LangChain
A composable framework for building agent and LLM applications with interchangeable model integrations.
- Source repo
- langchain-ai/langchain
- Stars
- ★ 149k
- Last updated
- today
- License
- MIT
- Primary language
- Python
- FA score
- 41/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platformOpenAI API
- Cost
- Free software; you pay for model usage
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- A Python developer who needs to invoke OpenAI GPT-5.5 with init_chat_model and pass the result into an application.
- Not a fit if
- Teams wanting a ready-made hosted service without writing code
- Teams wanting complex orchestration without adding LangGraph
- Source review
- 41/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
LangChain is a framework for building agents and LLM-powered applications, not a single hosted agent service. It combines interoperable components and third-party integrations through standard interfaces for models, embeddings, vector stores, and more. Developers can initialize a chat model with init_chat_model and obtain an invocation result with invoke. For more controllable complex workflows, the project points to LangGraph as its lower-level orchestration framework, while Deep Agents provides higher-level patterns such as planning, subagents, and file-system use. The supplied README does not document standalone LangChain deployment; it points to LangSmith Deployment for deploying and scaling agents.
Install the package with uv add langchain. In Python, import init_chat_model from langchain.chat_models, create a model with init_chat_model("openai:gpt-5.5"), then call model.invoke("Hello, world!") and store the returned value in result. The framework also exposes standard interfaces for models, embeddings, and vector stores, and connects to integrations including chat and embedding models, tools, and toolkits. The supplied material does not specify the configuration for those integrations or the exact output format.
- A Python developer who needs to invoke OpenAI GPT-5.5 with init_chat_model and pass the result into an application.
- An engineering team testing model choices that wants to swap model implementations without rebuilding the application.
- A team assembling an LLM application from model, embedding, and vector-store components.
- A developer who needs more controllable orchestration for complex tasks and can use LangGraph alongside LangChain.
- A team building complex agents that need planning, subagents, or file-system usage patterns and wants to evaluate Deep Agents.
How do you install or deploy this agent?
The documented installation command is: uv add langchain. The supplied README does not specify a Python version, how to set OpenAI credentials, environment-variable names, or provider authentication steps.
How do you use this agent?
Minimum invocation: from langchain.chat_models import init_chat_model; model = init_chat_model("openai:gpt-5.5"); result = model.invoke("Hello, world!"). This example explicitly uses an OpenAI model; the credentials and configuration needed to execute it are not documented in the supplied material. No further execution details are documented in the supplied material.
What are this agent's strengths and limitations?
- Provides standard interfaces for composable application components including models, embeddings, and vector stores.
- Explicitly emphasizes model interchangeability, which suits teams evaluating model choices over time.
- Has a documented layered ecosystem: LangGraph, Deep Agents, LangSmith, and integrations.
- The supplied material provides only the uv install command, not Python-version, authentication, or environment-variable setup.
- Its minimal example depends on the named OpenAI model openai:gpt-5.5, requiring provider access configuration that is not described here.
- No standalone deployment procedure for LangChain is documented in the supplied material; deployment is directed to LangSmith Deployment.
- More controllable workflows require LangGraph, while higher-level planning and subagent patterns are directed to Deep Agents.
How does this agent compare with similar options?
LangChain is positioned for composing general agent and LLM application components; LangGraph is positioned as the lower-level framework for controllable orchestration of complex tasks. For JavaScript and TypeScript, the README points to the equivalent LangChain.js library.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| LangChain This agent | 41 · Major gaps | Library / SDKFree + model costs | ★ 149k | today | Python | OpenAI API |
| Agentset | 33 · Major gaps | Hosted serviceFreemium | ★ 2.1k | 8d ago | TypeScript | — |
| Open Multi-Agent Canvas | 43 · Major gaps | Web appFree + model costs | ★ 556 | 5mo ago | TypeScript | — |
| LangGraph Course Repo — Build LLM Agents Hands-On | 33 · Major gaps | CLIFree + model costs | ★ 746 | 6d ago | — | OpenAI API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows MIT license and GitHub Actions workflows, but no specific implementation for least privilege, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. Therefore all trust criteria score 0.
Self-consistency: README and code structure are consistent, but no runtime verification. Dependency availability: Uses uv and lock files, but no guarantee of dependency availability. Failure messages: No explicit error handling or user-friendly failure messages found in code.
Audience and scenarios: README clearly targets developers, provides quickstart and ecosystem overview. Capability boundaries: Documentation mentions components and integrations, but boundaries not explicit. Trigger precision: No trigger mechanism provided. Environment fit: Supports Python and uv, but no other environment configurations.
Information architecture: README well-structured with documentation links. Install notes: Provides uv add langchain. Naming stability: No naming conventions provided. Examples and FAQ: Quickstart example provided, but no FAQ. Known limitations: Not mentioned. License: MIT license. Versioning and changelog: Not provided. Maintenance responsibility: Not explicit.
Output usability: Quickstart example provided, but no output format. Marginal value: Provides model interoperability and rapid prototyping, but not quantified. Cost-benefit: No cost information.
Claim traceability: Claims in README not supported by evidence. Cross-source corroboration: Not provided. Fact-inference separation: Not distinguished.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- No security mechanisms such as least privilege, user confirmation, or data flow transparency are provided; use with caution.
- Dependency security is not verified; there may be known vulnerabilities.
- No rollback mechanism is provided; updates may introduce risks.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
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Release notes excerpt
Changes since langchain-core==1.7.5 release(core): 1.7.6 (#41698) perf(core): merge streamed AIMessageChunks once when accumulating runnable streams (#41697) perf(core): stop parse_partial_json from retrying one character at a time (#41467)View on GitHub ↗ -
Release notes excerpt
Changes since langchain-fireworks==1.7.1 fix(fireworks): report streamed token usage once (#41693) release(fireworks): 1.7.2 (#41690) fix(fireworks): report the served model in response metadata (#41686) chore(model-profiles): refresh model profile data (#41669) fix(fireworks): promote reranking out of beta (#41551) chore(model-profiles): refresh model profile data (#41432) chore(infra): relax third-party dependency upper bounds (#41407) chore(model-profiles): refresh model profile data (#41354) chore(deps): bump certifi from 2025.8.3 to 2026.10.19 in /libs/partners/fireworks (#41387) …View on GitHub ↗ -
Release notes excerpt
Changes since langchain==1.4.8 release(langchain): 1.4.9 (#41684) chore(deps): require langchain-core>=1.7.5 for refusal handling (#41682) feat(core): standardize model refusal content blocks (#41582)View on GitHub ↗