From Single Agent to Multi-Agent

Most framework comparisons are written by people who have run demos, not production systems. Here is what actually matters after the honeymoon phase.

CrewAI has the gentlest learning curve and the most opinionated structure. You define Agents with roles, goals, and backstories; you define Tasks with descriptions and expected outputs; and CrewAI handles the orchestration. This structure is its strength and its constraint. When your use case fits the Crew mental model cleanly, it ships fast. When it doesn't, you fight the framework. Production verdict: excellent for knowledge work pipelines with well-defined roles (research → write → review). Struggles with dynamic task graphs and stateful long-running processes.

LangGraph is the most powerful option and the most demanding. It models your agent system as a directed graph with explicit state management at each node. This gives you complete control over execution flow, conditional branching, and human-in-the-loop interrupts. The cost is cognitive overhead. Production verdict: the right choice for teams building complex, stateful workflows where they need to reason precisely about what happens at every step. Not the right choice if you need to ship in a week.

AutoGen optimizes for conversational multi-agent interaction. Its model of "conversations between agents" is intuitive and powerful for tasks that benefit from back-and-forth refinement. It handles code execution natively and has strong support for human-in-the-loop patterns. Production verdict: strong choice for code generation, analysis, and tasks requiring iterative refinement. Less suited for structured pipelines with strict output requirements.

The single most common failure mode in multi-agent systems is the agent that works perfectly in a fresh session and fails mysteriously in session four. The culprit is almost always memory architecture — specifically, the absence of one.

L1: Conversation Buffer (always required) — The raw message history for the current session. Every framework gives you this for free, and every team forgets it has a context window limit. At ~32k tokens, your agent starts losing the beginning of the conversation. Mitigation: implement a rolling window with summary injection.

L2: Session Summarization (implement in week two) — A compressed representation of what happened in past sessions, injected into the system prompt at the start of each new conversation. Without this, your agent treats every session as if it has never worked with you before. Implementation: after each session ends, run a summarization call and store the result in a key-value store indexed by user/project ID.

L3: Persistent Vector Store (implement before scaling to teams) — Semantic search over accumulated knowledge: past decisions, project context, institutional patterns. This is what makes an agent feel like it actually knows your business rather than a stateless tool you have to re-educate every time. Implementation: embed key artifacts (decisions, summaries, code patterns) into a vector database (pgvector, Pinecone, Weaviate) and retrieve top-k on each new task.