Free open source alternative app list of AI Agent Development :

Agno

Key features Lightweight, model-agnostic Python library for building intelligent AI agents. Built-in support for memory, knowledge, tools, and reasoning. Multimodal framework capable of handling diverse input and output types. Free and open-source core under the MPL-2.0 license, with an optional paid cloud service. Platforms: Windows, macOS, Linux Links Official website: https://agno.com Source code: https://github.com/agno-ai/agno Pricing Freemium. The core library is free and open-source under the MPL-2.0 license; optional Agno Cloud paid services are available.

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Hexabot

Key features Self-hosted platform for building AI agents, chatbots, and automation workflows — a free, open-source alternative to ManyChat. Deploy your chatbots across multiple messaging channels from a single interface. Design automation workflows to handle complex, multi-step user interactions. Maintain full control over your data and infrastructure thanks to self-hosting. Completely free to use with no feature limitations under the GPL-3.0 license. Platforms: Linux Links Official website: https://hexabot.ai Source code: https://github.com/hexabot-ai/hexabot Pricing: Free. Completely free and open-source under the GPL-3.0 license; self-host without limitations.

FreeLinux

Agenta

Key features Build, evaluate, and improve AI agents and LLM applications in a single collaborative platform. Manage prompts with versioning and organization tools for LLM workflows. Run evaluations to test and compare AI agent and LLM outputs. Gain observability into LLM application behavior and performance. Support LLMOps practices for teams developing with large language models. Platforms Windows, macOS, Linux Links Official website: https://agenta.ai Source code: https://github.com/Agenta-AI/agenta Pricing Free. Free and open-source self-hosted platform under Apache-2.0 license.

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langgraph

langgraph is a free, open-source software / service you can self-host or use without paying. Low-level orchestration framework for building stateful agents. Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents. pip install -U langgraph Tip If you're looking to quickly build agents, check out Deep Agents — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks. For an equivalent JS/TS library, check out LangGraph.js and the JS docs . Why use LangGraph? LangGraph provides low-level supporting infrastructure for any long-running, stateful workflow or agent: Durable execution — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off. Human-in-the-loop — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution. Comprehensive memory — Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions. Debugging with LangSmith — Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics. Production-ready deployment — Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows. Tip For developing, debugging, and deploying AI agents and LLM applications, see LangSmith . LangGraph ecosystem While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with: Deep Agents – Build agents that can plan, use subagents, and leverage file systems for complex tasks. LangChain – Provides integrations and composable components to streamline LLM application development. LangSmith – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time. LangSmith Deployment – Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams – and iterate quickly with visual prototyping in LangSmith Studio . Documentation docs.langchain.com – Comprehensive documentation, including conceptual overviews and guides reference.langchain.com/python/langgraph – API reference docs for LangGraph packages LangGraph Quickstart – Get started building with LangGraph Chat LangChain – Chat with the LangChain documentation and get answers to your questions Discussions : Visit the LangChain Forum to connect with the community and share all of your technical questions, ideas, and feedback. Additional resources Guides – Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.). LangChain Academy – Learn the basics of LangGraph in our free, structured course. Case studies – Hear how industry leaders use LangGraph to ship AI applications at scale. Contributing Guide – Learn how to contribute to LangChain projects and find good first issues. Code of Conduct – Our community guidelines and standards for participation. Acknowledgements LangGraph is inspired by Pregel and Apache Beam . The public interface draws inspiration from NetworkX . LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.

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CopilotKit

CopilotKit is a free, open-source alternative to CommandBar . CopilotKit Docs · Examples · Enterprise Intelligence Platform · Discord Build agent-native applications — on any framework, on any surface. Generative UI, shared state, and human-in-the-loop workflows for React, Angular, Vue, React Native — and in Slack and Microsoft Teams. What is CopilotKit CopilotKit is a best-in-class SDK for building full-stack agentic applications, Generative UI, and chat applications. What started as a React library is now the horizontal layer between your agents and your users : the same agent can power your web app, your mobile app, and your team's Slack or Microsoft Teams workspace. We are the company behind the AG-UI Protocol - adopted by Google, LangChain, AWS, Microsoft, Mastra, PydanticAI, and more! Quick Start Up and running in under five minutes. All you need is an LLM key (OpenAI, Anthropic, Gemini, etc.). npx copilotkit@latest create Agent Skills CopilotKit ships agent skills that teach your coding agent (Claude Code, Codex, Cursor, Gemini, and others) how to set up, build with, integrate, debug, and upgrade CopilotKit. Install them into any project directory: npx copilotkit@latest skills install Run it again any time to refresh to the latest skills. Bring Your App to Life Whole.Generative.UI.v5.mp4 Add AI to your app in 1 minute Features: Chat UI – A fully customizable chat interface that supports message streaming, tool calls, and agent responses. Backend Tool Rendering – Enables agents to call backend tools that return UI components rendered directly in the client. Generative UI – Allows agents to generate and update UI components dynamically at runtime based on user intent and agent state. Shared State – A synchronized state layer that both agents and UI components can read from and write to in real time. Human-in-the-Loop – Lets agents pause execution to request user input, confirmation, or edits before continuing. Self-Learning (early access) – Agents that continuously improve from user feedback via in-context reinforcement learning (CLHF). 🧩 Works With Your Stack One agent backend. Every frontend. Platform Status Get Started ⚛️ React / Next.js ✅ GA Quickstart 🅰️ Angular ✅ Supported Source Code & Quickstart 💚 Vue ✅ Supported Source Code - Quickstart coming soon 📱 React Native ✅ Supported Quickstart 💬 Slack / Microsoft Teams ✅ Supported Channels · Quickstart 🔜 Discord / WhatsApp / Telegram / Google Chat / iMessage / SMS 🟡 Coming soon Channels Your agent logic stays the same — AG-UI handles the wire protocol, CopilotKit handles the UI layer for each framework and channel. 💬 Channels: One Agent, Every Chat App The Channels SDK takes the agent you already built and drops it into the chat apps your users live in — same tools, same shared state, same human-in-the-loop, no rewrite ( Learn more ). Slack – Agents as first-class Slack apps: threads, tool calls, and human-in-the-loop approvals right in the channel. Microsoft Teams – Bring agentic workflows to the enterprise, where your org already lives. 👉 Explore Channels → 🧠 Self-Learning Agents Improve your product by learning over time. With Continuous Learning from Human Feedback (CLHF) , part of the CopilotKit Intelligence Platform , agents improve with every interaction: In-context reinforcement learning – Agents automatically improve from user interactions, no model fine-tuning required. Automatic prompt augmentation – Agent behavior adapts based on recent interactions and outcomes. Per-user adaptation – Agents learn individual preferences and get better for each user over time. Threads & persistence – Full interaction history — generative UI, human-in-the-loop, shared state — captured across sessions. Available via CopilotKit Cloud or self-hosted. 🔒 Early access: We're onboarding teams now. 👉 Request early access → cpk-cli.mp4 What this gives you: CopilotKit installed – Core packages are fully set up in your app Provider configured – Context, state, and hooks ready to use Agent <> UI connected – Agents can stream actions and render UI immediately Deployment-ready – Your app is ready to deploy Complete getting started guide → How it works: CopilotKit connects your UI, agents, and tools into a single interaction loop. This enables: Agents that ask users for input Tools that render UI Stateful workflows across steps and sessions One agent, deployed across web, mobile, and chat platforms ⭐️ useAgent Hook The useAgent hook sits directly on AG-UI, giving you full programmatic control over the agent connection. // Programmatically access and control your agents const { agent } = useAgent ( { agentId : "my_agent" } ) ; // Render and update your agent's state return < div > < h1 > { agent . state . city } < / h1 > < button onClick = { ( ) => agent . setState ( { city : "NYC" } ) } > Set City < / b u t t o n > < / div > Check out the useAgent docs to learn more. CopilotKit.UseAgent.Graphic.Motion_2.mp4 Generative UI Generative UI is a core CopilotKit pattern that allows agents to dynamically render UI as part of their workflow. demo-generative-ui.mp4 Compare the Three Types Explore: Static (AG-UI Protocol) Declarative (A2UI) Open-Ended (MCP Apps & Open JSON) Generative UI educational repo → 🖥️ AG-UI: The Agent–User Interaction Protocol Connect agent workflows to user-facing apps, with deep partnerships and 1st-party integrations across the agentic stack—including LangChain, CrewAI, Mastra, PydanticAI, and more. npx create-ag-ui-app my-agent-app Learn more in the AG-UI README → 🤝 Community What's New Have questions or need help? Join our Discord → Read the Docs → Try the Enterprise Intelligence Platform → Stay up to date with our latest releases! Follow us on LinkedIn → Follow us on X → 🙋🏽‍♂️ Contributing Thanks for your interest in contributing to CopilotKit! 💜 We value all contributions, whether it's through code, documentation, creating demo apps, or just spreading the word. Here are a few useful resources to help you get started: For code contributions, CONTRIBUTING.md . For documentation-related contributions, check out the documentation contributions guide . Want to contribute but not sure how? Join our Discord and we'll help you out! 📄 License This repository's source code is available under the MIT License .

FreeAndroidiOS

Parlant

Parlant is a free, open-source software / service you can self-host or use without paying. Structured control layer for customer-facing AI agents

FreeWindowsmacOSLinux

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AI/LLM Inference delivers fast, scalable deployment of large language models for generating AI responses.