What LangChain believes
Distilled positions on specific topics, each backed by citations to their public posts. Older versions of a belief are preserved when the position evolves.
Agent harnesses are essential, enduring scaffolding for building LLM-based agents, managing tool interactions and context, including memory, and will persist as models improve.
Earlier version · 2026-05-07
BEFORE: Agent harnesses are the essential scaffolding for building LLM-based agents, managing tool interactions and context, including memory, and will persist as models improve. | AFTER: Agent harnesses are essential, enduring scaffolding for building LLM-based agents, managing tool interactions and context, including memory, and will persist as models improve.
Agent harnesses are the essential scaffolding for building LLM-based agents, managing tool interactions and context, including memory, and will persist as models improve.
Memory is a core function of an agent harness, not a separate plugin, and open, user-owned memory stores are crucial to avoid vendor lock-in and proprietary control by model providers.
Earlier version · 2026-05-07
BEFORE: Memory is a core function of the agent harness, not a separate plugin, and open harnesses are crucial for user-owned memory stores to prevent vendor lock-in by model providers. | AFTER: Memory is a core function of an agent harness, not a separate plugin, and open, user-owned memory stores are crucial to avoid vendor lock-in and proprietary control by model providers.
Memory is a core function of the agent harness, not a separate plugin, and open harnesses are crucial for user-owned memory stores to prevent vendor lock-in by model providers.
Asynchronous subagents, managed via the Agent Protocol, are critical for addressing blocking issues in complex agent workflows, allowing supervisors to manage multiple parallel tasks and maintain control.
Earlier version · 2026-05-07
BEFORE: Asynchronous subagents, managed via Agent Protocol, are critical for addressing blocking issues in complex agent workflows, enabling supervisors to manage multiple parallel tasks and scale effectively | AFTER: Asynchronous subagents, managed via the Agent Protocol, are critical for addressing blocking issues in complex agent workflows, allowing supervisors to manage multiple parallel tasks and maintain cont
Asynchronous subagents, managed via Agent Protocol, are critical for addressing blocking issues in complex agent workflows, enabling supervisors to manage multiple parallel tasks and scale effectively.
AI has significant potential to disrupt and solve long-standing problems in traditionally legacy fields like real estate and construction, catalyzing innovation and broader adoption.
Generative UI, spanning controlled, declarative, and fully open-ended approaches, enables agent-mediated interactions across various applications, with future emphasis on continuous learning from human feedback in production agents.
Earlier version · 2026-05-07
BEFORE: Generative UI, spanning from controlled to fully open-ended, enables agent-mediated interactions across various applications, with future developments focusing on continuous learning from human feedba | AFTER: Generative UI, spanning controlled, declarative, and fully open-ended approaches, enables agent-mediated interactions across various applications, with future emphasis on continuous learning from huma
Generative UI, spanning from controlled to fully open-ended, enables agent-mediated interactions across various applications, with future developments focusing on continuous learning from human feedback and emerging standards.
AI agents require domain-specific tacit knowledge and human judgment, integrated through an 'agent improvement loop' with rapid builds, production monitoring, and data-driven refinements, to handle unwritten conventions and technical nuances effectively.
Earlier version · 2026-05-07
BEFORE: AI agents require domain-specific tacit knowledge from human experts to enhance workflow design, tool selection, and context engineering, integrated through an 'agent improvement loop' with data-drive | AFTER: AI agents require domain-specific tacit knowledge and human judgment, integrated through an 'agent improvement loop' with rapid builds, production monitoring, and data-driven refinements, to handle un
AI agents require domain-specific tacit knowledge from human experts to enhance workflow design, tool selection, and context engineering, integrated through an 'agent improvement loop' with data-driven refinements.
Open-source Deep Agents, deployed with production infrastructure via a single command, offer model-agnostic deployment and avoid vendor lock-in by using open standards and self-hostable memory.
Earlier version · 2026-05-07
BEFORE: Open-source Deep Agents, combined with production infrastructure, allow for model-agnostic deployment and avoid vendor lock-in by utilizing open standards and self-hostable memory. | AFTER: Open-source Deep Agents, deployed with production infrastructure via a single command, offer model-agnostic deployment and avoid vendor lock-in by using open standards and self-hostable memory.
Open-source Deep Agents, combined with production infrastructure, allow for model-agnostic deployment and avoid vendor lock-in by utilizing open standards and self-hostable memory.