I'm building an AI assistant that uses tools and memory, recommend a stack

Here’s a solid, practical stack for an AI assistant with tools + memory, optimized for building quickly but still scaling well.

Recommended stack

1) Core agent/orchestration

Python + FastAPI

• Best ecosystem for LLM tooling and integrations
• Easy to expose your assistant as an API
• Good for streaming responses and tool execution

Agent framework: one of

• LangGraph if you want robust tool-calling, stateful workflows, retries, branches, and long-running agents
• LlamaIndex if your main focus is retrieval + memory + document workflows
• LangChain if you want a broad ecosystem, but I’d prefer LangGraph for production agent flows

My pick:

• LangGraph for the agent runtime
• LlamaIndex for retrieval/memory abstractions if needed

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2) LLM provider

Choose based on cost/quality/latency:

• OpenAI: strongest general-purpose tool calling, structured outputs, broad model options
• Anthropic: excellent reasoning and long-context behavior
• Google Gemini: strong context window and multimodal options
• Open-source via vLLM or TGI if you want to self-host

My pick for most teams:

• Start with OpenAI or Anthropic
• Add a fallback model later for resilience/cost control

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3) Tool execution

You’ll want a clean tool layer rather than direct ad hoc function calls.

Use:

• Python tool registry with typed schemas
• Pydantic for input/output validation
• Background jobs for long tasks: Celery, RQ, or Temporal

If you need serious workflow reliability:

• Temporal is excellent for retries, durable state, and human-in-the-loop steps

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4) Memory

Think of memory in 3 layers:

A. Short-term memory

• Conversation state stored in your app DB or agent state
• Keep recent turns + active task context

B. Long-term semantic memory

• Store embeddings in a vector DB
• Use for user preferences, past conversations, documents, and recalls

Good options:

• Postgres + pgvector for a simple, production-friendly default
• Pinecone, Weaviate, or Qdrant if you want a dedicated vector store

My pick:

• Postgres + pgvector unless you already know you need a specialized vector DB

C. Structured memory

• Store durable facts in relational tables or a knowledge graph
• Examples: user profile, preferences, settings, goals, permissions

Use Postgres for this too.

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5) Database

PostgreSQL

• Store users, sessions, messages, tool logs, memory items, audit trails
• Add pgvector for embeddings
• Very strong default for almost everything

Optional:

• Redis for caching, ephemeral state, rate limiting, and queues

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6) Retrieval / search

If your assistant uses documents or past interactions:

• Hybrid retrieval: keyword + vector search
• Reranking for better relevance

Options:

• Postgres full-text search + pgvector
• OpenSearch/Elasticsearch if search is central
• Rerankers from Cohere or model-based reranking if needed

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7) Frontend

Depending on product:

• Next.js for web app
• React Native if you need mobile
• Slack/Discord bot integration if that’s the main surface

For chat UI:

• Stream tokens over WebSockets or Server-Sent Events
• Keep tool progress visible to users

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8) Authentication and permissions

• Auth0, Clerk, or Firebase Auth
• Enforce per-user and per-tenant memory/tool access
• Important if your assistant can access external systems

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9) Observability and evaluation

This is critical for tool-using agents.

Use:

• LangSmith if using LangChain/LangGraph
• OpenTelemetry for traces
• Sentry for errors
• A logging pipeline for tool calls, prompts, and outcomes

Evaluation tools:

• Promptfoo
• Ragas for retrieval evaluation
• Custom golden test cases for tool behavior

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A very practical default architecture

Best “balanced” stack

• Frontend: Next.js
• Backend: FastAPI
• Agent orchestration: LangGraph
• LLM: OpenAI or Anthropic
• DB: PostgreSQL + pgvector
• Cache/queue: Redis
• Background jobs: Temporal or Celery
• Observability: OpenTelemetry + Sentry + LangSmith
• Auth: Clerk or Auth0

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If you want the simplest possible MVP

Use this:

• FastAPI
• OpenAI API
• PostgreSQL + pgvector
• Redis
• LangGraph
• Next.js
• Sentry

That gets you a production-ready baseline without too much complexity.

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If you expect complex workflows

Add:

• Temporal for durable tool workflows
• Qdrant or Pinecone if memory/retrieval becomes large-scale
• OpenSearch for advanced hybrid search

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Design advice for tool + memory assistants

A few important principles:

1. Separate ephemeral chat state from durable memory
2. Write memory explicitly, don’t store everything automatically
3. Validate tool inputs with schemas
4. Log every tool call and result
5. Use retrieval only when needed to avoid noisy context
6. Add permission checks at the tool layer, not just the UI

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My “best default” recommendation

If you want one stack choice:

Python, FastAPI, LangGraph, OpenAI/Anthropic, PostgreSQL + pgvector, Redis, Next.js, Temporal (if workflows are complex), Sentry + OpenTelemetry

If you want, I can also give you:

• a minimal MVP stack
• a production-scale stack
• or a reference architecture diagram for tools + memory.