Feature	LangChain	AutoGen	CrewAI	Loom
Context pressure management	❌	❌	❌	✅
Five-partition context	❌	❌	❌	✅
Background heartbeat (H_b)	❌	❌	❌	✅
Structured R→A→O→Δ loop	❌	partial	❌	✅
Context renewal (disk paging)	❌	❌	❌	✅
Veto authority	❌	❌	❌	✅
Self-improvement (E1–E4)	❌	❌	❌	✅
Progressive skill loading	❌	❌	partial	✅
MCP integration	partial	❌	❌	✅
Multi-provider (Anthropic/OpenAI/Gemini)	✅	✅	✅	✅
Built-in retry + circuit breaker	partial	❌	❌	✅

LangChain — chains and pipelines. Great for linear workflows, but the model is a step in your pipeline, not an autonomous agent.

AutoGen — conversation-based multi-agent. Agents talk to each other, but context management and safety are left to you.

CrewAI — role-based crews. Easy to get started, but limited control over what the model sees and when.

Loom — agent API first. Application developers assemble one AgentConfig, run one Agent, and get structured sessions, runtime inputs, safety, heartbeat, and context control underneath that single public surface.

Compared with other frameworks, Loom tries to keep the public contract narrower:

• one main assembly object: AgentConfig
• one main execution object: Agent
• one stateful continuity object: Session
• one run-scoped input object: RunContext

Everything else remains available, but advanced objects are intentionally pushed into loom.config and loom.runtime instead of competing with the top-level application path.