| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 13 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Captures LLM interaction traces across heterogeneous SDKs (Langchain, LiteLLM, OpenAI SDK, LlamaIndex) via unified ingestion API endpoints that normalize events into a PostgreSQL-backed trace graph. Uses event enrichment and masking pipelines to standardize observations (LLM calls, retrievals, tool executions) into parent-child relationships, enabling full execution path reconstruction without modifying user application code.
Unique: Unified ingestion API with automatic event enrichment and masking pipelines that normalize traces from 5+ SDK types into a single PostgreSQL schema, avoiding vendor lock-in and supporting self-hosted deployments with full data control
vs alternatives: Supports more SDK integrations (Langchain, LiteLLM, OpenAI, LlamaIndex, Anthropic) than Datadog APM or New Relic, with open-source self-hosting vs cloud-only competitors
Accepts OpenTelemetry Protocol (OTLP) traces via gRPC/HTTP endpoints and maps OTel semantic conventions (span attributes, events, status codes) to Langfuse trace domain model (observations, scores, metadata). Implements dual-write architecture to PostgreSQL and ClickHouse for real-time querying and historical analytics, with automatic schema validation and attribute masking for PII.
Unique: Native OTLP ingestion with automatic semantic convention mapping and dual-write to PostgreSQL + ClickHouse, enabling both transactional trace queries and analytical aggregations without ETL overhead
vs alternatives: Supports OpenTelemetry natively (vs Datadog requiring custom exporters), with self-hosted ClickHouse for cost-effective analytics vs cloud-only competitors charging per-span ingestion
Supports batch operations on multiple traces (export, delete, tag, score, assign to dataset) via async job queue with progress tracking and error recovery. Uses Redis-backed job queue for reliable processing with automatic retry logic and dead-letter queue for failed jobs. Implements batch selection UI with checkbox filtering and action confirmation, supporting 1k+ trace selections without UI blocking.
Unique: Redis-backed async batch processing with automatic retry logic and dead-letter queue, enabling 1k+ trace operations without UI blocking or manual job management
vs alternatives: Supports async batch operations (vs synchronous operations in competitors), with automatic retry and error recovery avoiding manual job resubmission
Implements configurable data retention policies at project level, automatically archiving or deleting traces based on age, cost, or custom criteria. Uses background scheduled jobs to enforce retention policies without manual intervention. Supports tiered storage (hot PostgreSQL, cold ClickHouse, archive S3) with automatic data migration based on retention tier. Provides audit trail of deleted traces for compliance.
Unique: Configurable retention policies with tiered storage and automatic archival, enabling cost-effective trace management without manual intervention or external archival tools
vs alternatives: Supports tiered storage with automatic migration (vs single-tier storage in competitors), with compliance audit trail for deleted data vs competitors lacking deletion audit
Streams new traces to connected clients via WebSocket or Server-Sent Events (SSE), enabling live dashboard updates without polling. Implements efficient delta updates (only changed fields) to minimize bandwidth. Uses tRPC subscriptions for real-time updates with automatic reconnection and backpressure handling. Supports filtering live streams by project, trace status, or custom criteria.
Unique: WebSocket-based real-time trace streaming with delta updates and automatic reconnection, enabling live dashboard updates without polling or external streaming infrastructure
vs alternatives: Supports real-time streaming (vs polling-based competitors), with delta updates reducing bandwidth vs full object updates
Executes automated evaluations on captured traces using LLM-as-Judge pattern via Redis-backed job queue (evalExecutionQueue, llmAsJudgeExecutionQueue). Supports configurable scoring rubrics with multi-step evaluation logic, integrates with OpenAI/Anthropic/custom LLM providers for judgment, and stores scores as observations linked to traces. Uses background worker processes to parallelize evaluation across multiple traces with configurable retry logic and error handling.
Unique: Redis-backed distributed evaluation queue with configurable LLM-as-Judge rubrics, parallel execution across worker processes, and automatic score linking to trace observations without requiring manual annotation
vs alternatives: Supports custom rubrics and multi-step evaluation logic (vs fixed evaluation templates in competitors), with self-hosted worker execution avoiding vendor lock-in and enabling cost control via local LLM providers
Implements multi-tenant isolation via project-scoped API keys and role-based access control (RBAC) with configurable permissions per user role. Supports SSO integration (OIDC, SAML) for enterprise deployments and API key management with automatic rotation and scoping. Uses tRPC internal API with authentication middleware and PostgreSQL-backed permission checks to enforce access control across all endpoints.
Unique: Project-scoped RBAC with SSO support and automatic API key management, using tRPC middleware for permission enforcement across all endpoints without requiring custom authorization code per route
vs alternatives: Supports both API key and SSO authentication (vs single-method competitors), with self-hosted RBAC avoiding third-party identity provider dependency and enabling offline operation
Stores prompt templates with version control, enabling side-by-side comparison of prompt variants via experiment framework. Integrates with trace capture to automatically tag observations with prompt version and experiment ID, enabling statistical analysis of prompt performance. Uses PostgreSQL for prompt storage and ClickHouse for aggregated experiment metrics (success rate, latency, cost per variant).
Unique: Integrated prompt versioning with automatic experiment tagging via trace observations, enabling statistical analysis of prompt performance without manual data correlation or external experiment tracking tools
vs alternatives: Combines prompt management and experiment tracking in single platform (vs separate tools like Weights & Biases or Evidently), with automatic trace-to-experiment linking avoiding manual data alignment
+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.
langfuse scores higher at 40/100 vs LlamaIndex at 40/100. langfuse 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