DeepAgents vs LangGraph

Side-by-side comparison of features, pricing, and ratings

Analysis reviewed Live tool data as of 2026-09-29
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At a glance

DimensionDeepAgentsLangGraph
Pricingfree · from Open Source $0/mofreemium · from Developer $0/seat per month
Best forDevelopers who want a production-ready harness with sub-agents, filesystem, and human oversight without assembling each piece, Teams building long-horizon, multi-step agent workflows where one context window can't hold the working stateBackend and platform engineers building production agents, Teams needing fine-grained control over agent workflows
Standout featuresSub-agents that delegate tasks with isolated context windows · Pluggable filesystem for read, write, edit, and search across local, sandboxed, or remote backends · Context management: summarize long threads and offload tool outputs to diskGraph-based state management for agent control flow · Human-in-the-loop checkpoints to steer and approve agent actions · Built-in memory storing conversation histories across sessions
Viability score76/10081/100
APIYesYes

DeepAgents is the stronger pick for developers who want a production-ready harness with sub-agents, filesystem, and human oversight without assembling each piece; LangGraph fits better for backend and platform engineers building production agents.

Built from live tool data, last verified 2026-09-29.

DeepAgents
DeepAgents

Deep Agents is an open-source agent harness with sub-agents, filesystem, shell access, and human-in-the-loop control.

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LangGraph
LangGraph

MIT-licensed agent runtime and low-level orchestration framework for building reliable, stateful AI agents.

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Pricing
Free
Freemium
Plans
$0/mo
$0/seat per month
$39/seat per month
Custom
Popularity
6.1k views
3.1k views
Skill Level
Advanced
Advanced
API Available
Platforms
APICLI
API
Categories
🕸️ Agent Frameworks & Orchestration
🕸️ Agent Frameworks & Orchestration📡 LLM Observability & Evals
Features
Sub-agents that delegate tasks with isolated context windows
Pluggable filesystem for read, write, edit, and search across local, sandboxed, or remote backends
Context management: summarize long threads and offload tool outputs to disk
Shell access for running commands in your sandbox of choice
Human-in-the-loop: approve, edit, or reject tool calls before execution
Persistent memory via pluggable state and store backends for cross-session recall
Skills: reusable agent behaviors loaded on demand
Bring your own tools or connect any MCP server
Model-agnostic: works with any LLM that supports tool calling
Self-hosted models supported via Ollama, vLLM, or llama.cpp
Open-weight model hosting via Baseten or Fireworks
Built on LangGraph with streaming, persistence, and checkpointing
Tracing, evaluation, and deployment through LangSmith
Deep Agents Code: pre-built terminal coding agent, curl install
Python library installable with uv add deepagents; JavaScript/TypeScript via deepagents.js
Graph-based state management for agent control flow
Human-in-the-loop checkpoints to steer and approve agent actions
Built-in memory storing conversation histories across sessions
Token-by-token streaming of agent reasoning and actions
Single-agent, multi-agent, and hierarchical architectures in one framework
Low-level primitives for fully custom agent design
Model-agnostic: works with any LLM provider
Sandboxed code execution for agent-generated code
Prompt caching
Agent self-evaluation
MCP support including stateless protocol and elicitation
Integrates with the Deep Agents harness for long-running tasks
LangSmith observability, tracing, and evaluation
LLM Gateway runtime controls: cost limits, rate limiting, fallbacks, PII redaction
Deployment with scale-to-zero and cron scheduling
Integrations
LangGraph
LangSmith
OpenAI
Anthropic
Google
Ollama
vLLM
llama.cpp
Baseten
Fireworks
MCP servers
Azure
AWS Bedrock
HuggingFace
Mistral
Meta
OpenRouter
NVIDIA

Frequently Asked Questions

Which is better, DeepAgents or LangGraph?

The best choice between DeepAgents and LangGraph depends on your specific use case — we compare them independently on features, current pricing, integrations, and real-world signals (with an on-demand sentiment scan available for each). See the side-by-side breakdown above to match them to your needs.

What are the main differences between DeepAgents and LangGraph?

The key differences include pricing model, feature set, platform support, and skill level requirements. Review the full comparison on RightAIChoice for a detailed breakdown.

Is there a free version of DeepAgents or LangGraph?

Check the pricing section in the comparison for the latest pricing details on both tools, including free tiers, trial options, and paid plans.

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Explore each tool further

DeepAgents
View DeepAgents reviewDeepAgents alternatives
LangGraph
View LangGraph reviewLangGraph alternatives

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Best AI Agent Frameworks & Orchestration toolsBest AI LLM Observability & Evals tools
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Last reviewed: May 12, 2026