LangChain vs LiteLLM
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | LangChain | LiteLLM |
|---|---|---|
| Pricing | Freemium; paid tiers for LangSmith | Freemium; open-source core with paid enterprise |
| Primary Focus | Agent lifecycle (build, trace, evaluate, deploy) | AI gateway (unified API, cost control) |
| Key Differentiator | LangGraph orchestration, LangSmith observability | Rust-based gateway, 140+ providers, auto router |
| Best For | Complex agent workflows, production deployment | Platform teams, multi-provider cost management |
| Recent Update | Tuned evaluators, >2x better issue detection | Shadow evaluations, day-0 Gemini 3.7 Flash support |
| Deployment | Cloud (LangSmith) or BYOC on AWS | Self-hosted or enterprise |
If you’re building complex, multi-step agents and need deep observability and evaluation, LangChain is your pick. If you’re a platform team unifying access to many models with strict cost and access controls, LiteLLM is the straightforward choice. For most teams, they complement each other: use LangChain for agent logic, LiteLLM in front as the gateway.
LangChain's agent platform: build agents with LangGraph and deepagents, then trace, evaluate and deploy them in LangSmith.
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Self-hosted AI gateway putting 140+ providers, MCP servers, and agents behind one OpenAI-compatible API.
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LangChain centers on the agent lifecycle: LangGraph gives low-level control for deterministic workflows, while Deep Agents add context compression and subagents for autonomy. LangSmith provides step-by-step tracing, SmithDB for fast trace queries, and LangSmith Engine now promises >2x better issue detection. Tuned evaluators (Perceived Error) and human feedback annotation improve evaluation accuracy. You can deploy agents with 30+ endpoints, durable checkpointing, and cron scheduling. LiteLLM, by contrast, is a gateway: it exposes a single OpenAI-compatible API to 140+ providers and 1,800+ models, with day-0 support for new models like Gemini 3.7 Flash. Its Rust core reduces latency, and Auto Router v2 offers semantic and adaptive routing. Virtual keys, SSO, and spend tracking by team/org enable cost controls. Both tools integrate with OpenTelemetry and LangSmith. While LangChain is for building agents, LiteLLM is for managing access to them.
Pricing compared
Both are freemium. LangChain’s open-source libraries are free, but LangSmith (observability/evaluation) uses paid tiers with usage-based costs; BYOC on AWS is available for enterprises. LiteLLM’s gateway core is open-source, so you can self-host for free, but enterprises can pay for advanced features like SSO and support. Recent news: a customer saved 51% on LLM costs using LiteLLM’s Auto Router—useful if you need cost savings. If you need a no-ops managed service, LiteLLM’s self-hosted model requires your own infrastructure; LangChain’s cloud is simpler but costs more at scale. Choose LangChain if you value managed agent infrastructure; choose LiteLLM if you prefer self-hosting and maximizing cost efficiency.
Who should pick which
- Platform EngineerPick: LiteLLM
Needs to give internal teams one API to many providers with virtual keys, budgets, and fallbacks—LiteLLM is built for this.
- AI Engineer building complex agentsPick: LangChain
LangGraph and Deep Agents provide the orchestration and context management needed for multi-step tasks, with LangSmith for tracing and evaluation.
- Enterprise with strict compliancePick: LangChain
LangSmith’s BYOC on AWS and durable checkpointing for long-running agents fit enterprise deployment and security needs.
- Cost-conscious startupPick: LiteLLM
LiteLLM’s open-source gateway can cut costs with auto-routing and spend tracking; recent case shows 51% savings.
- Team needing agent observabilityPick: LangChain
LangSmith Engine’s improved issue detection and SmithDB let you diagnose failures fast—critical for production agents.
Frequently Asked Questions
LangChain vs LiteLLM: which should you choose?
If you’re building complex, multi-step agents and need deep observability and evaluation, LangChain is your pick. If you’re a platform team unifying access to many models with strict cost and access controls, LiteLLM is the straightforward choice. For most teams, they complement each other: use LangChain for agent logic, LiteLLM in front as the gateway.
Can I use LangChain and LiteLLM together?
Yes. LiteLLM can be placed in front of LangChain as a gateway, providing unified API access, cost controls, and load balancing across LLM providers, while LangChain handles agent orchestration.
Which tool is easier to get started with?
If you need a quick API wrapper for many providers, LiteLLM is simple—just route requests. If you’re building agents, LangChain’s templates and Deep Agents speed up the initial setup, but there’s a steeper learning curve for production.
How does LangSmith Engine's detection work?
It autonomously analyzes traces to detect and diagnose issues, and recent updates claim more than doubled accuracy in issue detection.
Is LiteLLM secure after the supply chain attack?
LiteLLM is open-source; the incident affected 2,488 organizations, so you should audit your version and apply updates. For managed security, consider enterprise options or consult the project's security advisories.
What is Auto Router v2 in LiteLLM?
It uses complexity, semantic, and adaptive routing to select the best model per request. Shadow evaluations let you test it on a sample of your production traffic before full rollout.
Does LangChain support MCP servers?
The provided data doesn't mention MCP support for LangChain, but LiteLLM explicitly supports MCP servers and agents through its gateway. Check LangChain's latest docs for any updates.
Which tool is better for cost tracking?
LiteLLM has strong spend tracking by key, user, team, and org, with budget caps. LangChain focuses more on agent observability than cost management, though LangSmith may offer some usage insights.
Can I use LangChain for simple chatbots?
Technically yes, but it's overkill. LiteLLM or even direct API calls are simpler for single-turn Q&A. LangChain excels when you need multi-step reasoning, memory, or external tool use.
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Last reviewed: August 30, 2026