Microsoft AutoGen specializes in multi-agent conversations and code execution. Agents Squads focuses on persistent, CLI-first agent teams for development workflows. This guide helps you understand when to choose each.
| Feature | Agents Squads | AutoGen |
|---|---|---|
| Primary Use Case | CLI orchestration for dev teams | Conversational multi-agent systems |
| Abstraction | Squads and agents (file-based) | Agents with roles + conversation patterns |
| Memory | File-based persistent (Markdown/JSON) | In-memory (default), extensible |
| Setup | npm install -g squads-cli | pip install pyautogen |
| Language | TypeScript CLI + any agent language | Python (.NET in development) |
| Code Execution | Via shell (any language) | Built-in code executor agent |
| Deployment | Local-first, works anywhere | Local or Azure-hosted |
| Pricing | Open source (MIT) | Open source (MIT) |
Organizes agents into domain-aligned squads (e.g., engineering, marketing, customer). Each squad contains specialized agents with persistent memory and clear responsibilities.
.agents/memory/Builds systems where multiple conversable agents interact via structured conversations. Agents can play roles like Planner, Critic, Executor, or User Proxy.
File-based persistent memory that survives restarts, is version-controlled with Git, and can be read/edited by humans or other agents.
Why it matters: Agents remember across sessions, learnings accumulate over time, and memory is transparent and auditable.
Conversation history is the primary memory mechanism. Each agent maintains its message history and can query it. External memory is plugin-based.
• Chat history: Full conversation transcript per agent
• Memory plugins: Vector stores, SQL, custom
• Session-scoped: Memory resets between Python runs by default
Why it matters: Conversation history is excellent for collaborative reasoning tasks but requires custom implementation for cross-session persistence.
Try Agents Squads for CLI-first, transparent agent orchestration with Claude Code.