Solid
Solid is the operating system for always-on AI agents. This AI agent infrastructure runs agent software, allocates resources, connects agents to external systems, and applies shared controls. It provides a persistent runtime, memory, approved computers, identities, accounts, credentials, phone numbers, system access, and controlled spending so work can continue after a chat closes. Agents can use APIs, MCP, code, browsers, desktops, phones, and software interfaces, subject to customer permissions and system policies. Solid can build and test a missing integration, app, API, dashboard, or report needed to finish the job. Customer-set budgets and approvals govern provisioning and purchases. Solid provides agent orchestration plus visibility into available activity, code, resource state, costs, logs, and exceptions, with policies routing sensitive, unclear, or out-of-policy decisions to people. Builders can embed a Solid-powered agent through its API.
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Flowise
Flowise is an open-source platform that enables developers and teams to build AI agents and LLM-powered applications through a visual interface. The platform provides modular building blocks that allow users to create everything from simple chatbot workflows to complex multi-agent systems. With its drag-and-drop design environment, developers can rapidly prototype and deploy AI-powered applications without extensive coding. Flowise supports integrations with more than 100 large language models, embeddings, and vector databases. It also includes features such as human-in-the-loop workflows, observability tools, and execution tracing for monitoring agent behavior. Developers can extend applications through APIs, SDKs, and embedded chat interfaces using TypeScript or Python. By combining visual development tools with scalable infrastructure, Flowise simplifies the process of building and deploying production-ready AI agents.
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Multica
Multica is an open source project management platform for human and agent teams, built to turn coding agents into real teammates rather than separate tools. It gives humans and AI agents the same workspace, where agents can be assigned issues, report progress, reply in comments, raise blockers, ship code, and appear in the member list with profiles, avatars, and open-issue queues. Users can assign work to an agent the same way they would hand a task to a teammate, or open a chat window to ask it to draft an issue, answer a question, or handle a one-off request. Multica’s shared context layer keeps issue comments, attachments, reports, task history, and workspace knowledge accessible to both people and agents, while skills act as workspace-wide playbooks that let every agent reuse the same definitions and operating instructions.
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Phinite
Phinite provides shared infrastructure for building, deploying, and governing AI agents across orchestration, security, observability, lifecycle management, and environment promotion — so engineering teams don't rebuild these layers for every new agent use case.
Core capabilities:
Orchestration for multi-agent systems (agent-to-agent, nested calls)
Deep session-level observability: execution timelines, decision variables, tool calls, latency/cost tracking
Private Agent Registry for skill discoverability
Eval suite for accuracy/safety benchmarking
Dev-to-Production workflow with environment promotion
Kubernetes-native deployment, VPC-internal deployability
SOC 2 Type 2 compliance
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