guidance vs Supermaven — Comparison | Unfragile

guidance vs Supermaven

Supermaven ranks higher at 71/100 vs guidance at 22/100. Capability-level comparison backed by match graph evidence from real search data.

guidance

Framework

/ 100

Free

Supermaven

Extension

/ 100

Free

From $10/mo

Feature	guidance	Supermaven
Type	Framework	Extension
UnfragileRank	22/100	71/100
Adoption	0	1
Quality	0

guidance Capabilities

grammar-constrained text generation with token-aware parsing

Generates text from language models while enforcing constraints defined as an Abstract Syntax Tree (AST) of GrammarNode subclasses (LiteralNode, RegexNode, SelectNode, JsonNode). Uses TokenParser and ByteParser engines that work at the text level rather than token level, implementing token healing to correctly process text boundaries. The execution engine accumulates generated text into stateful lm objects that maintain both output and captured variables across generation steps.

Unique: Implements token healing at the text level rather than token level, allowing precise constraint enforcement across token boundaries without requiring model retraining. Uses immutable GrammarNode AST with TokenParser/ByteParser engines that integrate directly with model tokenizers via llguidance, enabling sub-token-level constraint enforcement.

vs alternatives: Faster and more reliable than post-processing validation because constraints are enforced during generation rather than after, and more flexible than LORA-based approaches because it works with any model backend without fine-tuning.

multi-backend model abstraction with unified api

Provides a unified interface for executing guidance programs across heterogeneous language model backends including local models (llama-cpp, Hugging Face Transformers) and remote APIs (OpenAI, Anthropic, Azure OpenAI, Google VertexAI). Each backend implements a common model interface that handles tokenization, state management, and generation, allowing the same guidance program to run on different models without code changes. The abstraction layer handles backend-specific details like API authentication, context window management, and token counting.

Unique: Implements a unified model interface that abstracts both local and remote backends, with token healing applied consistently across all backends through the llguidance tokenization layer. Unlike prompt-based abstractions, this works at the generation engine level, allowing grammar constraints to be enforced uniformly regardless of backend.

vs alternatives: More flexible than LangChain's model abstraction because it preserves grammar constraints across backends, and more performant than wrapper-based approaches because it integrates directly with model tokenizers rather than post-processing outputs.

caching and stateless execution modes for performance optimization

Supports both stateful and stateless execution modes, with optional caching of generation results. Stateless mode allows guidance programs to be executed without maintaining state between calls, reducing memory overhead. Caching can be enabled to store results of expensive generations (e.g., long prompts with complex constraints) and reuse them for identical inputs. The caching layer integrates with the model backend to avoid redundant API calls or model inference.

Unique: Integrates caching at the guidance framework level, allowing entire constrained generation results to be cached rather than just model outputs. Supports both stateful and stateless modes, enabling flexible tradeoffs between memory usage and state management.

vs alternatives: More efficient than application-level caching because it caches at the generation level, and more flexible than model-level caching because it can cache entire constrained generation pipelines including variable captures.

programmatic control flow with python integration

Allows guidance programs to interleave Python control flow (if/else, for loops, function calls) with constrained text generation using the @guidance decorator. The decorator transforms Python functions into guidance programs that can mix imperative logic with declarative grammar constraints. This enables complex workflows where generation decisions depend on previous outputs, external data, or application logic.

Unique: Uses the @guidance decorator to transform Python functions into guidance programs, enabling seamless interleaving of imperative control flow with declarative grammar constraints. Unlike prompt-based approaches, this allows full Python expressiveness within generation workflows.

vs alternatives: More flexible than pure prompt-based workflows because it allows arbitrary Python logic, and more readable than string-based prompt templates because it uses native Python syntax for control flow.

token-level constraint enforcement with llguidance integration

Integrates with the llguidance library to enforce grammar constraints at the token level during model inference. The grammar AST is compiled into a state machine that tracks which tokens are valid at each generation step, preventing the model from generating invalid tokens. This is implemented through a custom sampling function that filters the model's token logits based on the current grammar state, ensuring only valid tokens are sampled.

Unique: Compiles grammar constraints into a state machine that filters token logits during inference, implemented through llguidance C++ extension for performance. This is the core mechanism that enables reliable constraint enforcement without post-processing.

vs alternatives: More reliable than post-processing validation because constraints are enforced during generation, and more efficient than rejection sampling because invalid tokens are filtered rather than sampled and discarded.

recursive grammar rules and reusable constraint patterns

Supports RuleNode grammar constraints that define reusable patterns and recursive grammar rules. Rules can be defined once and referenced multiple times, reducing grammar duplication and improving maintainability. Recursive rules enable generation of nested structures (e.g., nested JSON, nested lists) without explicitly defining the nesting depth. Rules are compiled into the grammar AST and can be parameterized with arguments.

Unique: Implements RuleNode grammar constraints that support recursion and parameterization, enabling complex nested structures to be defined concisely. Rules are compiled into the grammar AST and can be referenced multiple times without duplication.

vs alternatives: More maintainable than inline grammar definitions because rules can be reused, and more flexible than hardcoded patterns because rules can be parameterized with arguments.

stateful execution with variable capture and context accumulation

Maintains execution state through immutable lm objects that accumulate generated text, captured variables, and model state across multiple generation steps. Variables are captured using named capture groups in regex patterns or JSON schema fields, and can be referenced in subsequent generation steps. The stateful model object preserves the full generation history, enabling introspection, debugging, and chaining of multiple constrained generations in sequence.

Unique: Uses immutable lm objects that preserve full generation history and captured variables, enabling transparent debugging and chaining. Unlike stateless prompt-response patterns, this allows variables to be extracted mid-generation and used in subsequent steps without re-prompting.

vs alternatives: More transparent than LangChain's memory abstractions because the full state is accessible and immutable, reducing bugs from hidden state mutations. More efficient than re-prompting with full history because only captured variables need to be passed forward.

json schema-based structured output generation

Generates valid JSON output that conforms to a provided JSON schema by using JsonNode grammar constraints. The schema is converted into a grammar that enforces field types, required fields, nested objects, and arrays at generation time. The generated JSON is automatically parsed and made available as Python objects in the captured variables, eliminating the need for post-generation validation or repair.

Unique: Converts JSON schemas into grammar constraints that are enforced during token generation, not after. This prevents invalid JSON from being generated in the first place, unlike post-processing approaches that must repair or reject malformed output.

vs alternatives: More reliable than JSON repair libraries (like json-repair) because it prevents invalid JSON generation, and faster than validation-retry loops because it guarantees correctness on the first pass.

+6 more capabilities

Supermaven Capabilities

codebase-aware inline code completion with 1m token context window

Generates single-line and multi-line code suggestions in real-time as developers type, using semantic indexing of the entire codebase to retrieve relevant type definitions, function signatures, and contextual patterns. The system maintains a 1M token context window (Pro/Team tiers) that enables suggestions informed by distant code definitions and cross-file dependencies, constructed via local codebase semantic search rather than simple token-based recency. Suggestions adapt to detected coding style on Pro/Team tiers through implicit pattern learning from recent edits.

Unique: 1M token context window with codebase-wide semantic indexing enables suggestions informed by distant code definitions and cross-file patterns, versus competitors (Copilot, Tabnine) that typically use fixed context windows (4K-32K tokens) or file-local context. Claimed 250ms latency suggests optimized retrieval pipeline, though indexing mechanism and performance at scale remain undisclosed.

vs alternatives: Larger context window than GitHub Copilot (8K-32K tokens) and faster latency than unnamed competitors (250ms vs 783ms claimed), enabling suggestions on large codebases with minimal typing delay; trade-off is cloud dependency and undisclosed free tier limitations.

multi-model conversational code chat with diff generation and application

Provides a separate chat interface supporting multiple LLM backends (GPT-4o, Claude 3.5 Sonnet, GPT-4, others) for conversational code assistance. Users attach files, reference recent edits, and trigger compiler diagnostic uploads; the system generates diffs and applies code changes directly to the editor. Model selection is per-conversation, and $5/month in credits (included in Pro/Team) covers external model API costs; overage pricing is undisclosed. Hotkey-driven workflow enables rapid context switching between inline completion and chat.

Unique: Multi-model chat interface with per-conversation model selection and integrated diff application, combined with compiler diagnostic auto-upload. Unlike Copilot Chat (single model per tier) or standalone ChatGPT, Supermaven Chat unifies multiple LLM backends in a single hotkey-driven workflow with direct editor integration for change application.

guidance vs Supermaven

guidance Capabilities

Supermaven Capabilities

Verdict

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