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| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 14 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Executes code in 13+ languages (Python, TypeScript, Go, Bash, Java, Rust, C#, PHP, Deno, Bun, Ansible, Nu, SQL) by routing to language-specific executors in windmill-worker that parse function signatures using language-specific parsers (windmill-parser-*) to automatically infer JSON schemas without manual type annotation. Workers poll PostgreSQL queue table using SELECT FOR UPDATE SKIP LOCKED, execute in sandboxed nsjail environments, and store results in completed_job table or S3, enabling polyglot workflow composition.
Unique: Uses language-specific AST parsers (not regex) to infer JSON schemas directly from function signatures, eliminating manual type annotation while supporting 13+ languages with isolated execution via nsjail per job
vs alternatives: Faster and more flexible than cloud-only solutions like Zapier because execution is local/self-hosted, and more polyglot-friendly than Temporal or Prefect which optimize for Python/TypeScript
Composes multi-step workflows using OpenFlow specification (openflow.openapi.yaml) where modules execute sequentially or in parallel with full state tracking in PostgreSQL flow_status JSONB column. Each step can branch on conditions, loop over arrays, or call other flows/scripts, with intermediate results passed between steps via variable interpolation. The worker processes flow definitions by parsing the DAG, executing modules in dependency order, and persisting state after each step for resumability and debugging.
Unique: Tracks full execution state in PostgreSQL JSONB (not just logs), enabling step-level resumability and debugging; OpenFlow spec is open and language-agnostic unlike proprietary workflow DSLs
vs alternatives: More transparent than Zapier (full state visibility) and simpler than Airflow (no DAG compilation step) while supporting both visual and code-based workflow definition
Provides official SDKs in TypeScript, Python, and PowerShell for programmatically calling Windmill scripts and flows from external applications. The SDKs handle authentication, request serialization, and response deserialization, with type hints generated from script schemas. Clients support both synchronous and asynchronous execution, polling for job completion, and streaming results. The SDKs are auto-generated from the OpenAPI spec (windmill-api/openapi.yaml) ensuring consistency with the API.
Unique: Auto-generated from OpenAPI spec ensuring consistency; provides type hints based on inferred script schemas; supports both sync and async execution patterns
vs alternatives: More convenient than raw HTTP clients because of type safety and built-in serialization, and more flexible than webhooks for request-response patterns
Stores job results in PostgreSQL completed_job table with full execution context (inputs, outputs, logs, duration), and provides a web UI for browsing results with filtering by status, date, and user. Large payloads (>1MB) are stored in S3 with references in the database. Results can be visualized as tables, charts, or raw JSON depending on output type, and artifacts (files, exports) are downloadable. The system maintains result history per script/flow for trend analysis and debugging.
Unique: Results stored with full execution context (inputs, outputs, logs, duration) in PostgreSQL; large payloads spilled to S3; web UI provides filtering and visualization
vs alternatives: More integrated than external logging systems because results are stored alongside execution metadata, and simpler than building custom dashboards
Automatically detects dependencies in scripts (imports, requires, use statements) and generates language-specific lockfiles (requirements.txt for Python, package-lock.json for Node.js, go.mod for Go, etc.) to ensure reproducible execution. Dependencies are cached on workers to avoid repeated downloads, and the system detects when lockfiles change to invalidate caches. The parser (windmill-parser-*) extracts imports from code and resolves them to specific versions, supporting both public registries and private package repositories.
Unique: Automatically detects and resolves dependencies from code without manual lockfile editing; generates language-specific lockfiles and caches on workers for fast execution
vs alternatives: More automatic than manual requirements management, and more reproducible than relying on latest versions
Exposes webhook endpoints for each script/flow that accept HTTP POST requests and enqueue jobs with the request payload as parameters. Webhooks support signature verification (HMAC-SHA256) to ensure requests come from trusted sources, and can be triggered by external services (GitHub, Slack, Stripe, etc.) without authentication. The system generates unique webhook URLs per script and supports custom headers and query parameters for routing. Webhook delivery is retried with exponential backoff if the job fails.
Unique: Generates unique webhook URLs per script with optional HMAC-SHA256 signature verification; integrates with external services without requiring API keys in Windmill
vs alternatives: More secure than unauthenticated webhooks because of signature verification, and simpler than building custom webhook handlers
Automatically exposes any script as a REST API endpoint and generates a web form UI by introspecting the inferred JSON schema. The API server (windmill-api) creates routes dynamically for each script, accepting JSON payloads that map to function parameters. The frontend (SvelteKit) renders form components based on schema type (string, number, object, array) with validation, and submits to the API which enqueues a job. Results are returned synchronously for short-running scripts or via polling/webhooks for long-running jobs, eliminating manual API/UI boilerplate.
Unique: Generates both REST API and web UI from a single source (function signature), with schema inference eliminating manual OpenAPI specs; form validation happens client-side and server-side
vs alternatives: Faster iteration than building custom APIs with FastAPI/Express, and more flexible than low-code platforms like Retool which require UI-first thinking
Schedules scripts and flows to run on cron expressions with timezone awareness, storing schedule definitions in PostgreSQL and using a background scheduler service to enqueue jobs at the specified times. The scheduler respects concurrency limits per script (preventing duplicate runs if previous execution hasn't completed) and supports both simple cron syntax and human-readable schedules. Failed scheduled jobs are retried with exponential backoff, and execution history is logged for audit and debugging.
Unique: Integrates scheduling directly into the platform with concurrency limits and timezone awareness, avoiding separate cron infrastructure; schedule definitions are version-controlled as code
vs alternatives: Simpler than Airflow for basic scheduling (no DAG compilation), and more reliable than system cron because execution is tracked in the database with retry logic
+6 more capabilities
LangChain provides a Chain abstraction that sequences LLM calls, prompt templates, and tool invocations into directed acyclic graphs (DAGs). Chains support sequential execution (SequentialChain), conditional branching (RouterChain), and parallel execution patterns. The framework uses a Runnable interface that standardizes input/output contracts across all chain components, enabling composition via pipe operators and method chaining. This allows developers to build complex multi-step workflows without managing state manually.
Unique: Uses a unified Runnable interface across all components (LLMs, tools, retrievers, parsers) enabling composability via pipe operators, unlike frameworks that require separate orchestration layers for different component types. Supports both sync and async execution with identical code paths.
vs alternatives: More flexible than simple prompt chaining (like OpenAI's function calling alone) because it abstracts orchestration logic, making chains reusable and testable; simpler than full workflow engines (Airflow, Prefect) because it's optimized for LLM-specific patterns rather than general data pipelines.
LangChain's PromptTemplate class provides structured prompt engineering with variable placeholders, automatic validation, and support for few-shot learning patterns. Templates use Jinja2-style syntax for variable substitution and support dynamic example selection via ExampleSelector. The framework includes specialized templates (ChatPromptTemplate for multi-turn conversations, FewShotPromptTemplate for in-context learning) that handle formatting differences across LLM types. This enables prompt reusability, version control, and systematic experimentation without string concatenation.
Unique: Provides first-class abstractions for few-shot learning (FewShotPromptTemplate) with pluggable ExampleSelector strategies, enabling dynamic example selection based on input similarity without requiring developers to implement selection logic. Separates system prompts, conversation history, and user input in ChatPromptTemplate, making multi-turn conversations composable.
Windmill scores higher at 59/100 vs LangChain at 41/100. Windmill also has a free tier, making it more accessible.
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vs alternatives: More structured than manual string formatting because it validates variable names and supports semantic example selection; more specialized than generic templating engines (Jinja2) because it understands LLM-specific patterns like chat message roles and few-shot formatting.
LangChain abstracts function calling across LLM providers by converting Python functions or Pydantic models into provider-specific schemas (OpenAI function_call, Anthropic tool_use, etc.). The framework automatically generates schemas, handles argument parsing, and routes calls to the correct provider. Developers define functions once and LangChain handles provider-specific formatting. This enables tool use without learning each provider's function calling API.
Unique: Automatically converts Python functions and Pydantic models into provider-specific function calling schemas (OpenAI, Anthropic, Cohere, etc.) and handles parsing and routing transparently. Developers define tools once and LangChain handles provider-specific formatting and execution.
vs alternatives: More portable than using provider SDKs directly because function definitions are provider-agnostic; more automated than manual schema management because schemas are generated from function signatures.
LangChain supports streaming LLM output at token granularity, enabling real-time user feedback as tokens are generated. The framework provides streaming iterators and async generators that yield tokens as they arrive from the LLM. Streaming is integrated into chains and agents, so developers can stream output from complex workflows without special handling. This enables responsive user experiences where output appears in real-time rather than waiting for full completion.
Unique: Integrates streaming at the framework level so chains and agents can stream output transparently without special handling. Provides both sync and async streaming iterators and handles provider-specific streaming formats uniformly.
vs alternatives: More integrated than provider-specific streaming APIs because streaming works across chains and agents; more responsive than buffering full output because tokens appear in real-time.
LangChain provides async/await support throughout the framework, enabling concurrent execution of LLM calls, chains, and agents. All major components (LLMs, chains, retrievers, agents) have async variants (e.g., arun() alongside run()). The framework uses asyncio for Python and native async/await for Node.js. This enables high-concurrency applications that can handle multiple requests simultaneously without blocking. Async execution is transparent; developers write the same code as sync but use async/await syntax.
Unique: Provides async/await support throughout the framework with parallel async implementations of all major components. Enables transparent concurrent execution without requiring developers to manage thread pools or explicit parallelization.
vs alternatives: More integrated than manual async management because async is built into the framework; more scalable than sync-only implementations because it enables handling multiple concurrent requests.
LangChain abstracts LLM APIs behind a common BaseLanguageModel interface, supporting OpenAI, Anthropic, Cohere, Hugging Face, Ollama, and 20+ other providers. The abstraction handles provider-specific details: token counting, streaming, function calling schemas, and cost tracking. Developers write LLM-agnostic code and swap providers via configuration. The framework includes built-in retry logic, rate limiting, and fallback chains for reliability. This enables portability and cost optimization without rewriting application logic.
Unique: Implements a unified BaseLanguageModel interface that abstracts away provider differences in token counting, streaming protocols, and function calling schemas. Includes built-in retry policies, rate limiting, and cost tracking at the framework level rather than requiring developers to implement these separately for each provider.
vs alternatives: More portable than using provider SDKs directly because swapping providers requires only configuration changes; more comprehensive than simple wrapper libraries because it handles streaming, retries, and cost tracking uniformly across 20+ providers.
LangChain provides a Retriever abstraction that enables RAG by connecting LLMs to external knowledge sources. The framework supports multiple retrieval strategies: vector similarity search (via VectorStore), BM25 keyword search, hybrid search, and custom retrievers. Documents are chunked, embedded, and stored in vector databases (Pinecone, Weaviate, Chroma, FAISS, etc.). The RetrievalQA chain automatically retrieves relevant documents and passes them as context to the LLM. This enables LLMs to answer questions grounded in custom data without fine-tuning.
Unique: Provides a unified Retriever interface that abstracts different retrieval strategies (vector, keyword, hybrid, custom) and integrates seamlessly with LLM chains via RetrievalQA. Includes built-in document loaders for 50+ formats (PDF, HTML, Markdown, code files) and automatic chunking strategies, reducing boilerplate for document ingestion.
vs alternatives: More integrated than building RAG from scratch because document loading, chunking, embedding, and retrieval are unified in one framework; more flexible than specialized RAG platforms (Pinecone, Weaviate) because it supports multiple vector stores and custom retrieval logic.
LangChain's Agent abstraction enables autonomous task execution by combining LLMs with tools (functions, APIs, retrievers). The agent uses an action-observation loop: the LLM decides which tool to call based on the task, executes the tool, observes the result, and repeats until the task is complete. Agents support multiple reasoning strategies: ReAct (reasoning + acting), chain-of-thought, and tool-use patterns. The framework handles tool schema generation, argument parsing, and error recovery. This enables building autonomous systems that can decompose complex tasks without explicit step-by-step instructions.
Unique: Implements a generalized Agent interface that supports multiple reasoning strategies (ReAct, chain-of-thought, tool-use) and automatically handles tool schema generation, argument parsing, and error recovery. The action-observation loop is abstracted, allowing developers to focus on defining tools rather than implementing agent logic.
vs alternatives: More flexible than simple function calling (OpenAI's tool_choice) because it implements multi-step reasoning and tool sequencing; more accessible than building agents from scratch because it handles schema generation, parsing, and error recovery automatically.
+5 more capabilities