Claude-Mem is a persistent memory compression system built for Claude Code. It automatically captures tool usage observations during coding sessions, generates semantic summaries using Claude's agent-sdk, and injects relevant context into future sessions to maintain continuity of project knowledge.
Orchestrates teams of role-based autonomous agents that collaborate on multi-step tasks, plus event-driven Flows for deterministic control. Built from scratch with no LangChain dependency; runs 450M+ agentic workflows monthly.
Python framework for building and serving LLM agents in production: a unified event bus for real-time frontends and human-in-the-loop, fine-grained tool permissions, multi-tenant serving, and tool/code execution sandboxed via Docker or E2B.
Runs AI-generated code in isolated, elastic sandboxes with SDK, API, and CLI access for agent workflows that need stateful execution and environment control.
Controls customer-facing LLM agents turn-by-turn against deterministic guidelines instead of one big system prompt, surfacing only the rules and tools that apply each turn. Adds journeys, pre-approved canned responses, and traces for auditable behavior.
Memory engine that lets AI apps remember users across conversations: it extracts facts, tracks updates, resolves contradictions, and auto-forgets stale info, returning context in ~50ms. Tops the LongMemEval, LoCoMo and ConvoMem memory benchmarks.
End-to-end framework for running and reproducing foundation-model research workflows — from data curation and tokenization to training and evaluation. Emphasizes reproducibility by recording every step (including failed runs) and expressing experiments as dependency-ordered steps.
Splits autonomous R&D into two cooperating agents: one proposes hypotheses, the other writes and tests code — iterating on quant-finance factors, Kaggle pipelines, and model research. Hits a ~30% medal rate on MLE-Bench, nearly double AIDE's.
Orchestrates and coordinates multiple coding agents in a shared, cross-device workspace — preserving context, files, and durable artifacts so teams can run parallel agent workflows without losing intent or state.
Build AI workflows once and run them across model providers — GoogleAI, OpenAI, Claude, Ollama — through one SDK. Composable primitives for RAG, tool use, and agents, plus a local dev UI for tracing and debugging, with SDKs in JS/TS, Go, and Python.
Provides a Python framework for building generative-AI agents and workflows with Pydantic-style type safety and composable capabilities. Model-agnostic provider support, built-in observability, human-in-the-loop tool approval, and durable execution for production use cases.