Features
Multi-turn conversational assistants with session management
Retrieval-Augmented Generation (RAG) with built-in vector stores
Supports OpenAI, Anthropic, Google, and local models via LiteLLM
Django admin integration for managing assistants and data sources
Key-value and summary memory types for conversations
File upload and processing for context injection
Streaming response support
Built-in vector store backends: Chroma, Pinecone, Qdrant, PGVector
Tool/function calling integration with Django ORM
Customizable system prompts and assistant personality
Conversation history persistence via Django models
Asynchronous support for high-concurrency scenarios
Live knowledge graph from code, commits, issues, and docs
Feasibility analysis: flags buildable vs risky items
Technical design document generation grounded in service topology
Impact assessment maps services, APIs, and dependencies across repos
Auto-scoping epics into Jira/Linear stories with effort estimates
One-shot production code generation grounded in service patterns
AI code reviews with cross-repo impact analysis
Production issue triage via MCP
Conversational learning from Slack and Jira
Accelerated onboarding via system-level Q&A in coding agents
Create Jira tickets and merge requests from Slack
MCP server for integration with Cursor, Claude Code, Codex
On-prem or cloud deployment
No code storage or model training
