LangChain vs Vellum
Compare coding AI Tools
Open source framework and platform for building reliable AI agents with LangChain LangGraph and LangSmith for tracing evaluation and deployment.
Vellum is an AI agent building platform that combines a prompt playground, evaluation tools, and hosted agent apps so teams can iterate on LLM workflows with debugging and knowledge base support, starting with a free tier and upgrading for more credits.
Feature Tags Comparison
Key Features
- Agent building blocks for tools memory and routing with templates and guards
- Graph based orchestration that models state steps and recovery
- Observability and evaluation with traces datasets and metrics
- Managed deployment for running agents with quotas and policies
- Integrations for models vector stores retrievers and tools
- Cost tracking tokens and latency dashboards for operators
- Free and Pro plans: Pricing starts at $0 with 50 credits and Pro at $25 with 200 builder credits so solo builders can scale testing
- Prompt playground: Compare models side by side and iterate prompts systematically instead of relying on subjective testing
- Evaluations framework: Run repeatable quality tests at scale to detect regressions and track improvements across prompt versions
- Hosted agent apps: Share working agents with teammates through hosted apps for demos
- reviews
- and stakeholder feedback cycles
Use Cases
- Stand up a retrieval augmented assistant with tool use and evals
- Run human in the loop workflows that enforce approvals
- Migrate prototypes from notebooks into traced services
- Standardize agent patterns across teams and languages
- Track costs and failures with span level visibility
- Stress test prompts and tools before a product launch
- Agent prototyping: Build an agent by chatting with AI then refine logic with low code steps and controlled prompt versions
- Prompt iteration: Compare LLM outputs side by side and select prompts that improve accuracy and reduce unwanted variation
- Regression testing: Run evaluations on a saved dataset before release to catch quality drops after model or prompt changes
- RAG apps: Attach a knowledge base and test retrieval behavior with representative questions and strict document scope rules
- Stakeholder demos: Publish hosted agent apps so product and compliance reviewers can test behavior without local setup steps
- Model selection: Evaluate providers and self hosted options with the same tasks to choose the best cost and latency mix for production
Perfect For
software engineers platform teams data engineers solution architects and researchers building production grade agentic applications
product managers, ML engineers, software engineers, data scientists, AI platform teams, prompt engineers, QA and reliability teams, startups building LLM features, teams shipping agent workflows
Capabilities
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