What to build
A working example that adds durable conversation persistence to a Deepgram Voice Agent session using SQLite: every turn (user utterance, agent response, tool calls, timing metadata) is persisted to a local database, and conversations can be resumed across sessions with full context restoration.
Why this matters
Production voice agents need conversation continuity — a customer who calls back should not have to repeat themselves. Currently, Deepgram voice agent examples are ephemeral: conversation context is lost when the WebSocket disconnects. Developers building customer service, healthcare intake, or sales qualification agents need a persistence pattern that works without infrastructure overhead. SQLite provides zero-configuration durability (no Redis/Postgres setup) while demonstrating the pattern that developers can later scale to their production database. This pattern also enables conversation analytics, compliance audit trails, and debugging replay.
Suggested scope
- Language: Python
- Deepgram APIs: Voice Agent API (WebSocket)
- Dependencies:
sqlite3 (stdlib), deepgram-sdk
- Components:
- SQLite schema: conversations, turns (role, content, timestamp, latency), tool_calls, session_metadata
- Conversation persistence middleware: automatically records every voice agent event
- Session resume: load previous conversation context when reconnecting with a conversation ID
- Context compression: summarize older turns to fit within LLM context window on resume
- CLI commands:
--new (start fresh), --resume <id> (continue), --list (show conversations), --replay <id> (print transcript)
- Complexity: Medium — single Python application with SQLite persistence layer
Acceptance criteria
Raised by the DX intelligence system.
What to build
A working example that adds durable conversation persistence to a Deepgram Voice Agent session using SQLite: every turn (user utterance, agent response, tool calls, timing metadata) is persisted to a local database, and conversations can be resumed across sessions with full context restoration.
Why this matters
Production voice agents need conversation continuity — a customer who calls back should not have to repeat themselves. Currently, Deepgram voice agent examples are ephemeral: conversation context is lost when the WebSocket disconnects. Developers building customer service, healthcare intake, or sales qualification agents need a persistence pattern that works without infrastructure overhead. SQLite provides zero-configuration durability (no Redis/Postgres setup) while demonstrating the pattern that developers can later scale to their production database. This pattern also enables conversation analytics, compliance audit trails, and debugging replay.
Suggested scope
sqlite3(stdlib),deepgram-sdk--new(start fresh),--resume <id>(continue),--list(show conversations),--replay <id>(print transcript)Acceptance criteria
Raised by the DX intelligence system.