Parallel + nested agent spawns can burn 200K+ tokens in a single conversation turn. From Anthropic to Microsoft, the industry is converging on tiered responses: compress → downgrade → stop, rather than a binary kill switch.
By 2026 nearly every mainstream coding agent supports subagents. Design philosophies split three ways: deterministic scripted orchestration (Claude Code Workflow), model-driven autonomy (Codex, Devin), and IDE command-center integration (Windsurf 2.0, VS Code). This overview maps product positioning, a capability matrix, and the design-philosophy spectrum.
Multi-agent orchestration splits into three camps: scripted determinism (LangGraph, Claude Code Workflow) is predictable but rigid, model-driven (Codex, Devin) is flexible but unpredictable, and hybrid (Windsurf 2.0) acts as a command center integrating multiple agents. The choice depends on how much predictability you need.
CrewAI 1.15.22 in three points: (1) a new `llm_overlay` context variable that routes a specific agent role to a different model at runtime, instead of hardcoding the model when the agent is created; (2) CrewAI Platform integration gains an application catalog, connection aliases, setup-time integration validation, and deployment-failure logging; (3) tracing now captures human feedback and pause events; no breaking changes in this release.
CrewAI 1.15.18 highlights: (1) conversational Flow is officially promoted from crewai.experimental to a stable API — the canonical implementation moves to crewai.flow, while crewai.experimental.conversational stays importable as a compatibility alias, so existing code doesn't break; (2) the shim currently emits no deprecation warning, so migrating is entirely opt-in for now; (3) also fixes a wrong Claude Sonnet 4.6 context-window mapping and a too-low Anthropic max_tokens default for large tool calls. No breaking changes.
The same Polly task — parallel git worktrees plus cross-vendor review — implemented four ways: Omnigent YAML governs at the Server layer, LangGraph controls flow with a StateGraph, CrewAI assembles roles quickly, and Goose ships a desktop Recipe, compared on tokens, latency, and maintainability.
CrewAI 1.15.17 highlights: (1) declarative Flow definitions can now enable conversational mode — the framework auto-synthesizes built-in conversation methods, no Python `Flow` subclass required; (2) conversational mode is explicitly marked as opt-in to reduce misuse risk; (3) fixes for AMP slug loss during slug-reference tool resolution and chunking of oversized single messages. No breaking changes.
CrewAI (GitHub 57.4k stars, MIT, PyPI 11.6M weekly downloads) defines agents by role, goal, and backstory, then groups them into crews for collaboration. Unlike LangGraph's graph-first and MAF's workflow-first approach, CrewAI is team-first — you don't draw nodes and edges, you describe who's on the team and what each person does. It fully removed its LangChain dependency in late 2024 and is now a standalone framework. The commercial side splits into the open-source package and AMP, a managed platform adding visual building, deployment, tracing, and compliance.
Sorted by GitHub Stars, a survey of 15 mainstream AI Agent frameworks in 2026 — their positioning, key features, and ideal use cases. Not a ranking — it's a map.