DevFlow FAQs

Question 1

Does DevFlow support tracking changes and temporal data?

Accepted Answer

Yes, DevFlow offers temporal awareness with full version history for entities and relations, enabling point-in-time graph retrieval. It also includes configurable confidence decay for relations, automatically adjusting their relevance over time if not reinforced.

Question 2

How does DevFlow store and manage its knowledge graph data?

Accepted Answer

DevFlow uses a zero-setup, single-file SQLite database for all knowledge graph data, including vector embeddings. This design eliminates complex setup, offering high performance and easy portability without external servers or configuration.

Question 3

What kind of search capabilities does DevFlow offer?

Accepted Answer

DevFlow provides advanced semantic search using vector embeddings and cosine similarity to find meaning-based connections. It also features hybrid search, combining semantic and keyword matching for comprehensive, contextually relevant results across the knowledge graph.

Question 4

What is DevFlow and its core purpose?

Accepted Answer

DevFlow is a persistent, searchable knowledge graph memory system specifically designed for AI agents in software development workflows. It gives AI a continuous, context-rich 'brain' that doesn't reset, allowing them to remember project details, decisions, and code between sessions.

Question 5

What are 'workflow-aware prompts' and how do they benefit AI agents?

Accepted Answer

Workflow-aware prompts are instructional messages that guide AI agents through structured development cycles (e.g., planner, task creator, coder, reviewer). They teach agents which tools to call and when, ensuring consistent, context-rich interactions with the knowledge graph and preserving workflow context.

DevFlow

DevFlow

Key Features

Use Cases

Key Features

Use Cases