Stop Comparing Agent Frameworks — First Decide Whether You Need a Product, a Chassis, or a Workflow
Agent tooling has split into three distinct layers — product, chassis, and workflow runtime — and teams that compare them on a single feature axis end up buying the wrong abstraction. The cost shows up as unowned state, duplicated governance, or a runtime too heavy for the actual job.
Pitting DeepSeek Harness against Codex, Claude Code, or LangGraph in a feature checklist conflates three distinct procurement layers. Codex and Claude Code are coding agent products that ship with a working terminal, repository workflow, and approval experience. DeepSeek Harness is a developer-preview agent runtime where the loop, session, tool registry, and surface all live inside a reversible plugin graph. LangGraph is a low-level orchestration runtime that forces teams to model business steps, state, and recovery points as explicit graphs with checkpoints and interrupts.
A team that needs delivery speed should buy a product. A team building multiple agent surfaces on shared primitives should evaluate Harness. A team whose core problem is pausable, auditable business logic should reach for LangGraph. The wrong choice loads the team with complexity it never needed, while the right one leaves a clear acceptance checklist for whatever the system does not handle.
No success-rate, cost, or latency ranking holds without a controlled experiment that fixes the model, task set, permissions, sandbox, and scoring rubric. The adoption matrix here is a starting filter, not a substitute for a same-conditions PoC.
Most “which agent framework is better” debates collapse because the participants are buying different things: a product, a runtime chassis, or a state-machine library. The feature lists overlap, but the ownership boundaries do not.
DeepSeek Harness’s reversible plugin lifecycle is architecturally unusual — effects unwind on unload and dependency changes re-trigger consumers — which shifts it from a coding tool into a platform-building substrate.
Claude Code’s extension model ties plugins, skills, agents, and hooks to a product lifecycle, not a runtime core; this makes it easier to distribute team assets but harder to replace the underlying agent loop.
LangGraph’s interrupt-and-resume semantics force a hard question most agent loops dodge: if a node re-executes from the top on resume, every side effect before the interrupt must be idempotent, and that burden falls entirely on the developer.
Combining two runtimes without first defining ID ownership, cancellation propagation, and checkpoint idempotency turns a recoverable system into a dual state machine with no single source of truth for task completion.