I'm building an agent that needs to use tools and memory, what stack should I use?

If you’re building an agent that needs tools + memory, I’d choose the stack based on whether you want to optimize for speed of prototyping or production reliability.

My default recommendation

Best general-purpose stack

• LLM orchestration: LangGraph or LlamaIndex Workflows
• Backend: Python + FastAPI
• Tool execution: native Python functions / API wrappers
• Short-term memory: conversation state in Postgres or Redis
• Long-term memory / retrieval: Postgres + pgvector or Pinecone
• Persistence / agent state: Postgres
• Observability: LangSmith, OpenTelemetry, or Helicone
• Queue/background jobs: Celery, RQ, or Temporal if workflows are complex

This gives you a clean separation:

• tools = actions the agent can take
• memory = what it can remember across turns and sessions
• state = the exact execution state of the agent

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If you want to move fast

Use:

• OpenAI Responses / Assistants-style API
• LangChain or LlamaIndex
• Redis for session memory
• Postgres + pgvector for long-term memory

This is good for an MVP, especially if your agent is mostly:

• chat + retrieval
• function calling
• simple state persistence

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If you want production-grade control

Use:

• LangGraph
• FastAPI
• Postgres
• Redis
• pgvector
• Temporal for durable workflows
• OpenTelemetry + Prometheus/Grafana for monitoring

This is better if your agent:

• has multi-step workflows
• needs retries / resumability
• uses many tools
• must survive crashes without losing state
• needs auditability

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Memory stack recommendation

1) Short-term memory

Store:

• current conversation
• active plan
• recent tool outputs
• scratchpad/state

Best options:

• Redis if you want speed and TTL-based sessions
• Postgres JSONB if you want durable state and easier querying

2) Long-term memory

Store:

• user preferences
• facts extracted from conversations
• past tasks and outcomes
• embeddings for semantic retrieval

Best options:

• Postgres + pgvector if you want simplicity
• Pinecone / Weaviate / Milvus if you need higher-scale vector search

3) Episodic memory

If your agent should remember “what happened before,” store events as:

• timestamped records
• tool calls
• model decisions
• final outcomes

This is often best as an append-only event log in Postgres.

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Tooling stack recommendation

If your agent has tools like:

• web search
• database queries
• calendar/email actions
• file operations
• internal APIs

Then define tools as:

• typed Python functions
• REST endpoints
• background jobs for slow tasks

Best practice:

• validate inputs with Pydantic
• make tools idempotent where possible
• log every call
• time out slow tools
• keep tool outputs structured

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A strong practical stack

If I were building this today, I’d use:

• Python
• FastAPI
• LangGraph
• Postgres
• Redis
• pgvector
• OpenAI / Anthropic model API
• LangSmith for tracing
• Docker for deployment

That’s a very solid baseline.

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When to use what

Use LangGraph if:

• you need branching workflows
• you need checkpoints
• you need durable state
• you want explicit control over agent flow

Use LlamaIndex if:

• your main challenge is retrieval / knowledge access
• memory is mostly document-centered
• you want a strong data ingestion pipeline

Use plain OpenAI function calling if:

• the agent is simple
• you don’t need complex control flow
• you want to avoid framework complexity

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Avoid these early mistakes

• putting all memory in prompts only
• using a vector DB as your only memory store
• letting tools return unstructured text only
• skipping persistence of agent state
• building complex agent loops without retries/checkpoints
• storing everything forever without retention rules

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My short answer

If you want the safest bet:

Python + FastAPI + LangGraph + Postgres + Redis + pgvector

That stack covers:

• tool use
• memory
• durable state
• production workflows
• easy scaling later

If you want, I can also give you:

1. a minimal MVP stack,
2. a production architecture diagram, or
3. a recommended folder structure for the agent.