AI Agent Frameworks, MCP Servers and SDKs

LangChainGo is a Go implementation of LangChain for developers adding language models to their own applications. It provides reusable components for connecting models to documents, tools, and conversation history.

Plano is a self-hosted AI gateway for developers building applications with multiple agents. It handles shared routing, orchestration and monitoring outside the agents, so developers don't need to build those functions into each one.

Omnigent coordinates AI agents for developers and teams who want to combine coding assistants, control their access, and supervise work in shared sessions. It runs locally or on a self-hosted server, with remote cloud sandboxes as another option.

Hexabot is an AI workflow automation platform for teams building customer service assistants and business automations. It links customer conversations to actions while letting teams keep workflow data on their own infrastructure.

BankMCP connects an AI assistant to your own European bank accounts so you can ask about spending, balances and incoming payments. It's a self-hosted connector for one user, with read-only access: it can't make payments.

llmwiki-cli gives AI agents a local Markdown wiki for building and maintaining a personal knowledge base. It's for people who want an agent to organize their information while keeping the wiki files on their own machine.

Palace gives AI coding agents persistent memory of project decisions, conventions and fixes. Developers can carry that context between sessions in Cursor, Claude Code, Codex and Claude Desktop.

SwarmLLM is a ChatGPT alternative for people who want to run AI models on their own hardware. It can use a single computer or combine several machines to handle a model that won't fit on one.

any-llm is a Python library for developers building applications that need to work with different LLM providers. A shared interface reduces the provider-specific code needed to switch between local servers and cloud APIs.

Guidance is a Python library for developers who need language model responses in a defined format. It controls output during generation for applications that expect structured data or specific syntax.

Claude Task Master helps developers turn product requirements into tasks they can work through with a coding assistant. It keeps task planning and progress tracking inside the editor or terminal where development happens.

Coding Tools MCP gives AI chat apps and coding agents access to a repository on your machine. It's for developers who want to use an existing MCP client to inspect code, make edits and check a running project.

smol developer is a Python coding agent with a self-hosted API for generating codebases from product specifications. It's for developers prototyping applications or adding code generation to their own software. Its default generation workflow uses GPT-4 through the OpenAI Python SDK, whose standard API endpoint sends model requests to OpenAI’s cloud. Self-hosting controls the agent server, while this standard model connection remains remote.

ZeroToken is a local browser automation server for people using AI agents to repeat web tasks such as collecting reports, logging into sites or testing browser workflows. It turns recorded actions into scripts so later runs don't need an LLM to reason through every step again.

Vercel AI SDK is a free TypeScript library for developers building apps and agents that use local models through Ollama or LM Studio. It runs within your application; those backends run the models. Its default model route uses Vercel AI Gateway, which sends requests to a cloud service, while the documented local integrations connect to your own model server.

EverShelf helps households keep track of food and choose meals that use what's already in the kitchen. It covers the pantry, fridge and freezer, with optional AI assistance.

LongMemory gives LLM applications and AI agents persistent memory across sessions. Developers can use it to retain project decisions and conversation context while distinguishing current facts from information that was true earlier.

OpenPipe ART is a Python framework for developers who want to train multi-step AI agents on the tasks their applications actually perform. It uses reinforcement learning through GRPO (Group Relative Policy Optimization) to improve model behavior based on experience and task rewards. It's open source under Apache 2.0.

swarmux is a command-line task runner for developers coordinating coding agents in tmux. Each task gets its own session and git worktree, so you can supervise separate jobs while keeping access to their terminals.

Zengram gives AI agents a shared memory across sessions and machines. It's for people using multiple assistants who want a discovery recorded by one agent to be available to the others.

FlashRAG is an open source Python toolkit for researchers and developers comparing retrieval-augmented generation (RAG) methods or building their own. It combines implemented research methods with prepared benchmark datasets and reported results, so experiments don't have to start with a separate codebase for each approach. It uses the MIT license.

Baserow is a self-hosted no-code database and application builder with a spreadsheet interface. Teams can deploy it through Docker or Helm, or use Baserow Cloud. The open-source core is MIT licensed, with additional commercial capabilities.

Guardrails AI is an open-source Python validation framework that runs inside your application or as a self-hosted service. It's for developers who need to check LLM inputs and responses before their apps use them. It provides a validation layer around model interactions; it doesn't supply local model inference.

Blades is an AI agent framework for Go developers building conversational applications and automated workflows. It lets developers combine model connections, application tools and conversation memory within their own software.

ContextGem is a Python framework for developers extracting structured information from documents such as contracts, invoices and reports. You describe what you need in natural language, and it handles extraction prompts and data modelling for text and image content.

Quicksand gives AI agents a Linux virtual machine for executing code and working with desktop apps on your own hardware. It's for developers who need a separate operating system for agent tasks, controlled through an async Python API.

GPT Researcher is an AI research agent for people who need detailed reports based on web evidence, their own documents, or both. It investigates a question and writes a report with inline citations readers can check.

Multi-Agent Shogun runs and coordinates separate AI coding assistants on your computer or a Linux server. It's for developers dividing implementation and research across parallel tasks. Coordination happens locally, but Claude Code’s standard cloud connection sends prompts and model outputs over the network to their model provider; other agents use their chosen CLI's model access and authentication.

OllamaSharp is a C# library for developers building .NET applications with Ollama. It gives those applications access to chat, embeddings and model management through the Ollama API.

OpenCrust is a self-hosted AI assistant platform for people who want agents to handle tasks through their messaging apps. Named agents can pass work to one another and retain conversation memory between sessions.

TradingAgents is an open-source Python framework for researchers and developers studying AI trading decisions. It assigns market analysis and risk assessment to separate LLM agents, then combines their findings into a proposed trade for a simulated exchange.

NVIDIA NeMo Data Designer is a Python framework for developers and data teams creating synthetic datasets with controlled variation and relationships between fields. It can start with an empty dataset or build on your own seed data. The framework is open source under Apache 2.0.

CascadeFlow is a library for developers who need to control AI agent spending and behavior during a run. It works inside the application, assessing model calls, tool calls and sub-agent handoffs before deciding how execution should continue.

LazyLLM is a Python framework for developers building applications with multiple AI agents. It brings application workflows, model training and evaluation together so teams can refine a prototype using task-specific data.