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Software like LeetLLM
What else does this job. Matched on what each project does, not on who links to whom.
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- LLM APIllmapi.devLLM API is a unified platform that provides developers with access to over 200 AI models from providers like OpenAI, Anthropic, Google, Meta, and more. It offers a single API endpoint, model catalog, and usage analytics, streamlining integration and management for AI-powered applications.
- LLLMlllm.oneLLLM, short for Low-Level Language Models, is a protocol and service layer for reusable agentic tactics. Its core idea is a Tactic: a typed unit of work that can run in process, stream results, sit behind a FastAPI service, and be described as a PsiHub package resource. The stated aim is to give different runtimes and surrounding tools a stable boundary for agentic work. The system centers on a small contract rather than on model execution itself. LLLM is not a model runtime; execution details such as tools, provider settings, tracing, eval hooks, and workflow state are handled by Pydantic AI, native LLLM objects, plain Python, or future adapters. The documentation says LLLM owns Tactic, TacticInfo, CallContext, and TacticEvent, along with local, async, streaming, proxy, and sandbox wrappers at the tactic boundary. It also provides FastAPI service adapters and remote tactic clients, exposing /run, /stream, and /info with stable envelopes. LLLM also includes metadata helpers that export tactics to PsiHub package resources. Those resources are described as discoverable, configurable, and composable, and the page shows ref resolution for local or remote tactic bindings. The text also notes that package storage, validation, cards, agent cards, config templates, semantic channels, event logs, artifacts, snapshots, local stores, service launch decisions, and operational orchestration stay outside LLLM’s responsibility. The tool is presented for apps, workers, robots, coding agents, package tools, and similar callers that need to understand the same tactic contract without caring whether the underlying implementation is an offline test agent, a Pydantic AI agent with tools, a native prompt/dialog workflow, or a remote HTTP service. No pricing or license information is stated in the provided material.
- LiteLLMlitellm.aiLiteLLM is an open-source gateway and proxy that enables developers to manage authentication, load balancing, and spend tracking across 100+ LLM providers using a unified OpenAI-compatible API. It streamlines integration and monitoring for AI applications.
- LLM Wikillm-wiki.netLLM Wiki is a knowledge base tool for AI agents. It compiles topic wikis from source material and supports parallel multi-agent research, thesis-driven investigation, source ingestion, wiki compilation, session memory, feedback curation, topic archiving, inventory tracking, dataset manifests, truth-seeking audits, token-efficient read-only querying, and artifact generation. Its workflow is built around turning raw sources into cross-referenced articles and then into outputs such as reports, slide decks, study guides, playbooks, implementation plans, timelines, glossaries, and comparisons. The tool can dispatch up to ten agents, search academic, technical, applied, news, and contrarian angles, and run a thesis mode that splits agents across supporting, opposing, mechanistic, meta, and adjacent perspectives to produce a verdict rather than a summary. It also ingests URLs, files, PDFs, inbox drops, Git doc repos, MediaWiki dumps, message archives, and Wayback CDX snapshots. Raw sources remain immutable while articles are synthesized on top, and the system can dedupe, catalog artifacts, track durable follow-up state, index large external data with manifests and profiles, and maintain human-owned schema.md topic guides. LLM Wiki ships as a Claude Code plugin, an OpenAI Codex plugin, an OpenCode instruction file, or a portable AGENTS.md. It is Obsidian-compatible. The documentation also describes a full workflow and a read-only querying path, with compact file-cited lookup available through wiki-query. Sessions are captured under .sessions/ with redacted events, state JSON, and Markdown digests, and feedback, librarian scoring, audit, lessons, and plan-generation functions are described as part of the same system. The page refers to it as llm-wiki by nvk. No pricing or license terms are stated in the provided material.
- LLM Scoutllmscout.coLLM Scout is a platform designed to help organizations measure and improve their brand's visibility within AI-generated responses across major large language model (LLM) platforms such as ChatGPT, Claude, Gemini, Perplexity, and AI Overviews. The tool addresses the growing importance of AI search models in brand discovery by providing analytics on how brands are mentioned, cited, and recommended in AI-generated answers. Users can see which brands and competitors are surfaced by AI models, track citations and sources, and identify gaps or opportunities in their AI visibility. The platform offers a range of features including multi-model coverage, competitor tracking, actionable guidance, and prompt-level insights. It allows users to track their brand's mentions across various AI models, view visibility trends over time, and analyze performance by region, market, and prompt intent. LLM Scout also captures which pages and prompts are most frequently referenced by AI models, and provides dashboards with live visibility scores, prompt coverage, and consistency scores reflecting cross-model agreement. Users can compare their brand's AI visibility against competitors, receive content recommendations, and generate weekly AI visibility reports. LLM Scout is intended for marketing and growth teams, SEO and content professionals, as well as agencies and consultants who are responsible for managing and improving brand visibility in the context of AI-driven discovery. The platform is built to support teams in understanding how AI models surface brands, where visibility breaks down, and how to adapt strategies to increase brand presence in AI answers. The setup process involves adding a brand and related topics, approving or tweaking auto-generated high-intent prompts, and then reviewing visibility data and recommendations. The service is delivered as a web-based platform and offers a 7-day free trial. 99 per month expands to 100 prompts, multi-model analytics, and additional reporting; and an Agency plan with custom pricing supports unlimited clients, prompts, and seats. LLM Scout positions itself as an analytics and benchmarking tool for AI visibility, enabling teams to adapt to the evolving landscape of AI-powered search and brand discovery.
- LLM Statsllm-stats.comLLM Stats is a web-based leaderboard for comparing large language models across intelligence benchmarks, speed, context size, and API pricing. It aggregates public benchmark results and live API metrics for developers, researchers, and AI teams evaluating models.
- BenchLLMbenchllm.comBenchLLM is a platform designed for evaluating large language model (LLM) applications. It enables developers to build test suites, generate quality reports, and choose between automated, interactive, or custom evaluation strategies. BenchLLM supports both API and CLI usage, making it suitable for AI developers and ML engineers seeking robust model evaluation tools.
- AnythingLLManythingllm.comAnythingLLM is an AI application for working with documents and AI agents in one interface. It is described as local and private by default, with desktop use centered on running AI tasks without requiring setup or code. The product supports chatting with documents, using AI agents, and working with custom models. It can run a preferred LLM locally, connect to local or cloud LLM engines, or use enterprise models from OpenAI, Azure, AWS, and more. It also supports text-only and multi-modal LLMs, with images or audio mentioned as usable in the same interface. Document support includes PDFs, Word documents, CSV files, codebases, and importing documents from online locations. The site also says it includes built-in defaults for the LLM, embedder, vector database, storage, and agents, and that nothing is shared unless allowed. AnythingLLM Desktop is available for MacOS, Windows, and Linux, and is presented as a one-click install. The desktop version is not SaaS and does not require a signup to use the full suite of tools locally. The same page also mentions hosted and self-hosted options for team use, with multi-user access, full isolation between tenants, and admin control over what users can do and see. It is open source, free to use, and MIT licensed. The page further describes a built-in developer API, plus a growing ecosystem of plugins and integrations, custom agents, data connectors, and data loaders.
- llm4freepypi.orgllm4free is an open-source command-line tool that provides access to various AI models, including chatbots, text-to-speech, and text-to-image generation. It also offers utilities like search, YouTube transcription, and temporary email, targeting developers and advanced users.
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