Local Coding Agent with Persistent Memory

Best for

Developers who want a coding assistant that remembers project conventions, past bug fixes, architecture decisions, and preferred patterns across days or weeks of work.

Core tools

• Continue
• Ollama
• Letta (MemGPT)
• mem0
• Chroma

Recommended models

• Qwen3 8B — fast iteration with strong coding and tool-call support
• DeepSeek R1 14B — chain-of-thought reasoning for architecture decisions and debugging
• Mistral Small 3.1 — reliable long-context recall for multi-file discussions

Hardware notes

8–16 GB VRAM for the coding model. Memory storage (Chroma or SQLite) is CPU-based and lightweight. The memory retrieval layer adds negligible latency — the bottleneck is always the LLM inference step.

Setup steps

1. Install Ollama and pull a coding model: ollama pull qwen3:8b
2. Install mem0 for managed memory: pip install mem0ai (configure with a local Chroma or SQLite backend).
3. Create a memory store for your project: mem0.add("Project uses TypeScript strict mode, no any types", user_id="project-myapp").
4. On each new session, retrieve relevant memories before sending the first prompt: memories = mem0.search(query, user_id="project-myapp").
5. Inject retrieved memories into the system prompt or context window before the user query.
6. Configure Continue to use a custom system prompt that includes memory context at session start.
7. After significant decisions (architecture changes, bug fixes, new conventions), write a memory entry: mem0.add("Decided to use Zod for all API validation — see PR #42").
8. Prune stale memories periodically — outdated conventions cause more confusion than no memory.

Trade-offs

Persistent memory improves continuity but introduces a retrieval step that can inject irrelevant or outdated context. Memory quality degrades if not curated. Start with explicit, structured memory writes rather than auto-capturing all model outputs.

Alternatives

• Use Aider with its built-in CONVENTIONS.md for lightweight project conventions without a memory database.
• Use a long-context model (Llama 4 Scout, 10M context) to fit more session history directly in the context window.
• Use Cursor or Windsurf for a managed memory alternative if you prefer a hosted solution over self-hosted memory infrastructure.

Related resources

Not sure if your PC has enough VRAM for this workflow?

FAQ