I'm building an agent that talks to our CRM and docs, what stack should I use?

A good stack for an agent that talks to a CRM and internal docs usually has 5 layers:

1. LLM layer
2. Tool / integration layer
3. Retrieval layer for docs
4. Orchestration / state layer
5. Safety / observability / evals

A practical default stack

1) Model

• OpenAI GPT-4.1 / GPT-4o for strong tool use, structured outputs, and general reasoning
• If you need lower cost/high throughput: a smaller OpenAI model for routine tasks, with a larger model for escalations

2) Agent orchestration

Pick one:

• LangGraph if you want robust multi-step flows, state, retries, branching, approvals
• OpenAI Responses API / Assistants-style tool calling if you want a simpler implementation and can keep orchestration mostly in your app
• LlamaIndex if the center of gravity is document retrieval and knowledge workflows

My default recommendation for enterprise CRM + docs is:

• LangGraph for agent workflow control
• OpenAI tool calling for the actual model interaction

3) CRM + docs integration

• Build thin, explicit API wrappers around:
  • CRM: Salesforce, HubSpot, etc.
  • Docs: Google Drive, Confluence, Notion, SharePoint, Slack, etc.
• Use typed schemas for every tool input/output.
• Add idempotency keys for write actions.

4) Retrieval for docs

• Postgres + pgvector is a great default if your scale is moderate
• If you expect large-scale semantic search, use:
  • Pinecone, Weaviate, or Qdrant
• For ingestion:
  • chunking + metadata
  • hybrid retrieval if possible (keyword + vector)
  • source citations in responses

5) App/backend

• Python + FastAPI is the easiest choice for agent backends
• Use:
  • Pydantic for schemas
  • Postgres for state, audit logs, and user data
  • Redis for queueing, caching, and short-lived session state
  • Celery / Temporal / Prefect for async jobs and longer workflows

6) Observability and evals

• LangSmith or OpenTelemetry + your APM
• Store:
  • prompts
  • tool calls
  • retrieved documents
  • final outputs
  • user feedback
• Add offline evals with a small golden dataset of CRM/doc tasks

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If you want the simplest good stack

If you’re optimizing for speed to production:

• Frontend: Next.js
• Backend: FastAPI
• Agent orchestration: LangGraph
• LLM: OpenAI GPT-4.1 or GPT-4o
• Vector store: Postgres + pgvector
• DB: Postgres
• Cache/queue: Redis
• Auth: OAuth / SSO
• Observability: LangSmith + OpenTelemetry

That’s a very solid “default enterprise agent” stack.

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If the agent will take actions in CRM

Make sure you add:

• approval steps for risky actions
• read-only mode first
• tool allowlists
• role-based access control
• audit logs
• human-in-the-loop for updates/deletes
• rate limits and retries

For example:

• Read customer info: automatic
• Draft email: automatic but reviewable
• Update CRM fields: require approval for first release
• Delete records / close deals: never autonomous at first

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Architecture pattern I’d recommend

A clean pattern is:

• Agent planner
  • decides whether to answer, search docs, query CRM, or take action
• Tool executor
  • strictly typed API calls
• Retriever
  • fetches relevant docs and knowledge
• Policy layer
  • checks permissions, safety, and business rules
• Audit layer
  • logs everything

This avoids a “free-roaming chatbot” and makes it production-friendly.

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What I would choose today

If I were building this now, I’d pick:

• Python + FastAPI
• LangGraph
• OpenAI GPT-4.1
• Postgres + pgvector
• Redis
• Temporal if workflows are long-running
• LangSmith for tracing/evals
• Next.js for the UI
• OAuth/SSO + RBAC

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Quick rule of thumb

• Mostly docs Q&A? LlamaIndex + pgvector
• Complex multi-step CRM workflows? LangGraph + tool calling
• Need enterprise control/auditability? FastAPI + Postgres + explicit tools + approvals

If you want, I can suggest:

1. a minimal MVP stack,
2. a scalable enterprise stack, or
3. a reference architecture diagram for your exact CRM/docs setup.