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Appears in 4 awesome lists
The reference architecture for stateful agents: three-tier memory (core / archival / recall) maps directly to harness state management design. Their agent loop redesign post is the most thorough public analysis of how memory structure shapes the harness.
Section: Memory & State · The reference architecture for stateful agents: three-tier memory (core / archival / recall) maps directly to harness state management design. Their agent loop redesign post is the most thorough public analysis of how memory structure shapes the harness.
Section: Other LLM Frameworks · Platform for stateful agents with advanced memory that learn and self-improve
Section: Memory Management · the stateful agents framework with memory, reasoning, and context management
Section: 4. Agentic AI & Multi-Agent Systems · Platform for building stateful agents with advanced memory that learn and self-improve over time.
(MIT) provides modules for structured outputs at different levels of abstraction, including output parsers for text completion endpoints, Pydantic programs for mapping prompts to structured outputs using function calling or output parsing, and pre-defined Pydantic programs for specific output types.
Mem0 is an intelligent memory layer for Large Language Models that enhances personalized AI experiences by retaining and utilizing contextual information across various applications. github | website | docs | discord | twitter | github profile | linkedin
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search,…
Microsoft's multi-agent conversation framework with a complete AgentChat layer covering agent loop, tool integration, termination conditions, and human-in-the-loop. The most comprehensive open-source reference for large-scale multi-agent harness design.
(MIT) is a framework for algorithmically optimizing LM prompts and weights. DSPy introduced typed predictor and signatures to leverage Pydantic for enforcing type constraints on inputs and outputs, improving upon string-based fields.
Test your prompts, models, RAGs. Evaluate and compare LLM outputs, catch regressions, and improve prompt quality. LLM evals for OpenAI/Azure GPT, Anthropic Claude, VertexAI Gemini, Ollama, Local & private models like Mistral/Mixtral/Llama with CI/CD
Semantic Kernel is an SDK that integrates Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. Semantic Kernel achieves this by allowing you to define plugins that can be chained together in just a few lines of…