Archive
Haystack is a Python framework by deepset, Apache 2.0 licensed, for building AI applications as pipelines of connected components: retrievers, generators and routers. Since the 2.0 rewrite it allows cycles, which lets you add an Agent component that calls tools in a loop. It serves both RAG and production agents.
Agno, the Python framework formerly known as Phidata, builds AI agents with one clear goal: performance. It creates each agent in about two microseconds and uses roughly 3.75 KiB of memory, so it scales to thousands of concurrent agents. It offers agents, teams and workflows, over twenty model providers and the AgentOS runtime.
Mastra is an open-source TypeScript framework for building AI agents and applications with a single package: agents, tools, workflows, memory, RAG and evals. It was built by the team behind Gatsby, hit version 1.0 in January 2026 and connects to over 40 model providers through one interface.
Semantic Kernel is Microsoft's open-source kit for integrating AI models into C#, Python and Java through a kernel that orchestrates plugins and functions. In 2026 its successor is Microsoft Agent Framework, which merges Semantic Kernel and AutoGen; Semantic Kernel itself keeps support for at least one more year.
LlamaIndex is a Python framework focused on connecting your data to a language model. Its AgentWorkflow module lets you build agents that reason and call tools: FunctionAgent for models with function calling, and ReActAgent for any model. This guide shows how to turn a RAG pipeline into an agent that acts over your own documents.
Google ADK (Agent Development Kit) is Google's open-source framework for building, evaluating and deploying AI agents. It is code-first, works with Gemini and other models via LiteLLM, and offers workflow agents, tools and memory. Version 2.5.0 landed in July 2026 and deploys straight to Vertex AI.
smolagents is Hugging Face's agent library: in under a thousand lines of code it lets you build an agent that reasons by writing Python instead of filling in JSON. Its CodeAgent runs those actions as code, takes around 30% fewer steps than classic tool calling and works with any model, local or API.
Pydantic AI is the agent framework from the Pydantic team: you build agents in Python where every model output is validated against a type you define. You declare the expected answer as a Pydantic model and the agent guarantees that structure, with dependency injection, tools and support for more than twenty model providers.
The OpenAI Agents SDK is a lightweight Python framework for building agents and multi-agent workflows with few primitives: agents, tools, handoffs, guardrails, sessions and built-in tracing. It is the production-ready evolution of Swarm, works with the OpenAI API and with over 100 other LLMs.
Continue is an open-source coding assistant for VS Code and JetBrains that brings chat, autocomplete, inline editing and an agent mode into a single panel. Under the Apache 2.0 licence it works with more than a hundred models, including ones you run locally with Ollama, so you pick the model and keep control of your code.
Windsurf is the agentic code editor created by Codeium: a VS Code fork whose agent reads your project, plans and edits several files with your approval. In July 2025 Cognition, the company behind Devin, bought it, and on 2 June 2026 it was renamed Devin Desktop; its agent Cascade reached end of life on 1 July 2026 and was replaced by Devin Local.
Gemini CLI is Google's coding agent for the terminal: open source, with a one-million-token context window and the Gemini 3 model. Since 18 June 2026 its free tier for individuals moved to Antigravity CLI, but the tool still works with an API key from AI Studio or an enterprise licence.
Goose is an open-source AI agent created at Block that runs on your own machine as a desktop app, CLI and API. It reads and writes files, runs commands and tests, and works with more than fifteen model providers and MCP extensions. It is free, licensed under Apache 2.0, and you pay only for model usage.
OpenHands, formerly OpenDevin, is an open-source platform that solves programming tasks end to end: it takes a request, opens a sandbox container and edits files, runs commands and browses the web until it is done. It runs with Docker on your own machine and works with the model you choose.
The Codex CLI is OpenAI's coding agent that works inside your terminal: you describe a task, it reads your repository, proposes the changes and runs commands inside a sandbox you control. It is open source, installs with npm and works with your ChatGPT account or with an API key.
Roo Code is an open-source VS Code extension, born as a fork of Cline, that turns the editor into a team of agents with specialised modes (Code, Architect, Debug, Ask and Orchestrator). The project was archived in May 2026 at version 3.54.0, but its community continuation ZooCode keeps the same features alive.
Cline is a VS Code extension that turns your editor into an autonomous coding agent: it reads your project, plans changes in Plan mode and carries them out in Act mode, showing every edit as a diff you approve. It is open source and works with your own API key or with local models.
A multi-agent system splits a task across several specialised agents coordinated by a design pattern. The three most common are orchestrator-workers, where a lead agent delegates to parallel subagents; hierarchical, with teams of teams; and network, where any agent hands control to another through a handoff.
ReAct and plan-and-execute are the two control patterns for an AI agent. ReAct decides one step at a time, reasoning and acting in a loop; plan-and-execute draws up a full plan first and then executes it step by step. The former adapts better to surprises; the latter uses fewer calls and plans long tasks with more order.
Context engineering is the craft of deciding what information enters a model's window at each step of an agent. Beyond prompt engineering, it manages the whole set of tokens: instructions, tools, memory and history. Its goal is the smallest possible set of high-signal tokens that still completes the task.
Human-in-the-loop is the pattern that keeps a person inside an AI agent's decision loop: the agent stops at an approval point before an irreversible action, waits for your confirmation and resumes with its state intact. Frameworks such as LangGraph and OpenAI's Agents SDK implement it with interruptions and tool approval.
Memory is what lets an AI agent remember beyond a single conversation. Its working memory is the context window, ephemeral and limited; its long-term memory stores facts, experiences and procedures in an external store, almost always a vector database, and retrieves them when they are needed to keep acting coherently.
The reflection pattern makes an agent critique its own output and rewrite it before accepting it. One model generates, a second step evaluates and flags mistakes, and a third revises, in a loop of one or two rounds. It improves quality on tasks with clear criteria, but each cycle adds model calls, tokens and latency.
Planning lets an AI agent solve long tasks: instead of improvising step by step, it first breaks the goal into an ordered list of subtasks and then runs them. The planner-executor pattern separates thinking from acting, cuts the number of model calls and lets the agent replan when a step fails midway through the job.
