langchain-alternative

AINL helps turn AI from "a smart conversation" into "a structured worker." It is designed for teams building AI workflows that need multiple steps, state and memory, tool use, repeatable execution, validation and control, and lower dependence on long prompt loops. AINL is a compact, graph-canonical, AI-native programming system for (READ: README)

Open-source self-hosted AI agent runtime and multi-agent framework for autonomous agent swarms. Agent memory, MCP tools, schedules, delegation, and 23+ LLM providers (Claude, GPT, Gemini, OpenRouter, Ollama). A practical Claude Code and LangChain alternative.

Deploy intelligence. Open-source infrastructure for AI agents in production.

Self-hosted framework for orchestrating fleets of specialist AI agents — ensemble reasoning and a full agentic coding pipeline, model-agnostic and local-friendly.

Production-ready RAG Framework (Python/FastAPI). 1-line config swaps: 6 Vector DBs (Weaviate, Pinecone, Qdrant, ChromaDB, pgvector, MongoDB), 5 LLMs (Gemini, OpenAI, Claude, Ollama, OpenRouter). OpenAI-compatible API. 2100+ tests.

AI Security Platform: Defense (61 Rust engines + Micro-Model Swarm) + Offense (39K+ payloads)

Your SDK solves all of this. One interface. Unified logic. Local + hosted models. Fine-tuning. Agent tools. Enterprise-ready. Hybrid RAG.Star 🌟 if you like it!

Deploy production-ready AI services in minutes. One YAML file for agents, RAG pipelines, and MCP servers — run anywhere. Inspired by docker-compose.

Online-adaptive multi-agent framework for enterprise API orchestration. Adaptive circuit breakers, SLA-driven routing, hybrid LLM+rule intent parser. Apache 2.0.

Universal autonomous agent framework with ReAct loop, multi-provider LLM routing, reasoning graph, and MCP integration, domain-agnostic for building specialized AI agents.

Minimal, async-first Python framework for production LLM apps- 2 hard deps, no magic, no SaaS.

Ultra-lightweight AI agent framework. Shannon Entropy routing, zero-trust sandbox, GraphRAG memory. Only 2 dependencies.

Lightweight Python SDK for LLMs with unified API across 9 providers. Built-in ReAct & Plan-Execute agents, streaming, native tool calling, context injection, structured outputs, and observability.

A lightweight, enterprise-ready multi-agent orchestration framework for the .NET ecosystem. Build autonomous AI agents, equip them with tools, and orchestrate complex RAG workflows in C#.

Production-ready agentic AI framework. High-performance, lightweight, simple. Built-in safety, memory, and 4 reasoning patterns. Ships to production fast.

Production-ready Python framework for AI agents with built-in guardrails, audit logging, cost tracking, and hybrid RAG. Supports OpenAI, Anthropic, Gemini, Ollama. By NichevLabs.

Claude Code-like AI agent framework for your app. Planning, task tracking, clarifying questions - one dependency, stateless, serverless-ready.

TypeScript AI agent framework — 40+ LLM providers, 100+ tools, multi-agent orchestration, RAG, guardrails, circuit breakers, HITL, budget enforcement. Open-source CrewAI / LangChain alternative. — For Building Production-Grade Multi Agents in Minutes at Scale

TypeScript first class memory primitive for AI agents. Embed → store → retrieve → inject, backed by Postgres + pgvector. Swappable embedders, consumer owns the connection, zero framework coupling. MIT.

JavaScript for MultiMindSDK – bring advanced context transfer and multi-LLM orchestration, Node.js apps, and web tools