Agno vs LangChain: agent frameworks compared
Updated By Agent Radar3 min read
Short answer
Agno vs LangChain compared from repo metadata and docs: Agno targets running agents as a service, LangChain a model-agnostic agent API.
Top pick: Agno. Agno's docs say it helps companies run agents as a service, exposing them through a REST API and MCP server. The repository describes it as a way to build, run, and manage agent platforms.
Agno and LangChain are both open-source Python agent frameworks, but their documented emphasis differs. Agno's docs describe running agents as a service through a REST API, MCP server and AgentOS. LangChain's docs show a create_agent API across many model providers. Checked 2026-10-14; ordering is editorial.
Quick comparison
| Tool | Best fit (from sources) | License | Pricing as stated | Primary source |
|---|---|---|---|---|
| Agno | Running agents as a service with durable execution, REST API and MCP server | Apache-2.0 (GitHub API) | not stated | Agno docs |
| LangChain | Building agents with create_agent across multiple model providers | MIT (GitHub API) | not stated | LangChain overview |
How the options differ
Focus and surfaces
Agno's docs say it helps companies run agents as a service. They describe it as the orchestration and service layer around agents, exposed through a REST API and MCP server. Agents can be made available in Slack, WhatsApp and Telegram using Interfaces.
LangChain's repository describes itself as "The agent engineering platform." The docs overview shows agents created with create_agent, a list of tools and a system prompt. The excerpts do not describe a hosted service layer comparable to AgentOS.
Build and run model
Agno separates building from running. Per its docs, you build agents with the Agno SDK, Claude Agent SDK, Codex SDK or another supported framework. You then run them via AgentOS and manage them with the AgentOS UI.
LangChain's examples show a single create_agent call that takes a model string. The docs excerpts show examples for OpenAI, Google GenAI, Anthropic, OpenRouter, Fireworks, Baseten, Ollama, Azure OpenAI, Bedrock and Hugging Face.
License and repository
Agno's repository reports Apache-2.0 and was created on 2022-05-04. LangChain's repository reports MIT and was created on 2022-10-17. The LangChain repository topics include langgraph, and a LangGraph hub is listed as an alternative.
How we compared
We read the GitHub repository metadata and the product documentation pages for both tools, checked 2026-10-14. We did not install either framework, run code, benchmark, or test any deployment path. Claims come only from those pages.
Limitations
The cited sources do not state pricing for either tool, hosted-service costs, performance, or adoption figures. They do not compare the two tools directly. They do not say whether Agno supports the same model providers as LangChain, or whether LangChain offers durable execution or multi-replica scaling.
See the hubs for Agno, langchain and Frameworks, plus the related guide Agno vs Google ADK.
The tools
In rank orderAgno's docs say it helps companies run agents as a service, exposing them through a REST API and MCP server. The repository describes it as a way to build, run, and manage agent platforms.
Pros
- Docs describe durable execution and multi-replica scaling for running agents as a service
- Agents can be built with the Agno SDK, Claude Agent SDK, Codex SDK or another supported framework
- Apache-2.0 license reported by the GitHub API
Cons
- Pricing not stated in the cited sources
- Model provider support not detailed in the cited excerpts
The LangChain repository describes it as the agent engineering platform. Its docs show agents created with create_agent across many model providers.
Pros
- Docs show examples for providers including OpenAI, Anthropic, Google GenAI, Bedrock, Ollama and Hugging Face
- MIT license reported by the GitHub API
- Repository topics include python, typescript, rag and multiagent
Cons
- Pricing not stated in the cited sources
- Hosted deployment or service layer not described in the cited excerpts
Questions
What is the main difference between Agno and LangChain?
Agno's docs emphasize running agents as a service through a REST API, MCP server and AgentOS. LangChain's docs emphasize creating agents with create_agent across many model providers.
Are Agno and LangChain open source?
The GitHub API reports Apache-2.0 for Agno and MIT for LangChain. Hosted pricing is not stated in the cited sources.
Can Agno agents be used in chat apps?
Agno's docs say agents can be made available in Slack, WhatsApp and Telegram using Interfaces.
Which model providers does LangChain show examples for?
The LangChain docs show examples for OpenAI, Google GenAI, Anthropic, OpenRouter, Fireworks, Baseten, Ollama, Azure OpenAI, Bedrock and Hugging Face.
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