{"generated":"2026-06-21T03:49:00.182Z","count":24,"tools":[{"slug":"mcp-servers","name":"MCP Servers","owner":"modelcontextprotocol","repo":"servers","category":"mcp","lang":"TypeScript","blurb":"The reference collection of Model Context Protocol servers — connect agents to files, GitHub, databases, and more.","use_cases":"[\"tool integration\",\"agent connectors\",\"MCP ecosystem\"]","alternatives":["fastmcp"],"stars":87490,"pushed_at":"2026-06-17T01:40:52Z","synced_at":"2026-06-20T20:37:28.456Z","useCases":["tool integration","agent connectors","MCP ecosystem"]},{"slug":"autogen","name":"AutoGen","owner":"microsoft","repo":"autogen","category":"framework","lang":"Python","blurb":"Microsoft's framework for multi-agent conversation, with a programming model for agents that talk to each other and tools.","use_cases":"[\"conversational multi-agent\",\"code execution\",\"research\"]","alternatives":["langgraph","crewai","semantic-kernel"],"stars":59094,"pushed_at":"2026-04-15T11:59:09Z","synced_at":"2026-06-20T20:37:28.555Z","useCases":["conversational multi-agent","code execution","research"]},{"slug":"mem0","name":"Mem0","owner":"mem0ai","repo":"mem0","category":"memory","lang":"Python","blurb":"A memory layer for AI agents — extracts, stores, and retrieves user/agent facts across sessions.","use_cases":"[\"personalized agents\",\"cross-session memory\",\"user preference recall\"]","alternatives":["letta","zep"],"stars":58989,"pushed_at":"2026-06-20T17:40:16Z","synced_at":"2026-06-20T20:37:28.689Z","useCases":["personalized agents","cross-session memory","user preference recall"]},{"slug":"crewai","name":"CrewAI","owner":"crewAIInc","repo":"crewAI","category":"framework","lang":"Python","blurb":"Role-playing autonomous agents that collaborate as a 'crew' with defined roles, goals, and task delegation.","use_cases":"[\"role-based multi-agent teams\",\"task delegation\",\"rapid prototyping\"]","alternatives":["langgraph","autogen"],"stars":54036,"pushed_at":"2026-06-20T17:38:38Z","synced_at":"2026-06-20T20:37:28.782Z","useCases":["role-based multi-agent teams","task delegation","rapid prototyping"]},{"slug":"llama-index","name":"LlamaIndex","owner":"run-llama","repo":"llama_index","category":"framework","lang":"Python","blurb":"Data framework for connecting LLMs to private data — indexing, retrieval, and agentic RAG over your documents.","use_cases":"[\"RAG\",\"document agents\",\"knowledge assistants\"]","alternatives":["langgraph","haystack"],"stars":50241,"pushed_at":"2026-06-20T00:09:30Z","synced_at":"2026-06-20T20:37:28.899Z","useCases":["RAG","document agents","knowledge assistants"]},{"slug":"milvus","name":"Milvus","owner":"milvus-io","repo":"milvus","category":"vectordb","lang":"Go","blurb":"Cloud-native vector database built for billion-scale similarity search.","use_cases":"[\"billion-scale search\",\"enterprise RAG\",\"distributed\"]","alternatives":["qdrant","weaviate"],"stars":44856,"pushed_at":"2026-06-20T03:27:11Z","synced_at":"2026-06-20T20:37:28.999Z","useCases":["billion-scale search","enterprise RAG","distributed"]},{"slug":"langgraph","name":"LangGraph","owner":"langchain-ai","repo":"langgraph","category":"framework","lang":"Python","blurb":"Graph-based orchestration for stateful, multi-actor agent workflows with explicit control flow and checkpointing.","use_cases":"[\"stateful multi-agent workflows\",\"human-in-the-loop\",\"long-running agents\"]","alternatives":["crewai","autogen","llama-index"],"stars":35293,"pushed_at":"2026-06-19T10:05:20Z","synced_at":"2026-06-20T20:37:30.035Z","useCases":["stateful multi-agent workflows","human-in-the-loop","long-running agents"]},{"slug":"dspy","name":"DSPy","owner":"stanfordnlp","repo":"dspy","category":"framework","lang":"Python","blurb":"Programming — not prompting — language models: compile declarative pipelines into optimized prompts/weights.","use_cases":"[\"prompt optimization\",\"compound AI systems\",\"self-improving pipelines\"]","alternatives":["langgraph","llama-index"],"stars":35180,"pushed_at":"2026-06-18T16:57:05Z","synced_at":"2026-06-20T20:37:29.106Z","useCases":["prompt optimization","compound AI systems","self-improving pipelines"]},{"slug":"qdrant","name":"Qdrant","owner":"qdrant","repo":"qdrant","category":"vectordb","lang":"Rust","blurb":"High-performance vector search engine with rich filtering, written in Rust for production-scale retrieval.","use_cases":"[\"production RAG\",\"filtered search\",\"high throughput\"]","alternatives":["chroma","weaviate","milvus"],"stars":32492,"pushed_at":"2026-06-20T19:44:04Z","synced_at":"2026-06-20T20:37:29.221Z","useCases":["production RAG","filtered search","high throughput"]},{"slug":"langfuse","name":"Langfuse","owner":"langfuse","repo":"langfuse","category":"observability","lang":"TypeScript","blurb":"Open-source LLM engineering platform — tracing, evals, prompt management, and metrics for agent apps.","use_cases":"[\"LLM tracing\",\"prompt management\",\"production monitoring\"]","alternatives":["phoenix","helicone"],"stars":29428,"pushed_at":"2026-06-20T09:29:08Z","synced_at":"2026-06-20T20:37:30.140Z","useCases":["LLM tracing","prompt management","production monitoring"]},{"slug":"chroma","name":"Chroma","owner":"chroma-core","repo":"chroma","category":"vectordb","lang":"Rust","blurb":"Open-source embedding database designed for simplicity — the default vector store for many RAG prototypes.","use_cases":"[\"RAG\",\"semantic search\",\"local prototyping\"]","alternatives":["qdrant","weaviate","milvus"],"stars":28507,"pushed_at":"2026-06-20T00:03:04Z","synced_at":"2026-06-20T20:37:29.335Z","useCases":["RAG","semantic search","local prototyping"]},{"slug":"fastmcp","name":"FastMCP","owner":"jlowin","repo":"fastmcp","category":"mcp","lang":"Python","blurb":"The fast, Pythonic way to build MCP servers and clients — decorators over boilerplate.","use_cases":"[\"building MCP servers\",\"Python tooling\",\"rapid integration\"]","alternatives":["mcp-servers"],"stars":25723,"pushed_at":"2026-06-06T01:30:50Z","synced_at":"2026-06-20T20:37:29.947Z","useCases":["building MCP servers","Python tooling","rapid integration"]},{"slug":"letta","name":"Letta (MemGPT)","owner":"letta-ai","repo":"letta","category":"memory","lang":"Python","blurb":"Stateful agents with long-term memory and self-editing context, evolved from the MemGPT research.","use_cases":"[\"persistent agents\",\"self-editing memory\",\"long conversations\"]","alternatives":["mem0","zep"],"stars":23429,"pushed_at":"2026-05-14T17:14:23Z","synced_at":"2026-06-20T20:37:29.447Z","useCases":["persistent agents","self-editing memory","long conversations"]},{"slug":"promptfoo","name":"promptfoo","owner":"promptfoo","repo":"promptfoo","category":"eval","lang":"TypeScript","blurb":"Test-driven prompt and agent development — evals, red-teaming, and side-by-side model comparison from the CLI.","use_cases":"[\"prompt evals\",\"red-teaming\",\"model comparison\"]","alternatives":["deepeval","ragas"],"stars":22406,"pushed_at":"2026-06-20T18:01:55Z","synced_at":"2026-06-20T20:37:30.678Z","useCases":["prompt evals","red-teaming","model comparison"]},{"slug":"pgvector","name":"pgvector","owner":"pgvector","repo":"pgvector","category":"vectordb","lang":"C","blurb":"Vector similarity search inside Postgres — keep embeddings next to your relational data.","use_cases":"[\"RAG on existing Postgres\",\"no new infra\",\"transactional + vector\"]","alternatives":["qdrant","chroma"],"stars":21842,"pushed_at":"2026-06-18T19:57:02Z","synced_at":"2026-06-20T20:37:29.652Z","useCases":["RAG on existing Postgres","no new infra","transactional + vector"]},{"slug":"temporal","name":"Temporal","owner":"temporalio","repo":"temporal","category":"runtime","lang":"Go","blurb":"Durable execution platform — write long-running, failure-resilient agent workflows as ordinary code.","use_cases":"[\"durable agent workflows\",\"retries & recovery\",\"long-running orchestration\"]","alternatives":["inngest"],"stars":21102,"pushed_at":"2026-06-20T16:26:45Z","synced_at":"2026-06-20T20:37:29.538Z","useCases":["durable agent workflows","retries & recovery","long-running orchestration"]},{"slug":"pydantic-ai","name":"Pydantic AI","owner":"pydantic","repo":"pydantic-ai","category":"framework","lang":"Python","blurb":"Type-safe agent framework from the Pydantic team — structured outputs, dependency injection, and model-agnostic agents.","use_cases":"[\"type-safe agents\",\"structured outputs\",\"production Python\"]","alternatives":["langgraph","crewai"],"stars":17869,"pushed_at":"2026-06-20T19:44:00Z","synced_at":"2026-06-20T20:37:30.576Z","useCases":["type-safe agents","structured outputs","production Python"]},{"slug":"weaviate","name":"Weaviate","owner":"weaviate","repo":"weaviate","category":"vectordb","lang":"Go","blurb":"Open-source vector database with hybrid search and built-in modules for vectorization and RAG.","use_cases":"[\"hybrid search\",\"RAG\",\"multimodal\"]","alternatives":["qdrant","chroma","milvus"],"stars":16379,"pushed_at":"2026-06-20T09:35:23Z","synced_at":"2026-06-20T20:37:29.754Z","useCases":["hybrid search","RAG","multimodal"]},{"slug":"deepeval","name":"DeepEval","owner":"confident-ai","repo":"deepeval","category":"eval","lang":"Python","blurb":"Pytest-like framework for unit-testing LLM outputs with metrics for hallucination, relevancy, and bias.","use_cases":"[\"LLM unit tests\",\"regression testing\",\"CI gates\"]","alternatives":["ragas","promptfoo"],"stars":16337,"pushed_at":"2026-06-18T17:41:24Z","synced_at":"2026-06-20T20:37:30.375Z","useCases":["LLM unit tests","regression testing","CI gates"]},{"slug":"ragas","name":"Ragas","owner":"explodinggradients","repo":"ragas","category":"eval","lang":"Python","blurb":"Evaluation toolkit for RAG pipelines — faithfulness, answer relevancy, and context metrics without ground truth.","use_cases":"[\"RAG evaluation\",\"faithfulness scoring\",\"CI for retrieval\"]","alternatives":["deepeval","promptfoo"],"stars":14447,"pushed_at":"2026-02-24T07:47:19Z","synced_at":"2026-06-20T20:37:30.276Z","useCases":["RAG evaluation","faithfulness scoring","CI for retrieval"]},{"slug":"e2b","name":"E2B","owner":"e2b-dev","repo":"E2B","category":"runtime","lang":"TypeScript","blurb":"Secure cloud sandboxes for running AI-generated code — the runtime layer for code-executing agents.","use_cases":"[\"code execution\",\"agent sandboxes\",\"data analysis agents\"]","alternatives":["modal"],"stars":12661,"pushed_at":"2026-06-20T12:53:39Z","synced_at":"2026-06-20T20:37:30.478Z","useCases":["code execution","agent sandboxes","data analysis agents"]},{"slug":"phoenix","name":"Phoenix","owner":"Arize-ai","repo":"phoenix","category":"observability","lang":"Jupyter Notebook","blurb":"Arize's open-source observability for LLM apps — OpenTelemetry-based tracing and evaluation.","use_cases":"[\"OTel tracing\",\"evaluation\",\"debugging RAG\"]","alternatives":["langfuse","helicone"],"stars":10215,"pushed_at":"2026-06-20T18:26:28Z","synced_at":"2026-06-20T20:37:30.773Z","useCases":["OTel tracing","evaluation","debugging RAG"]},{"slug":"helicone","name":"Helicone","owner":"Helicone","repo":"helicone","category":"observability","lang":"TypeScript","blurb":"Open-source observability for LLM apps via a proxy — logging, caching, and cost tracking with one header.","use_cases":"[\"cost tracking\",\"request logging\",\"caching\"]","alternatives":["langfuse","phoenix"],"stars":5841,"pushed_at":"2026-06-11T19:46:29Z","synced_at":"2026-06-20T20:37:30.896Z","useCases":["cost tracking","request logging","caching"]},{"slug":"zep","name":"Zep","owner":"getzep","repo":"zep","category":"memory","lang":"Go","blurb":"Long-term memory store for agents with a temporal knowledge graph of facts and their validity over time.","use_cases":"[\"temporal memory\",\"knowledge graphs\",\"fact recall\"]","alternatives":["mem0","letta"],"stars":4684,"pushed_at":"2026-06-19T18:13:20Z","synced_at":"2026-06-20T20:37:31.056Z","useCases":["temporal memory","knowledge graphs","fact recall"]}]}