BAML

LLM Space v4 is a local-first desktop workbench for prototyping and developing AI agents. It lets users version prompts, system messages, tools, and model settings; trace each model call and tool run in an agent loop; replay historical runs to debug failures; and evaluate agent performance across runs. It can generate prompts and tools with AI and turn a thread into a runnable LangGraph agent. Threads, project files, and API keys remain on the user's computer, while the project describes the app as cloud-ready for managed agents. The desktop application uses Electrobun with a React, Tailwind CSS, and shadcn/ui interface, and is built as a Bun monorepo around Pi Agent Core.

Embabel is an open-source framework for authoring agentic flows on the JVM. Written in Kotlin with a natural Java usage model and built on Spring, it combines LLM-prompted interactions with manually written code and strongly typed domain models. Flows are expressed through actions, goals, conditions, and domain objects; they can be authored with Spring-style annotations such as @Agent, @Goal, @Condition, and @Action, or with an idiomatic Kotlin DSL. The framework dynamically formulates plans toward goals using a pluggable, non-LLM planning step. Its default approach is Goal Oriented Action Planning (GOAP), while Utility AI is also supported for selecting actions by utility scores rather than strict preconditions and postconditions. Conditions are reassessed after each action, and the system replans based on new information and observed effects, allowing known actions to be combined in novel orders and enabling runtime decisions such as parallelization. The planning model is independent of the actions, goals, and plans themselves, and can extend application capabilities by adding domain objects, actions, goals, and conditions without changing finite-state-machine definitions. Execution is provided through an AgentPlatform with focused, closed, and open modes. Focused mode runs a requested agent from application code; closed mode classifies user intent or an incoming event to select an agent; and open mode searches across available goals and actions to build a custom agent, while allowing developers to restrict goal selection. Open mode can find paths not explicitly envisioned by developers and combine functionality from multiple providers, but individual steps must still be defined by the application. Embabel supports mixing language models, including local models for selected tasks, separates the programming model from platform internals for local or production execution, and integrates with Spring dependency injection, AOP, persistence, transactions, and testing. The framework is designed for unit and end-to-end agent testing.

AgentLoop is a developer tool that runs unattended coding loops on a user's machine, executing plans authored in ChatGPT by launching Codex-based workers and independent critic processes. It automates iterative code generation and evaluation until configured quality standards are met.

Lemmalog is a Datalog engine for LLM-agent memory, distributed as a Rust crate with an MCP server, REPL, and agent skill. It treats extracted statements as provenance-tracked base facts, then uses runtime-parsed stratified Datalog to derive temporal projections, closures, contradiction candidates, relevance relationships, and aggregates. The engine supports seminaive incremental fixpoint evaluation, stratified negation with negative-cycle rejection, bi-temporal facts, confidence and provenance annotations, proof trees through why(), scoped retraction recomputation, demand-driven ask_deep queries using magic sets, and persistence of episodes, rules, and base facts while rebuilding derived relations on load. Its AgentMemory facade connects an extractor to deterministic ADD, UPDATE, NOOP, and escalation policies, and assembles budgeted contexts from distilled facts and their source episodes. The MCP server exposes the engine to agent harnesses such as Claude Code and Kimi CLI through tools for observing facts, querying, explaining proofs, retrieving context, saving state, installing rules, and running hypotheticals.

LangFlow is an open-source, low-code visual builder for creating and running language-model pipelines, agent workflows, and retrieval-augmented generation (RAG) applications. The project is developed and maintained by the open-source community led by the GitHub user logaretm and integrates with language-model providers and tools such as LangChain.