Every tool tracked by The Stack — frameworks, memory, vector databases, MCP servers, evals, and observability — with live GitHub data and our coverage.
Libraries for orchestrating LLM agents, tools, and multi-step control flow.
Microsoft's framework for multi-agent conversation, with a programming model for agents that talk to each other and tools.
Role-playing autonomous agents that collaborate as a 'crew' with defined roles, goals, and task delegation.
Data framework for connecting LLMs to private data — indexing, retrieval, and agentic RAG over your documents.
Batteries-included agent runtime — built-in memory, knowledge/RAG, and AgentOS, a control plane you run in your own cloud. Formerly Phidata.
Graph-based orchestration for stateful, multi-actor agent workflows with explicit control flow and checkpointing.
Programming — not prompting — language models: compile declarative pipelines into optimized prompts/weights.
OpenAI's lightweight agent framework — a small set of primitives (Agents, Handoffs, Guardrails, Sessions); provider-agnostic via LiteLLM. Evolved from Swarm.
Google's Agent Development Kit — a code-first, model-agnostic toolkit for building, evaluating, and deploying multi-agent systems. Optimized for Gemini.
Type-safe agent framework from the Pydantic team — structured outputs, dependency injection, and model-agnostic agents.
Anthropic's SDK for building agents on Claude — the harness behind Claude Code, with in-process tools, hooks, and an interactive client. Formerly the Claude Code SDK.
AWS's model-driven agent SDK — give it a prompt and tools and let the model plan, call tools, and reflect in a loop. First-class Bedrock, MCP-native, OpenTelemetry tracing.
TypeScript-first SDK for stateful agents where each agent is a Durable Object — embedded SQLite, WebSockets, cron scheduling, and hibernation, running globally on Cloudflare's edge.
Long-term and working memory for agents that persist across runs.
A memory layer for AI agents — extracts, stores, and retrieves user/agent facts across sessions.
Stateful agents with long-term memory and self-editing context, evolved from the MemGPT research.
Long-term memory store for agents with a temporal knowledge graph of facts and their validity over time.
Embedding stores powering retrieval for RAG and agent recall.
Cloud-native vector database built for billion-scale similarity search.
In-process analytical database whose vss extension adds an HNSW vector index — vector search alongside your columnar analytics.
High-performance vector search engine with rich filtering, written in Rust for production-scale retrieval.
Open-source embedding database designed for simplicity — the default vector store for many RAG prototypes.
Vector similarity search inside Postgres — keep embeddings next to your relational data.
Open-source vector database with hybrid search and built-in modules for vectorization and RAG.
Embedded, in-process vector database on the columnar Lance format — versioned, updatable, larger-than-RAM retrieval with no server.
A single-file SQLite extension for vector search — exact brute-force KNN that lives inside the database you already ship.
Model Context Protocol servers and tool-calling infrastructure.
Measuring agent and LLM output quality, regressions, and safety.
Test-driven prompt and agent development — evals, red-teaming, and side-by-side model comparison from the CLI.
Pytest-like framework for unit-testing LLM outputs with metrics for hallucination, relevancy, and bias.
Evaluation toolkit for RAG pipelines — faithfulness, answer relevancy, and context metrics without ground truth.
Tracing, logging, and monitoring for LLM and agent systems.
Open-source LLM engineering platform — tracing, evals, prompt management, and metrics for agent apps.
Arize's open-source observability for LLM apps — OpenTelemetry-based tracing and evaluation.
Open-source observability for LLM apps via a proxy — logging, caching, and cost tracking with one header.
Sandboxes and execution environments for running agent code/tools.
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