isolation. More flexible than hard-coded isolation because it supports both isolated and shared memory patterns.
Supports incremental indexing of codebase changes rather than full re-indexing, using file modification timestamps or git diff to detect changed files and update only affected embeddings. The system can track which files have been indexed, detect changes since last indexing, and update only the changed code units in the vector database. Enables efficient maintenance of large codebase indices without full re-embedding on every update.
Unique: Implements incremental indexing with change detection, avoiding expensive full re-indexing of large codebases. Uses file timestamps or git integration to identify changed files and updates only affected embeddings in Qdrant.
vs alternatives: More efficient than full re-indexing for large codebases, enabling live code search indices. More reliable than polling-based approaches because it uses explicit change detection rather than periodic full scans.
+1 more capabilities
Automatically inspects tabular data sources (Google Sheets, Airtable, Excel, CSV, SQL databases) to extract column names, infer field types (text, number, date, checkbox, etc.), and create bidirectional data bindings between UI components and source columns. Uses declarative component-to-column mappings that persist schema changes in real-time, enabling components to automatically reflect upstream data structure modifications without manual rebinding.
Unique: Glide's approach combines automatic schema introspection with declarative component binding, eliminating manual field mapping that competitors like Airtable require. The bidirectional sync model means changes to source column structure automatically propagate to UI components without developer intervention, reducing maintenance overhead for non-technical users.
vs alternatives: Faster to initial app than Airtable (which requires manual field configuration) and more flexible than rigid form builders because it adapts to evolving data structures automatically.
Provides 40+ pre-built, data-aware UI components (forms, tables, calendars, charts, buttons, text inputs, dropdowns, file uploads, maps, etc.) that automatically render responsively across mobile and desktop viewports. Components use a declarative binding syntax to connect to spreadsheet columns, with built-in support for computed fields, conditional visibility, and user-specific data filtering. Layout engine uses CSS Grid/Flexbox under the hood to adapt component sizing and positioning based on screen size without requiring manual breakpoint configuration.
Unique: Glide's component library is tightly integrated with data binding — components are not generic UI elements but data-aware objects that automatically sync with spreadsheet columns. This eliminates the disconnect between UI and data that exists in traditional form builders, where developers must manually wire component values to data sources.
vs alternatives: Faster to build than Bubble (which requires manual component-to-data wiring) and more mobile-optimized than Airtable's grid-centric interface, which prioritizes desktop spreadsheet metaphors over mobile-first design.
Glide scores higher at 70/100 vs @13w/local-rag at 28/100. @13w/local-rag leads on ecosystem, while Glide is stronger on adoption and quality.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Enables multiple team members to edit apps simultaneously with role-based access control. Supports predefined roles (Owner, Editor, Viewer) with different permission levels: Owners can manage team members and publish apps, Editors can modify app design and data, Viewers can only view published apps. Team member limits vary by plan (2 free, 10 business, custom enterprise). Real-time collaboration on app design is not mentioned, suggesting changes may not be synchronized in real-time between editors.
Unique: Glide's team collaboration is built into the platform, meaning team members don't need separate accounts or complex permission configuration — they're invited via email and assigned roles directly in the app. This is more seamless than tools requiring external identity management.
vs alternatives: More integrated than Airtable (which requires separate workspace management) and simpler than GitHub-based collaboration (which requires version control knowledge), though less sophisticated than enterprise platforms with audit logging and approval workflows.
Provides pre-built app templates for common use cases (inventory management, CRM, project management, expense tracking, etc.) that users can clone and customize. Templates include sample data, pre-configured components, and example workflows, reducing time-to-first-app from hours to minutes. Templates are fully editable, allowing users to modify data sources, components, and workflows to match their specific needs. Template library is curated by Glide and updated regularly with new templates.
Unique: Glide's templates are fully functional apps with sample data and workflows, not just empty scaffolds. This allows users to immediately see how components work together and understand app structure before customizing, reducing the learning curve significantly.
vs alternatives: More complete than Airtable's templates (which are mostly empty bases) and more accessible than building from scratch, though less flexible than code-based frameworks where templates can be parameterized and generated programmatically.
Allows workflows to be triggered on a schedule (daily, weekly, monthly, or custom intervals) without manual intervention. Scheduled workflows execute at specified times and can perform batch operations (process pending records, send daily reports, sync data, etc.). Execution time is in UTC, and the exact scheduling mechanism (cron, quartz, custom) is undocumented. Failed scheduled tasks may or may not retry automatically (retry logic undocumented).
Unique: Glide's scheduled workflows are integrated with the workflow engine, meaning scheduled tasks can execute the same complex logic as event-triggered workflows (conditional logic, multi-step actions, API calls). This is more powerful than simple scheduled email tools because scheduled tasks can perform data transformations and cross-system synchronization.
vs alternatives: More integrated than Zapier's schedule trigger (which is limited to simple actions) and more accessible than cron jobs (which require server access and scripting knowledge), though less transparent about execution guarantees and failure handling than enterprise job schedulers.
Offers Glide Tables, a proprietary managed database alternative to external spreadsheets or databases, with automatic scaling and optimization for Glide apps. Glide Tables are stored in Glide's infrastructure and optimized for the data binding and query patterns used by Glide apps. Scaling limits are plan-dependent (25k-100k rows), with separate 'Big Tables' tier for larger datasets (exact scaling limits undocumented). Automatic backups and disaster recovery are mentioned but details are undocumented.
Unique: Glide Tables are optimized specifically for Glide's data binding and query patterns, meaning they're tightly integrated with the app builder and don't require separate database administration. This is more seamless than connecting external databases (which require schema design and optimization knowledge) but less flexible because data is locked into Glide's proprietary format.
vs alternatives: More managed than self-hosted databases (no administration required) and more integrated than external databases (no separate configuration), though less portable than standard databases because data cannot be easily exported or migrated.
Provides basic chart components (bar, line, pie, area charts) that visualize data from connected sources. Charts are configured visually by selecting data columns for axes, values, and grouping. Charts are responsive and adapt to mobile/tablet/desktop. Real-time updates are supported; charts refresh when underlying data changes. No custom chart types or advanced visualization options (3D, animations, etc.) are available.
Unique: Provides basic chart components with automatic real-time updates and responsive design, suitable for simple dashboards — most visual builders (Bubble, FlutterFlow) require chart plugins or custom code
vs alternatives: More integrated than Airtable's chart view because real-time updates are automatic; weaker than BI tools (Tableau, Looker) because no drill-down, filtering, or advanced visualization options
Allows users to query data using natural language (e.g., 'Show me all orders from last month with revenue > $5k') which is converted to structured database queries without SQL knowledge. Also includes AI-powered data extraction from unstructured text (emails, documents, images) to populate spreadsheet columns. Implementation details (LLM model, context window, fine-tuning approach) are undocumented, but the feature appears to use prompt-based query generation with fallback to manual query building if AI fails.
Unique: Glide's natural language query feature bridges the gap between spreadsheet users (who think in English) and database queries (which require SQL). Rather than teaching users SQL, it translates natural language to structured queries, lowering the barrier to data exploration. The data extraction capability extends this to unstructured sources, automating data entry from emails and documents.
vs alternatives: More accessible than Airtable's formula language or traditional SQL, and more integrated than bolt-on AI query tools because it's built directly into the data layer rather than as a separate search interface.
+7 more capabilities