The tools we engineer with.

An open-source, self-improving AI agent built by Nous Research. Runs from the terminal, messaging platforms, and IDE workflows with persistent memory, reusable skills, cron jobs, browser automation, and multi-agent delegation. Its built-in learning loop creates skills from experience and improves them during use.

An open-source agent framework that turns LLMs into autonomous systems capable of executing code, managing persistent state, and interacting with external APIs. Runs locally on your hardware, connects to 20+ messaging channels, and supports any model provider. Over 330,000 GitHub stars.

Stateful agent orchestration for complex multi-step reasoning. Used when an agent needs to maintain context across tool calls, manage branching logic, and recover from failure without losing state. LangGraph gives us a directed-graph runtime where every node is inspectable and every edge is traceable.

Lightweight multi-agent coordination for parallel task execution. Used when specialized agents need to collaborate on independent subtasks within a shared goal. CrewAI handles role assignment, task delegation, and inter-agent communication without the overhead of a full orchestrator.

Node-based workflow engine for deterministic, auditable automation. Handles API orchestration, data pipelines, human-in-the-loop approval gates, and event-driven triggers. Every workflow is version-controlled, testable, and deployable through CI/CD. Our default for any automation that needs to be governed.

Visual platform for building AI agents and automation across 3,000+ apps. Used when rapid integration breadth outweighs the need for custom logic. Strong visual scenario builder with branching, routing, and error handling. Complements n8n for scenarios where connector availability matters most.

Rapid integration layer for connecting SaaS tools with minimal overhead. Used for high-volume, low-complexity automations where speed of connection matters more than custom logic. We wrap Zapier workflows with monitoring to ensure they remain observable like the rest of the stack.

General-purpose reasoning for structured tasks. Chosen when the work requires broad knowledge, instruction following, and reliable output formatting. We bind GPT-4o to typed tool interfaces so it operates within explicit boundaries rather than freeform generation.

Precision reasoning for complex analysis. Used when the task demands careful adherence to guidelines, multi-step verification, and appropriate refusal boundaries. Claude's constitution-based training aligns well with systems that require strict operational guardrails.

Low-latency reasoning for real-time agent workflows. Grok's architecture emphasizes tool-calling efficiency and large context windows, making it suitable for agents that need to process long conversation histories or documents before acting. Evaluated per-task against cost and reliability benchmarks.

Open-source visual agent builder supporting hundreds of LLMs with built-in RAG, ReAct, and function calling. Used for rapid prototyping of agent workflows before committing to a production orchestration layer. Its visual debugger speeds up prompt iteration and tool configuration.

Goal-decomposition agent framework that breaks complex objectives into sub-tasks, executes them autonomously, and iterates based on results. Used for research-heavy workflows where the path to completion is not known in advance and must be discovered at runtime.

Persistent memory stores for agent context. Enable agents to recall past interactions, retrieve relevant knowledge, and maintain state across sessions. We use vector search for semantic retrieval and structured metadata filtering to narrow results before they reach the model.

Every decision, API call, and workflow branch is logged and measurable. Execution latency, context size, token usage, and failure modes are surfaced through structured logs and dashboards. We treat observability as a non-negotiable layer of every deployment, regardless of the underlying model or framework.

Every autonomous agent and automated workflow operates under a verifiable identity. RBAC binds each agent to scoped permissions, defining exactly which data it can read, which APIs it can call, and which operations it can trigger. No agent acts outside its authorized envelope.