---
title: 'LangChain — company profile'
canonical: 'https://feeny.ai/companies/langchain'
type: 'company'
updated: '2026-07-02'
---

# LangChain

> The open-source frameworks (LangChain, LangGraph) and LangSmith platform developers use to build, observe, and ship AI agents.

- **Website:** https://www.langchain.com/
- **Total raised:** $160M
- **Latest round:** Series B · $125M · Oct 2025 · $1.25B valuation
- **Investors:** IVP, Sequoia Capital, Benchmark, CapitalG, Sapphire Ventures, ServiceNow Ventures, Workday Ventures, Cisco Investments, Datadog, Databricks, Frontline
- **Business model:** Open-core: free open source frameworks feed a usage-and-seat-priced commercial platform (LangSmith).
- **Industries:** Artificial Intelligence
- **Open roles:** 97
- **Profile:** https://feeny.ai/companies/langchain

## What they do

LangChain builds the open source frameworks (LangChain, LangGraph) developers use to build AI agents, and sells LangSmith, a commercial platform to observe, evaluate, deploy, and improve those agents in production. It is framework-agnostic, working with any model or agent stack.

## Overview

**The side project that became the default way to build AI agents**

LangChain started in late 2022 as Harrison Chase's side project: one Python package pushed from a personal GitHub account with no grand plan behind it. Then ChatGPT launched and the timing turned an experiment into infrastructure. Chase teamed up with Ankush Gola to incorporate the company in early 2023, and the open source frameworks LangChain and LangGraph became the tools a generation of developers reached for first.

The company now sells LangSmith, a commercial platform for observing, evaluating, and deploying agents, and it has the metrics to back the pitch: over 1 billion open source downloads, more than 6,000 paying LangSmith customers, and 35% of the Fortune 500 using its products. In October 2025 it raised a $125M Series B at a $1.25 billion valuation. The bet from day one has not changed: agents are still hard to make reliably good, and whoever owns the tooling to fix that owns a lot of the AI stack.

## What They Do

**Open source frameworks up top, a paid engineering platform underneath**

LangChain runs a two-layer strategy. On top sit the open source frameworks, LangChain and LangGraph, that developers use to build agents fast with any model provider. Underneath sits LangSmith, the commercial agent engineering platform that watches those agents run in production, scores their quality, and ships them.

The connective idea is that traces, not code, are the only real record of what an agent did and why, since an LLM decides every output on the fly. LangSmith turns that trace data into fuel for improvement: observability to debug failures, evaluation to measure quality against real production runs, deployment on a durable runtime, and a newer Engine that clusters failures into issues and proposes fixes. It is framework-agnostic on purpose, so a team can trace an agent built on OpenAI, Anthropic, or anything else, not just LangChain's own stack.

## Problems

**Agents break in ways normal software does not, and nobody could see why**

Agents are hard to debug because you cannot plan for every input and the model decides each step at runtime. Long context, branching logic, and dozens of tool calls make it hard to pinpoint where a run went wrong, and a single conversation can spit out megabytes of nested trace data. Traditional web apps do not work this way, so the usual monitoring and databases were not built for how teams actually query agent behavior.

LangChain's answer is to treat the whole agent lifecycle as one problem: trace every step so failures are visible, turn production runs into test cases and score them, then deploy on a runtime built for long-running, bursty, human-in-the-loop workloads. It even built its own database, SmithDB, because general-purpose stores choked on trace queries at volume.

### Problems addressed

- Agents are hard to debug because the model decides each step at runtime
- Long context, branching logic, and many tool calls hide where a run went wrong
- Production trace data is huge and nested; general-purpose databases query it slowly
- No systematic way to measure whether an agent got better after a change
- Deploying long-running, bursty, human-in-the-loop agents needs a runtime web apps do not provide
- Non-technical teams cannot build their own agents without code

## Who It's For

**Built for the engineers shipping agents to production, not the ones demoing them**

The core user is a developer or engineering team moving an agent from prototype to something a company can rely on. That spans solo builders on the free tier all the way up to platform teams at global enterprises: 5 of the Fortune 10 and 35% of the Fortune 500 run LangSmith in production.

Fleet widens the audience past engineers. It is a no-code layer that lets non-technical teams describe a task in plain language, sales research, daily briefings, competitor tracking, and get a working agent that asks permission before it does anything sensitive. So the buyer is the developer or platform lead who needs reliability and control, and increasingly the ops or GTM team that just wants the work done.

### Ideal customer profiles

- **AI/ML engineer** — can't see what an agent actually did; no reliable way to eval changes before shipping
- **Platform / infrastructure engineer** — scaling high-throughput trace ingestion; operating agents reliably at production scale
- **Engineering leader** — standardizing how the org builds and deploys agents; controlling cost, latency, and reliability across teams
- **Non-technical ops / GTM team** — routine tasks eat the day; want an agent without writing code, with approvals for sensitive actions

## Products

**Five products across the agent development lifecycle**

The lineup splits cleanly into open source frameworks and the paid LangSmith platform, and they are designed to feed each other. LangChain and LangGraph are the free entry point that drives adoption; LangSmith is where the company makes money once those agents hit production.

Each piece targets a different stage: build with the frameworks, then observe, evaluate, deploy, and improve with LangSmith and its newer additions, Engine, Fleet, and Sandboxes.

## Business Model

**Free frameworks feed a usage-priced platform**

LangChain gives away the frameworks and charges for the platform. LangSmith pricing is seat plus usage: a free Developer tier for solo builders, a $39 per seat per month Plus tier for teams, and custom Enterprise pricing for advanced hosting, security, and support. Most costs scale with what you actually consume, from trace volume to deployment uptime to compute units.

The open source funnel is the growth engine. Over a billion downloads a year create the pool of developers who eventually need observability and deployment, and 6,000-plus of them pay for it. Startups get discounted rates and credits, which keeps the top of that funnel full.

### Plans

The open source frameworks are free (MIT-licensed). LangSmith is priced per seat plus usage: a free Developer tier for solo builders, a $39 per seat per month Plus tier for teams, and custom Enterprise pricing. Cost scales with what you consume, trace volume, deployment uptime, Fleet runs, Engine compute units, and sandbox compute, so most metrics are pay-as-you-go beyond included allotments.

- **Developer** — $0 / seat per month, then pay as you go · Solo users getting started
  Free single seat with a base trace allotment
  - Up to 5k base traces / mo, then pay-as-you-go
  - 1 seat only
  - Community support
  - Full Observability & Evaluation feature set
- **Plus** — $39 / seat per month, then pay as you go · Teams building and deploying agents
  Unlimited seats plus access to Deployment, Sandboxes, and Engine
  - Up to 10k base traces / mo, then pay-as-you-go
  - Add unlimited seats
  - Access to Deployment, Sandboxes, and Engine
  - 1 free Dev deployment with unlimited runs
  - 500 Fleet runs / mo included
  - Email support
- **Enterprise** — Custom · Teams with advanced hosting, security, and support needs
  Self-hosted and hybrid deployment with custom security and SLA
  - Self-hosted and hybrid deployment options
  - Custom SSO and RBAC
  - Support SLA and deployed engineers
  - Custom seats and workspaces
  - Annual invoice and custom terms

Good to know: Base traces retained 14 days at $2.50 per 1k; extended traces retained 400 days at $5.00 per 1k; Deployment uptime billed per minute ($0.0036/min production, $0.0007/min dev); additional deployment runs $0.005 each; Engine metered in LangChain Compute Units (LCUs) at $1.50 / LCU; Additional Fleet runs $0.05 each beyond the plan allotment; LLM usage billed separately by your model provider; Seats billed monthly (self-serve); Enterprise invoiced annually upfront; Discounted Startup Plan with credits for VC-backed startups; LangChain does not train on customer data

## Competition

**Owning the whole lifecycle while staying model-neutral**

LangChain competes with LLM observability and eval tools on one side and agent frameworks on the other, and its edge is refusing to pick just one lane. It sells the full lifecycle, build with the open source frameworks, then evaluate, deploy, and operate on LangSmith, as a vertically integrated stack, while staying framework- and model-agnostic so it does not lose the teams that build on OpenAI or Anthropic directly.

The real moat is the open source community: LangChain and LangGraph are among the most-used agent frameworks anywhere, with 3,500-plus contributors and a billion-plus downloads a year. That adoption is both the top of the sales funnel and a talent magnet. The catch is that the same ubiquity draws sharp criticism about the frameworks' abstractions, which is a reputational risk the platform business has to keep outrunning.

### Their edge

- **Owns the whole lifecycle** — Build with the open source frameworks, then evaluate, deploy, and operate on LangSmith, as one integrated stack rather than stitched-together point tools.
- **Model- and framework-neutral** — LangSmith traces any agent stack (OpenAI, Anthropic, custom) via OpenTelemetry and SDKs, so it does not lose teams that build outside LangChain.
- **Open source moat** — 1000+ integrations, 3,500+ contributors, and 1B+ downloads a year make its frameworks a default choice and a talent magnet.
- **Purpose-built infrastructure** — SmithDB is built specifically for agent trace query patterns, claiming 6x to 15x faster queries than general-purpose databases.

### Where they're betting

- Agent reliability via observability + evals
- Autonomous improvement (LangSmith Engine)
- No-code agents for whole companies (Fleet)
- Enterprise deployment (hybrid, self-hosted, BYOC)

## Proof

**The numbers LangChain puts on the table**

The traction is not subtle. LangChain reports over 1 billion open source downloads, more than 6,000 active LangSmith customers, and over 1 billion events ingested per day on LangSmith. Five of the Fortune 10 and 35% of the Fortune 500 use its products in production.

Named customers span AI-first startups and large enterprises: Klarna's assistant, Rippling's HR and payroll agents, Podium cutting engineering intervention by 90%, plus Coinbase, Workday, Cloudflare, Harvey, Vanta, LinkedIn, Monday.com, Nvidia, Cisco, Vodafone, and C.H. Robinson. SmithDB, its purpose-built trace database, claims 6x to 15x faster queries than general-purpose stores.

## What People Say

**Loved as the default, criticized for its abstractions**

Developers reach for LangChain first because it is the fastest way to get an agent working: proven patterns, 1000-plus integrations, and no vendor lock-in on models or tools. LangSmith earns praise for making non-deterministic agent behavior debuggable, which is exactly the pain it set out to solve.

The recurring criticism is the flip side of that reach. Experienced engineers complain the frameworks stack abstractions on abstractions, that the same thing can be done three different ways, and that dependency bloat, breaking changes, and uneven docs make production use harder than it should be. It is the classic tradeoff of a tool that tries to serve both beginners and experts, and it is a live debate in the community.

## Funding

**$125M at a $1.25B valuation, and a 525% markup in under two years**

LangChain closed a $125M Series B in October 2025 at a $1.25 billion valuation, led by IVP. That is a roughly 525% jump in company value in less than two years, a pace that tracks the download and revenue curve.

The cap table reads like a customer list, which is the point. Existing backers Sequoia and Benchmark returned, joined by CapitalG, Sapphire Ventures, and strategic investors ServiceNow Ventures, Workday Ventures, Cisco Investments, Datadog, and Databricks, several of which also show up as named LangSmith customers.

## Team & Culture

**A builders' shop that ships v0s earlier than is comfortable**

LangChain describes itself as a team of owners who ship early and iterate in public. Its four operating principles, build something great, embrace hot takes, maximum agency, and run to the roar, add up to a fast, opinionated, high-ownership culture that leans into a hyper-competitive space rather than shrinking from it.

Headquartered in San Francisco with offices in New York, Boston, and Amsterdam, the company is hiring across engineering, sales, product, and customer-facing roles worldwide. Most US roles expect in-office presence, some are hybrid or fully remote by team, and the engineering culture prizes shipping production systems over demos.

## Compensation

**Real bands, meaningful equity, and OTE that swings wide in sales**

Comp is disclosed on most roles and grounded in base salary, meaningful equity, and benefits. Engineering bases run wide, from roughly $116K for customer-facing engineers up past $270K for principal and staff-level systems roles. Sales roles are quoted as on-target earnings and stretch from about $110K to $350K, since commission does the heavy lifting.

Every role includes equity and standard US benefits: medical, dental, and vision, a 401(k), flexible vacation, and in-office meals. Team members in the EU, UK, and APAC get locally competitive equivalents.

## Security & Legal

**A Delaware entity built for enterprise data control**

The operating entity is LangChain, Inc., a Delaware corporation, and its terms govern the LangSmith Platform. The security story is aimed squarely at enterprises that cannot let data leave their walls: SaaS with US or EU data residency, a hybrid deployment that keeps the data plane in the customer's own infrastructure, and a fully self-hosted option that runs inside the customer's VPC.

Enterprise controls include SSO and SAML, SCIM, RBAC and ABAC, audit logs, and encryption. LangChain is explicit that it does not train on customer data and that traces, prompts, and outputs stay private to the organization. Default cloud data lives in GCP.

## In the News

**Unicorn status, a new database, and a steady drip of agent releases**

The headline moment was October 2025, when Fortune broke the $125M Series B and LangChain crossed into unicorn territory. Since then the company has kept a heavy publishing cadence: launching LangSmith Engine for autonomous agent improvement, open-sourcing agents like OpenWiki, and detailing the internals of SmithDB, its purpose-built trace database.

Harrison Chase's In the Loop essays and the Max Agency podcast round out a content engine that doubles as developer marketing.

### Coverage

- [Early AI darling LangChain is now a unicorn with a fresh $125 million in funding](https://fortune.com/2025/10/20/exclusive-early-ai-darling-langchain-is-now-a-unicorn-with-a-fresh-125-million-in-funding/) — Fortune (2025-10-20)
- [LangChain raises $125M to build the platform for agent engineering](https://blog.langchain.com/series-b/) — LangChain Blog (2025-10-20)
- [LangChain Hits $1.25 Billion Valuation In Series B Led By IVP](https://dataconomy.com/2025/10/21/langchain-hits-1-25-billion-valuation-in-series-b-led-by-ivp/) — Dataconomy (2025-10-21)
- [Introducing LangSmith Engine](https://www.langchain.com/blog) — LangChain Blog (2026-05-13)
- [Introducing OpenWiki, an open source agent for repo documentation](https://www.langchain.com/blog) — LangChain Blog (2026-07-01)
- [LangChain valuation, funding & news](https://sacra.com/c/langchain/) — Sacra (2025)

## Outlook

**Betting that reliable agents need an engineering discipline, and a vendor to sell it**

LangChain's wager is that agents graduate from demos to dependable software only with real engineering tooling, and that it can be the vendor that supplies it end to end. The download numbers, enterprise logos, and a $1.25B valuation say the market believes that story for now.

The open questions are the ones its own community keeps raising. The framework abstractions that made LangChain ubiquitous also draw the most criticism, and staying model-neutral means never leaning on lock-in the way a single-cloud vendor might. If the platform business keeps compounding on top of the open source funnel, the side project becomes durable infrastructure. If the frameworks lose developer goodwill, the funnel narrows.

## Company details

- **Mission:** Make intelligent agents ubiquitous by figuring out what the future of agents looks like and building the tools that make them easy to build reliably.
- **Products:** LangSmith, LangGraph, LangChain, Deep Agents, LangSmith Fleet, LangSmith Engine, SmithDB
- **Notable customers:** Klarna, Rippling, Podium, Pigment, ServiceNow, Monday.com, C.H. Robinson, PagerDuty, Cisco, Unify, Vodafone, Trellix, Coinbase, Workday, Cloudflare, Harvey, Vanta, LinkedIn, Nvidia, Bridgewater, Lyft, Clay, Replit
- **Customer segments:** Developers, AI/ML engineering teams, Enterprises, VC-backed startups
- **Buyers / users:** AI/ML engineers, Platform engineers, Engineering leaders, Non-technical ops and GTM teams
- **Competitors:** LLM observability/eval tools, agent framework alternatives, general-purpose APM/tracing vendors
- **What sets them apart:** Full agent lifecycle in one integrated stack; Framework- and model-agnostic (no vendor lock-in); Dominant open source community as adoption and talent funnel; Purpose-built trace database (SmithDB); Multiple deployment models incl. self-hosted and hybrid
- **Tech stack:** Python, TypeScript, Go, Java, OpenTelemetry, Kubernetes, Postgres, GCP, AWS, Azure, MCP, A2A
- **Integrations:** OpenAI SDK, Anthropic SDK, Vercel AI SDK, LlamaIndex, OpenTelemetry, PagerDuty, Salesforce, Gmail, Slack, BigQuery, GitHub, Linear, Google Calendar, Tavily, MCP servers

## Open roles (97)

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- [Deployed Engineer (Seattle)](https://jobs.ashbyhq.com/langchain/60d3bd4f-e80f-45fb-9801-725d8b681d54) — Seattle, WA
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- [Deployed Engineer (Denver)](https://jobs.ashbyhq.com/langchain/3f793a73-9b0d-456b-9a7c-16441e3e6e69) — Remote
- [Customer Marketing Manager](https://jobs.ashbyhq.com/langchain/e82ce858-bbaf-4444-885f-cfe357ed4292) — San Francisco, CA
- [Solutions Architect (Austin)](https://jobs.ashbyhq.com/langchain/da7cbabd-ceed-4d7d-ae25-f226cac0c1c1) — Remote
- [Deployed Engineer (Stockholm)](https://jobs.ashbyhq.com/langchain/31483b31-68c5-4603-8a84-0051634100c0) — Sweden
- [JavaScript Engineer (Open Source Team)](https://jobs.ashbyhq.com/langchain/05efa205-8560-43fd-bfcc-3f7697561cfb) — San Francisco, CA
- [Senior Platform Engineer, Ingestion](https://jobs.ashbyhq.com/langchain/ea3df015-477b-4197-a53f-c44029632a7d) — Remote
- [Deployed Engineer (Bay Area)](https://jobs.ashbyhq.com/langchain/e773649c-4ea7-47c2-9987-74d525474e82) — San Francisco, CA
- [Enterprise Account Executive (San Francisco Bay Area)](https://jobs.ashbyhq.com/langchain/9930dd01-d12e-42e1-b751-5240c3814f85) — San Francisco, CA
- [Enterprise Account Executive (Dallas)](https://jobs.ashbyhq.com/langchain/fd3767ba-6247-47bd-85ae-7e76be3871e6) — Remote
- [Senior Backend Engineer, LangSmith Deployments](https://jobs.ashbyhq.com/langchain/cb61f821-d8c4-4ec5-940d-3fd83be63a5f) — San Francisco, CA
- [Partner Alliance Lead - EMEA](https://jobs.ashbyhq.com/langchain/cd5f346a-df71-476b-8382-abd33bed6ed8) — London, United Kingdom
- [Solutions Architect (APAC)](https://jobs.ashbyhq.com/langchain/c882af7e-59aa-4017-b791-9a8094c0ef6f) — Remote
- [Controller](https://jobs.ashbyhq.com/langchain/6799c4fc-893c-4ed6-ad14-26ea80105984) — San Francisco, CA
- [Product Manager, Fleet](https://jobs.ashbyhq.com/langchain/5ac59bcb-0245-4a82-bab0-0ebdeae07ccd) — San Francisco, CA
- [Deployed Engineer (Phoenix)](https://jobs.ashbyhq.com/langchain/2a1f67e3-7509-4c92-9ec8-2e645d00c3eb) — Remote
- [People Partner](https://jobs.ashbyhq.com/langchain/72eefb77-605d-4616-adda-820eb5208ac8) — San Francisco, CA
- [Education Marketing Lead](https://jobs.ashbyhq.com/langchain/baf1486a-6329-40bd-8304-5d450efb17b7) — New York, NY
- [Deployed Engineer (Las Vegas)](https://jobs.ashbyhq.com/langchain/dea6e10a-5cc8-4956-85b4-1ca88b8c0a95) — Remote
- [Senior Backend Engineer, Enterprise Readiness & Identity](https://jobs.ashbyhq.com/langchain/6f32998b-111c-447b-b593-2b64c7de245d) — New York, NY
- [Solutions Architect (Remote)](https://jobs.ashbyhq.com/langchain/0a5dd30c-6da1-4095-bd96-b16f27eeb333) — Remote
- [Senior Fullstack Engineer,  AI Observability & Evals Platform](https://jobs.ashbyhq.com/langchain/a03b3e54-495d-4960-aee7-0194a3cef57d) — San Francisco, CA
- [Senior Fullstack Engineer, Growth & Monetization](https://jobs.ashbyhq.com/langchain/f07bfbad-6daf-4b11-b5bb-2c3bbc47c53c) — New York, NY
- [Solutions Architect (Dallas)](https://jobs.ashbyhq.com/langchain/933a41a3-43ca-44de-a0ae-f541149151b7) — Remote
- [Enterprise Account Executive (Missouri)](https://jobs.ashbyhq.com/langchain/f159330a-a8a0-4523-96b5-0ac58f9e109b) — Remote
- [Solutions Architect (San Francisco)](https://jobs.ashbyhq.com/langchain/e12d7176-8b9f-438a-8f3b-0cbbffab9c5c) — San Francisco, CA
- [Product Marketing - Observability](https://jobs.ashbyhq.com/langchain/fc61254d-2dd2-4b92-b01a-162e805b921c) — San Francisco, CA
- [Enterprise Account Executive, South EMEA (UK)](https://jobs.ashbyhq.com/langchain/df3a645d-5c08-4ea2-8823-7e40a5cbf9a6) — London, United Kingdom
- [FullStack Engineer,  AI Observability & Evals Platform (LangSmith)](https://jobs.ashbyhq.com/langchain/ddf92275-1cc3-49c0-9f25-e8ded43b07f6) — San Francisco, CA
- [Software Engineering Manager, AI Observability & Evals Platform (New York, NY)](https://jobs.ashbyhq.com/langchain/e1263bfe-b638-4019-b4d5-0aacca93b2b3) — New York, NY
- [Commercial Account Manager](https://jobs.ashbyhq.com/langchain/e408177e-54e8-4c08-8a70-d9bbd510b7ec) — London, United Kingdom
- [Enterprise Account Executive (Missouri)](https://jobs.ashbyhq.com/langchain/df72edb4-6342-45f8-b728-83b8b932a397) — St Louis, MO
- [Fullstack Software Engineer, Applied AI](https://jobs.ashbyhq.com/langchain/c75915ba-a32b-4e17-873d-19b47564170d) — San Francisco, CA
- [Engagement Manager  (San Francisco)](https://jobs.ashbyhq.com/langchain/36bfd743-8e8d-4393-8d47-d69097fb1f51) — San Francisco, CA
- [Controller](https://jobs.ashbyhq.com/langchain/dc1193e6-f874-4234-aa65-fdb54f8c4f24) — New York, NY
- [Product Marketing - LangSmith Fleet](https://jobs.ashbyhq.com/langchain/8f50c37d-61ca-4127-8605-bff4f6811d79) — San Francisco, CA
- [Content Lead](https://jobs.ashbyhq.com/langchain/aa54089b-56df-4795-bb86-83f5ca2359ca) — San Francisco, CA
- [Enterprise Account Executive (SoCal)](https://jobs.ashbyhq.com/langchain/49e5ee0c-7293-4ce6-80fc-70199e4172f8) — Remote
- [Sales Engineer (Bay Area)](https://jobs.ashbyhq.com/langchain/b47dbcea-222c-427d-bf4d-9e30a6b60d4a) — San Francisco, CA
- [Enterprise Account Executive (Minneapolis)](https://jobs.ashbyhq.com/langchain/925d5539-ea78-4d33-87e0-4b5f2d7f15e6) — Remote
- [Sales Development Representative](https://jobs.ashbyhq.com/langchain/3efc7bc8-657c-4986-b39b-ce30e5d6cb17) — New York, NY
- [GTM Engineer](https://jobs.ashbyhq.com/langchain/32c6fe13-76e0-49a2-849d-991014416987) — San Francisco, CA
- [Education Marketing Lead](https://jobs.ashbyhq.com/langchain/9c4cc9a5-f8b1-440a-826a-067e432d3e65) — San Francisco, CA
- [Commercial Account Executive (UK)](https://jobs.ashbyhq.com/langchain/65dbb419-ca80-4fda-b1b7-b81c08878992) — London, United Kingdom
- [Enterprise Account Executive (Federal)](https://jobs.ashbyhq.com/langchain/6847e38f-ec98-4fb2-a2cb-a97cdd9be108) — Remote
- [Commercial Account Executive](https://jobs.ashbyhq.com/langchain/426359a5-3a48-4b81-ba25-b2f32678b153) — San Francisco, CA

_…and 47 more at https://feeny.ai/companies/langchain/jobs_

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_Source: https://feeny.ai/companies/langchain · profile updated 2026-07-02_