For developers building agents that need to persist state across steps, LangGraph is a graph-based orchestration framework from LangChain that manages long-running, multi-step agent workflows with built-in memory and control flow.
Presence & Market Position
#38 of 103
GTM Developer Tools
▲ 329 60-day move
Measures earned, engagement-weighted share of voice across the GTM voices panel. Methodology →
LangGraph is LangChain's lower-level orchestration framework — where LangChain offers a quick way to stand up an agent, LangGraph gives developers explicit control over state, memory, and execution flow for long-running, multi-step agents, including human-in-the-loop checkpoints and support for single-agent, multi-agent, and hierarchical designs. It's positioned as the layer for production-grade, custom agent architectures rather than off-the-shelf setups. Attention on the framework itself is rising quickly, building its own footprint as a reference point distinct from the broader LangChain platform it's part of.
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RedisLinkedIn, Clay, Uber, Cisco, Cloudflare, Coinbase, Klarna, Rippling, Harvey, Elastic
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Business Profile
LangGraph is LangChain's lower-level agent orchestration framework, open source since it was introduced in 2023 alongside the rest of the LangChain stack. It ships and scales as part of LangChain rather than as a separately financed business, drawing on the same roughly 325-person team, $160M in funding, $16M in 2025 ARR, and $1.25B valuation (as of October 2025) that span the company's full product line. Within that stack, LangGraph is positioned as the framework for teams that have outgrown quick-start agent setups and need explicit control over state and execution for production systems.
Employees & founded inherited from LangChain (parent).
Agent Readiness
Measures how easily your agents can build on it — API, MCP, CLI, SDK, docs depth. Methodology →
LangGraph ships with API, SDK, and CLI surfaces, and its build-out centers on production deployment and control: one-click deployment through LangSmith, native streaming so an agent's intermediate steps are visible in real time, and human-in-the-loop checkpoints that let a person approve or redirect an agent mid-run. The emphasis is on governance for agents already running in production, beyond the framework used to construct them.
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