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Type-Safe Markdown-based Agents

A Reliable Open Source AI studio to build core infrastructure stack for your LLM Applications. It allows you to gain visibility, make your application reliable, and prepare it for production with features such as caching, rate limiting, exponential retry, model fallback, and more.

Deploy a ML inference service on a budget in less than 10 lines of code.

Web framework to create vertical AI agents. FastAPI based, plugin system inspired to WordPress, admin panel, vector DB included

Self-hosted context engine for AI agents with persistent conversation memory and recall. Works as a drop-in OpenAI-compatible proxy, OpenClaw plugin, or memory SDK — no code changes required.

Stream large multimodal datasets to achieve near 100% GPU utilization. Query, visualize, & version control data. Access data w/o the need to recompute the embeddings for the model finetuning.

Cost-effective LLM development in any cloud (AWS, GCP, Azure, Lambda, etc).

Cloud-Native LLM Routing Engine. Improve LLM app resilience and speed.

Creating semantic cache to store responses from LLM queries.

Open-source AI agent framework for building goal-driven, self-improving autonomous agents with auto-generated graphs, evolution loops, and MCP integration.

Open-source prompt lifecycle management and gateway with a Web UI.

Open-source all-in-one platform for engineering AI products. Traces, Evals, Datasets, Labels.

An effortless way to experiment and prototype LangChain flows with drag-and-drop components and a chat interface.

Out-of-the-box LLM telemetry collection library that extracts features and profiles prompts, responses and metadata about how your LLM is performing over time to find problems at scale.

LLM App is a Python library that helps you build real-time LLM-enabled data pipelines with few lines of code.

LLMFlows is a framework for building simple, explicit, and transparent LLM applications such as chatbots, question-answering systems, and agents.

CLI/TUI tool for managing localization files (.resx, JSON, Android, iOS) with LLM-powered translation via Ollama, validation, and code scanning for unused/missing keys.

Open-source memory infrastructure for AI agents. Provides semantic (entities/facts), episodic (conversations), and procedural (learned behaviors) memory with auto-reflection. Python SDK, JS SDK, MCP server, and REST API.

Intuitive convenience tooling for lightning-fast, efficient development and ensuring quality in LLM-based applications

OpenLIT is an OpenTelemetry-native GenAI and LLM Application Observability tool and provides OpenTelmetry Auto-instrumentation for monitoring LLMs, VectorDBs and Frameworks. It provides valuable insights into token & cost usage, user interaction, and performance related metrics.

Pezzo is the open-source LLMOps platform built for developers and teams. In just two lines of code, you can seamlessly troubleshoot your AI operations, collaborate and manage your prompts in one place, and instantly deploy changes to any environment.

A declarative, extensible, and composable approach for developing LLM prompts using Markdown and JSX.

Open-source tool to simplify the process of creating and managing LLM workflows and prompts as a self-hosted solution.

A lightweight Python library for prompt lifecycle management that helps you version control, track, experiment and debug with your LLM prompts with ease. Minimal setup, no servers, databases, or API keys required - works directly with your local filesystem, ideal for data scientists and engineers t

Handle OpenAI Errors (overloaded OpenAI servers, rotated keys, or context window errors) for your production LLM Applications.

Universal index and routing layer for AI agents. Aggregates agent metadata from multiple registries (NANDA, MCP, Virtuals, OpenRouter, A2A, X402 Bazaar) across web2 and web3, normalizes profiles, and provides protocol translation between agent ecosystems.

Open-source testing infrastructure for LLM and agentic applications. Collaborative platform enabling teams to define quality metrics, run evaluations, and ship confidently with version control and peer review workflows built for AI engineering.

Distributed semantic cache and stateful routing system that cuts LLM API costs by returning cached responses for semantically similar queries. Uses ANN vector search (cosine ≥ 0.8) and consistent hashing to pin requests to the same worker, achieving ~7× latency reduction on cache hits while scaling

Build and control your personal LLMs with fast and efficient fine-tuning.

Open-source framework for orchestrating, experimenting and deploying production-grade ML solutions, with built-in langchain & llama index integrations.

Self-hosted multi-agent AI runtime with 23+ LLM providers, persistent memory, skills, schedules, sub-agent spawning, and MCP client + server support. Ships as desktop app, CLI, or Docker.

Open-source self-hostable end-to-end agent engineering and optimization platform unifying tracing, evals, simulations, datasets, gateway, and guardrails for LLM and AI agent applications.

An easy way to turn any app into searchable data for LLMs.

An easy to use Neural Search Engine. Index latent vectors along with JSON metadata and do efficient k-NN search.

AI Native database for embedding vectors

A 10x faster, cheaper, and better vector database