--- title: "chat-langchain vs agents-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/langchain-ai-chat-langchain-vs-pguso-agents-from-scratch" tools: ["langchain-ai-chat-langchain", "pguso-agents-from-scratch"] --- # chat-langchain vs agents-from-scratch *GraphCanon updated Aug 15, 2026* ## Verdict Pick chat-langchain if chat-langchain is a documentation assistant that leverages managed deep agents and LangChain middleware to provide on-topic responses and support knowledge base queries; pick agents-from-scratch if agents-from-scratch is for those who want absolute control over their AI agent development using only local resources and Python, focusing on deep learning without relying on external frameworks or cloud dependencies. [chat-langchain](https://chat.langchain.com) reports 6.4k GitHub stars, 1.5k forks, and 68 open issues, last pushed Aug 13, 2026. [agents-from-scratch](https://github.com/pguso/agents-from-scratch) has 954 stars, 240 forks, and 3 open issues, last pushed Jul 25, 2026. Figures are from public GitHub metadata via [chat-langchain's repository](https://github.com/langchain-ai/chat-langchain) and [agents-from-scratch's repository](https://github.com/pguso/agents-from-scratch). | | [chat-langchain](/tools/langchain-ai-chat-langchain.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Tagline | A documentation assistant demonstrating managed deep agent deployment and LangChain agents. | Build AI agents locally without relying on frameworks or cloud APIs. | | Stars | 6,433 | 954 | | Forks | 1,488 | 240 | | Open issues | 68 | 3 | | Language | TypeScript | Python | | Adopt for | Chat-langchain is a documentation assistant that leverages managed deep agents and LangChain middleware to provide on-topic responses and support knowledge base queries. | agents-from-scratch is for those who want absolute control over their AI agent development using only local resources and Python, focusing on deep learning without relying on external frameworks or cloud dependencies. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes. | | Categories | AI Agents, Inference & Serving | AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [chat-langchain](/tools/langchain-ai-chat-langchain.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 1d | 18d | | Open issues (now) | 68 | 3 | | Stars delta | +27 (30d) | Unknown | | Open issues delta | +20 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/langchain-ai-chat-langchain/trust.md) | [trust report](/tools/pguso-agents-from-scratch/trust.md) | ## Shared compatibility - **Python**: [chat-langchain](/tools/langchain-ai-chat-langchain.md) - Python runtime; [agents-from-scratch](/tools/pguso-agents-from-scratch.md) - Python runtime ## Decision facts: chat-langchain - **Adopt for:** Chat-langchain is a documentation assistant that leverages managed deep agents and LangChain middleware to provide on-topic responses and support knowledge base queries. ## Decision facts: agents-from-scratch - **Requirements:** Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs. - **Adopt for:** agents-from-scratch is for those who want absolute control over their AI agent development using only local resources and Python, focusing on deep learning without relying on external frameworks or cloud dependencies. - **License detail:** MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes. ## Choose when ### Choose chat-langchain if… - chat-langchain is primarily TypeScript; agents-from-scratch is Python. - Tags unique to chat-langchain: conversation guardrails, documentation assistant, langchain agents, managed deep agents. - Also covers Inference & Serving. - You need a specialized tool for accessing help and information about LangChain technologies, such as LangGraph and LangSmith. ### Choose agents-from-scratch if… - agents-from-scratch is primarily Python; chat-langchain is TypeScript. - Requirements: Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs.. - Tags unique to agents-from-scratch: agent-architecture, ai-agents, llm, local-llm. - Also covers Developer Tools. - You plan to teach yourself or others about the fundamentals of creating AI agents from ground zero with complete transparency into each layer of architecture. ## When NOT to use chat-langchain - Your team prefers to use general-purpose AI agents over those specialized for a specific technology stack like LangChain. - You do not require managed deployment services and prefer more control over deployment configurations through traditional methods rather than Managed Deep Agents. ## When NOT to use agents-from-scratch - You are working on an application that needs to be deployed quickly. The tool's approach from first principles can be time-consuming compared to using established frameworks. - If you need scalability or cloud capabilities such as easy scaling with demand, this tool will not provide these features since it strictly operates on local infrastructure. ## Common questions ### What is the difference between chat-langchain and agents-from-scratch? chat-langchain: A documentation assistant demonstrating managed deep agent deployment and LangChain agents.. agents-from-scratch: Build AI agents locally without relying on frameworks or cloud APIs.. See the comparison table for live GitHub stats and shared categories. ### When should I choose chat-langchain over agents-from-scratch? Choose chat-langchain over agents-from-scratch when chat-langchain is primarily TypeScript; agents-from-scratch is Python; Tags unique to chat-langchain: conversation guardrails, documentation assistant, langchain agents, managed deep agents; Also covers Inference & Serving; You need a specialized tool for accessing help and information about LangChain technologies, such as LangGraph and LangSmith. ### When should I choose agents-from-scratch over chat-langchain? Choose agents-from-scratch over chat-langchain when agents-from-scratch is primarily Python; chat-langchain is TypeScript; Requirements: Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs.; Tags unique to agents-from-scratch: agent-architecture, ai-agents, llm, local-llm; Also covers Developer Tools; You plan to teach yourself or others about the fundamentals of creating AI agents from ground zero with complete transparency into each layer of architecture. ### When should I avoid chat-langchain? Your team prefers to use general-purpose AI agents over those specialized for a specific technology stack like LangChain. You do not require managed deployment services and prefer more control over deployment configurations through traditional methods rather than Managed Deep Agents. ### When should I avoid agents-from-scratch? You are working on an application that needs to be deployed quickly. The tool's approach from first principles can be time-consuming compared to using established frameworks. If you need scalability or cloud capabilities such as easy scaling with demand, this tool will not provide these features since it strictly operates on local infrastructure. ### Is chat-langchain or agents-from-scratch more popular on GitHub? chat-langchain has more GitHub stars (6,433 vs 954). Stars measure visibility, not whether either tool fits your constraints. ### Are chat-langchain and agents-from-scratch open source? Yes - both are open-source projects on GitHub (chat-langchain: MIT, agents-from-scratch: MIT). ### Where can I find alternatives to chat-langchain or agents-from-scratch? GraphCanon lists graph-backed alternatives at [chat-langchain alternatives](/tools/langchain-ai-chat-langchain/alternatives) and [agents-from-scratch alternatives](/tools/pguso-agents-from-scratch/alternatives) ([chat-langchain markdown twin](/tools/langchain-ai-chat-langchain/alternatives.md), [agents-from-scratch markdown twin](/tools/pguso-agents-from-scratch/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/langchain-ai-chat-langchain-vs-pguso-agents-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, chat-langchain or agents-from-scratch? chat-langchain: Very active. agents-from-scratch: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for chat-langchain and agents-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [chat-langchain trust report](/tools/langchain-ai-chat-langchain/trust); [agents-from-scratch trust report](/tools/pguso-agents-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=langchain-ai-chat-langchain`](/api/graphcanon/graph?tool=langchain-ai-chat-langchain) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_