| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 15 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Provides a single high-level API for defining models and layers that transparently dispatches numerical computation to JAX, TensorFlow, PyTorch, or OpenVINO backends selected at import time via KERAS_BACKEND environment variable or ~/.keras/keras.json. The framework maintains a backend-agnostic source of truth in keras/src/ with generated public API surface in keras/api/, enabling seamless backend switching without code changes. Runtime dispatch follows two paths: symbolic execution during model construction (shape/dtype inference via compute_output_spec on KerasTensor objects) and eager execution during training/inference (forwarded to active backend implementation).
Unique: Implements true multi-backend abstraction through keras/src/ source-of-truth architecture with auto-generated keras/api/ public surface, enabling compile-time API consistency across backends while maintaining separate backend-specific implementations in keras/src/backend/{jax,torch,tensorflow,openvino}/ directories. Uses symbolic execution path (compute_output_spec) for shape inference and eager path for actual computation, avoiding backend lock-in.
vs alternatives: Unlike TensorFlow (TF-only) or PyTorch (PyTorch-only), Keras 3 provides true write-once-run-anywhere semantics with equal support for JAX, TensorFlow, and PyTorch through a unified API rather than framework-specific wrappers.
Defines neural network layers (Dense, Conv2D, LSTM, etc.) and operations (numpy-compatible ops, neural network ops, core backend ops) in keras/src/ that are completely decoupled from backend implementation. Each layer inherits from a base Layer class that implements compute_output_spec() for symbolic shape/dtype inference and call() for eager execution. Backend-specific implementations are injected at runtime through the active backend module, allowing the same layer code to execute on JAX, TensorFlow, PyTorch, or OpenVINO without modification.
Unique: Implements layers as backend-agnostic Python classes with dual-path execution: symbolic path uses compute_output_spec() to infer output shapes/dtypes without computation, eager path delegates to backend-specific implementations via keras.ops.* namespace. Layer definitions in keras/src/layers/ contain zero backend-specific code; all dispatch happens through the ops module.
vs alternatives: Compared to PyTorch (backend-specific) or TensorFlow (TF-centric), Keras layers achieve true backend independence by separating layer logic from backend implementation, allowing identical layer code to run on JAX, PyTorch, or TensorFlow without conditional logic.
Provides a callback system (keras/src/callbacks/) that enables monitoring and controlling training through hooks at various training stages: on_epoch_begin, on_epoch_end, on_batch_begin, on_batch_end, on_train_begin, on_train_end. Built-in callbacks include EarlyStopping (stop training when validation metric plateaus), ModelCheckpoint (save best model), ReduceLROnPlateau (reduce learning rate), TensorBoard (visualization), and CSVLogger (log metrics). Callbacks are executed synchronously during training and have access to training state (epoch, batch, metrics, model weights).
Unique: Implements callback system in keras/src/callbacks/ with hooks at multiple training stages (epoch/batch begin/end) and built-in callbacks for common use cases (EarlyStopping, ModelCheckpoint, ReduceLROnPlateau). Callbacks are executed synchronously during training with access to training state, enabling monitoring and control without modifying training loop code.
vs alternatives: Unlike PyTorch (no built-in callback system) or TensorFlow (callbacks are TensorFlow-specific), Keras provides a unified callback system across all backends with built-in callbacks for common use cases like early stopping and model checkpointing.
Provides a metric system (keras/src/metrics/) for computing and tracking statistics during training and evaluation. Metrics are stateful objects that accumulate values across batches and compute aggregate statistics (accuracy, AUC, precision, recall, etc.). Metrics are compiled into models via model.compile(metrics=[...]) and automatically computed during training/evaluation. The framework provides built-in metrics for classification, regression, and ranking tasks. Metrics support both eager and graph execution modes and work identically across all backends.
Unique: Implements metrics as stateful objects in keras/src/metrics/ that accumulate values across batches and compute aggregate statistics. Metrics are compiled into models and automatically computed during training/evaluation, with support for both eager and graph execution modes across all backends.
vs alternatives: Unlike PyTorch (requires manual metric computation) or TensorFlow (metrics are TensorFlow-specific), Keras provides a unified metric system across all backends with built-in metrics for common use cases and automatic computation during training.
Provides optimizer implementations (keras/src/optimizers/) including SGD, Adam, RMSprop, and others that update model weights based on gradients. Optimizers are backend-agnostic and delegate gradient updates to backend-specific implementations. Learning rate scheduling is supported through LearningRateSchedule objects that adjust learning rate during training based on epoch or batch number. Optimizers support momentum, weight decay, gradient clipping, and other advanced features. All optimizers work identically across backends.
Unique: Implements optimizers as backend-agnostic objects in keras/src/optimizers/ that delegate gradient updates to backend-specific implementations. Learning rate scheduling is supported through LearningRateSchedule objects that adjust learning rate during training, with all optimizers working identically across backends.
vs alternatives: Unlike PyTorch (requires manual learning rate scheduling) or TensorFlow (optimizers are TensorFlow-specific), Keras provides a unified optimizer system across all backends with built-in learning rate scheduling and advanced features like gradient clipping and weight decay.
Provides loss functions (keras/src/losses/) for training objectives including classification losses (categorical_crossentropy, sparse_categorical_crossentropy), regression losses (mean_squared_error, mean_absolute_error), and ranking losses. Loss functions are compiled into models via model.compile(loss=...) and automatically computed during training. The framework automatically computes gradients with respect to loss using the active backend's autodiff system (JAX's jax.grad, PyTorch's autograd, TensorFlow's GradientTape). Loss computation and gradient backpropagation are handled transparently without user code.
Unique: Implements loss functions as backend-agnostic objects in keras/src/losses/ with automatic gradient computation through the active backend's autodiff system. Loss computation and backpropagation are handled transparently during training without user code, leveraging JAX's jax.grad, PyTorch's autograd, or TensorFlow's GradientTape.
vs alternatives: Unlike PyTorch (requires manual loss computation and backpropagation) or TensorFlow (loss functions are TensorFlow-specific), Keras provides a unified loss system across all backends with automatic gradient computation and built-in loss functions for common use cases.
Provides APIs for inspecting model structure and accessing weights: model.summary() displays layer structure and parameter counts, model.get_weights() returns all weights as NumPy arrays, model.set_weights() updates weights, model.get_config() returns model configuration as JSON, model.get_layer() retrieves specific layers by name. These APIs work identically across all backends and enable model analysis, weight manipulation, and configuration serialization without backend-specific code.
Unique: Implements model introspection APIs in keras/src/models/model.py that work identically across all backends, providing access to model structure, weights, and configuration without backend-specific code. Weight access converts from backend-native tensors to NumPy arrays, enabling framework-agnostic weight manipulation.
vs alternatives: Unlike PyTorch (requires framework-specific APIs like state_dict()) or TensorFlow (requires TensorFlow-specific APIs), Keras provides unified introspection APIs across all backends with automatic conversion to NumPy for framework-agnostic weight access.
Exposes a NumPy-compatible operation API (keras.ops.numpy.*) that mirrors NumPy's function signatures and behavior while dispatching to backend-specific implementations. Operations include array manipulation (reshape, concatenate, transpose), mathematical functions (sin, exp, matmul), and linear algebra (linalg.solve, linalg.eigh). The dispatch mechanism routes each operation call to the active backend's implementation in keras/src/backend/{backend}/numpy.py, ensuring numerical consistency across backends while leveraging backend-specific optimizations.
Unique: Implements NumPy API compatibility layer that maps NumPy function signatures to backend-specific implementations without requiring users to learn backend APIs. Each operation in keras/ops/numpy/ delegates to backend-specific versions in keras/src/backend/{jax,torch,tensorflow,openvino}/numpy.py, maintaining API consistency while preserving backend optimizations.
vs alternatives: Unlike raw JAX/PyTorch/TensorFlow APIs (which require learning framework-specific syntax), Keras ops.numpy provides familiar NumPy semantics across all backends; unlike NumPy itself, it supports automatic differentiation and GPU acceleration through any backend.
+7 more capabilities
Provides a language-agnostic Kernel abstraction (Microsoft.SemanticKernel.Kernel in .NET, semantic_kernel.Kernel in Python) that orchestrates LLM invocations, plugin registration, and function execution across C#, Python, and Java. The kernel acts as a central coordinator that manages AI service connections, maintains execution context, and routes function calls through a consistent pipeline regardless of underlying language runtime. Implements a decorator-based plugin system where functions are registered as KernelFunction objects with metadata for discovery and invocation.
Unique: Implements a true language-agnostic kernel abstraction with parallel implementations in .NET, Python, and Java that share conceptual models but use language-native patterns (C# decorators, Python decorators, Java annotations). Unlike frameworks that wrap a single language implementation, SK maintains separate codebases with consistent APIs, enabling native performance and idiomatic code in each language while preserving orchestration semantics.
vs alternatives: Offers better multi-language consistency than LangChain (which has divergent Python/JS implementations) and deeper enterprise integration than LlamaIndex through tight Azure/Microsoft 365 coupling, though at the cost of smaller ecosystem compared to LangChain.
Implements a provider-agnostic function calling system that translates semantic kernel function definitions into provider-specific schemas (OpenAI JSON schema, Anthropic tool_use format, etc.) and routes tool calls back through a unified handler. Uses a connector abstraction layer (IChatCompletionService, IEmbeddingGenerationService) that abstracts away provider-specific API differences, allowing seamless switching between OpenAI, Azure OpenAI, Anthropic, Ollama, and other LLM providers. Function metadata is extracted via reflection/introspection and automatically converted to the target provider's tool schema format.
Unique: Uses a reflection-based schema extraction pipeline that automatically converts native function signatures into provider-specific tool schemas at runtime, with a pluggable connector architecture (IChatCompletionService) that allows new providers to be added without modifying core orchestration logic. This differs from LangChain's tool_utils which require manual schema definition, and from Anthropic's SDK which is provider-locked.
Semantic Kernel scores higher at 72/100 vs keras at 27/100. keras leads on ecosystem, while Semantic Kernel is stronger on adoption and quality.
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vs alternatives: Provides tighter provider abstraction than LangChain's BaseLLM + Tool pattern through explicit connector interfaces, and better multi-provider support than single-provider SDKs, though with slightly higher complexity and latency overhead from schema translation.
Provides patterns and utilities for coordinating multiple agents in a single application, enabling agents to communicate with each other and delegate tasks. The framework supports agent composition where one agent can invoke another agent's capabilities, and agent hierarchies where a coordinator agent manages multiple specialist agents. Communication between agents is mediated through the kernel, allowing agents to share context and results. Supports both sequential agent chains (agent A → agent B → agent C) and parallel agent execution with result aggregation. Agents maintain separate conversation histories but can share semantic memory and function registries.
Unique: Supports multi-agent patterns through agent composition and shared kernel resources, enabling agents to communicate and delegate tasks. Unlike AutoGen which has built-in multi-agent orchestration, SK requires explicit coordination code but provides more flexibility for custom agent topologies. Agents can share semantic memory and function registries while maintaining separate conversation histories.
vs alternatives: More flexible than single-agent frameworks, though less mature than AutoGen for complex multi-agent scenarios; requires more custom code but provides better control over agent interactions.
Provides a configuration system for LLM execution settings that abstracts provider-specific parameters (temperature, max_tokens, top_p, etc.) into a unified PromptExecutionSettings object. Developers can configure settings globally on the kernel or per-function invocation, with automatic translation to provider-specific formats (OpenAI compat, Anthropic, etc.). Supports fallback configurations where if a setting is not supported by a provider, a sensible default is used. Settings can be serialized to JSON for persistence and reloaded at runtime. Enables A/B testing of different model configurations without code changes.
Unique: Implements a unified PromptExecutionSettings abstraction that translates to provider-specific parameters at invocation time, enabling configuration portability across OpenAI, Anthropic, Azure OpenAI, and other providers. Unlike LangChain's model-specific parameter classes, SK provides a single configuration object that works across providers.
vs alternatives: More portable than provider-specific configuration classes, and more flexible than hardcoded settings, though with less comprehensive parameter coverage than direct provider APIs.
Implements streaming support for LLM responses, allowing applications to receive and process tokens as they are generated rather than waiting for the complete response. The system provides streaming APIs for both chat completion and semantic functions, returning async iterables or streams of token chunks. Streaming is transparent to the developer; the same function invocation API works for both streaming and non-streaming modes. Supports streaming with function calling, where tool calls are streamed and executed incrementally. Enables real-time UI updates and reduced perceived latency in conversational applications.
Unique: Implements transparent streaming support where the same function invocation API works for both streaming and non-streaming modes, with automatic provider detection and fallback. Supports streaming with function calling, enabling incremental tool execution. Unlike LangChain's separate streaming APIs, SK provides unified interfaces.
vs alternatives: More transparent than LangChain's separate streaming APIs, and better integrated with function calling than basic streaming implementations, though with less mature error handling for mid-stream failures.
Implements a custom prompt template language (documented in PROMPT_TEMPLATE_LANGUAGE.md) that uses {{variable}} syntax for dynamic prompt composition, supporting variable substitution, conditional blocks, and function composition. Semantic functions are defined as YAML or inline C#/Python with embedded prompts that are parsed and compiled into executable functions. The system maintains a PromptTemplateEngine that interpolates variables from kernel arguments at execution time, enabling dynamic prompt construction without string concatenation. Supports both simple variable replacement and complex prompt engineering patterns like few-shot examples and chain-of-thought templates.
Unique: Implements a declarative prompt template system with YAML-based semantic function definitions that separates prompt logic from orchestration code, using a custom PromptTemplateEngine for variable interpolation. Unlike LangChain's PromptTemplate which is primarily Python-based, SK provides language-agnostic template definitions that compile to native functions in .NET, Python, or Java, enabling true prompt portability across language runtimes.
vs alternatives: Offers better prompt-code separation than inline prompt strings in LangChain, and more flexible templating than Anthropic's prompt caching (which is provider-specific), though with less ecosystem tooling for prompt management compared to specialized platforms like Prompt Flow.
Provides a memory abstraction layer (ISemanticTextMemory, TextMemoryPlugin) that decouples embedding generation from vector storage, allowing developers to use any embedding model (OpenAI, Azure OpenAI, Hugging Face) with any vector database (Chroma, Weaviate, Pinecone, in-memory). The system implements a two-stage pipeline: (1) text is converted to embeddings via an IEmbeddingGenerationService, and (2) embeddings are stored/retrieved via an IMemoryStore implementation. Supports semantic search by converting queries to embeddings and performing similarity matching, enabling RAG patterns where retrieved context is injected into prompts. Memory operations are exposed as kernel plugins (TextMemoryPlugin) for seamless integration with function calling.
Unique: Implements a two-tier abstraction (IEmbeddingGenerationService + IMemoryStore) that fully decouples embedding generation from vector storage, allowing independent provider selection. This is more modular than LangChain's VectorStore pattern which couples embedding and storage, and provides better multi-backend support than LlamaIndex's single-backend approach. Exposes memory operations as kernel plugins (TextMemoryPlugin) for native integration with function calling.
vs alternatives: More flexible than LangChain's tightly-coupled embedding+storage pattern, and better integrated with function calling than LlamaIndex, though with less mature vector store support compared to LangChain's ecosystem of 20+ integrations.
Provides a planning framework (documented in PLANNERS.md) that decomposes complex user goals into executable steps using LLM-based reasoning. The system includes multiple planner implementations: SequentialPlanner (breaks tasks into ordered steps), HandlebarsPlanner (uses Handlebars templates for step generation), and FunctionCallingPlanner (leverages native function calling for step execution). Planners generate a Plan object containing a sequence of steps, each mapping to a kernel function. The Kernel then executes steps sequentially, passing outputs from one step as inputs to the next, enabling multi-step agent workflows. Supports dynamic replanning if steps fail or return unexpected results.
Unique: Implements multiple planner strategies (Sequential, Handlebars, FunctionCalling) with pluggable plan execution, allowing developers to choose planning approach based on reliability/cost tradeoffs. The FunctionCallingPlanner uses native tool calling for step execution, which is more reliable than prompt-based planning. Unlike LangChain's ReAct pattern which is primarily prompt-based, SK provides structured Plan objects that are inspectable and modifiable before execution.
vs alternatives: Offers more planning flexibility than LangChain's single ReAct implementation, and better structured plans than LlamaIndex's query engines, though with higher latency due to multiple LLM calls and less mature multi-agent support compared to specialized frameworks like AutoGen.
+5 more capabilities