Trace And Span Data Retrieval With Filtering

via “span-level trace querying and filtering via graphql”

Open-source LLM observability — tracing, evaluation, OpenTelemetry, span analysis.

Unique: Strawberry GraphQL schema specifically designed for LLM trace patterns (model names, token counts, retrieval metadata) rather than generic span attributes, with built-in support for RAG-specific filters like 'retrieval_source' and 'embedding_model'

vs others: More intuitive than raw SQL queries for non-database engineers, and more flexible than Jaeger's UI-only filtering for programmatic access

via “trace querying and filtering via graphql api”

AI Observability & Evaluation

Unique: Uses Strawberry GraphQL framework with type-safe schema generation from Python dataclasses, enabling automatic schema validation and IDE autocomplete for query construction. Translates GraphQL queries directly to optimized SQL rather than loading full datasets into memory.

vs others: More flexible than REST APIs for complex filtering scenarios and more efficient than full-dataset retrieval; GraphQL schema is self-documenting and supports introspection for dynamic client generation.

Model Context Protocol (MCP) implementation for Opik enabling seamless IDE integration and unified access to prompts, projects, traces, and metrics.

Unique: Exposes Opik's hierarchical trace structure (traces → spans → metadata) as queryable MCP resources with native filtering by project, time, status, and custom attributes. Handles nested span serialization and pagination to work within MCP message constraints.

vs others: More accessible than raw Opik API because it integrates trace querying directly into IDE and agent workflows via MCP, eliminating the need for separate observability dashboards or API clients.

via “distributed trace retrieval and span correlation”

** - Navigate your OpenTelemetry resources, investigate incidents and query metrics, logs and traces on [Dash0](https://www.dash0.com/).

Unique: Reconstructs distributed traces through MCP tools with automatic parent-child span correlation, presenting the full call graph without requiring clients to manually fetch and assemble individual spans

vs others: Simpler trace analysis than raw Jaeger/Zipkin APIs because it automatically correlates spans and presents the call graph structure, versus requiring manual span fetching and tree construction

via “trace event filtering and time-range queries”

MCP server: perfetto-mcp

Unique: Provides in-memory filtering of parsed Perfetto events with support for multi-dimensional criteria (process, thread, category, time range). Implements sequential filtering passes to handle complex queries without requiring a separate indexing layer or database.

vs others: Simpler than building a full trace database or index, but slower than indexed queries — suitable for interactive analysis of medium-sized traces where latency is acceptable but complexity must be minimized.

via “trace-aware debugging with span-level filtering and aggregation”

** - Query and analyze your Axiom logs, traces, and all other event data in natural language

Unique: Axiom's MCP server understands trace structure (span hierarchies, parent-child relationships) and enables the LLM to query traces by span attributes and duration thresholds, then correlate slow/failed spans with logs. This allows conversational trace debugging without requiring users to navigate trace UIs.

vs others: More accessible than learning Jaeger or Zipkin UIs, and faster than manually clicking through trace waterfalls, but lacks visual span waterfall diagrams and is limited to Axiom's trace schema and indexing capabilities.

via “batch trace filtering and search”