Core agent frameworks

Core agent frameworks are the foundation for building production AI agents—especially when you need multi-step reasoning, tool use, memory, retrieval, and coordination across multiple agents. We help teams pick the right framework for the job, then implement agent systems that are reliable, observable, and straightforward to evolve over time.

LangGraph

LangGraph is a graph-based way to design agent workflows, which makes it well-suited to stateful, multi-step and multi-agent systems.

It's a strong option for long-running, tool-heavy agents where you want explicit control over flow (routing, retries, interrupts) and the ability to pause and resume safely.

LangChain

LangChain is still a common default in real-world stacks because it provides a broad set of building blocks: tools, memory patterns, retrieval, and integrations.

We use it when speed-to-value matters and when a team wants an ecosystem that supports many common agent and LLM app patterns without over-customizing upfront.

AutoGen (Microsoft)

AutoGen is designed for multi-agent collaboration and orchestration, including asynchronous coordination and structured "agent chat" patterns.

It also supports human-in-the-loop workflows (for example, using a UserProxyAgent), which is useful for review-heavy domains and operational guardrails.

CrewAI

CrewAI focuses on role-based multi-agent execution: a "crew" of specialized agents splits work, coordinates, and hands off tasks to each other.

It's a strong fit for business process agents and content/ops workflows where teams want clear responsibilities and repeatable execution.

LlamaIndex

LlamaIndex is retrieval-first and widely used for data-heavy AI systems, and it now supports agentic patterns over both structured and unstructured sources.

We often pair it with LangChain or LangGraph so the retrieval layer (ingestion, indexing, querying) stays robust while the agent layer handles planning and tool use.

What we deliver

  • Framework selection + reference architecture based on workflow complexity, data access needs, and governance requirements.
  • Production implementation: tool integration, state management, human approvals where needed, and safety guardrails.
  • Operational hardening: deployment, evaluation, and observability so the system performs consistently and can be improved without rewrites.