For developers building GTM agents, LangChain is a suite of open-source tools and a managed runtime that orchestrates stateful, multi-step agent graphs for production LLM applications.
Presence & Market Position
#11 of 124
GTM Developer Tools
▲ 4 60-day move
Measures earned, engagement-weighted share of voice across the GTM voices panel. Methodology →
LangChain is positioning itself as the platform for building and operating production agents, pairing open-source frameworks with LangSmith's evaluation, observability, and managed infrastructure. Its established presence in GTM developer tools spans technical education and enterprise adoption. Practitioner conversation focuses on how agents retain context, how teams evaluate their work, and which parts of the execution system to build or buy. Enterprise sales hiring and implementation roles also draw attention. LangGraph and Deep Agents give builders distinct starting points, while LangSmith anchors the discussion of running those agents in production.
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BasetenLinkedIn, Uber, Cisco, Vodafone, Cloudflare, Workday, ServiceNow, Klarna, Lyft, Monday.com
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Business Profile
LangChain was founded by Harrison Chase and Ankush Gola and has grown from an open-source framework into a company with a commercial layer, LangSmith, for tracing, evaluation, and improving agents once they're in production. It has raised $160M, reported $16M in ARR in 2025, and was last valued at $1.25B as of October 2025, with strategic backing that includes several of the enterprises it sells into. The company runs independently with a team of roughly 325, and frames its work around agent engineering — treating an agent's reliability in production, not just its construction, as the core problem it sells against.
Agent Readiness
Measures how easily your agents can build on it — API, MCP, CLI, SDK, docs depth. Methodology →
LangChain ships API, MCP, SDK, and CLI surfaces, and its recent build-out has centered on operating and evaluating agents rather than just building them: a free sandboxed runtime for agents to execute code safely, an installable skill that writes evals from an agent's own traces, and an internal data stack queryable through Slack, a CLI, or MCP instead of dashboards.
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