I'm building an AI assistant that needs memory and tool use, what should I use?

If you’re building an AI assistant with memory and tool use, a good default choice is:

Recommended stack

• LLM: a strong chat model with function/tool calling
• Memory:
  • Short-term memory: conversation state in your app
  • Long-term memory: a database + vector store for retrieval
• Tool use: function calling / tool routing layer
• Orchestration: a small agent loop that decides when to call tools, store memory, and answer

Practical options

1) Fastest path: managed AI platform

Use a hosted model API that supports:

• tool/function calling
• structured outputs
• retrieval or file search
• conversation threads

This is best if you want to move quickly and avoid building everything yourself.

2) More control: build your own agent layer

Use:

• LLM API for generation
• Redis/Postgres for session state
• Vector DB like pgvector, Pinecone, Weaviate, or Qdrant for semantic memory
• Tools as normal backend functions
• Workflow/agent framework if helpful

Good if you want custom memory policies and complex tool use.

Memory design

A solid memory system usually has:

• Conversation buffer: recent messages
• Summary memory: compressed long-running context
• Episodic memory: past interactions indexed for retrieval
• User profile memory: stable preferences, saved explicitly
• Tool results cache: reused outputs

Important: don’t dump everything into context. Retrieve only what’s relevant.

Tool use design

Model tools as explicit functions, like:

• search
• calendar
• database query
• send email
• create ticket

Best practice:

• define strict schemas
• validate inputs
• log tool calls
• make tools idempotent when possible
• add permission checks for sensitive actions

If you want a simple recommendation

• Prototype: use a hosted LLM with tool calling + Postgres + pgvector
• Production: add Redis for session state, explicit memory write policies, and a tool execution sandbox

A good architecture

1. User sends message
2. Assistant checks short-term context
3. Retrieve relevant long-term memories
4. Model decides:
   • answer directly, or
   • call a tool
5. Execute tool
6. Feed result back to model
7. Store important facts back into memory

What to avoid

• relying on the model to “remember” everything
• putting private/user-sensitive data into prompts without controls
• allowing tools to execute without validation
• unbounded context growth

If you want, I can also recommend a specific stack based on your language and deployment style, like:

• Python
• JavaScript/TypeScript
• local/open-source
• cloud production