LangChain

LangChain provides the agent engineering platform and open source frameworks developers need to ship reliable agents fast.

Agent Framework

Visit Website

Founded

2022

Location

San Francisco, CA

Employees

150

Funding

$125M Series B (2025)

LangChain + LangGraph: Agent Engineering Platform Dossier

Company Snapshot

**What it is:** Open-source frameworks (**LangChain**, **LangGraph**) plus the commercial **LangSmith** platform for observability, evaluation, and deployment.

**Focus:** Build, test, and run reliable, long‑running, stateful AI agents with **visibility, control, and scale**.

**Who uses it:** Startups and enterprises including LinkedIn, Replit, Klarna, Rakuten, Morningstar, Cloudflare, Workday, and Cisco.

**Website:** [LangChain](https://www.langchain.com)

Platform Overview

LangChain provides an end‑to‑end stack for designing, evaluating, and operating AI agents:

**LangChain (framework):** Prebuilt agent architecture and model/tool integrations to speed early development. See the [Docs](https://docs.langchain.com) and [GitHub](https://github.com/langchain-ai/langchain).

**LangGraph (framework):** Low‑level control for stateful, multi‑step, and multi‑agent workflows, with built‑in memory and human‑in‑the‑loop patterns. Explore the [Docs](https://docs.langchain.com).

**LangSmith (platform):** Production toolkit for:

[Observability](https://www.langchain.com/langsmith/observability): tracing, prompt/chain step visibility, and LLM call auditing

[Evaluation](https://www.langchain.com/langsmith/evaluation): test suites, regression checks, and quality gates

[Deployment](https://www.langchain.com/langsmith/deployment): manage agent deployments with usage controls and rollbacks

The stack is **model‑neutral**, with broad integrations across model providers, vector databases, tools, and enterprise data sources.

Why It Matters

**Ship faster:** Prebuilt agent components reduce boilerplate and accelerate prototypes to pilots.

**Gain control:** LangGraph enables deterministic routing, memory, and human approval in complex agent workflows.

**Operate with confidence:** LangSmith delivers the **observability, testing, and deployment** controls teams need for production reliability and compliance.

**Scale flexibly:** Run in cloud, hybrid, or self‑hosted environments with **BYO frameworks** supported.

Key Capabilities

Agent architecture for **long‑running, stateful** workflows

**Human‑in‑the‑loop** checkpoints and approvals

Integrated **tracing**, **prompt/version management**, and **evaluation**

**Model/tool neutrality** with 1,000+ integrations (models, vector DBs, connectors)

Production‑grade **deployment**, rollbacks, and usage governance

Learn more in the [Docs](https://docs.langchain.com).

Primary Use Cases

**App copilots** for code, analytics, design, and domain tasks (e.g., [Replit](https://www.langchain.com))

**Enterprise GPT** with permissions, knowledge retrieval, and audit trails (e.g., [Rakuten](https://www.langchain.com))

**Customer support assistants** that reduce handle time and improve accuracy (e.g., [Klarna](https://www.langchain.com))

**Research and analysis** assistants for finance and knowledge work (e.g., [Morningstar](https://www.langchain.com))

**Data access agents** (e.g., SQL bots that convert questions to queries, as showcased by [LinkedIn](https://www.langchain.com))

Explore examples on the [homepage](https://www.langchain.com).

Integrations

Model providers: OpenAI, Anthropic, Azure OpenAI, AWS Bedrock

Vector databases: Pinecone, Weaviate, pgvector

Connectors and tools: GitHub, Gmail, Google Drive, and more

Entry point for integrations and guides: [Docs](https://docs.langchain.com)

Pricing and Hosting

**Free Developer plan:** 1 seat and 5k base traces/month in LangSmith

**Plus plan:** $39 per seat/month (up to 10 seats), includes 10k base traces and one free dev deployment

**Usage‑based** pricing for additional traces and deployments

**Hosting options:** cloud, hybrid, or self‑hosted

Full details: [Pricing](https://www.langchain.com/pricing)

Customers and Traction

Used by teams at LinkedIn, Replit, Klarna, Rakuten, Morningstar, Cloudflare, Workday, and Cisco

Open source footprint: **90M monthly downloads** and **100k+ GitHub stars** (combined)

Claim: **35% of the Fortune 500** use the services (see the [Series B blog](http://blog.langchain.com/series-b/))

Funding: **$125M Series B at a $1.25B valuation** (Oct 2025), led by IVP with participation from Sequoia, Benchmark, Amplify, CapitalG, Sapphire Ventures, and strategic investors including ServiceNow Ventures, Workday Ventures, Cisco Investments, Datadog Ventures, Databricks Ventures, and Frontline. Read the [announcement](http://blog.langchain.com/series-b/).

Product Lineup

**LangChain (OSS)**

**LangGraph (OSS)**

**LangSmith Observability** — [Learn more](https://www.langchain.com/langsmith/observability)

**LangSmith Evaluation** — [Learn more](https://www.langchain.com/langsmith/evaluation)

**LangSmith Deployment** — [Learn more](https://www.langchain.com/langsmith/deployment)

**Agent Builder** (private preview)

Pros and Cons from User Sentiment

Pros:

**Strong integrations and model neutrality**—mix models, tools, and vector DBs in one place

**Developer velocity**—prebuilt components reduce early boilerplate

**Useful tracing**—clear visibility for debugging and audits via LangSmith

**Improved control with LangGraph** for stateful, multi‑step, multi‑agent workflows (community reports)

**Production stories**—teams report success putting agents in production and diagnosing failures (community reports)

Cons:

**Steep learning curve**—abstractions can feel heavy

**UX churn**—concerns about frequent UI changes in LangSmith

**Perceived framework lock‑in**—some prefer direct model/tool calls (community/HN discussion)

**Pricing sensitivity at scale**—evaluation of alternatives as usage grows

**Advanced workflows often require LangGraph**—basic patterns may not suffice as complexity rises

Browse reviews on [G2](https://www.g2.com/products/langchain/reviews).

Who It’s For

**Product and platform teams** building agent copilots, data assistants, and workflow agents

**Enterprises** requiring observability, evaluation, and governance for compliance and reliability

**Startups** seeking fast prototyping with a clear path to production

Notable Differentiators

**End‑to‑end agent engineering**: frameworks + observability + evals + deployment

**Stateful, long‑running agents** with memory and human‑in‑the‑loop built in

**Model and tool neutrality** across the AI stack

**Production‑ready operations**: tracing, testing, and controlled rollouts

Getting Started

1. Explore the platform and examples on the [LangChain website](https://www.langchain.com).

2. Read the [Docs](https://docs.langchain.com) to decide between LangChain and LangGraph for your workflow.

3. Start with the free tier on [Pricing](https://www.langchain.com/pricing) and enable tracing in LangSmith for immediate observability.

4. Plan for production with [LangSmith Evaluation](https://www.langchain.com/langsmith/evaluation) and [Deployment](https://www.langchain.com/langsmith/deployment).

Key Links

[Website](https://www.langchain.com)

[Docs](https://docs.langchain.com)

[Pricing](https://www.langchain.com/pricing)

[LangSmith Observability](https://www.langchain.com/langsmith/observability)

[LangSmith Evaluation](https://www.langchain.com/langsmith/evaluation)

[LangSmith Deployment](https://www.langchain.com/langsmith/deployment)

[GitHub](https://github.com/langchain-ai/langchain)

[Series B announcement](http://blog.langchain.com/series-b/)

[G2 Reviews](https://www.g2.com/products/langchain/reviews)

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