KForge

Gimlet Labs is an applied research lab and AI-infrastructure company building systems for serving AI workloads. Its Gimlet Cloud provides serverless inference for AI agents, supporting multi-agent systems, custom logic and data sources, model chains with non-model stages such as search, and platform-managed scheduling, orchestration and optimization. The company describes a multi-silicon inference architecture that divides workloads into fine-grained pieces and maps them to suitable hardware, including GPUs and specialized accelerators. Its research also covers workload partitioning, heterogeneous-hardware compilation, dynamic datacenter scheduling, hybrid edge/cloud execution and cost-aware optimization. KForge is its multi-agent system for generating optimized low-level kernels directly from PyTorch, with correctness checks and support for CUDA, ROCm and Metal backends.

TrueForge is an open-source, vendor-neutral agent harness developed by TrueFoundry. It runs the agent execution loop, including model calls, MCP tool use, skills, sandboxed code and file execution, approvals, context management, and session state. Agents can use configured model providers, remote MCP servers, git-backed SKILL.md instruction packs, and on-demand sandboxes; context features include subagents, deferred tool loading, Code Mode, large-result offloading, and compaction. TrueForge exposes agents through a bundled chat UI, an HTTP API with a TypeScript SDK, and an embeddable UI SDK. It supports local mode with SQLite and hosted deployments using Postgres and Redis through Docker Compose or Helm; the repository warns that local mode is intended for personal use on localhost rather than production or internet-facing deployment.

KTransformers is an open-source research framework for efficient large-language-model inference and fine-tuning through heterogeneous CPU-GPU computing. Its user-facing capabilities are provided through the kt-kernel source tree: high-performance inference and supervised fine-tuning (SFT). For mixture-of-experts models, it uses heterogeneous expert scheduling, placing frequently used or “hot” experts on GPUs and other experts on CPUs with optimized AMX, AVX512, or AVX2 kernels and NUMA-aware memory management. It supports CPU-side INT4/INT8 quantized weights, GPU-side GPTQ, native BF16 and FP8 precision, and multiple CPU, GPU, and NPU backends. The SFT capability integrates with LlamaFactory for hybrid CPU-GPU fine-tuning of large MoE models, including LoRA and full-parameter training, with INT4/INT8 quantization options.

OKF Agent Memory is a Git-native persistent memory layer for AI coding agents, developed as a pure Go CLI and library. It stores project knowledge as plain Markdown files with YAML front matter in a repository, following the vendor-neutral Open Knowledge Format (OKF) v0.2 rather than relying on an external vector database. The tooling parses and validates OKF bundles, builds an in-memory BM25 index for local lexical search, and provides progressive disclosure through hierarchical index files and link graphs. Its search-before-write workflow queries existing concepts before creating new ones; concepts can include provenance, trust tiers, lifecycle metadata, and code references for linking knowledge to source files. The standalone executable also supports creating and updating concepts, bootstrapping the memory structure into a project, and running an embedded Model Context Protocol (MCP) server over stdio for agent platforms. The repository reports sub-300-microsecond concept search and approximately 4-millisecond graph validation for its benchmark cases, with no external databases or API costs for retrieval. It is distributed under the MIT License.

Gimlet Cloud is Gimlet Labs' serverless inference platform for AI agents and multi-agent systems. It can run agentic pipelines with custom logic, data sources, chained models, and non-model stages such as search; the platform manages scheduling, orchestration, and optimization. Its multi-silicon approach splits inference workloads into fine-grained pieces and maps them to suitable hardware, including Nvidia, AMD, AWS Trainium, Cerebras, D-Matrix, and Intel Gaudi systems. Gimlet describes this heterogeneous execution model as achieving 3–10× faster inference than homogeneous systems. The company also develops kforge, a multi-agent system that generates and checks optimized low-level kernels from PyTorch for CUDA, ROCm, and Metal backends.