Overmind
Overmind is a model training and deployment platform for teams building AI agents. It ingests production traces through OpenTelemetry and creates datasets from trace data, file uploads, and connectors for Langfuse, LangSmith, Braintrust, and Galileo. Teams can inspect agent context and runtime behavior, prepare and score datasets, evaluate prompts and models against their own examples, and optimize agent behavior through scored changes. The platform supports LoRA and full fine-tuning of open-weight models, benchmarking with configured evaluators, and deployment through an OpenAI-compatible endpoint; trained weights can also be downloaded. Users can work in the hosted console or run the platform on their own infrastructure with Docker Compose. It is designed for AI engineering and product teams operating agents in production that want to test, improve, or specialize models using their own data.
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Molted
Molted is a managed operating environment for autonomous AI agents. It lets teams deploy, host, monitor, recover, and scale OpenClaw-based agents without building their own cloud, DevOps, integration, and recovery stack.
Molted provides agent-ready runtimes with persistent workspaces, browser automation, 1,000+ app integrations, dedicated email and voice per agent, monitoring, automatic recovery, and lifecycle management. Agents can use tools, access websites without APIs, communicate through email/voice/SMS, and keep working continuously.
It is built for AI agencies, SaaS builders, OpenClaw consultants, and teams running agent fleets for customer-facing or internal workflows. Molted supports multi-agent management, versioned filesystems, restore points, REST API control, and cloud, on-premise, or sovereign deployments.
Molted is not generic hosting; it is the run layer for production AI agents.
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evalship
evalship helps engineering teams test and improve LLM-powered software. At setup, your coding agent reviews every scorer, judge and prompt in your evals and finds their flaws, such as a judge that scores 1.0 when its call fails, each with fix instructions. On every pull request, evalship compares results with the default branch, reports the evals a change really broke and the likely cause, sets flaky evals aside, and flags AI calls no eval exercises. Its check can block a merge that breaks an eval. Optimize has your coding agent try prompt and model changes on your machine, such as a cheaper model or clearer instructions, and evalship says which win beyond chance and by how much in pass rate, cost and time. Evals run with your own keys, in your own GitHub Actions or on your machine. evalship reads results from pytest, Vitest, Jest, promptfoo, Langfuse, Braintrust and LangSmith, with no SDK to add. Public repositories are free; paid plans start at $19 a month.
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Deep Agents
Deep Agents is an open-source developer library and agent harness for building LLM-powered agents that can work through multi-step tasks. It provides an agent loop with built-in capabilities for planning, persistent memory, file-system interaction, and delegation to subagents, while allowing developers to connect model providers and tools. The library is intended for software developers and teams creating custom agent applications, including research, coding, and other workflows that require an agent to plan and act across several steps. Developers can extend its behavior with custom tools, middleware, and backends, and integrate it with LangGraph and LangSmith for orchestration, tracing, and deployment workflows. Deep Agents is a software development framework rather than a ready-made end-user agent application; users implement and run agents within their own applications and environments.
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