Liquid Inference

Co-Invest is Liquid's AI trading assistant for researching markets, sizing positions, and placing trades through Claude, ChatGPT, iMessage, a Chrome extension, or the Liquid account. It uses market data such as positioning, funding, liquidation maps, on-chain flows, news, macroeconomic events, earnings, and ETF flows to produce trade ideas with a direction, size, named catalyst, and source links. It supports crypto, stocks, ETFs, indices, commodities, foreign exchange, and other markets available through Liquid, including long and short positions, multipliers, stop-losses, and take-profits. Trades require explicit user confirmation: the assistant returns a confirmation card containing the symbol, direction, size, multiplier, and rationale, and the user taps to confirm or cancel. According to the product page, Co-Invest can read a portfolio and propose trades but cannot transfer funds, change account settings, or submit an order without confirmation. The page distinguishes this assistant from Co-Invest Computer, which it identifies as Liquid's option for scheduled, hands-off execution; the video description presents scheduled trading routines as part of Liquid's AI trading offering. Co-Invest itself is described as free, with Liquid's standard trading fees and applicable perpetual-futures funding rates. It also provides a paper-trading mode using simulated balances against live market data.

LiteLLM is an open-source AI gateway and Python SDK from BerriAI for calling more than 100 LLM providers through a unified OpenAI-compatible interface. It supports direct library integration and a self-hosted proxy server, with provider-specific formats translated into common endpoints such as chat completions, responses, embeddings, image generation, audio, batches, reranking, messages, and agent-to-agent calls. The proxy provides centralized authentication and authorization, virtual keys, per-project and per-user spend tracking, logging, guardrails, caching, load balancing, routing with retry and fallback logic, and an administration dashboard. LiteLLM also exposes MCP tools and A2A agents through the gateway and can be deployed as a componentized stack with separate gateway, backend, and UI services. The repository describes it as open source and documents Docker, local development, and Terraform deployment options for AWS and Google Cloud.

Langfuse is an open-source LLM engineering platform for developing, monitoring, evaluating, and debugging AI applications. It ingests traces for LLM calls and related operations such as retrieval, embeddings, and agent actions, with integrations including OpenTelemetry, LangChain, the OpenAI SDK, LiteLLM, and other LLM frameworks. Its features include prompt management with version control and collaborative iteration, LLM-as-a-judge and code-based evaluations, user feedback and manual labeling, datasets for test sets and benchmarks, trace replay and debugging, metrics, and a playground for testing prompts and model configurations. Langfuse exposes APIs and typed Python and JavaScript/TypeScript SDKs for custom LLMOps workflows. It is available as a managed cloud service or for self-hosting with Docker Compose, virtual machines, Kubernetes via Helm, or Terraform templates.

GuideLLM is an open-source platform for benchmarking and evaluating real-world LLM inference deployments. It simulates end-to-end interactions with OpenAI-compatible and vLLM-native servers, using real or synthetic text and multimodal datasets and configurable execution profiles such as synchronous, concurrent, throughput, constant-rate, Poisson, and sweep workloads. It measures SLO-oriented latency and token statistics, including time to first token, inter-token latency, and end-to-end behavior, then produces console, JSON, CSV, or HTML reports for deployment tuning, capacity planning, and regression tracking. The tool provides both a CLI and API, with multiprocessing, threading, asynchronous execution, and Hugging Face, file, synthetic, or custom data sources.

vLLM is an open-source library and inference-serving engine for deploying large language models on GPUs and other hardware accelerators. Originally developed in UC Berkeley's Sky Computing Lab, it is maintained by a broad community and provides an OpenAI-compatible API server, as well as Anthropic Messages API and gRPC support. Its serving stack manages attention key-value memory with PagedAttention, continuous batching, chunked prefill, prefix caching, streaming generation, and disaggregated prefill, decode, and encode. It also supports speculative decoding, quantization, optimized attention and GEMM/MoE kernels, automatic kernel generation and graph-level transformations, structured outputs, tool calling, multiple decoding algorithms, and distributed inference through tensor, pipeline, data, expert, and context parallelism. The project integrates with Hugging Face models and supports decoder-only, mixture-of-experts, hybrid state-space, multimodal, embedding, retrieval, reward, and classification models. It can run across NVIDIA, AMD, Intel, and other supported accelerators, as well as x86, ARM, and PowerPC CPUs, and can be installed with uv or pip or built from source.