Framework Comparison Matrix
| Framework | Model | License Cost | Learning Curve | Best For | GitHub Stars |
|---|---|---|---|---|---|
| LangGraph | Open Source | Free | Steep | Enterprise, durable workflows | 12K+ (May 2026) |
| CrewAI | Open Source | Free | Low | Role-based multi-agent teams | 25K+ (May 2026) |
| AutoGen (AG2) | Open Source | Free | Medium | Conversational multi-agent | 40K+ (May 2026) |
| LlamaIndex Workflows | Open Source | Free | Medium | RAG-heavy agent pipelines | 38K+ (May 2026) |
| AWS Bedrock Agents | Managed (AWS) | Pay-per-API-call | Low (if AWS native) | AWS ecosystem, production scale | N/A |
| Palantir AIP Logic | Enterprise (custom) | Custom (enterprise) | Low (no-code builder) | Non-technical operator workflows | N/A |
| Google Vertex AI Agents | Managed (GCP) | Pay-per-use | Low-Medium | GCP ecosystem, Gemini native | N/A |
GitHub star counts from May 2026 public data. Framework maturity and API stability vary — check release notes before production adoption. Open-source license costs are $0; infrastructure and model API costs apply in all cases.
Framework Deep Dives
LangGraph models agents as state machines with nodes (functions), edges (transitions), and persistent state. The graph model gives precise control over execution order, conditional routing, and checkpoint/restart for long-running workflows. It reached production stability (v1.0) in late 2024 and has become the default runtime for LangChain agents in enterprise deployments.
When to choose LangGraph: Durable workflows that must survive failures and restart mid-execution. Auditable pipelines where every step must be logged. Enterprise use cases where a single agent failure shouldn't lose the entire job. Human-in-the-loop workflows requiring pause, review, and resume.
Tradeoff: Verbose. The state graph mental model requires upfront design. For simple single-agent tasks, it's overkill. For complex, long-running enterprise workflows, it's the right tool. Source: LangGraph documentation, accessed May 2026.
CrewAI models agents as a team: you define Agents (with roles and goals), Tasks (with descriptions and expected outputs), and a Crew that orchestrates them. The abstraction is intuitive — a "researcher" agent, an "analyst" agent, a "writer" agent working together. 20 lines of Python to get started.
When to choose CrewAI: Rapid prototyping of multi-agent workflows. Role-based content generation pipelines. Research and analysis tasks where parallel agents specialize in different domains. Teams new to agentic AI who need quick wins.
Tradeoff: Token efficiency. A CrewAI crew with 5 agents can consume 5× the tokens of an equivalent single-agent LangGraph workflow. Multi-agent conversation overhead adds up at scale. Test your cost profile carefully before production. Source: comparative analysis from CrewAI vs LangGraph vs AutoGen (DEV Community, 2026), accessed May 2026.
AutoGen implements multi-agent systems through conversation: agents communicate with each other in multi-turn dialogues, with a GroupChat coordinator determining who speaks next. AG2 (v0.4) introduced the GroupChat pattern as the primary coordination mechanism.
When to choose AutoGen: Multi-turn conversational agent pipelines. Code generation with automated execution and feedback loops. Research scenarios where agents debate, critique, and refine outputs. Microsoft ecosystem — AutoGen integrates well with Azure OpenAI and GitHub Copilot workflows.
Note: Microsoft has shifted active development focus toward the broader Microsoft Agent Framework, with AutoGen moving toward maintenance mode for new large features. AG2 remains actively maintained for current capabilities. Source: OpenAgents framework comparison, Feb 2026, accessed May 2026.
Decision Framework — Which to Choose
| Your Need | Best Choice | Reason |
|---|---|---|
| Durable, restartable enterprise workflows | LangGraph | State checkpointing, precise execution control |
| Quick multi-agent prototype | CrewAI | Lowest learning curve; role-based DSL |
| Conversational agent teams | AutoGen | GroupChat pattern; code execution loops |
| RAG + agents combined | LlamaIndex Workflows | Best-in-class retrieval + orchestration |
| AWS production deployment | AWS Bedrock Agents | Managed, SLA-backed, AWS ecosystem |
| Non-technical ops team users | Palantir AIP | No-code builder; operator-facing UI |
| GCP + Gemini native | Vertex AI Agents | Native Gemini integration; GCP ecosystem |
The most common mistake with agent frameworks: underestimating token consumption. A multi-agent workflow that calls 5 agents, each with a 2K token system prompt and 1K context, can cost 5–10× a well-engineered single-agent solution doing the same task. Benchmark your agent pipeline's per-task token cost before committing to production scale. Multi-agent parallelism adds latency management complexity, not just cost.