| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 13 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Wraps LLM application methods using the @instrument decorator to automatically generate structured OpenTelemetry spans (RECORD_ROOT, GENERATION, RETRIEVAL, EVAL) without modifying core application logic. The decorator integrates with a TracerProvider that captures execution context, method inputs/outputs, and timing metadata, then exports spans to configured backends (SQLite, PostgreSQL, Snowflake). This enables zero-friction observability for framework-agnostic applications.
Unique: Uses a decorator-based instrumentation model that generates structured OTEL spans with semantic span kinds (GENERATION, RETRIEVAL, EVAL) specific to LLM workflows, rather than generic HTTP/RPC spans. Integrates directly with TruSession for unified span collection and evaluation lifecycle management.
vs alternatives: Simpler than manual OTEL instrumentation and more LLM-aware than generic APM tools; requires less boilerplate than Langsmith's tracing while maintaining OTEL standard compliance.
Computes evaluation metrics (groundedness, relevance, coherence, custom metrics) by executing feedback functions that call LLM APIs with structured prompts. The Feedback class defines metric logic; LLMProvider interface abstracts over OpenAI, Bedrock, Cortex, HuggingFace, and LiteLLM endpoints. Evaluation runs asynchronously via a background Evaluator thread, storing results linked to application spans. Supports both synchronous (blocking) and deferred (async) evaluation modes.
Unique: Abstracts LLM provider selection behind LLMProvider interface, enabling same feedback function to run against OpenAI, Bedrock, Cortex, or local models without code changes. Integrates evaluation lifecycle with span collection via RunManager, enabling automatic metric computation on application traces.
vs alternatives: More flexible than Langsmith's built-in metrics (supports custom LLM providers and deferred evaluation); more integrated than standalone evaluation frameworks (metrics tied directly to application spans and session lifecycle).
Exports OTEL spans directly to Snowflake event tables for server-side querying and analysis. SnowflakeEventTableDB connector implements DBConnector interface, batching span exports asynchronously. Enables server-side evaluation pipeline where feedback functions execute in Snowflake Cortex (LLM provider) rather than client-side, reducing data transfer and enabling SQL-based metric computation. Integrates with Snowflake's native OTEL support.
Unique: Exports OTEL spans directly to Snowflake event tables and enables server-side evaluation in Snowflake Cortex, avoiding data export and enabling native SQL querying. Tighter integration than generic OTEL exporters.
vs alternatives: More efficient than client-side evaluation for large-scale deployments; enables SQL-based analytics on trace data within data warehouse.
RunManager class orchestrates application runs, tracking run metadata (ID, timestamp, app name, version), linking spans and metrics to runs, and managing run lifecycle. Supports external agent integration for distributed evaluation — agents can retrieve pending runs, execute feedback functions, and report results back to central database. Enables horizontal scaling of evaluation workload across multiple workers.
Unique: Provides RunManager for tracking run lifecycle and metadata, with support for external agents to execute distributed evaluation. Enables horizontal scaling of evaluation workload.
vs alternatives: More integrated than generic job queues; provides run-level abstraction specific to LLM evaluation workflows.
This package (trulens-eval) provides backwards-compatible API for applications built against trulens_eval<1.0.0, mapping old API calls to new trulens-core>=1.0.0 implementations. Enables existing applications to upgrade without code changes. Acts as compatibility shim during migration period, allowing gradual adoption of new API.
Unique: Provides compatibility shim mapping trulens_eval<1.0.0 API to trulens-core>=1.0.0 implementations, enabling zero-change upgrades for existing applications.
vs alternatives: Enables gradual migration path vs requiring immediate rewrite; reduces upgrade friction for existing users.
TruSession class provides centralized orchestration for database connections, OTEL setup, evaluation scheduling, and run lifecycle. Manages DBConnector abstraction (SQLAlchemy, Snowflake event tables) for span/metric persistence, coordinates Evaluator thread for async feedback execution, and maintains context across application invocations. Session acts as entry point for developers: initialize once, wrap application, retrieve results.
Unique: Centralizes database, OTEL, and evaluation orchestration in single TruSession object that manages DBConnector abstraction, Evaluator thread lifecycle, and run context. Enables context manager pattern (with statement) for automatic resource cleanup.
vs alternatives: Simpler than manual OTEL setup and database connection management; more integrated than standalone database libraries because it couples persistence with evaluation scheduling and span collection.
DBConnector interface abstracts storage backend selection (SQLAlchemy for SQLite/PostgreSQL/MySQL, SnowflakeEventTableDB for Snowflake). Stores spans, feedback metrics, and run metadata in normalized schema. SQLAlchemy backend uses ORM models for relational storage; Snowflake backend exports OTEL spans directly to event tables for server-side querying. Enables schema migrations and versioning for database evolution.
Unique: Provides DBConnector abstraction that supports both relational (SQLAlchemy) and cloud-native (Snowflake event tables) backends with unified API. Snowflake backend exports OTEL spans directly to event tables, enabling server-side querying without ETL.
vs alternatives: More flexible than single-backend solutions; Snowflake integration is deeper than generic OTEL exporters because it uses event table schema optimized for trace data.
Provides framework-specific wrapper classes (TruChain for LangChain, TruGraph for LangGraph, TruLlama for LlamaIndex, TruBasicApp/TruCustomApp for custom apps) that intercept application execution and generate semantically-typed spans (GENERATION for LLM calls, RETRIEVAL for vector search, EVAL for feedback). Wrappers preserve original framework APIs while injecting instrumentation transparently.
Unique: Provides framework-specific wrappers that generate semantically-typed spans (GENERATION, RETRIEVAL, EVAL) tailored to LLM workflows, rather than generic function call spans. Wrappers intercept framework-level operations (LLM calls, vector search) to assign correct span kinds automatically.
vs alternatives: More semantic than generic OTEL instrumentation; more framework-aware than manual span creation; preserves original framework APIs unlike some observability solutions that require code rewriting.
+5 more capabilities
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.
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.
Supermaven scores higher at 71/100 vs trulens-eval at 24/100. trulens-eval leads on ecosystem, while Supermaven is stronger on adoption and quality.
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vs alternatives: Supports multiple LLM backends (GPT-4o, Claude 3.5 Sonnet) in one interface with included credits, whereas GitHub Copilot Chat is single-model per tier and requires separate ChatGPT subscription for model switching; trade-off is credit limits and unknown overage pricing.
Supermaven Chat can automatically upload compiler diagnostic messages (errors, warnings) alongside code context to provide error-aware suggestions and fixes. The mechanism is described as 'automatically uploading your code together with compiler diagnostic messages,' but specific language/compiler support and the upload trigger mechanism are undisclosed. This feature is Chat-only and not available in inline completion.
Unique: Automatic compiler diagnostic upload in Chat for error-aware suggestions, versus competitors (Copilot, Tabnine) that require manual error context or have limited diagnostic integration. Supermaven's approach reduces friction but with undisclosed language/compiler support.
vs alternatives: Automatic diagnostic upload reduces manual context-gathering compared to manual copy-paste; trade-off is undisclosed language support and unclear upload trigger mechanism.
Supermaven offers a 30-day free trial of the Pro tier ($10/month), providing full access to 1M token context window, largest model, style adaptation, and $5/month chat credits. No credit card is required to start the trial (implied), and trial conversion to paid is automatic after 30 days unless cancelled. Trial terms and auto-renewal policy are not explicitly detailed.
Unique: 30-day free trial of Pro tier with full feature access (1M context, largest model, chat credits), versus competitors (Copilot 2-month free trial, Tabnine free tier only) with different trial lengths and feature access. Supermaven's approach is generous but with undisclosed auto-renewal terms.
vs alternatives: Full Pro feature access during trial compared to limited free tier; trade-off is undisclosed auto-renewal policy and potential unexpected charges if not cancelled.
Supermaven requires internet connectivity and server-side inference; no offline mode or local inference capability is mentioned or available. All code completion requests are sent to Supermaven's backend servers for processing, and responses are returned over the network. This creates a hard dependency on network connectivity and Supermaven's service availability; if the service is down or network is unavailable, code completion is not available.
Unique: Supermaven has no offline mode or local inference capability; all processing is server-side. GitHub Copilot also requires server-side inference, but Tabnine offers local inference options for some use cases. Supermaven's lack of offline capability is a significant limitation for developers with connectivity constraints.
vs alternatives: Supermaven's server-side-only approach is comparable to GitHub Copilot; Tabnine offers local inference options, making Tabnine more suitable for offline work. Supermaven's lack of offline capability is a weakness vs. Tabnine.
Analyzes recent code edits and inferred coding patterns to adapt inline suggestions to match team conventions, naming patterns, and structural preferences. The mechanism is implicit (not explicit fine-tuning) and operates only on Pro/Team tiers, suggesting pattern learning from editor activity rather than explicit configuration. Free tier uses a single base model without personalization.
Unique: Implicit style adaptation via editor activity analysis without explicit configuration, versus competitors (Copilot, Tabnine) that require manual style guides or explicit fine-tuning. Supermaven's approach is transparent to the user but also non-configurable and undisclosed in mechanism.
vs alternatives: Requires no manual style configuration compared to tools requiring explicit style guides; trade-off is lack of transparency and inability to control or export learned styles.
Delivers code suggestions to the editor inline as the developer types, with a claimed baseline latency of 250ms from keystroke to suggestion display. The system uses a cloud inference backend and local editor plugin to minimize round-trip time. Latency claim is positioned against an unnamed competitor (783ms), but methodology is undisclosed and no independent verification is provided.
Unique: Claimed 250ms latency via optimized cloud inference pipeline and editor plugin architecture, versus competitors with higher latency (783ms unnamed baseline). Actual differentiation is undisclosed; mechanism may involve request batching, model quantization, or edge caching, but specifics are not public.
vs alternatives: Faster than unnamed competitor (250ms vs 783ms claimed); trade-off is cloud dependency and unverified latency claim with no SLA or performance guarantee.
Provides native editor extensions for VS Code, JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.), and Neovim, enabling inline suggestion rendering, hotkey-driven chat access, and compiler diagnostic integration directly within the editor. Each plugin variant is maintained separately and integrates with the editor's native autocomplete UI, keybinding system, and file context APIs.
Unique: Native plugins for three major editor ecosystems (VS Code, JetBrains, Neovim) with integrated chat and diff application, versus competitors (Copilot, Tabnine) that support broader editor ecosystems but with less deep integration in some cases. Supermaven's approach prioritizes depth over breadth.
vs alternatives: Deep integration with VS Code and JetBrains (native autocomplete UI, hotkey system) compared to web-based tools or lighter integrations; trade-off is limited editor support (no Sublime, Vim, Emacs) and undisclosed Neovim support details.
+5 more capabilities