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| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 13 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Captures execution traces across LLM applications using language-specific SDKs (Python, TypeScript) that instrument framework-native hooks for LangChain, LlamaIndex, Claude SDK, Pydantic AI, and others. The SDK batches trace events and sends them asynchronously via HTTP to the backend, which persists them in a relational database with Redis Streams for async processing, enabling full visibility into multi-step agent and RAG workflows without code modification.
Unique: Uses framework-native hook integration (e.g., LangChain callbacks, LlamaIndex instrumentation) combined with SDK-level batching and Redis Streams async processing, avoiding the need for OpenTelemetry overhead while maintaining framework compatibility across 10+ LLM frameworks
vs alternatives: Faster and simpler than OpenTelemetry-based solutions for LLM-specific use cases because it leverages framework-native APIs and batches traces at the SDK level rather than requiring separate collector infrastructure
Executes evaluation metrics against trace data using a pluggable evaluation framework that supports LiteLLM for multi-provider LLM access (OpenAI, Anthropic, Ollama, etc.) and custom Python evaluators. The system runs evaluations asynchronously via a Python backend service, storing results as feedback scores linked to traces, enabling comparison of model outputs against ground truth or custom criteria without manual annotation.
Unique: Integrates LiteLLM for provider-agnostic LLM evaluation combined with a pluggable Python evaluator framework, allowing users to mix LLM-based judges (GPT-4, Claude, etc.) with custom Python logic in a single evaluation pipeline without provider lock-in
vs alternatives: More flexible than closed-source evaluation platforms because it supports any LLM provider via LiteLLM and allows custom Python evaluators, while being simpler than building evaluation infrastructure from scratch
Provides a web-based playground in the frontend that allows users to test prompts and model configurations against LLM providers (OpenAI, Anthropic, Ollama, etc.) in real-time. The playground supports variable substitution, message history, and cost estimation, with results automatically captured as traces for later analysis. Users can iterate on prompts without leaving the browser and save successful configurations as reusable prompts.
Unique: Integrates a multi-provider LLM playground directly into the Opik UI with automatic trace capture and cost estimation, avoiding the need for external playground tools or manual result tracking
vs alternatives: More integrated than standalone playgrounds because results are automatically captured as traces and linked to prompt versions, enabling seamless iteration from playground to production
Provides a separate Python backend service that runs safety and content filtering checks on LLM inputs and outputs using configurable rules and external safety APIs. Guardrails can be applied at trace collection time or as a post-processing step, with results stored as feedback scores. The system supports custom guardrail definitions and integrates with popular safety frameworks.
Unique: Provides a dedicated guardrails backend service that runs safety checks asynchronously on traces, with results stored as feedback scores, enabling safety monitoring without modifying application code
vs alternatives: More integrated than external safety services because guardrail results are stored alongside trace data, enabling correlation between safety violations and application behavior
Uses Redis Streams as a message queue for asynchronous processing of trace events, enabling decoupling of trace collection from persistence and evaluation. Trace events are published to Redis Streams, consumed by background workers, and processed (persisted, evaluated, guardrails checked) without blocking the SDK. This architecture supports high-throughput trace collection and enables scaling of evaluation and guardrails processing independently.
Unique: Uses Redis Streams for asynchronous trace processing with decoupled workers for persistence, evaluation, and guardrails, enabling independent scaling of different processing stages
vs alternatives: More scalable than synchronous trace processing because it decouples collection from processing, while being simpler than Kafka-based architectures for LLM-specific use cases
Manages datasets (collections of input-output pairs) and experiments (runs of an application against a dataset) with automatic comparison of results across runs. The system stores datasets in the relational database, executes applications against them, and computes aggregate metrics (accuracy, latency, cost) across experiment runs, enabling side-by-side comparison of different prompts, models, or configurations without manual result aggregation.
Unique: Combines dataset management with automatic experiment execution and metric aggregation in a single system, using the trace data collected during execution to compute metrics without requiring separate result collection or post-processing
vs alternatives: Tighter integration than external experiment tracking tools because datasets and experiments are native concepts in Opik, enabling automatic metric computation from trace data without manual result parsing
Provides a web-based frontend (React/TypeScript) that renders traces as interactive trees showing span relationships, inputs, outputs, and metadata. The frontend queries the REST API to fetch trace data, renders message content with syntax highlighting for code and JSON, and allows filtering/searching traces by project, tags, and metadata. Users can drill down into individual spans to inspect LLM calls, tool invocations, and intermediate results without leaving the browser.
Unique: Renders traces as interactive trees with syntax-aware message rendering (code highlighting, JSON formatting) and integrated filtering, avoiding the need for external trace viewers or log aggregation tools
vs alternatives: More intuitive than CLI-based trace inspection because it visualizes span relationships as trees and provides interactive filtering, while being more specialized than generic log viewers for LLM-specific trace structures
Automatically extracts token counts from LLM provider responses (OpenAI, Anthropic, etc.) and computes costs using a pricing database that syncs daily with provider pricing data. The system aggregates costs at multiple levels (per trace, per project, per experiment) and stores them alongside trace data, enabling cost analysis without requiring manual token counting or external billing APIs.
Unique: Automatically extracts token counts from LLM responses and syncs pricing data daily from providers, computing costs without requiring manual configuration or external billing integrations
vs alternatives: More accurate than manual cost tracking because it captures actual token counts from provider responses, and more current than static pricing tables because it syncs daily with provider pricing
+5 more capabilities
Automatically loads and parses documents from diverse sources (PDFs, Word docs, HTML, Markdown, code files, databases) into a unified in-memory representation using format-specific loaders and node-based document abstractions. Each document is decomposed into Document objects containing metadata, content, and relationships, enabling downstream processing without format-specific handling in application code.
Unique: Provides a unified loader abstraction (BaseReader interface) that normalizes 100+ data source connectors into a single Document/Node API, eliminating format-specific branching logic in application code. Loaders are composable and chainable, allowing sequential transformations (e.g., load → split → extract metadata → embed).
vs alternatives: Broader out-of-the-box loader coverage than LangChain's document loaders and more structured node-based decomposition than raw text splitting, reducing boilerplate for multi-source RAG pipelines.
Splits documents into semantically coherent chunks using multiple strategies (character-based, token-aware, recursive, semantic) with configurable overlap and chunk size. Preserves document hierarchy and metadata through a node tree structure, enabling retrieval systems to maintain context relationships and enable hierarchical re-ranking or parent-document retrieval patterns.
Unique: Implements a node-tree abstraction that preserves document hierarchy and enables parent-document retrieval patterns. Supports multiple splitting strategies (recursive, semantic, code-aware) with pluggable custom splitters, and automatically propagates metadata through the node tree.
vs alternatives: More sophisticated than LangChain's text splitters because it preserves hierarchical relationships and supports semantic splitting; better for complex document structures than simple character-based splitting.
opik scores higher at 40/100 vs LlamaIndex at 40/100. opik also has a free tier, making it more accessible.
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Processes documents containing mixed content (text, images, tables, code) by extracting and understanding each modality separately, then synthesizing information across modalities. Uses vision models for image understanding, specialized parsers for tables and code, and integrates results into a unified document representation for retrieval and generation.
Unique: Integrates vision models, table parsers, and code extractors into a unified multi-modal document processing pipeline that synthesizes information across modalities. Preserves modality-specific structure (table schemas, code formatting) while enabling cross-modal retrieval and generation.
vs alternatives: More comprehensive multi-modal support than text-only RAG; built-in vision integration reduces boilerplate for document understanding compared to manual vision API calls.
Enables streaming of LLM responses token-by-token and real-time retrieval updates, allowing applications to display partial results as they become available. Supports streaming from retrieval (progressive document discovery) and generation (token-by-token output) with backpressure handling and cancellation support for responsive user experiences.
Unique: Provides first-class streaming support for both retrieval and generation with automatic backpressure handling and cancellation. Enables progressive result display without custom async/streaming code in application layer.
vs alternatives: More integrated streaming support than manual LLM API streaming; built-in retrieval streaming and backpressure handling reduce complexity compared to custom streaming implementations.
Tracks API costs for LLM calls, embeddings, and other operations with per-query and per-session cost attribution. Provides cost optimization recommendations (e.g., batch processing, model selection, caching) and enables cost-aware query planning to balance quality and expense. Integrates with multiple LLM providers to normalize cost tracking across models.
Unique: Provides automatic cost tracking across multiple LLM providers with per-query attribution and cost optimization recommendations. Integrates with query execution to enable cost-aware planning without manual cost calculation.
vs alternatives: More integrated cost tracking than manual API billing review; built-in optimization recommendations reduce guesswork for cost reduction.
Enables building custom RAG pipelines by composing modular components (retrievers, synthesizers, agents, tools) through a declarative or programmatic API. Supports complex workflows with branching, loops, and conditional logic, with automatic dependency resolution and execution optimization. Pipelines are reusable, testable, and can be deployed as APIs or batch jobs.
Unique: Provides a flexible pipeline composition API supporting both declarative and programmatic definitions, with automatic dependency resolution and execution optimization. Enables complex workflows with branching and conditional logic without custom orchestration code.
vs alternatives: More flexible pipeline composition than fixed RAG architectures; better workflow support than manual component chaining.
Generates embeddings for documents/nodes using pluggable embedding providers (OpenAI, Hugging Face, local models) and stores them in a unified vector store interface that abstracts over multiple backends (Pinecone, Weaviate, Milvus, FAISS, Chroma, etc.). The abstraction layer enables switching vector stores without changing application code, and handles batching, retry logic, and metadata indexing.
Unique: Provides a unified VectorStore interface that abstracts 10+ vector database backends, enabling zero-code switching between providers. Handles embedding batching, retry logic, and metadata propagation automatically. Supports both cloud and local embedding models through a pluggable EmbedModel interface.
vs alternatives: Broader vector store coverage and more seamless provider switching than LangChain's vectorstore integrations; better abstraction consistency across backends than using raw vector store SDKs directly.
Retrieves semantically similar documents from vector stores using embedding-based similarity search, with optional re-ranking, filtering, and fusion strategies (hybrid search combining dense and sparse retrieval). Supports multiple retrieval modes (similarity, MMR, fusion) and enables custom retrieval logic through a pluggable Retriever interface that can combine multiple strategies.
Unique: Implements a pluggable Retriever abstraction supporting multiple retrieval strategies (similarity, MMR, fusion, custom) that can be composed and chained. Built-in support for re-ranking via LLM or cross-encoder, and hybrid search combining dense and sparse retrieval without custom integration code.
vs alternatives: More flexible retrieval composition than LangChain's retrievers; built-in re-ranking and fusion strategies reduce boilerplate for advanced retrieval pipelines.
+6 more capabilities