Workflow Search And Query With Temporal Indexing

via “workflow visibility and querying with sql-like search”

Durable execution for distributed workflows.

Unique: Maintains a separate Visibility Store indexed by searchable fields, enabling fast queries without scanning the full event log. Custom attributes are user-defined and indexed, allowing application-specific search (e.g., by customer ID or order ID) without schema changes.

vs others: More flexible than Airflow's UI (which only supports basic filtering) because Temporal supports SQL-like queries on custom attributes. More scalable than scanning the event log directly (which would require full table scans) because the Visibility Store is optimized for search.

via “temporal knowledge graphs with version tracking and time-aware queries”

The memory for your AI Agents in 6 lines of code

Unique: Stores temporal metadata (timestamps, version numbers) as native graph properties rather than in a separate temporal database, enabling temporal queries to leverage the same graph traversal engine as structural queries. Supports both point-in-time snapshots and range-based temporal queries, allowing agents to reason about knowledge at different temporal granularities.

vs others: More integrated than external temporal databases because temporal queries use the same graph engine as structural queries, reducing latency and complexity; more flexible than immutable event logs because it preserves the full graph structure at each point in time, enabling complex temporal reasoning.

via “advanced search filtering with temporal and entity extraction”

Hi HN,I built an open-source AI agent that has already indexed and can search the entire Epstein files, roughly 100M words of publicly released documents.The goal was simple: make a large, messy corpus of PDFs and text files immediately searchable in a precise way, without relying on keyword search

Unique: Combines NER with temporal filtering specifically for investigative workflows, likely building a knowledge graph of entity relationships extracted from documents rather than relying on external databases

vs others: More powerful than simple keyword filtering because it understands entity relationships and temporal context, enabling complex queries like 'all meetings between X and Y in Q3 2015'

Hey HN. Graph Compose is a hosted platform for orchestrating API workflows on Temporal. You define workflows as graphs of nodes (HTTP calls, AI agents, iterators, error boundaries) and everything runs as a durable Temporal workflow under the hood.Three ways to build the same graph: a React Flow visu

Unique: Integrates with Temporal's search attribute system to enable structured queries on workflow metadata, rather than treating workflows as opaque execution records

vs others: Understands Temporal's workflow model to provide targeted search on workflow type, status, and custom attributes, whereas generic log search treats workflows as unstructured event streams

via “real-time query processing”

MCP server for https://grep.app

Unique: Combines caching with indexing to achieve real-time query processing, enhancing performance for frequently accessed documents.

vs others: Faster than traditional search systems that require full re-indexing for each query.

via “workflow-search-and-filtering”