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LangChain, Inc.

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www.langchain.comSan Francisco, CA, United States

LangChain helps companies own their AI agent intelligence through open-source frameworks and enterprise-grade tooling.

Overview

LangChain is a company that provides an open, model-agnostic platform and open source frameworks to help teams build, control, and own their AI agent intelligence. The organization emphasizes enabling enterprises to develop, observe, evaluate, and govern agent-powered workflows while aligning AI with business needs and governance. It highlights a mission to empower every company to own their intelligence and supports a broad ecosystem of tools and community-driven projects.

Mission statement

Our mission is to enable every company to own their intelligence.

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What we offer

LangSmith Platform

Streamline AI agent development with observability, evaluation, and deployment solutions.

www.langchain.com/langsmith-platform

Deep Agents

Enables complex, long-running tasks with autonomous agents for seamless workflows.

www.langchain.com/deep-agents

LangChain

Empower teams to create versatile agent-powered applications effortlessly and flexibly.

www.langchain.com/langchain

LangGraph

Empower your agents with low-level control and orchestration for sophisticated workflows.

www.langchain.com/langgraph

Deep Agents Code

Empower coding tasks with customized models and execution governance.

www.langchain.com/dcode

Market segments

Market size by segment

Growth potential (CAGR)

Agent orchestration and workflow automation

11.02 Billion USD22.3% CAGR

Design, configure, coordinate, and execute AI agents and automated workflows that perform multi-step tasks, integrate with enterprise applications, and manage handoffs across teams.

Agent development frameworks

14.58 Billion USD35.36% CAGR

Frameworks and SDKs for building customizable, model-agnostic agent-powered applications and coding agents with multi-model support and developer extensibility.

AI model management and observability

3 Billion USD25% CAGR

Centralized dashboards to manage multiple models and agents, monitor usage and performance, and provide analytics, user management, and governance controls.

Ephemeral development sandboxes

1 Billion USD18% CAGR

Disposable, isolated microVM-based environments that enable safe execution, testing, and automation of development tasks while containing risk and enabling quick teardown and reprovisioning.

Semantic graph and persistent memory platforms

3.5 Billion USD25% CAGR

Systems that provide a shared semantic graph or persistent memory layer to maintain context, state, and governance across an agent fleet, improving coherence, personalization, and auditability.

More information about our offering

LangSmith Platform

LangSmith Platform is LangChain's framework-agnostic agent engineering platform for observing, evaluating, and deploying agents. It helps teams debug every agent decision, run evaluations, and govern deployment across the production lifecycle. The platform includes features for deployment, observability, evaluation, performance improvement, and building no-code agents.

  • Deploy Agents Enterprise-Wide
    Ensure seamless deployment and management of agents across your organization with version control and multi-agent orchestration.
  • Improve Agent Performance Proactively
    Utilize the Engine to continuously analyze production data, diagnose issues, and suggest actionable fixes to enhance agent quality.
  • Continuously Improve Agent Quality
    Implement robust evaluation methods to ensure high performance and reliability of agents through meticulous testing and feedback.
  • Gain Full Visibility into Agent Behavior
    Leverage detailed insights and monitoring tools to track agent performance effectively, helping to identify and address issues promptly.
  • Build No-Code Agents
    Empower non-technical teams to create agents effortlessly with user-friendly templates, enabling quick deployment and scaling.
  • Run Untrusted Code Safely
    Utilize secure environments for running experimental code without risking core infrastructure, ensuring safe execution of agent-generated tasks.

Deep Agents

Deep Agents is an open-source agent harness built for long-running tasks. It provides autonomous task decomposition, parallel work, and persistent memory to support complex agent workflows. Features include context management, model neutrality, and seamless integration with LangSmith for effective monitoring.

  • Retain Knowledge
    Ensure crucial information and skills are preserved for future tasks.
  • Knowledge Storage
    Keep a consistent understanding of tasks and contexts for more informed decision-making.
  • Adaptive Task Handling
    Break complex tasks into manageable steps and track their completion effectively.
  • Control Data Flow
    Manage the flow of information effectively to enhance task efficiency.
  • Seamless Monitoring
    Leverage comprehensive monitoring and evaluation features integrated with LangSmith.
  • Execute Independently
    Improve efficiency by handling multiple tasks simultaneously without interference.
  • Custom Coding Agent
    Utilize a coding agent that aligns with your specific requirements and processes.
  • Flexible Model Use
    Adapt the platform to different models as per changing requirements.

LangChain

LangChain is an open, model-agnostic framework to help teams build agent-powered applications. It provides quick-start capabilities with any model provider and supports diverse use cases, enabling organizations to build custom agents and applications efficiently while retaining flexibility in model choice.

  • Support Any Model Provider
    Provides users the flexibility to integrate various AI models without vendor lock-in, facilitating innovation and adaptability.
  • Leverage Open-Source Technologies
    Utilizes community-driven development and shared knowledge to accelerate the evolution of agent systems, supporting collaboration and rapid iteration.
  • Design Custom Agent Workflows
    Empowers developers to tailor agent behaviors and processes, ensuring solutions fit specific business needs while maintaining user control.
  • Ensure Contextual Continuity
    Supports agents in maintaining knowledge over time, enhancing their ability to deliver personalized experiences based on historical interactions.

LangGraph

LangGraph provides low-level control for building stateful, long-running workflows and agents, enabling fine-grained orchestration across tasks. It integrates seamlessly with LangChain components to support advanced agent patterns.

  • Build Complex Solutions
    Facilitate precise orchestration of agent behaviors and workflows tailored to your needs.
  • Enhance Decision-Making
    Implement quality controls and moderation to ensure agent actions remain aligned with organizational goals.
  • Integrate Seamlessly
    Leverage interoperability with LangChain to build complex agent solutions easily.
  • Enable Rich Interactions
    Allow agents to recall previous conversations and deliver tailored experiences across sessions.
  • Show Real-Time Reasoning
    Enhance user experience by displaying the agent's reasoning process and actions as they occur.

Deep Agents Code

Deep Agents Code is an open-source terminal coding agent built on the Deep Agents SDK. It enables using your own model, customizing the harness, and governing code execution with critical features such as model support and execution governance.

  • Customize Model Execution
    Seamlessly integrate models of your choice, allowing for flexibility in coding tasks.
  • Ensure Code Safety
    Maintain strict oversight on code execution processes, including auditing and approval workflows.
  • Execute Code Securely
    Ensure that code runs in a safe, ephemeral environment without affecting underlying infrastructure.
  • Tailor Agent Functionality
    Modify harness components to fit your specific needs, enhancing the agent's performance and relevance.
  • Enhance Code Learning
    Facilitate long-running interactions by maintaining context, thus improving efficiency and relevance.
  • Access and Modify Code
    Leverage the open-source community for collaboration, improvements, and transparency in development.

References

Methodology and sourcing behind the market figures shown above.

Agent orchestration and workflow automation

Primary estimate uses MarketsandMarkets’ AI Orchestration report, which explicitly sizes the agent-orchestration market (USD 11.02B in 2025) and forecasts a 22.3% CAGR to 2030. This is corroborated by specialist agentic/agent orchestration reports (FMI, SNS Insider) that show similar high-growth forecasts (~21–22% CAGR). Adjacent workflow/process automation sizing (MarketResearchFuture) is larger but projects lower CAGR (~12%), indicating agent orchestration is a faster-growing, more specialized subset.

Agent development frameworks

Primary estimate uses Spherical Insights’ explicit sizing for the agentic AI development platform market (USD 14.58B in 2025; CAGR 35.36%), which specifically covers platforms, frameworks, and tools for building agentic applications. MarketsandMarkets’ broader “AI Agents” market (USD 7.84B in 2025 to USD 52.62B by 2030; CAGR 46.3%) corroborates rapid adoption across agent use-cases and implies strong upside for framework/platform vendors as a subset of the larger agent ecosystem.

AI model management and observability

Used AI-specific observability estimates as primary inputs (Nextmsc: USD 2.94B in 2025) and reconciled with Market.us (USD 1.4B in 2023; USD 10.7B by 2033, 22.5% CAGR), broader observability forecasts (MarketsandMarkets: USD 11.71B in 2026, 12.1% CAGR), and data-observability sizing (Precedence: USD 1.10B in 2025, 11.57% CAGR). The chosen baseline (~USD 3.0B) aligns with recent AI-observability reports; the CAGR (~25%) reflects faster growth for AI/model-focused observability versus the broader observability market, averaging across the published ranges.

Ephemeral development sandboxes

Estimation based on related sandboxing market reports in the search results. Network sandboxing is a large security-focused market (USD 12.5B in 2025; 15.7% CAGR) while AI- and cloud-focused sandbox reports show smaller current bases but higher growth (AI sandboxes USD 3.42B in 2024 at ~22.1% CAGR; cloud sandboxes ~USD 1.2B in 2024 with ~16.5% CAGR). Ephemeral development sandboxes (microVM-based, dev/preview environments, and agent execution sandboxes) are a subset of these markets—closer in use-case to AI/cloud sandboxes—so I estimate an addressable market around USD 1.0B today with higher growth potential than legacy network sandboxes; selected CAGR (18%) is between cloud sandbox and AI-sandbox growth rates reflecting developer tooling adoption plus AI-agent acceleration.

Semantic graph and persistent memory platforms

Estimate is a blended 2025–2026 market size for platforms combining semantic/knowledge-graph capabilities and persistent agent memory. I weighted recent market reports for semantic/knowledge-graph markets (~$1.5–$4.9B) and a focused AI agent memory estimate ($1.2B) to produce a mid-point (~$3.5B). Growth potential (CAGR ~25%) reflects published semantic/web/knowledge-graph CAGRs (≈12–38%) and high agent-memory forecasts (up to 62%), using a conservative midpoint to represent combined-technologies demand from enterprise GenAI and GraphRAG adoption.

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