---
title: "Deep Agents vs Traditional ReAct Loops: When CallSphere Picks What"
description: "LangChain's deepagents harness brings planning, filesystems, and subagents on top of LangGraph. Here is when to pick deep agents vs a classic ReAct loop."
canonical: https://callsphere.ai/blog/vw1g-deepagents-vs-react-loops-when-to-use-which
category: "Agentic AI"
tags: ["LangGraph", "Agents", "Multi-Agent", "Tool Use", "Claude"]
author: "CallSphere Team"
published: 2026-04-27T00:00:00.000Z
updated: 2026-05-07T06:44:59.318Z
---

# Deep Agents vs Traditional ReAct Loops: When CallSphere Picks What

> LangChain's deepagents harness brings planning, filesystems, and subagents on top of LangGraph. Here is when to pick deep agents vs a classic ReAct loop.

> [Deep Agents](https://github.com/langchain-ai/deepagents) is LangChain's harness for complex agentic tasks: planning tools, filesystem backend, and the ability to spawn subagents. It uses the same core tool-calling loop as ReAct but adds primitives that traditional ReAct loops lack.

## What changed

The 2022 ReAct paper from Yao et al. (Princeton + Google) formalized the core agent loop: think, act, observe, repeat. Until 2025, almost every production agent ran some variant of this loop. The pattern works, but it has limits — long-horizon tasks blow context, tool choice gets confused with 20+ tools, and there is no native way to spawn subagents.

Deep agents are not a new loop. They are a harness on top of the ReAct loop with three additions:

1. **Planning tool.** The agent can write a plan to a virtual scratchpad, refer back to it, and update it. This stabilizes long-horizon work.
2. **Filesystem backend.** The agent gets a virtual filesystem to read and write intermediate work products. Replaces context-bloat with explicit state.
3. **Subagent spawning.** The agent can spawn focused subagents for sub-tasks (search, summarize, refactor). Each subagent has its own context.

Anthropic also published an Agent SDK alongside Claude 4.6 with similar primitives — extended thinking, computer use, MCP integration, persistent memory. Two implementations, same idea: agents need more than the bare ReAct loop for hard tasks.

## Why it matters for production agent teams

Three concrete cases where deep agents beat ReAct:

**Long-horizon tasks (10+ minutes of agent work).** ReAct loops drown in their own context. Deep agents externalize state to the virtual filesystem; the model sees a stable scratchpad rather than a growing context.

**Branching exploration.** "Research these 5 candidate vendors and compare them" — five subagents work in parallel, each with focused context. ReAct would serialize this and run out of context.

**Iterative refinement.** "Draft this document, then review it, then revise" — deep agents naturally split into draft / review / revise subagents. Each subagent prompt is small and focused.

For shorter, single-domain tasks (the vast majority of voice agent turns) the bare ReAct loop is faster and cheaper.

## How CallSphere applies this

CallSphere uses both patterns deliberately:

- **All voice conversation turns:** classic ReAct via the OpenAI Agents SDK. Single specialist, focused tool surface, sub-second turn time. Adding a deepagent harness here would make the conversation slower without quality benefit.
- **GTM lead-research workflows:** deep agents with subagent spawning. "Research this prospect" spawns a Web Research subagent, a Public Filings subagent, and a Synthesis subagent in parallel. Each subagent gets focused context.
- **Internal due-diligence pipelines:** deep agents for multi-step refactoring of internal docs and KB articles. Plan + filesystem + iterative refinement is exactly the right shape for this work.
- **Behavioral health intake forms:** classic ReAct. The conversation is too time-sensitive for deep agent overhead.

The mental model: deep agents are for tasks that humans would say "give me 10 minutes to think." ReAct is for tasks that humans would say "let me grab that for you."

## Migration / build steps

1. **Classify your workloads.** Sub-minute / single-domain = ReAct. Multi-minute / multi-step = deep agents.
2. **Don't rebuild the world.** ReAct works for 80% of voice agent turns. Reach for deep agents only when ReAct demonstrably falls down.
3. **Start with the deepagents library.** It is a thin layer on LangGraph; the learning curve is small.
4. **Define a planning tool first.** Even before subagents, the planning scratchpad alone delivers most of the long-horizon win.
5. **Bound subagent depth.** Allow at most 1-2 levels of subagent spawning. Unbounded recursion is a cost trap.

```mermaid
graph TD
    A[Master Deep Agent] --> B[Planning Tool]
    A --> C[Filesystem]
    A -->|spawn| D[Subagent: Web Research]
    A -->|spawn| E[Subagent: Public Filings]
    A -->|spawn| F[Subagent: Synthesis]
    D --> G[Tool Calls + Findings]
    E --> G
    F --> H[Final Output]
    B -.->|consults| A
    C -.->|persists| A
```

## FAQ

**Are deep agents just multi-agent under another name?** Partly. The differentiator is the harness — planning tool, filesystem, controlled subagent lifecycle. Multi-agent without those primitives is brittle.

**Does this work with Claude?** Yes. The deepagents library supports any model that supports tool calling. Claude 4.6 / 4.7 are particularly strong on the underlying capabilities (extended thinking, MCP).

**Should voice conversation turns ever use deep agents?** Almost never. The latency is wrong for voice. Use deep agents for the async work that surrounds voice conversations.

**What about Anthropic's official Agent SDK?** Same idea, different implementation. Most teams pick based on their existing stack — LangGraph users go with deepagents; Anthropic-first teams use the Agent SDK.

**Where do I start?** Pick a single async workflow that already runs longer than 5 minutes and rebuild it as a deep agent. Compare cost, latency, and output quality. See our [demo](/demo) for live agent examples and our [trial](/trial) for a tenant to experiment in.

## Sources

- [Deep Agents - LangChain Docs](https://docs.langchain.com/oss/python/deepagents/overview)
- [LangChain deepagents GitHub](https://github.com/langchain-ai/deepagents)
- [The Anatomy of an Agent Loop](https://stevekinney.com/writing/agent-loops)

---

Source: https://callsphere.ai/blog/vw1g-deepagents-vs-react-loops-when-to-use-which