What are the main differences between Langchain and SciPhi?

Langchain and SciPhi differ in features, pricing, and target audience. Langchain excels in specific use cases while SciPhi offers different strengths. Our detailed comparison covers all key differences to help you choose.

Which is better: Langchain or SciPhi?

The better choice between Langchain and SciPhi depends on your specific needs. Consider factors like budget, required features, team size, and technical requirements. Our comparison tool helps you evaluate both options side-by-side.

How much does Langchain cost compared to SciPhi?

Pricing varies between Langchain and SciPhi. Both offer different pricing tiers and models. Check our detailed pricing comparison to see which fits your budget and requirements better.

Can I switch from Langchain to SciPhi easily?

Migration between Langchain and SciPhi depends on your current setup and data. Many tools offer migration assistance or import features. Review our migration guide section for specific steps and considerations.

What integrations do Langchain and SciPhi support?

Both Langchain and SciPhi offer various integrations with popular tools and platforms. Our comparison includes a detailed integration matrix to help you ensure compatibility with your existing tech stack.

Langchain vs SciPhi

Make an informed decision with our comprehensive comparison. Discover which RAG solution perfectly fits your needs.

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Priyansh KhodiyarDevRel at CustomGPT.ai

Fact checked and reviewed by Bill Cava

Published: 01.04.2025Updated: 25.04.2025

In this comprehensive guide, we compare Langchain and SciPhi across various parameters including features, pricing, performance, and customer support to help you make the best decision for your business needs.

Overview

Welcome to the comparison between Langchain and SciPhi!

Here are some unique insights on Langchain:

LangChain is a developer library, not a SaaS. It lets you wire up LLMs, retrievers, and tools however you like. That freedom thrills coders but means you’re responsible for every piece of the puzzle.

And here's more information on SciPhi:

SciPhi is an open-source RAG platform that touts massive scale, hybrid search, and auto-generated knowledge graphs. It’s developer-friendly and highly flexible—if you’re ready for the learning curve.

Enjoy reading and exploring the differences between Langchain and SciPhi.

Detailed Feature Comparison

Features	Langchain	SciPhi	CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources	Takes a code-first approach: plug in document-loader modules for just about any file type—from PDFs with PyPDF to CSV, JSON, or HTML via Unstructured. Lets developers craft custom ingestion and indexing pipelines, so niche or proprietary data sources are no problem.	Handles 40 + formats—from PDFs and spreadsheets to audio—at massive scale Reference. Async ingest auto-scales, crunching millions of tokens per second—perfect for giant corpora Benchmark details. Ingest via code or API, so you can tap proprietary databases or custom pipelines with ease.	Lets you ingest more than 1,400 file formats—PDF, DOCX, TXT, Markdown, HTML, and many more—via simple drag-and-drop or API. Crawls entire sites through sitemaps and URLs, automatically indexing public help-desk articles, FAQs, and docs. Turns multimedia into text on the fly: YouTube videos, podcasts, and other media are auto-transcribed with built-in OCR and speech-to-text. View Transcription Guide Connects to Google Drive, SharePoint, Notion, Confluence, HubSpot, and more through API connectors or Zapier. See Zapier Connectors Supports both manual uploads and auto-sync retraining, so your knowledge base always stays up to date.
Integrations & Channels	Ships without a built-in web UI, so you’ll build your own front-end or pair it with something like Streamlit or React. Includes libraries and examples for Slack (and other platforms), but you’ll handle the coding and config yourself.	Ships a REST RAG API—plug it into websites, mobile apps, internal tools, or even legacy systems. No off-the-shelf chat widget; you wire up your own front end API snippet.	Embeds easily—a lightweight script or iframe drops the chat widget into any website or mobile app. Offers ready-made hooks for Slack, Zendesk, Confluence, YouTube, Sharepoint, 100+ more. Explore API Integrations Connects with 5,000+ apps via Zapier and webhooks to automate your workflows. Supports secure deployments with domain allowlisting and a ChatGPT Plugin for private use cases. Hosted CustomGPT.ai offers hosted MCP Server with support for Claude Web, Claude Desktop, Cursor, ChatGPT, Windsurf, Trae, etc. Read more here. Supports OpenAI API Endpoint compatibility. Read more here.
Core Chatbot Features	Provides retrieval-augmented QA chains that blend LLM answers with data fetched from vector stores. Supports multi-turn dialogue through configurable memory modules; you’ll add source citations manually if you need them. Lets you build agents that call external APIs or tools for more advanced reasoning.	Core RAG engine serves retrieval-grounded answers; hook it to your UI for multi-turn chat. Multi-lingual if the LLM you pick supports it. Lead-capture or human handoff flows are yours to build through the API.	Reduces hallucinations by grounding replies in your data and adding source citations for transparency. Benchmark Details Handles multi-turn, context-aware chats with persistent history and solid conversation management. Speaks 90+ languages, making global rollouts straightforward. Includes extras like lead capture (email collection) and smooth handoff to a human when needed.
Customization & Branding	Gives you the framework to design any UI you want, but offers no out-of-the-box white-label or branding features. Total freedom to match corporate branding—just expect extra lift to build or integrate your own interface.	Fully bespoke—design any UI you want and skin it to match your brand. SciPhi focuses on the back end, so front-end look-and-feel is entirely up to you.	Fully white-labels the widget—colors, logos, icons, CSS, everything can match your brand. White-label Options Provides a no-code dashboard to set welcome messages, bot names, and visual themes. Lets you shape the AI’s persona and tone using pre-prompts and system instructions. Uses domain allowlisting to ensure the chatbot appears only on approved sites.
L L M Model Options	Is completely model-agnostic—swap between OpenAI, Anthropic, Cohere, Hugging Face, and more through the same interface. Easily adjust parameters and pick your embeddings or vector DB (FAISS, Pinecone, Weaviate) in just a few lines of code.	LLM-agnostic—GPT-4, Claude, Llama 2, you choose. Pick, fine-tune, or swap models anytime to balance cost and performance Model options.	Taps into top models—OpenAI’s GPT-4, GPT-3.5 Turbo, and even Anthropic’s Claude for enterprise needs. Automatically balances cost and performance by picking the right model for each request. Model Selection Details Uses proprietary prompt engineering and retrieval tweaks to return high-quality, citation-backed answers. Handles all model management behind the scenes—no extra API keys or fine-tuning steps for you.
Developer Experience ( A P I & S D Ks)	Comes as a Python or JavaScript library you import directly—there’s no hosted REST API by default. Extensive docs, tutorials, and a huge community smooth the learning curve—but you do need programming skills. Reference	REST API plus a Python client (`R2RClient`) handle ingest and query tasks Docs and GitHub repos offer deep dives and open-source starter code SciPhi GitHub.	Ships a well-documented REST API for creating agents, managing projects, ingesting data, and querying chat. API Documentation Offers open-source SDKs—like the Python `customgpt-client`—plus Postman collections to speed integration. Open-Source SDK Backs you up with cookbooks, code samples, and step-by-step guides for every skill level.
Integration & Workflow	Chain together LLM calls, retrievers, and prompt templates directly in code to create custom workflows. Fits into CI/CD and event-driven architectures, though you’ll script the automation yourself.	Runs as a microservice—slot it into doc pipelines, CRMs, or any enterprise workflow. Trigger ERP updates or support tickets straight from query escalations Integration overview.	Gets you live fast with a low-code dashboard: create a project, add sources, and auto-index content in minutes. Fits existing systems via API calls, webhooks, and Zapier—handy for automating CRM updates, email triggers, and more. Auto-sync Feature Slides into CI/CD pipelines so your knowledge base updates continuously without manual effort.
Performance & Accuracy	Accuracy hinges on your chosen LLM and prompt engineering—tune them well for top performance. Response speed depends on the model and infra you choose; any extra optimization is up to your deployment.	Hybrid search (dense + keyword) keeps retrieval fast and sharp. Knowledge-graph boosts (HybridRAG) drive up to 150 % better accuracy Sub-second latency—even at enterprise scale.	Delivers sub-second replies with an optimized pipeline—efficient vector search, smart chunking, and caching. Independent tests rate median answer accuracy at 5/5—outpacing many alternatives. Benchmark Results Always cites sources so users can verify facts on the spot. Maintains speed and accuracy even for massive knowledge bases with tens of millions of words.
Customization & Flexibility ( Behavior & Knowledge)	Gives you full control over prompts, retrieval settings, and integration logic—mix and match data sources on the fly. Makes it possible to add custom behavioral rules and decision logic for highly tailored agents.	Add new sources, tweak retrieval, mix collections—everything’s programmable. Chain API calls, re-rank docs, or build full agentic flows	Lets you add, remove, or tweak content on the fly—automatic re-indexing keeps everything current. Shapes agent behavior through system prompts and sample Q&A, ensuring a consistent voice and focus. Learn How to Update Sources Supports multiple agents per account, so different teams can have their own bots. Balances hands-on control with smart defaults—no deep ML expertise required to get tailored behavior.
Pricing & Scalability	LangChain itself is open-source and free; costs come from the LLM APIs and infrastructure you run underneath. Scaling is DIY: you manage hosting, vector-DB growth, and cost optimization—potentially very efficient once tuned.	Free tier plus a $25/mo Dev tier for experiments. Enterprise plans with custom pricing and self-hosting for heavy traffic Pricing.	Runs on straightforward subscriptions: Standard (~$99/mo), Premium (~$449/mo), and customizable Enterprise plans. Gives generous limits—Standard covers up to 60 million words per bot, Premium up to 300 million—all at flat monthly rates. View Pricing Handles scaling for you: the managed cloud infra auto-scales with demand, keeping things fast and available.
Security & Privacy	Security is fully in your hands—deploy on-prem or in your own cloud to meet whatever compliance rules you have. No built-in security stack; you’ll add encryption, authentication, and compliance tooling yourself.	Customer data stays isolated in SciPhi Cloud; self-host for full control. Standard encryption in transit and at rest; tune self-hosted setups to meet any regulation.	Protects data in transit with SSL/TLS and at rest with 256-bit AES encryption. Holds SOC 2 Type II certification and complies with GDPR, so your data stays isolated and private. Security Certifications Offers fine-grained access controls—RBAC, two-factor auth, and SSO integration—so only the right people get in.
Observability & Monitoring	You’ll wire up observability in your app—LangChain doesn’t include a native analytics dashboard. Tools like LangSmith give deep debugging and monitoring for tracing agent steps and LLM outputs. Reference	Dev dashboard shows real-time logs, latency, and retrieval quality Dashboard. Hook into Prometheus, Grafana, or other tools for deep monitoring.	Comes with a real-time analytics dashboard tracking query volumes, token usage, and indexing status. Lets you export logs and metrics via API to plug into third-party monitoring or BI tools. Analytics API Provides detailed insights for troubleshooting and ongoing optimization.
Support & Ecosystem	Backed by an active open-source community—docs, GitHub discussions, Discord, and Stack Overflow are all busy. A wealth of community projects, plugins, and tutorials helps you find solutions fast. Reference	Community help via Discord and GitHub; Enterprise customers get dedicated support Open-source core encourages community contributions and integrations.	Supplies rich docs, tutorials, cookbooks, and FAQs to get you started fast. Developer Docs Offers quick email and in-app chat support—Premium and Enterprise plans add dedicated managers and faster SLAs. Enterprise Solutions Benefits from an active user community plus integrations through Zapier and GitHub resources.
Additional Considerations	Total freedom to pick and swap models, embeddings, and vector stores—great for fast-evolving solutions. Can power innovative, multi-step, tool-using agents, but reaching enterprise-grade polish takes serious engineering time.	Advanced extras like GraphRAG and agentic flows push beyond basic Q&A Great fit for enterprises needing deeply customized, fully integrated AI solutions.	Slashes engineering overhead with an all-in-one RAG platform—no in-house ML team required. Gets you to value quickly: launch a functional AI assistant in minutes. Stays current with ongoing GPT and retrieval improvements, so you’re always on the latest tech. Balances top-tier accuracy with ease of use, perfect for customer-facing or internal knowledge projects.
No- Code Interface & Usability	Offers no native no-code interface—the framework is aimed squarely at developers. Low-code wrappers (Streamlit, Gradio) exist in the community, but a full end-to-end UX still means custom development.	No no-code UI—built for devs to wire into their own front ends. Dashboard is utilitarian: good for testing and monitoring, not for everyday business users.	Offers a wizard-style web dashboard so non-devs can upload content, brand the widget, and monitor performance. Supports drag-and-drop uploads, visual theme editing, and in-browser chatbot testing. User Experience Review Uses role-based access so business users and devs can collaborate smoothly.

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Final Thoughts

We hope you found this comparison of Langchain vs SciPhi helpful.

Choose LangChain when you want to craft something unique and don’t mind handling setup, hosting, and maintenance yourself.

SciPhi can power a deeply customized RAG stack, but expect significant engineering effort. If speed to launch trumps fine-grained control, you might look to a simpler managed service.

Stay tuned for more updates!

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Priyansh Khodiyar

DevRel at CustomGPT.ai. Passionate about AI and its applications. Here to help you navigate the world of AI tools and make informed decisions for your business.