What memory architecture works best for a coding assistant that tracks project context?

A coding assistant that forgets the project's tech stack, architecture decisions, and coding conventions between sessions forces the developer to re-explain context that should be known. A good memory architecture for a coding assistant maintains three distinct layers.

Layer 1: Project-level semantic memory

Stable facts about the project true across all sessions: language and framework choices, architecture patterns, team conventions, external service dependencies, deployment environment. Retrieved on virtually every query. Examples: 'This is a Next.js 14 app with a FastAPI backend.' 'We use PostgreSQL with Prisma.' 'All API responses follow the {data, error, meta} shape.' 'ESLint config enforces no-any.'

Layer 2: Decision memory

Preserves decisions made during coding sessions that inform future recommendations: 'decided to use optimistic updates for this form,' 'avoided React Query here due to bundle size concerns,' 'this function is intentionally non-async.' These prevent the assistant from suggesting approaches the developer has already rejected.

Layer 3: Per-developer preference memory

Individual developers on the same codebase have different working styles. One prefers functional patterns; another is comfortable with class-based code. Per-developer memory stores these preferences separately from project-level facts.

from mem0 import Memory

memory = Memory()

# Project-level facts — stored once, shared across all developers
memory.add([
    {"role": "user", "content": "Next.js 14, TypeScript strict mode, Tailwind, deployed on Vercel"}
], agent_id="project_atlas")

# Decision memory — stored after a coding session
memory.add([
    {"role": "user", "content": "We decided against Zustand — Context API is enough since state is shallow"},
    {"role": "assistant", "content": "Got it, I'll avoid suggesting Zustand for this project."}
], agent_id="project_atlas")

# At next session — retrieve project context and decisions
context = memory.search("state management and architecture", agent_id="project_atlas", limit=8)
# Returns project stack + the Zustand decision

What not to store in conversational memory

Do not store code snippets in conversational memory — they become stale as the codebase evolves. Code context belongs in a code indexing layer (embeddings of actual files), not in the semantic memory store. Memory is for decisions and facts about the project; retrieval of current code state should come from the live codebase.