- Builders evaluating this category
Agents
LangChain
Framework for building LLM-powered applications with chains, agents, tools, and 100+ integrations.
122K Stars
MIT License
langchain-ai Maintainer
2026-06-16 Verified
Overview
What is LangChain?
Framework for building LLM-powered applications with chains, agents, tools, and 100+ integrations.
FAQ
Frequently asked questions
What should I check before using LangChain?
Build one two-step agent flow with a tool call, a state transition, and an observable failure path.
Is LangChain open source?
LangChain is listed on OpenAgent.bot with MIT based on the current resource metadata. Re-check the official repository, docs, and license before production use.
Decision brief
JSON
Should you use LangChain?
- Production adoption without checking source links, permissions, and maintenance.
- Verified 2026-06-16
- License: MIT
- Repo: langchain-ai/langchain
- Open-source signal
Deployment
Check source
Permission surface
memory, external services
Decision signals
Local first, API, CLI
Agent packet
Structured decision data for LangChain
This packet is the compact machine-readable view agents should use before following source links or taking action.
Capabilities
tool calling, workflow orchestration, rag, connectors
Constraints
open source
Deployment
Check source
Permission surface
memory, external services
Recommended workflows
Connector or protocol layer
Overview
What LangChain does
What it is
LangChain is a agent in the agents category. Framework for building LLM-powered applications with chains, agents, tools, and 100+ integrations.
Why it matters
LangChain matters when builders need a clearer way to choose tools by workflow fit, constraints, source quality, and operational risk rather than by category labels alone.
How to evaluate it
Evaluate LangChain by starting from the official sources, checking its interface surface, and running one narrow workflow before expanding scope.
Facts
Known metadata and operating surface
These fields are separated from editorial interpretation so agents can reason over facts and missing checks.
Resource type agent
Category Agents
Maturity active
Difficulty Unknown
License MIT
Pricing open source
Verified 2026-06-16
Source confidence high
Risk level moderate
Fit matrix
Where LangChain fits in an agent stack
strong
Connector or protocol layer
LangChain has multiple signals for connector or protocol layer, including matching tags, capabilities, category, or positioning.
- Connect one low-risk service, then inspect schemas, auth scope, errors, and logs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Coding agent workflow
LangChain has at least one signal for coding agent workflow, but should be checked against a real task before adoption.
- Run a small repository change and inspect the diff, tests, and rollback path.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Memory or RAG workflow
LangChain has at least one signal for memory or rag workflow, but should be checked against a real task before adoption.
- Create, update, retrieve, correct, and delete memory or retrieval objects with real data.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Reusable skill workflow
LangChain has at least one signal for reusable skill workflow, but should be checked against a real task before adoption.
- Run one skill end to end and check whether it produces evidence or structured output.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak
Browser automation
LangChain is not primarily positioned for browser automation in the current metadata.
- Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak
Evaluation and observability
LangChain is not primarily positioned for evaluation and observability in the current metadata.
- Add one repeatable test case and confirm results can run again in review or CI.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
Inputs and outputs
What an agent should inspect
Likely inputs
- Documents, user facts, entities, context, or retrieval queries
- Official setup instructions and a small real workflow
Likely outputs
- Retrieved context, memory updates, graph relations, or citations
- A decision on whether this resource fits the target workflow
Evidence
Sources, claims, and missing checks
Claims are marked separately from source links so future crawlers and reviewers can update them without rewriting the page.
GitHub github
Repository source for code, license, issues, releases, and implementation details.
Docs docsDocumentation source for setup, API shape, and operational behavior.
LangChain is listed as open source.
License metadata: MITLangChain has a recorded GitHub repository: langchain-ai/langchain.
Resource facts and GitHub source link.- Repository freshness has not been recorded.
Next action
How to start evaluating LangChain
Inspect repository
Check license, recent activity, issues, examples, and security-sensitive code paths.
Open sourceRead setup docs
Use docs as the source of truth for installation and supported interfaces.
Open source Compare
Alternatives and nearby resources
Use related resources to compare category fit, license, deployment model, and first-workflow behavior.
FAQ
Common questions about LangChain
What is LangChain used for?
LangChain is used as a agent for agents workflows. The most relevant recorded capabilities are tool calling, workflow orchestration, rag, connectors.
Is LangChain open source?
LangChain is listed as open source with MIT license metadata. Re-check the official repository or source link before production use.
Can agents use LangChain directly?
LangChain has recorded interfaces such as official source links. Agents should prefer the JSON or Markdown profile first, then follow official docs for real execution.
What should I check before production use?
Check source confidence (high), risk level (moderate), license, maintenance freshness, permission surface, required credentials, and whether the first workflow succeeds in a sandbox.