Laguna by Poolside vs LangChain: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Laguna by Poolside and LangChain — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Laguna by Poolside
Poolside
Poolside's family of open Mixture-of-Experts foundation models for agentic coding — XS.2 runs locally, M.1 reaches 72.5% on SWE-bench Verified.
Key features
- Two Model Sizes: Laguna XS.2 (33B total / 3B active) and Laguna M.1 (225B total / 23B active) target different latency and capability needs.
- Mixture-of-Experts Architecture: Routes each token through a subset of experts for efficiency at large scale.
- Local Deployment: XS.2 is small enough to run on a Mac with 36 GB of RAM via Ollama under an Apache 2.0 license.
- Strong SWE-bench Results: XS.2 hits 68.2% and M.1 reaches 72.5% on SWE-bench Verified.
- Bundled Coding Agent: Ships 'pool,' a lightweight terminal-based coding agent.
- Agent Client Protocol: Includes a dual ACP client-server used internally for agent RL training and evaluation.
Best for
- Local Agentic Coding: Running XS.2 on a laptop for private, offline code generation and editing.
- High-Capability Code Tasks: Using M.1 for harder, long-horizon software engineering work.
- Self-Hosted Deployments: Building on open weights to avoid third-party API dependencies.
- Research & Fine-Tuning: Adapting permissively licensed weights for custom coding workflows.
- Benchmarking: Evaluating agentic coding performance against SWE-bench Verified and Pro.
LangChain
LangChain Inc.
Framework for building LLM-powered applications with chains, agents, integrations, retrieval, and vector store support.
Key features
- Unified Model Abstractions: Provides a standard interface to connect and swap LLM providers and models, allowing consistent calls to completions, chat, and embeddings across backends.
- Chains and Pipelines: Compose modular chains of prompts, parsers, and logic to build multi-step application flows and reusable pipelines for reasoning and data processing.
- Agent Framework and Tool Calling: Offers agent patterns enabling LLMs to decide actions, call external tools/APIs, observe results, and iterate to solve complex tasks autonomously.
- Retrieval-Augmented Generation (RAG): Built-in support for vector stores, dense retrieval, and RAG workflows to ground responses in external documents and knowledge bases.
- Integrations Ecosystem: Connectors for popular vector databases, storage systems, LLM providers, and third-party tools so applications can access real data and services securely.
- LangGraph and Orchestration: Complementary tooling (e.g., LangGraph) for designing, visualizing, and running controllable, multi-actor agent workflows and stateful graphs.
- Multi-language SDKs and Community Ports: Official and community implementations (Python, JavaScript/TypeScript, Java, Elixir, etc.) to support diverse deployment environments.
- Extensive Documentation and Guides: Tutorials, how-to guides, conceptual references, and a community forum to help developers implement best practices and advanced patterns.
- Standardized interfaces for models, embeddings, and vector stores
- Chains to compose prompt flow, parsers, and multi-step logic
- Agent abstractions with tool calling, observation loop and orchestration
- LangGraph for controllable, production-grade agent workflows and multi-actor graphs
- Retrieval-Augmented Generation (RAG) patterns and retrieval integrations
- Broad integrations with third-party LLM providers, vector DBs and tools
- Multi-language SDKs and ports (Python, TypeScript/JavaScript, LangChain4j for Java, Elixir implementations)
- Supported JS/TS runtime environments: Node.js (ESM & CommonJS 18.x–22.x), Cloudflare Workers, Vercel/Next.js (Browser/Serverless/Edge), Supabase Edge Functions, Browser, Deno
- Extensive docs, tutorials, how-to guides, and API reference
- Package installation and distribution (pip package for Python: pip install -U langchain; npm/ts packages for JS)
Best for
- RAG Chatbots and Assistants: Build chat interfaces that retrieve and synthesize information from company documents, knowledge bases, or indexed files for accurate, context-aware answers.
- Autonomous Agents and Automation: Create agents that call APIs, run code, and orchestrate external services to complete multi-step tasks like booking, debugging, or data processing.
- Semantic Search and Document Understanding: Implement semantic retrieval and QA over large document collections using embeddings and vector stores for discovery and analytics.
- Tool-Enhanced Workflows: Enable LLMs to invoke domain-specific tools (calculators, search, databases) safely for actions such as financial analysis, content generation, or system queries.
- Prototype to Production LLM Apps: Rapidly prototype chains and agents locally and scale to production with standardized abstractions and integrations across model providers.
- Multi-Actor and Stateful Applications: Design complex, stateful applications involving multiple agents or actors using graph-based orchestration to model interactions and data flow.
- Build retrieval-augmented chatbots and assistants using vector stores and embeddings
- Create autonomous agents that call tools and orchestrate multi-step tasks
- Prototype and productionize LLM-powered features in web, serverless and edge environments
- Integrate LLM capabilities into Java and enterprise applications via LangChain4j
- Compose complex workflows and multi-actor applications using LangGraph
- Implement RAG pipelines, knowledge-grounded QA, and document understanding systems