You'd reach for this when you want your AI coding assistant to remember patterns, decisions, and context across your team's engineering work. It exposes 47 MCP tools backed by pgvector for semantic search, so the assistant can store and retrieve architectural decisions, code patterns, bug contexts, and team conventions. Think of it as persistent memory that survives beyond a single chat session. The semantic search means you can ask about similar problems your team solved months ago and actually get relevant hits. Useful for teams who want their AI to learn from past work rather than starting fresh every conversation.
DATABASE_URL*PostgreSQL connection string
OPENAI_BASE_URL*Embedding API base URL (OpenAI-compatible)
OPENAI_API_KEY*secretEmbedding API key
OPENAI_EMBEDDING_MODEL*Embedding model name (e.g. BAAI/bge-m3)
io.github.ericm1018/skillfm-llm-cost-optimizer-openai-anthropic-usage
io.github.mikerawsonnz/llm-orchestration-agent
io.github.mikerawsonnz/authenticated-llm-agent
labforgedev/copilot-memory-mcp
csoai-org/agent-prompt-injection-firewall-mcp
io.github.mikerawsonnz/authenticated-multi-llm-agent