If you're building AI agents in Python right now, two frameworks are competing for your attention: LangChain Deep Agents (launched March 15, 2026) and the OpenAI Agents SDK (early March 2026). Both promise production-ready multi-agent orchestration. Both have real traction -- Deep Agents hit 9.9k GitHub stars in 5 hours, while the Agents SDK formalized patterns thousands of teams were already hacking together with OpenAI's experimental Swarm library. But they solve the problem from fundamentally different directions. Deep Agents is an agent harness -- batteries-included with planning, filesystem context management, and subagent spawning baked in. The Agents SDK is a lightweight toolkit -- minimal primitives (agents, handoffs, guardrails) that you compose with Python. Picking the wrong one means rewriting your orchestration layer in three months. This comparison breaks down the architectures, shows code side-by-side, and gives you a decision framework so you can pick the right tool for your use case. TL;DR Deep Agents wins for long-horizon, stateful tasks (research sessions, coding agents, multi-step analysis) where you need built-in planning and filesystem-based context management. OpenAI Agents SDK wins for multi-agent handoff workflows (triage + specialists) where you want the simplest possible setup with built-in tracing and guardrails. Neither wins for teams that want agent capabilities without writing orchestration code -- that's where managed platforms like Nebula fit. Skip to the comparison table or the decision framework. Quick Comparison Table Feature LangChain Deep Agents OpenAI Agents SDK Architecture Agent harness on LangGraph Lightweight standalone SDK Language Python (+ TypeScript SDK) Python + TypeScript Planning Built-in write_todos tool Manual (you build it) Memory LangGraph Memory Store + filesystem Sessions (persistent working context) Multi-Agent Subagent via task tool (context isolation) Handoffs + Triage pattern Context Management Auto-summarization + file offload Conversation context (ephemeral) Tracing LangSmith / LangGraph Studio OpenAI Dashboard (built-in, zero config) Guardrails Via LangGraph middleware Input/output guardrails built-in Human-in-the-Loop LangGraph interrupts SDK pause/resume Model Support Any LLM (model-agnostic) OpenAI-first (others via params) MCP Support Via LangChain MCP integration Built-in MCP server tool calling Learning Curve Medium-High (LangGraph required) Low-Medium Best For Long-running stateful tasks Multi-agent handoff workflows Pricing Free (OSS) + LLM costs Free (OSS) + LLM costs What LangChain Deep Agents Brings to the Table Deep Agents is what LangChain calls an "agent harness" -- a layer above the basic agent loop that packages planning, context management, and subagent delegation into sensible defaults. Harrison Chase built it by reverse-engineering the patterns behind Claude Code, Deep Research, and Manus. Planning That Doesn't Require Prompt Hacking The built-in write_todos tool forces the agent to decompose tasks into explicit steps. This isn't a side feature -- on trajectories of 50-100 tool calls, it's the difference between an agent that stays on track and one that drifts. from deepagents import create_deep_agent agent = create_deep_agent( model="openai:gpt-4o", tools=[web_search, analyze_data], system_prompt="You are a research assistant." ) # The agent automatically gets planning, filesystem, # shell execution, and subagent tools -- no extra config result = agent.invoke( "messages": [ "role": "user", "content": "Research the top 5 AI agent frameworks, compare their architectures, and write a summary report." ] ) With that single create_deep_agent() call, your agent can plan tasks, read/write files, spawn subagents, and manage its own context window. You didn't request these features -- they're built in. Filesystem-Based Context Management This is Deep Agents' most underappreciated feature. Instead of cramming everything into the LLM's context window, agents offload intermediate results to a virtual filesystem using write_file, read_file, edit_file, ls, glob, and grep. Why this matters: a research agent processing 200 pages of documentation would overflow any context window. With filesystem tools, it writes findings to research.md, code to app.py, and reads them back as needed. The filesystem acts as a shared workspace where agents and subagents collaborate. Deep Agents supports pluggable backends: StateBackend (default): Stored in LangGraph state, transient per-thread LangGraph Store: Cross-thread persistence LocalFilesystem: Standard disk storage CompositeBackend: Mix multiple backends Remote sandboxes: Modal, Runloop, Daytona Subagents for Context Isolation The task tool spawns specialized subagents with isolated context windows. The main agent stays clean while su