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LangChain

About LangChain

LangChain provides an open agent engineering platform and open source frameworks for building, controlling, and owning agent intelligence. The company's commercial platform is called LangSmith. LangSmith covers building, testing, deploying, monitoring, and iterating on agents. Users can build agents with or without code and choose their own model.

Products

  • LangGraph: Gives developers control over complex agent systems with a runtime built for production.
  • Deep Agents: An agent harness for autonomous, long-horizon tasks.
  • Fleet: Lets every team build agents in natural language.
  • Engine: Detects production issues automatically as an agent for agent engineering.
  • LLM Gateway: Controls model calls, spend, and sensitive data.

Used for

  • Debugging agent runs with step-by-step tracing
  • Querying complex traces using SmithDB
  • Turning expert review into standardized evaluations
  • Measuring quality across production agent runs
  • Tracking cost, latency, errors, and tool behavior

Who it is for

  • Used by teams building and deploying agents.
  • Used by solo users getting started.

From LangChain's own website, read 2026-10-23. How we build listings.

How to evaluate LLM Infrastructure & APIs

This is how the CIOPages Research Team evaluates this category. It is not an assessment of LangChain. It comes from our Generative AI & LLM Platforms buyer guide.

25%
Model Capability & Task Fit
Reasoning and instruction-following on your tasks (not public leaderboards), multimodal coverage (text, image, audio, video) where you need it, context-window length for your documents, breadth of the model menu (frontier, mid, small), and measured quality on a held-out set of your real prompts
20%
Deployment, Data Residency & Governance
Where inference physically runs (multi-tenant API, your VPC, on-prem, air-gapped), explicit no-training-on-your-data and retention commitments, regional residency, SOC 2 / ISO 27001 / HIPAA / FedRAMP and EU AI Act alignment, and DLP, content filtering, and full prompt/response audit logging
15%
Build & Customization Tooling
First-class retrieval-augmented generation and grounding, managed fine-tuning and (where relevant) continued pre-training, an integrated evaluation and regression-testing harness, prompt/version management, and native or tightly integrated vector search over your corpora
15%
Agentic & Orchestration Readiness
Reliable tool/function calling, support for open agent protocols (MCP, A2A), a managed agent runtime with memory and state, multi-agent orchestration, and guardrails — permissions, human-in-the-loop checkpoints, and step-level tracing for autonomous workflows
15%
Portability & Lock-in Risk
How model-agnostic the API is, whether multiple model families are reachable through one interface, the real cost of swapping providers (prompt, tool-schema, and eval rework), reliance on open standards, and whether you can take fine-tuned weights or your data with you on exit
10%
Cost, Throughput & Operability
Token economics at your projected volume, batch and provisioned-throughput options, prompt-caching support, rate limits and quota headroom for production, p95 latency under load, regional availability and uptime SLAs, and built-in usage, cost, and quality observability

CIOPages put this listing together from public sources. It’s information, not an endorsement. How we build listings. Work here? Claim this listing or .

Quick Facts

www.langchain.com Pricing page
CategoryAI & ML Platforms
SubcategoryLLM Infrastructure & APIs

We publish a detail only when we can point at the page it came from. Know when LangChain was founded or where it is based? Send it to us, free.

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