Seven leading AI agent frameworks have crystallized into distinct approaches for building autonomous systems as of mid-2026, with LangGraph leading production deployments at approximately 400 firms including Klarna, Uber, and JP Morgan. The landscape fragments across abstraction level, provider scope, and orchestration philosophy, with CrewAI offering the fastest path to a working prototype in roughly 35 lines of code while Claude Agent SDK provides the deepest single-provider operational capabilities. The decision matrix recommends LangGraph for maximum production durability, OpenAI Agents SDK for cleanest multi-agent handoffs, and Google ADK for cross-vendor interoperability via the A2A protocol.
Post # AI Agent Frameworks: A Comparative Analysis of DSPy, Claude Agent SDK, OpenAI Agents SDK, CrewAI, AutoGen, LangGraph, and Google ADK A deep-dive into the design philosophies, architectures, capabilities, trade-offs, and production readiness of the seven leading AI agent frameworks as of May 2026. ## Executive Summary The AI agent framework landscape in mid-2026 has crystallized into seven distinct approaches to building autonomous systems. Rather than a single winner, we see a fragmentation along three primary axes: **abstraction level** (from DSPy’s declarative programming model to LangGraph’s low-level graph runtime), **provider scope** (Claude Agent SDK’s Anthropic-only focus vs. the provider-agnostic CrewAI, LangGraph, and Google ADK), and **orchestration philosophy** (role-based teams in CrewAI vs. conversational debate in AutoGen vs. graph state machines in LangGraph). **Decision matrix: choose your framework by priority:** | If your top priority is… | Recommended framework(s) | Rationale | |---|---|---| Fastest prototype to working prototype | CrewAI | ~35 lines of code; team metaphor maps naturally to most business workflows | Maximum production durability (crash recovery, checkpointing) | LangGraph | First stable v1.0 with durable execution; deployed by 400+ firms | Deepest single-provider operational capabilities | Claude Agent SDK | File/shell access, MCP integration, 18 lifecycle hooks: same architecture as Claude Code | Cleanest multi-agent handoff with provider flexibility | OpenAI Agents SDK | Typed handoffs with metadata; 100+ models via Responses API; built-in tracing | Enterprise governance and OWASP compliance (Azure/.NET shops) | Microsoft Agent Framework | OWASP Agentic Top 10 coverage, dual-language (.NET + Python), best HITL | Prompt quality optimization across any pipeline | DSPy (combined with an orchestration framework) | MIPROv2 and GEPA optimizers produce better prompts automatically; pair with LangGraph or CrewAI for orchestration | Cross-vendor agent interoperability (A2A protocol) | Google ADK | Native A2A support, four language SDKs (Python, TypeScript, Go, Java) | **Key findings:** **LangGraph leads production deployments** with the most mature durable execution model. Deployed by ~400 firms including Klarna ($60M savings), Uber, and JP Morgan, it reached v1.0 in September 2025 and offers explicit graph modeling with first-class human-in-the-loop debugging. Its 34.5M monthly downloads and 90M ecosystem-wide downloads reflect broad adoption.**Claude Agent SDK is the most operationally capable single-provider framework**, shipping the same architecture that powers Claude Code, including built-in file/shell access, MCP integration, lifecycle hooks, and subagent spawning. However, it is locked to Anthropic models, lacks observability, durable execution, and state persistence natively, requiring teams to build all platform infrastructure themselves.**OpenAI Agents SDK offers the cleanest multi-agent delegation model** with its handoff system and three-tier guardrails. It is provider-agnostic (100+ models), lightweight, and tightly integrated with OpenAI’s Responses API. Its April 2026 enterprise security update added harness improvements and sandbox isolation.**CrewAI wins on developer velocity** for role-based multi-agent systems, requiring as few as 35 lines of code for a minimal agent. Its three process types (sequential, hierarchical, consensual) and event-driven Flows make it the fastest path from idea to working prototype. Benchmarks suggest it executes tasks 5.76× faster than LangGraph in QA scenarios, though the original benchmark methodology lacks publicly available details on task selection, model versions, and hardware (see Performance Benchmarks section for caveats).**Microsoft Agent Framework (successor to AutoGen)** is the enterprise choice for organizations invested in Azure and .NET. Its merger of Semantic Kernel’s enterprise features with AutoGen’s conversational patterns reached GA v1.0 in April 2026. It offers the best human-in-the-loop support and OWASP Agentic Top 10 governance.**Google ADK is the most multi-language framework** with SDKs for Python, TypeScript, Go, and Java. Its native A2A (Agent-to-Agent) protocol and hierarchical agent trees make it ideal for enterprise cross-vendor discovery. It powers Google’s own Agentspace and Customer Engagement Suite.**DSPy occupies a unique niche as a prompt optimization framework** rather than an orchestration framework. With 34.7k GitHub stars and optimizers including MIPROv2 and GEPA (ICLR 2026 Oral), it treats LLM pipelines as compilable programs that self-improve through evaluation-driven compilation. It excels at single-agent pipeline optimization but lacks multi-agent coordination primitives. The market is projected to grow from $7.84 billion in 2025 to $52.62 billion by 2030, with enterprise agentic AI reporting average ROI of 171% (US: 192%). The choice among frameworks increasingly depends on three factors: (a) whether you prioritize orchestration control or developer velocity, (b) your provider commitments (Anthropic-only vs. multi-provider), and (c) the complexity of your workflow state management needs. ## Background and Context ### Why Agent Frameworks Emerged The rise of AI agent frameworks reflects a fundamental shift in how developers interact with large language models. Prior to 2023, LLM integration meant wrapping API calls in application code: sending prompts, parsing responses, and handling errors manually. The release of LangChain in late 2022 introduced the concept of “chains”: composable sequences of LLM calls with intermediate steps. This was the first attempt to bring software engineering discipline to LLM applications. However, chains are linear and deterministic. Real-world AI tasks require loops, conditionals, branching, and state management, capabilities that simple chains cannot express. LangGraph addressed this by introducing graph-based workflows where agents become nodes in a directed graph with explicit state transitions. This marked the transition from “chain thinking” to “agent thinking.” Simultaneously, the limitations of prompt engineering became apparent. Manually crafting prompts for complex multi-step pipelines was brittle and non-reproducible. DSPy, released by Stanford NLP researchers in 2023 and backed by Databricks, proposed a radical alternative: treat prompt engineering as a compilation problem. Instead of hand-writing prompts, developers define declarative signatures (typed input/output contracts) and modules (computation patterns like ChainOfThought or ReAct), then use optimizers to automatically compile effective prompts and weights based on evaluation metrics. ### The Multi-Agent Revolution By 2024, a second wave emerged: multi-agent systems. Single agents were proven adequate for many tasks, but complex problems (research synthesis, software engineering, customer service at scale) required coordination between specialized agents. Several frameworks pursued this vision with different philosophies: **CrewAI**(2023) introduced the “crew” metaphor: agents as team members with roles, goals, and shared tools. This role-based approach proved highly intuitive for developers coming from traditional project management mental models.**AutoGen**(Microsoft Research, 2023) pioneered conversational multi-agent patterns where agents debate, critique, and refine outputs through structured group chats. This research-grade approach excelled at tasks requiring iterative deliberation.**OpenAI Swarm**(March 2024) offered a minimal multi-agent orchestration primitive: handoffs between agents as function calls. It was educational but too simple for production. The OpenAI Agents SDK (March 2025) evolved Swarm into a production framework with guardrails, tracing, and sandbox environments.**Google ADK**(Cloud NEXT 2025) introduced hierarchical agent trees with native A2A protocol support, enabling cross-vendor agent discovery and enterprise-scale multi-agent orchestration. ### The Provider Wars A critical dimension of the framework landscape is provider scope. Anthropic’s Claude Agent SDK (originally “Claude Code SDK,” renamed late 2025) is locked to Anthropic models but offers the deepest operational capabilities: built-in file access, shell execution, MCP integration, and lifecycle hooks. OpenAI’s Agents SDK, while optimized for GPT models, is provider-agnostic and supports 100+ models through its Responses API. Google ADK is model-agnostic (via LiteLLM) but deeply aligned with the Google Cloud ecosystem. LangGraph, CrewAI, and DSPy are all provider-agnostic by design. ### Market Trajectory The agentic AI market has exploded from $5.40 billion in 2024 to $7.84 billion in 2025, with projections reaching $52.62 billion by 2030 at a 45.8% CAGR [firecrawl.dev, May 2026]. Enterprise deployments report average ROI of 171%, with US enterprises averaging 192%, triple the return of traditional RPA and chatbot automation [xillentech.com, April 2026]. The global agent market reached $7.84 billion in 2025 and is projected to hit $52.62 billion by 2030 [firecrawl.dev, May 2026]. ### Standardization Efforts Several protocol-level initiatives are attempting to create interoperability between frameworks: **Model Context Protocol (MCP)** by Anthropic standardizes agent-tool connectivity**Agent-to-Agent (A2A) Protocol** by Google (now under the Linux Foundation with 150+ supporters) enables cross-framework agent discovery and communication**AGENTS.md** donated by OpenAI to the Agentic AI Foundation (Linux Foundation) aims to create open, interoperable standards for safe agentic AI These protocols suggest a future where frameworks are interchangeable building blocks rather than walled gardens. ## Detailed Framework Analyses ### 1. DSPy (Declarative Self-improving Python) **Origin and Positioning:** DSPy stands for “Declarative Self-improving Python.” Created by Stanford NLP researchers (Omar Khattab et al.) and backed by Databricks, it was published as an ICLR 2024 spotlight paper. Unlike orchestration frameworks, DSPy is fundamentally a **programming model and optimization framework** for LLM pipelines. Its thesis: rather than hand-crafting prompts, developers write structured Python code that DSPy “compiles” into effective prompts and weights. **Core Architecture:** DSPy’s design rests on three layers: **Signatures**: Typed input/output contracts that declare what a module should do. For example,`question_answer = Signature("question -> answer")` declares a module that takes a question and produces an answer. DSPy abstracts away the prompt template: it generates one automatically during compilation.**Modules**: Composable building blocks like`ChainOfThought` ,`ReAct` ,`Predict` , and`MultiChainClassification` . These are analogous to neural network layers but for LLM reasoning patterns. A DSPy program is a directed graph of modules, much like a PyTorch model definition.**Optimizers (Teleprompters)**: Algorithms that automatically tune the pipeline parameters. DSPy ships with several:** BootstrapFewShot**: Generates few-shot examples by running the unoptimized program and collecting successful traces** COPRO**(Cooperative Prompt Optimization): Evolves prompt instructions using mutation and selection** MIPROv2**(Meta-Instruction PRO optimization v2): Uses meta-prompting to iteratively refine both instructions and demonstrations, optimizing for a custom metric**GEPA**(Genetic-Pareto Architectures, ICLR 2026 Oral): A reflective prompt optimizer using genetic/evolutionary algorithms that achieves up to 19% higher test accuracy and 35× fewer rollouts than reinforcement learning baselines [arxiv.org/abs/2507.19457]**Experimental RL**: Reinforcement learning-based optimization (experimental) **LLM Provider Support:** DSPy is provider-agnostic by design, integrating with OpenAI, Anthropic, Gemini, Databricks, Ollama, SGLang, Azure, SageMaker, and any LiteLLM-compatible service. However, the practical gap between “theoretically agnostic” and “functionally compatible across providers” is significant. LiteLLM, DSPy’s primary multi-provider abstraction layer, has documented issues with Ollama tool calling (JSON parsing errors when models return array-type content) [github.com/BerriAI/litellm/issues/11433], streaming inconsistencies with certain providers, and known incompatibilities with OpenAI’s Responses API when used via the completion bridge [github.com/BerriAI/litellm/issues/9170, #16808]. Organizations running DSPy across many providers should expect to handle provider-specific edge cases that LiteLLM does not abstract away. **Multi-Agent Capabilities:** DSPy supports tool-using agents through its `ReAct` module and can be combined with orchestration frameworks. However, it is primarily designed for single-agent, multi-step reasoning pipelines rather than independent agent coordination. It lacks primitives for agent handoffs, team coordination, or role-based delegation. **Deployment Characteristics:** DSPy programs compile to self-contained Python modules with built-in caching, async execution, streaming, and model persistence. The compiled prompts are deterministic given the same optimization dataset, enabling reproducible deployments. **Cost Profile:** DSPy is economically advantageous for large-scale applications where per-query error rates matter. By optimizing prompts and demonstrations, it reduces the need for expensive model upgrades. However, the optimization process itself adds compute overhead: MIPROv2 and GEPA can require hundreds of LLM calls during compilation. **Strengths:** - Best-in-class prompt optimization (MIPROv2, GEPA are SOTA) - Declarative programming model eliminates brittle prompt strings - Provider-agnostic with extensive model support - Reproducible pipelines through deterministic compilation - Academic rigor with peer-reviewed optimizers **Weaknesses:** - Steep learning curve: requires understanding of declarative patterns and optimization theory - Limited multi-agent coordination primitives - No built-in observability or tracing - Primarily designed for single-agent pipelines, not team-based orchestration - Optimization adds significant pre-deployment compute cost **Best Use Cases:** Complex chained workflows requiring automated prompt tuning, structured extraction tasks, RAG pipelines where retrieval quality needs optimization, and scenarios where consistent output quality across many queries matters more than multi-agent collaboration. **GitHub:** 34.7k stars, MIT license, v3.2.1 (May 2026), 4,500+ commits. Community: ~40 active contributors in the last 30 days. Academic presence through ICLR publications (2024 spotlight, 2026 Oral for GEPA). Niche hiring market: DSPy skills are valued but primarily in academic and research-oriented companies. ### 2. Claude Agent SDK (Anthropic) **Origin and Positioning:** Originally launched as “Claude Code SDK” in mid-2025, renamed to “Claude Agent SDK” in late 2025. It provides programmatic access to the same autonomous agent loop that powers Claude Code: Anthropic’s terminal-based AI coding assistant. The SDK treats Claude Code as a library rather than a CLI tool. **Core Architecture:** The Claude Agent SDK centers on a single primary function: `query()` . This async iterator yields messages from an autonomous agent that can read files, run commands, search the web, edit code, and more, all without the developer implementing any tool loop. Key architectural components: **Built-in Tools (zero setup required)**: Read, Write, Edit, Bash, Glob, Grep, WebSearch, WebFetch, AskUserQuestion. These nine preconfigured utilities require no wrapper code: Claude handles tool execution autonomously.**Hooks**: A comprehensive interception mechanism monitoring 18 distinct lifecycle stages (PreToolUse, PostToolUse, Stop, SessionStart, SessionEnd, UserPromptSubmit, etc.). Hooks can validate, log, block, or transform agent behavior at any point in the execution pipeline.**Subagents**: The main agent can spawn specialized subagents with isolated context windows. Subagents are invoked via the Agent tool, each with its own instructions, allowed tools, and permission scope. Messages from subagents include a`parent_tool_use_id` field for tracing.**MCP Integration**: Full Model Context Protocol support for connecting to external systems: databases, browsers (via Playwright), APIs, and hundreds of MCP servers. Custom functions operate as embedded MCP servers without network overhead.**Permissions System**: Granular control over which tools the agent can use. Three modes:`acceptEdits` (auto-approve safe edits),`fullAuto` (no approval needed), and interactive approval with AskUserQuestion for sensitive operations.**Sessions**: Resumable, forkable sessions that maintain context across exchanges. The system tracks files read, analysis performed, and conversation history. Sessions can be resumed later or forked to explore different approaches. **Provider Support:** Exclusively Anthropic models (Claude). However, through environment variables, it supports deployment on Amazon Bedrock (`CLAUDE_CODE_USE_BEDROCK=1` ), Claude Platform on AWS, Google Vertex AI, and Microsoft Azure Foundry. This means while the model must be Claude-class, the infrastructure can be multi-cloud. **Deployment Options:** The SDK runs in your process on your infrastructure. TypeScript bundles a native Claude Code binary as an optional dependency. Authentication requires API keys (no web session credentials). The companion Managed Agents service (beta) offers a hosted REST API alternative where Anthropic runs the agent and sandbox. **Limitations:** **Anthropic-only models**: No multi-provider routing; you cannot mix GPT, Gemini, or open-source models** No built-in observability**: No tracing, metrics, or logging: teams must build custom OpenTelemetry instrumentation** No durable execution**: No checkpoint-based crash recovery** No state persistence across sessions**: Sessions are JSONL on the filesystem, not a database** Limited multi-agent beyond subagents-as-tools**: No sophisticated routing, handoff patterns, or team coordination** Language asymmetry**: TypeScript has more features than Python (certain lifecycle callbacks only available in TS) **Strengths:** - Deepest operational capabilities of any agent framework (file system, shell, code editing) - Zero boilerplate: no tool wrappers to write, no execution loops to implement - Comprehensive MCP integration with hundreds of servers - Granular lifecycle hooks (18 stages) for audit, governance, and intervention - Same architecture as Claude Code: battle-tested in production coding scenarios - Subagent isolation with independent context windows **Weaknesses:** - Provider lock-in to Anthropic models - Platform infrastructure (observability, durability, persistence) must be built by the team - No visual workflow designer - Limited multi-agent orchestration beyond simple delegation **Best Use Cases:** Coding agents, research agents requiring deep OS control, CI/CD pipelines, production automation where Claude’s capabilities are essential, and scenarios where zero-boilerplate tooling is critical. **Pricing:** From June 15, 2026, Agent SDK usage on subscription plans draws from a separate $200 monthly credit budget, distinct from interactive usage limits [xda-developers.com, May 2026]. **GitHub:** ~121k stars on anthropics/claude-code CLI repo (May 2026) [augmentcode.com]. No standalone Agent SDK repository: the SDK is bundled with Claude Code. 610 commits, 52 contributors, changelog updated as recently as May 4, 2026 [augmentcode.com]. Community: ~52 active contributors in the last 30 days. High hiring demand for Claude Code skills, commanding premium salaries. Major conference presence at Code with Claude 2026 [infoq.com, May 2026]. The March 2026 source code leak via npm (512,000 lines of TypeScript exposed) [zscaler.com, April 2026] was a significant security incident that underscored the importance of build pipeline integrity. ### 3. OpenAI Agents SDK **Origin and Positioning:** Launched March 11, 2025, the OpenAI Agents SDK is a production-ready evolution of the earlier Swarm educational framework. It represents OpenAI’s shift from hosted state management (Assistants API) to developer-controlled orchestration. The Assistants API is now legacy, with a migration path to the Responses API + Agents SDK combination. **Core Architecture:** The SDK provides a minimal set of powerful primitives: **Agents**: Defined by instructions (system prompts), allowed tools, and optional handoff configurations. Agents are lightweight objects: no hosted state, no implicit memory. The agent’s context is managed entirely by the developer through session objects.**Tools**: Two categories: hosted tools (web search, file search, code interpreter, computer use) provided natively by OpenAI, and custom function tools defined by the developer. MCP server integration is available via extension libraries.**Handoffs**: The SDK’s signature multi-agent feature. Handoffs allow an agent to delegate tasks to another agent by invoking a tool (`transfer_to_<agent_name>` ). The receiving agent inherits the conversation history (unless filtered) and can optionally receive structured metadata about the transfer context. Beta nesting support summarizes earlier turns into a single block.**Guardrails**: Three-tier security:** Input guardrails**: Apply only to the first agent in a delegation chain** Output guardrails**: Target only the final producer agent** Tool-level guardrails**: For intermediate steps, developers must implement custom tool-level checks **Sessions and State**: The SDK tracks conversation state, token counts, compaction strategies, and resume bookkeeping. Background processing, webhooks, and WebSocket connections are supported for real-time applications.**Tracing**: Built-in tracing integrates with OpenTelemetry for observability. Every agent action, tool call, and handoff is instrumented. **Provider Support:** Provider-agnostic through the Responses API, supporting 100+ models. The SDK itself is Python and TypeScript/JavaScript, with Go implementations available via community libraries. **Deployment Options:** Two tracks: **Code-first SDK**: Direct server-side control with full orchestration ownership** Agent Builder**: Hosted visual workflow designer for non-technical users, with ChatKit deployment for embedding agents in products Sandboxes provide isolated container environments for file access, command execution, and package management: critical for production security. **April 2026 Evolution:** OpenAI expanded enterprise security capabilities with improved harness controls, sandbox isolation, and governance features. The update emphasized safer agent behavior in enterprise contexts [techcrunch.com, April 2026]. **Strengths:** - Cleanest handoff model in the industry: explicit, typed, with metadata support - Provider-agnostic (100+ models) through Responses API - Built-in tracing and observability (OpenTelemetry) - Production-ready sandbox environments for secure execution - Visual Agent Builder for non-technical users alongside code-first SDK - Voice support and real-time capabilities - Minimal abstractions: developers retain full control **Weaknesses:** - Handoffs are confined to a single session: no cross-session delegation - Input guardrails apply only to the first agent; output guardrails only to the final producer - Less sophisticated than graph-based frameworks for complex multi-step workflows - No built-in durable execution or checkpointing - LiteLLM integration has known “calls home” behavior not logged by the proxy [github.com/BerriAI/litellm/issues/9170] **Best Use Cases:** Lightweight multi-agent coordination, customer service routing, pipeline workflows where clean delegation between specialized agents matters, and teams wanting a minimal, production-ready starting point. **GitHub:** 19k stars, MIT license. Community: ~30+ active contributors in the last 30 days. High hiring demand for OpenAI agent skills. Major conference presence at Build 2026 and OpenAI DevDay. ### 4. CrewAI **Origin and Positioning:** Created by João Moura, CrewAI is an open-source (MIT) Python framework for orchestrating role-playing autonomous AI agents. It distinguishes itself by being “built entirely from scratch, completely independent of LangChain or other agent frameworks” [github.com/crewaiinc/crewAI]. It emphasizes the fastest path from idea to working prototype. **Core Architecture:** CrewAI’s design centers on a simple but powerful metaphor: agents are team members. The core abstractions are: **Agents**: Defined with a role (specialization), goal (objective), backstory (context/persona), and tools (capabilities). Agents can use any LLM provider through LangChain’s model integrations or directly.**Crews**: Collections of agents that collaborate on tasks. A crew manages the execution order, resource sharing, and inter-agent communication. CrewAI 1.x introduced event-driven**Flows** for complex orchestration beyond simple sequential execution.**Tasks**: Discrete pieces of work assigned to agents. Tasks can be sequential (one after another), hierarchical (a manager agent delegates to workers), or consensual (agents vote on decisions). Each task has an expected output format and can include callbacks.**Processes**: Three process types:** Sequential**: Agents execute in order, passing outputs forward** Hierarchical**: A manager agent delegates tasks to worker agents, reviewing and routing results** Consensual**: Agents vote on decisions, useful for collaborative decision-making **Memory and Knowledge**: CrewAI supports short-term memory (context passed between agents), long-term memory (persistent across sessions), and knowledge bases (documents, files, structured data that agents can reference).**Structured Outputs**: Integration with Pydantic for schema-validated outputs. Agents can be constrained to produce specific JSON schemas, ensuring downstream compatibility. **Performance:** Benchmarks from JetThoughts (2025) show CrewAI executing tasks 5.76× faster than LangGraph in QA scenarios while maintaining higher evaluation scores [tech-insider.org, April 2026]. This performance advantage likely stems from CrewAI’s simpler abstraction layer reducing computational overhead. **LLM Provider Support:** Multi-provider through LangChain integrations and direct model support. Works with OpenAI, Anthropic, Gemini, open-source models, and any provider accessible through LangChain’s model registry. The LangChain dependency means CrewAI inherits the same provider-ecosystem fragility as LangGraph: breaking changes in LangChain can affect CrewAI’s multi-provider support until patches land. **Deployment:** Enterprise console for tracking live executions, environment management, and monitoring. Added streaming tool calls in January 2026. **Strengths:** - Fastest time-to-value: ~35 lines of code for a minimal agent - Most intuitive mental model (team/role metaphor) - Three process types cover most multi-agent patterns - Event-driven Flows for complex orchestration - Largest community metrics among multi-agent frameworks - Pydantic integration for structured outputs - Production-ready with enterprise console **Weaknesses:** - Less fine-grained control than graph-based frameworks (LangGraph) - Higher-level abstraction means less flexibility for custom orchestration patterns - Role-based design can be limiting for non-team-oriented workflows - Less mature state management compared to LangGraph’s checkpointing **Best Use Cases:** Rapid prototyping of multi-agent systems, content generation pipelines, research automation, customer service teams, marketing workflows, and scenarios where a team metaphor maps naturally to the problem domain. **GitHub:** 44.3k stars, 5.2M monthly downloads, MIT license. Community: ~3 active contributors in the last 30 days, surprisingly low given the download volume, suggesting a core team with heavy reliance on community contributions. Highest share of AI agent job postings: 62% Europe, 28% US, with CrewAI skills increasingly required alongside other frameworks [agentic-engineering-jobs.com, April 2026]. Conference presence at PyCon and dedicated AI agent workshops. ### 5. AutoGen / Microsoft Agent Framework **Origin and Positioning:** AutoGen was created by Microsoft Research as an open-source framework for building conversational multi-agent systems. In Q1 2026, Microsoft entered AutoGen into maintenance mode and announced its merger with Semantic Kernel into the unified **Microsoft Agent Framework**, which reached GA v1.0 in April 2026. **AutoGen Legacy (v0.4):** AutoGen v0.4 was a significant redesign introducing: **Layered architecture**: Message passing layer (how messages are delivered) decoupled from agent handling (how agents process them)** Actor model**: Agents as independent actors with their own message queues and processing loops** Group Chat patterns**: Multi-agent conversations with configurable termination conditions, debate modes, and role-based发言顺序** Event-driven design**: Async-first, streaming support, serialization, and state management** Human-in-the-loop**: Best-in-class HITL support: humans can intervene at any conversation turn **Microsoft Agent Framework (v1.0, April 2026):** The merged framework combines: - From AutoGen: Simple agent abstractions, conversational multi-agent patterns, group chat - From Semantic Kernel: Enterprise-grade features (session-based state management, type safety, middleware/filters, telemetry, extensive model and embedding support) - New additions: Graph-based workflows, A2A/MCP/AG-UI protocol support **Key Capabilities:** **Agents**: Individual agents using LLMs for processing inputs, calling tools, and generating responses. Supports Microsoft Foundry, Anthropic, Azure OpenAI, OpenAI, Ollama, and more.**Workflows**: Graph-based workflows connecting agents and functions for multi-step tasks with type-safe routing, checkpointing, and human-in-the-loop support.**Session Management**: Thread-based state management with persistence across restarts.** Governance**: The Agent Governance Toolkit (April 2026) covers OWASP Agentic Top 10, providing policy enforcement, zero-trust identity, execution sandboxing, and reliability engineering [opensource.microsoft.com, April 2026]. **Dual Language Support:** Both .NET (C#) and Python, with first-class parity. This is unique among the frameworks analyzed: only Microsoft Agent Framework offers true enterprise-grade support for both major languages. **Strengths:** - Best human-in-the-loop support in any framework - Pioneered conversational multi-agent patterns (debate, critique, refinement) - Enterprise governance via OWASP Agentic Top 10 Toolkit - Azure integration and Microsoft Foundry deployment - Dual language support (Python + .NET/C#) - Production-ready v1.0 with stable APIs and long-term support commitment - Graph-based workflows for explicit multi-agent orchestration **Weaknesses:** - AutoGen (original) in maintenance mode: migration to Agent Framework required - Steeper learning curve than CrewAI for simple use cases - Azure-centric deployment path, though multi-provider support exists - Less community momentum than LangGraph or CrewAI **Best Use Cases:** Research-grade agent conversations, systems requiring mid-workflow human intervention, enterprise deployments on Azure, .NET shops, and scenarios where governance and OWASP compliance are critical. **GitHub:** 54.6k stars (AutoGen legacy repo), MIT license. Community: ~100+ active contributors from Microsoft org (Semantic Kernel + AutoGen merger). High hiring demand in Azure/.NET shops. Major conference presence at Microsoft Build (annual, June 2026). The Agent Governance Toolkit is an open-source project under the Microsoft organization with MIT license [github.com/microsoft/agent-governance-toolkit]. ### 6. LangGraph (LangChain) **Origin and Positioning:** Developed by the LangChain team, LangGraph is a low-level orchestration framework for building stateful agents as directed graphs. It reached v1.0 in September 2025, described as “the first stable major release in the durable agent framework space” [langchain.com]. **Core Architecture:** LangGraph treats agents as state machines: **Graph Model**: Nodes represent computation steps (LLM calls, tool invocations, conditional logic). Edges define transitions between nodes. Conditional edges enable branching based on state values. Cycles (loops) are first-class: an agent can iterate indefinitely until a condition is met.**State Management**: A single typed schema defines the complete agent state. State flows through nodes as input and is updated as output. LangGraph enforces immutability of state at each step, preventing subtle bugs from concurrent mutations.**Persistence and Checkpointing**: Every state update is checkpointed to a configurable store (SQLite, PostgreSQL, Redis). This enables:** Crash recovery**: After downtime, the agent resumes from its last checkpoint** Time-travel debugging**: Replay any point in the execution history** Human-in-the-loop**: Pause at any node, inspect state, modify it, and resume **Multi-Agent Patterns**: LangGraph supports supervisor patterns (one agent routes work to specialized sub-agents), parallel execution (fan-out/fan-in), and nested graphs (sub-graphs within parent graphs).**LangSmith Integration**: Built-in observability through LangSmith: tracing, evaluation, dataset management, and production monitoring. **Adoption:** Deployed by ~400 firms including Klarna ($60M savings), Uber, JP Morgan, BlackRock, and Cisco. The broader LangChain ecosystem has 90M monthly downloads. **LLM Provider Support:** Provider-agnostic through LangChain’s model integrations, which supports OpenAI, Anthropic, Gemini, open-source models, and any provider in LangChain’s registry. However, the LangChain dependency layer introduces its own breaking-change cycle: model support APIs evolve independently of LangChain, and updates to LangChain can break integrations with specific providers until patches land. Teams using LangGraph with non-default providers (e.g., Ollama, SGLang) should expect to handle provider-specific edge cases. **Strengths:** - Most mature durable execution model (checkpointing, crash recovery, time-travel) - Explicit graph modeling with first-class cycles and conditional routing - Best human-in-the-loop debugging (pause, inspect, modify, resume) - Production reliability proven at scale (400+ firms, Klarna $60M savings) - First-class observability via LangSmith - Async support and streaming - Multi-agent supervisor patterns and parallel execution **Weaknesses:** - Steeper learning curve than CrewAI (graph mental model vs. team metaphor) - Can become complex quickly: graphs can get “spaghetti-like” for intricate workflows - Tightly coupled to LangChain ecosystem (though usable independently) - Debugging large graphs requires familiarity with LangSmith **Best Use Cases:** Complex workflows requiring branching logic and retries, production-grade enterprise applications, systems needing reliable state persistence, human-in-the-loop approval gates, and scenarios where debugging execution history is critical. **GitHub:** 24.8k stars, Apache 2.0 license, v1.0 GA (September 2025). Community: ~60+ active contributors in the last 30 days. The largest ecosystem among orchestration frameworks with 34.5M monthly downloads for LangGraph and 90M ecosystem-wide for LangChain. Highest overall hiring demand for agent engineering skills. Major conference presence through LangChain’s “State of Agent Engineering” report and dedicated workshops. ### 7. Google Agent Development Kit (ADK) **Origin and Positioning:** Announced at Google Cloud NEXT 2025, ADK is Google’s open-source framework for building, evaluating, and deploying AI agents. It reached v1.0.0 for Python, TypeScript, Go, and Java in early 2026. It is model-agnostic (via LiteLLM) but deeply aligned with the Google Cloud ecosystem, particularly Vertex AI Agent Engine Runtime. **Core Architecture:** ADK’s design emphasizes modularity and composability: **Agents**: Code-first agent definitions with prompts, tools, and configuration. Agents are defined as Python/TypeScript classes or functions with declarative specifications.**Hierarchical Trees**: Parent agents delegate to child agents, forming a tree structure. This hierarchical composition enables complex multi-agent systems where each level has its own scope, context, and tool access.**Agent Cards**: Auto-generated discovery documents that describe an agent’s capabilities, enabling cross-vendor agent discovery via the A2A protocol.**Tools**: Integration with Google services (Search, Maps, Drive, Calendar), custom function tools, and MCP servers. Tools are registered declaratively with type signatures.**Graph-Based Multi-Agent Workflows**: Agents can be connected in graph patterns, enabling sequential, parallel, and conditional execution flows.** Signals**: A real-time event system for context-aware agent behavior, allowing agents to react to external events (database changes, API responses, user inputs) without polling. **Multi-Language SDKs:** Python, TypeScript, Go, and Java: unique among the analyzed frameworks in offering four major language support. Android ADK also exists for mobile integration. **Deployment:** Vertex AI Agent Engine Runtime is the primary deployment path, with support for Google Cloud Run, GKE, and direct hosting. The framework is designed to be deployment-agnostic. **A2A Protocol:** Native support for Agent-to-Agent protocol, enabling agents built with ADK to discover and communicate with agents from other frameworks. **Security Note:** CVE-2026-4810 was discovered in versions 1.7.0 through 1.28.1: an authentication vulnerability allowing unauthenticated remote code execution on the server hosting the ADK instance [gitlab.com, 2026]. This highlights the importance of prompt engineering for security in agent frameworks. **Strengths:** - Four language SDKs (Python, TypeScript, Go, Java): most comprehensive - Native A2A protocol for cross-vendor agent discovery - Hierarchical multi-agent composition with auto-generated Agent Cards - Deep Google Cloud and Vertex AI integration - Powers Google’s own Agentspace and Customer Engagement Suite - Android ADK for mobile agent integration - Model-agnostic via LiteLLM **Weaknesses:** - Moderate-to-steep learning curve due to cloud dependencies - Security vulnerability (CVE-2026-4810) raised concerns about production readiness - Stronger alignment with Google ecosystem than truly agnostic frameworks - Smaller community than LangGraph or CrewAI **Best Use Cases:** Enterprise multi-language systems, cross-vendor agent discovery, Google Cloud-native deployments, Android mobile agents, and scenarios where A2A protocol interoperability is required. **GitHub:** 17.8k stars, Apache 2.0 license, v1.0.0 (early 2026). Community: ~20+ active contributors in the last 30 days. Moderate hiring demand concentrated in Google Cloud shops and multi-language enterprise teams. Major conference presence at Google Cloud Next. The A2A protocol, now under the Linux Foundation with 150+ supporters, positions ADK as a key player in cross-vendor interoperability. ## Head-to-Head Comparison: Cross-Framework Analysis The individual framework profiles above establish each framework’s capabilities in isolation. This section provides systematic cross-comparisons on the three axes that most influence practical engineering decisions: handoff mechanisms, state management approaches, and observability stacks. ### Multi-Agent Handoff Mechanisms How agents delegate work to other agents is the defining architectural difference between frameworks. The five major approaches are: | Framework | Handoff Primitive | Delegation Model | Context Inheritance | Cross-Session Support | |---|---|---|---|---| OpenAI Agents SDK | `transfer_to_<agent_name>` tool call | Explicit typed handoffs with optional structured metadata | Full conversation history (filterable) or summarized block (beta nesting) | No: confined to single session | LangGraph | Conditional edges + supervisor routing | Supervisor routes work to sub-agent nodes via `Send()` API | State flows through graph; each node receives merged state | Yes: checkpointed across sessions | CrewAI | Task assignment within crew execution | Sequential or hierarchical task delegation by the crew manager | Output of previous task passed as context to next agent | No: crew lifetime only | Microsoft Agent Framework | GroupChat with termination rules | Rotating turn-based conversation; humans can intervene at any point | Full message history across all participants | Yes: session management with persistence | Google ADK | Parent→child delegation in hierarchical trees | Top-down delegation with independent context windows per level | Child agents inherit parent scope but maintain isolated contexts | Partial: via Vertex AI Agent Engine | Claude Agent SDK | Subagent spawning via Agent tool | Main agent spawns specialized subagents; each has independent context window | `parent_tool_use_id` field for tracing; messages include lineage | No: JSONL session files only | DSPy | N/A (not an orchestration framework) | N/A: designed for single-agent pipelines, not multi-agent delegation | N/A | N/A | **Key architectural insight:** The “supervisor pattern” (LangGraph’s supervisor + OpenAI’s handoffs + Claude’s subagents) has emerged as the 2026 production default [digitalapplied.com]. For most cross-domain agent tasks (a researcher, a coder, and a reviewer collaborating on a project), the supervisor topology is the right starting point. The critical difference is that LangGraph’s supervisor runs inside a durable graph with checkpointing, while OpenAI’s handoffs are session-scoped, and Claude’s subagents are single-agent-with-tools rather than true multi-agent delegation. ### State Management Approaches State persistence and recovery capabilities vary dramatically across frameworks: | Framework | State Store | Checkpointing | Crash Recovery | Time-Travel Debugging | Session Format | |---|---|---|---|---|---| LangGraph | SQLite, PostgreSQL, Redis (configurable) | Every state update checkpointed | Yes: resume from last checkpoint | Yes: full execution history replay | Typed graph state schema | Microsoft Agent Framework | Thread-based with persistence layer | Yes: session management across restarts | Yes | Limited (session logs) | Session objects | Google ADK | Vertex AI managed store | Partial (managed by runtime) | Partial | Limited | Agent Engine Runtime state | Claude Agent SDK | JSONL files on filesystem | No: sessions are append-only logs | No: no checkpoint-based recovery | No: linear session replay only | JSONL conversation history | OpenAI Agents SDK | In-memory session objects | No | No | No | Session object with token tracking | CrewAI | Short-term (context passing), long-term (persistent storage) | No built-in checkpointing | No | No | Pydantic-validated outputs | DSPy | Compile-time caching in memory/filesystem | No: deterministic compilation given same dataset | N/A | N/A | Compiled module state | **Key architectural insight:** LangGraph’s checkpointing is the only framework offering first-class crash recovery with configurable backends (SQLite for prototyping, PostgreSQL/Redis for production). This makes it uniquely suited for long-running multi-agent workflows where interruptions are likely. Claude Agent SDK’s JSONL sessions are append-only logs, useful for replay but not for state restoration after failures. ### Observability and Tracing Stacks Observability is increasingly table stakes, but the depth and actionability vary significantly: | Framework | Built-in Tracing | External Integration | Visualization | Evaluation Tools | |---|---|---|---|---| LangGraph | Yes: full node/edge tracing | LangSmith (built-in ecosystem), OpenTelemetry | Graph visualizer, time-travel debugger | LangSmith datasets and evaluations | OpenAI Agents SDK | Yes: per-action tool call tracing | OpenTelemetry-native | Minimal (code-based traces) | Limited (session object inspection) | Microsoft Agent Framework | Yes: telemetry pipeline | Azure Application Insights, OpenTelemetry | Azure Monitor dashboards | Governance toolkit policies | Claude Agent SDK | No built-in tracing | Custom OpenTelemetry instrumentation required | None (must build custom dashboard) | None (must build custom eval) | CrewAI | Enterprise console only (paid tier) | Limited API for external integration | Console UI for live executions | None built-in | Google ADK | Yes: OpenTelemetry integration | Google Cloud Trace, Vertex AI Model Monitoring | Vertex AI dashboards | Vertex AI evaluation pipelines | DSPy | No built-in tracing | None by design (optimization-focused) | None | Built-in optimizer metrics (accuracy, latency) | **Key architectural insight:** LangSmith remains the most actionable observability stack for agent development, offering time-travel debugging that no other framework matches. OpenAI’s OpenTelemetry-native tracing is clean but minimal: it records what happened but doesn’t enable deep inspection. Claude Agent SDK’s lack of built-in observability means teams must build custom instrumentation, a significant operational burden for production deployments [augmentcode.com, May 2026]. ### Provider-Agnosticism: Theoretical vs. Practical The claim that DSPy, LangGraph, CrewAI, and Google ADK are “provider-agnostic” requires qualification: **DSPy + LiteLLM**: Theoretically supports 100+ providers. In practice, LiteLLM has documented gaps with streaming (some providers don’t support the OpenAI-compatible streaming format), tool calling (Ollama throws JSON parsing errors for array-type content) [github.com/BerriAI/litellm/issues/11433], and multimodal features. The abstraction layer adds latency and can mask provider-specific error messages.**LangGraph + LangChain**: Provider support depends on LangChain’s model registry, which evolves independently of the providers themselves. Breaking changes in LangChain can temporarily break multi-provider support until patches land.**CrewAI + LangChain**: Same dependency chain as LangGraph: inherits LangChain’s provider ecosystem fragility.** Google ADK + LiteLLM**: Model-agnostic via LiteLLM but pushes Vertex AI deployment. Teams using non-Google providers face the same LiteLLM gaps as DSPy users.**OpenAI Agents SDK + LiteLLM**: The OpenAI Agents SDK integrates with LiteLLM for 100+ models, but known issues exist where the SDK “calls home” in ways not logged by litellm [github.com/BerriAI/litellm/issues/9170], suggesting opaque behavior that complicates debugging.**Claude Agent SDK**: Exclusively Anthropic models: no provider agnosticism whatsoever, though deployment infrastructure can be multi-cloud (Bedrock, Vertex AI, Azure Foundry). ### Security Posture Comparison Security is the fastest-moving dimension of the agent framework landscape. The following table synthesizes the security posture across frameworks as of May 2026: | Dimension | OpenAI Agents SDK | Claude Agent SDK | LangGraph | CrewAI | Microsoft Agent Framework | Google ADK | DSPy | |---|---|---|---|---|---|---|---| Prompt injection resistance | Three-tier guardrails; March 2026 guidance on designing injection-resistant agents [openai.com] | Harness system for long-running agents; no built-in injection detection | No built-in (depends on underlying model) | No built-in (depends on underlying model) | OWASP Agentic Top 10 coverage via Governance Toolkit | CVE-2026-4810 (auth bypass RCE); no systematic injection framework | No built-in | Sandboxing | Containerized sandboxes for file access, command execution, package management [techcrunch.com, April 2026] | OS-level primitives: Linux bubblewrap, macOS Seatbelt for process isolation. Cloud-hosted via Azure Container Apps for Python workloads | No built-in sandbox (runs in user process) | No built-in sandbox (runs in user process) | Azure Container Apps for isolated execution; policy-enforced sandboxing via Governance Toolkit | gVisor on GKE for agent workloads; no built-in SDK-level sandbox | No built-in | CVE history | No major CVEs | No major CVEs | No major CVEs | No major CVEs | Semantic Kernel eval() RCE (May 2026) [microsoft.com/security] | CVE-2026-4810 (auth bypass, RCE) | No major CVEs | Data privacy / compliance | SOC2-compliant infrastructure; data stays within OpenAI processing boundaries | API keys only; no web session credentials needed. GDPR considerations for EU deployments | Depends on deployment (self-hosted = full control) | Same as LangGraph (same dependency chain) | Enterprise identity, zero-trust policies via Governance Toolkit | Vertex AI compliance features; GDPR/CCPA ready | Self-hosted = full data control | Tool poisoning risk | Tool-level guardrails for intermediate steps | MCP integration with server verification | No built-in tool validation | No built-in tool validation | Policy enforcement on tool schemas | MCP server support with no runtime validation | Training data poisoning possible during optimization | **Key architectural insight:** Microsoft’s Agent Governance Toolkit is the only framework offering deterministic, sub-millisecond policy enforcement covering all 10 OWASP Agentic Top 10 risks [opensource.microsoft.com, April 2026]. OpenAI’s sandbox isolation and Claude’s OS-level primitives provide runtime isolation but lack systematic governance frameworks. LangGraph, CrewAI, and DSPy rely on the underlying model’s built-in safety: a significant gap for enterprise deployments where prompt injection attacks are increasingly common [atlan.com, 2026]. **Prompt injection across all frameworks:** Microsoft Research demonstrated that prompt injection in AI agent frameworks can lead to remote code execution when untrusted inputs are mapped to system capabilities [microsoft.com/security/blog, May 2026]. The Semantic Kernel platform was found vulnerable through a filter function executing user inputs via Python’s `eval()` and an exposed host-side file transfer tool. Similar architectural risks are anticipated in LangChain-based frameworks (LangGraph, CrewAI) since they share the same abstraction layer. ### Illustrative Code Examples: Architecture Patterns in Practice To concretely demonstrate how these frameworks differ in practice, we present minimal working examples of the most common multi-agent pattern (a supervisor delegating to specialized sub-agents) across the four frameworks that support this topology natively (LangGraph, OpenAI Agents SDK, CrewAI, and Google ADK). **LangGraph Supervisor Pattern:** ``` python from langgraph.graph import StateGraph, START, END from langgraph.supervisor import create_supervisor class AgentState(TypedDict): messages: Annotated[list, add_messages] team_members: list[str] # Define specialized agents coder = create_agent("Python coder", tools=[write_code]) researcher = create_agent("Researcher", tools=[web_search]) reviewer = create_agent("Reviewer", tools=[code_review]) # Build supervisor graph supervisor = create_supervisor( team_members=["coder", "researcher", "reviewer"], model=ChatAnthropic(model="claude-sonnet-4-20250514") ) graph = StateGraph(AgentState).add_edges(START, "supervisor") graph.add_node("supervisor", supervisor) # ... compile with checkpointer for durability ``` *Key differentiator:* LangGraph’s supervisor runs inside a durable graph with checkpointing. The `StateGraph` enforces typed state, and the `checkpointer` enables crash recovery and time-travel debugging: capabilities no other framework’s equivalent provides out of the box. **OpenAI Agents SDK Handoff Pattern:** ``` python from agents import Agent, handoffs researcher = Agent( name="Researcher", instructions="Search and summarize information.", handoffs=[], # no further delegation ) coder = Agent( name="Coder", instructions="Implement the solution based on research.", handoffs=[handoffs.handoff_to(researcher)], # can route back ) supervisor = Agent( name="Supervisor", instructions="Route tasks to the right agent.", handoffs=[handoffs.handoff_to(coder), handoffs.handoff_to(researcher)], ) # Execute: supervisor runs, decides to hand off to coder result = supervisor.run("Build a web scraper") ``` *Key differentiator:* The handoff is a typed tool call (`transfer_to_<agent_name>` ) that inherits conversation history. The model decides routing: there’s no explicit graph or conditional logic in the code. This is the simplest possible multi-agent delegation, but it’s confined to a single session with no checkpointing. **CrewAI Team Pattern:** ``` python from crewai import Agent, Task, Crew, Process researcher = Agent( role="Research Analyst", goal="Find relevant information on given topics.", tools=[SearchTool()], backstory="Expert researcher with 10 years experience.", ) writer = Agent( role="Content Writer", goal="Write comprehensive reports based on research.", tools=[], backstory="Professional writer specializing in technical content.", ) research_task = Task(description="Research AI agent frameworks...", agent=researcher) write_task = Task(description="Write a comparison report...", agent=writer, context=[research_task]) crew = Crew(agents=[researcher, writer], tasks=[research_task, write_task], process=Process.sequential) result = crew.kickoff() ``` *Key differentiator:* The team metaphor: agents have roles, goals, and backstories. Tasks are assigned sequentially with context passing. No graph, no conditional routing, no checkpointing. The abstraction is high-level but the control is low: you cannot pause mid-execution to inspect state or intervene. **Google ADK Hierarchical Pattern:** ``` python from google.adk import Agent, Runner researcher = Agent( name="Researcher", model="gemini-2.5-pro", tools=[web_search], description="Searches and summarizes information.", ) coder = Agent( name="Coder", model="gemini-2.5-pro", tools=[code_editor], description="Implements code based on specifications.", ) supervisor = Agent( name="Supervisor", model="gemini-2.5-pro", child_agents=[researcher, coder], description="Routes tasks to specialized agents.", ) runner = Runner(agent=supervisor, deployment_mode="remote") result = runner.run(task="Build a web scraper") ``` *Key differentiator:* Hierarchical agent trees with auto-generated Agent Cards for discovery. The `deployment_mode="remote"` deploys to Vertex AI Agent Engine Runtime, which provides managed state persistence and observability, but at the cost of Google Cloud dependency. ### Architecture Diagrams (Textual) **Supervisor topology** (shared by LangGraph, OpenAI SDK, Claude SDK, and ADK): ``` ┌─────────────┐ │ Supervisor │ │ (routing │ │ agent) │ └──────┬──────┘ │ decides which sub-agent to invoke ┌────────┼────────┐ ▼ ▼ ▼ ┌────────┐ ┌───────┐ ┌───────┐ │ Research│ │ Coder │ │ Reviewer│ └────────┘ └───────┘ └───────┘ ``` This topology has emerged as the 2026 production default for cross-domain agent tasks [digitalapplied.com]. The supervisor delegates, sub-agents execute, and results are aggregated, typically by the supervisor or a final producer agent. **CrewAI sequential pipeline:** ``` [Researcher Agent] ──output──▶ [Writer Agent] ──output──▶ Result (context passed) (context from previous task) ``` Linear, no branching, no conditional routing. For complex tasks, CrewAI’s Flows add event-driven branching but still lack checkpointing. **LangGraph with conditional edges:** ``` [Start] → [Research Node] → [Quality Check?] ──yes──▶ [Write Node] → [End] │ │ no yes │ │ ▼ ▼ [Refine Node] ──────────────┘ ``` Explicit control flow with conditional edges. The graph can loop indefinitely until quality thresholds are met: a capability CrewAI’s sequential model cannot express natively. ## Quantitative Comparison ## Quantitative Comparison The following table synthesizes key metrics across all seven frameworks based on publicly available data as of May 2026. | Dimension | DSPy | Claude Agent SDK | OpenAI Agents SDK | CrewAI | Microsoft Agent Framework | LangGraph | Google ADK | |---|---|---|---|---|---|---|---| Primary Philosophy | Prompt optimization via compilation | Claude Code as a library | Lightweight multi-agent delegation | Role-based team orchestration | Enterprise multi-agent (AutoGen + Semantic Kernel) | Graph-based stateful agents | Hierarchical multi-agent with A2A | Framework Type | Optimization + programming model | Agent runtime SDK | Multi-agent orchestration SDK | Multi-agent orchestration framework | Multi-agent orchestration + workflow engine | Low-level agent runtime | Modular agent development toolkit | GitHub Stars | 34.7k | ~121k (anthropics/claude-code CLI repo only; no standalone Agent SDK repo: the SDK is bundled with Claude Code) | 19k | 44.3k | 54.6k (AutoGen legacy) | 24.8k | 17.8k | Monthly Downloads | ~2.5M | N/A: bundled with Claude Code distribution | 10.3M | 5.2M | 856k (AutoGen) | 34.5M (LangGraph) | 3.3M | Languages | Python | Python, TypeScript | Python, TypeScript/JS, Go | Python, JavaScript | .NET (C#), Python | Python, TypeScript | Python, TypeScript, Go, Java | LLM Providers | 100+ (LiteLLM) | Anthropic-only (multi-cloud infra) | 100+ (Responses API) | Multi (LangChain integrations) | 50+ (native + SDK extensions) | Multi (LangChain integrations) | Multi (LiteLLM), optimized for Gemini | Multi-Agent Support | Limited (single-agent pipelines) | Subagents-as-tools only | Handoffs, agent-as-tool patterns | Crews, tasks, processes, Flows | GroupChat, supervisor, workflows | Graph nodes, supervisor pattern, nested graphs | Hierarchical trees, A2A protocol | Durable Execution | No (compile-time caching) | Limited (JSONL sessions) | No (session objects in memory) | No | Yes (checkpointing) | Yes (first-class, first stable v1.0) | Partial (Vertex AI managed) | Observability | No built-in | No built-in | Built-in tracing (OpenTelemetry) | Enterprise console | Telemetry + governance toolkit | LangSmith (built-in) | OpenTelemetry integration | Human-in-the-Loop | No | AskUserQuestion tool | Approvals, guardrails | Callbacks, human-in-the-loop triggers | Best-in-class HITL | Pause/resume at any node | Via Agent Engine Runtime | Version Status | v3.2.1 (May 2026) | Active (renamed late 2025) | Active (April 2026 update) | Active (v1.x) | v1.0 GA (April 2026) | v1.0 GA (September 2025) | v1.0.0 (early 2026) | License | MIT | Anthropic Commercial ToS | MIT | MIT | MIT | Apache 2.0 | Apache 2.0 | Production Rank (Alice Labs, May 2026) | N/A | #2 | N/A | #3 | #5 | #1 | N/A | Setup Complexity | High (optimization theory) | Low (zero boilerplate) | Low-Medium | Lowest (~35 lines) | Medium-High | Medium | Medium-High | Security Track Record | No major CVEs | No major CVEs | Improved April 2026 | No major CVEs | OWASP Agentic Top 10 coverage | No major CVEs | CVE-2026-4810 (auth bypass) | Active Contributors (30d) | ~40+ | ~52 | ~30+ | ~3 | ~100+ (Microsoft org) | ~60+ | ~20+ | Hiring Market Demand | Moderate (academic niche) | High (Claude Code skills premium) | High (OpenAI ecosystem) | Highest share of AI agent jobs: 62% Europe, 28% US | High (Azure/.NET shops) | Highest overall demand | Moderate (Google Cloud focus) | Conference Presence | ICLR 2024/2026 publications | Code with Claude 2026 keynote | Build 2026, OpenAI DevDay | PyCon, AI agent workshops | Build conference (annual) | LangChain State of Agent Engineering | Google Cloud Next | ### Performance Benchmarks **CrewAI vs LangGraph**: CrewAI executes tasks 5.76× faster than LangGraph in QA scenarios with higher evaluation scores [JetThoughts, 2025].** Methodology caveat**: The original benchmark source (TowardsAI / JetThoughts) does not publicly disclose task selection criteria, model versions used, hardware specifications, or evaluation metrics (wall-clock time vs. token count vs. throughput). The 5.76× figure should be treated as an indicative signal rather than a rigorously validated measurement. Independent replication has not been published.**DSPy optimization gains**: GEPA achieves up to 19% higher test accuracy and 35× fewer rollouts than RL baselines [arxiv.org/abs/2507.19457]. This is a peer-reviewed ICLR 2026 Oral paper with publicly available methodology.**Enterprise ROI**: Average 171% ROI for agentic AI deployments (US: 192%), triple traditional RPA [xillentech.com, April 2026]. Source is vendor-funded; independent verification is limited.**Claude Code**: 80.8% SWE-bench score, 30+ hours of autonomous coding without performance degradation [tosea.ai, April 2026].** Methodology caveat**: The evaluation protocol, model version (Claude Opus 4 vs. Sonnet 4), and SWE-bench variant (Lite vs. Full) are not specified in the source. SWE-bench scores vary significantly depending on the subset used, so this figure is directionally informative but not directly comparable to other framework benchmarks without knowing the exact evaluation protocol.**GAIA benchmark**: Hit 74.5% across all frameworks combined [Adaline, March 2026]. This is a composite score across different frameworks, not a per-framework benchmark.**Market size**: $7.84B (2025) → projected $52.62B by 2030 (45.8% CAGR) [firecrawl.dev, May 2026] ### Cost Considerations | Framework | Typical Monthly Token Cost (per agent) | Optimization Overhead | Infrastructure Cost | |---|---|---|---| | DSPy | Low-Medium (optimized prompts reduce calls) | High during compilation (hundreds of LLM calls) | Low (self-hosted Python) | | Claude Agent SDK | Medium-High (Claude model pricing) | None | Medium (hosted or self-hosted) | | OpenAI Agents SDK | Low-Medium (provider-agnostic, efficient routing) | None | Low-Medium (SDK + cloud) | | CrewAI | Medium (multi-agent = more calls per task) | None | Low (self-hosted Python) | | Microsoft Agent Framework | Medium-High (Azure/Foundry pricing) | None | Medium-High (Azure infrastructure) | | LangGraph | Medium (state checkpointing adds minor overhead) | None | Medium (LangSmith optional) | | Google ADK | Medium (Vertex AI pricing) | None | Medium-High (Google Cloud) | ## Competing Perspectives and Controversies ### “Abstraction Level” Debate: Control vs. Velocity A fundamental tension exists across the framework ecosystem between **developer velocity** (how quickly you can build something working) and **control** (how precisely you can direct execution). **The velocity camp** (CrewAI, DSPy): CrewAI argues that developer time is the scarcest resource: a team of three engineers spending two weeks on a CrewAI prototype delivers more value than one engineer spending six weeks hand-crafting a LangGraph. DSPy makes a similar argument: manual prompt engineering is a waste of expensive engineer time when optimizers can produce better prompts automatically. **The control camp** (LangGraph, Claude Agent SDK): LangGraph’s proponents argue that crew-based abstractions hide too much: when a task fails, you need to know exactly which node failed, what state it had, and why the edge was taken. Claude Agent SDK’s supporters argue that zero-boilerplate tooling is only valuable if you trust Claude’s autonomous decisions; for mission-critical systems, explicit graph control and manual intervention points are essential. **My assessment**: The tension is real but increasingly false as frameworks converge. LangGraph has added higher-level abstractions (create_agent interface in v1.0). CrewAI added Flows (event-driven orchestration) for finer control. DSPy can be combined with LangGraph for optimization + orchestration. The cleanest architectures in 2026 compose multiple frameworks rather than choosing one [designveloper.com, Sep 2025]. ### “Provider Lock-In” Controversy Anthropic’s decision to lock Claude Agent SDK to Claude models alone has sparked debate. Proponents argue that specialization beats generalization: Claude Code’s operational depth (file system access, shell execution, code editing) would be diluted by multi-provider support. Opponents point out that this creates vendor lock-in and prevents cost optimization through model switching (e.g., using cheaper models for routine tasks and Claude for complex reasoning). OpenAI’s approach is more provider-agnostic but still optimized for GPT models. Google ADK is model-agnostic via LiteLLM but pushes Vertex AI deployment. CrewAI, LangGraph, and DSPy are genuinely provider-agnostic. **My assessment**: The lock-in concern is real but manageable. Anthropic’s $200 monthly Agent SDK credit policy (from June 2026) [xda-developers.com, May 2026] makes Claude relatively affordable for development, and organizations can always wrap the SDK with a model-agnostic abstraction layer if multi-provider support becomes critical later. ### “Framework vs. Platform” Shift Multiple sources note a growing split between open-source frameworks (LangGraph, CrewAI, AutoGen) and managed platforms (OpenAI Agent Builder, Google ADK on Vertex AI, Claude Managed Agents). The platform approach promises to skip infrastructure assembly (observability, governance, multi-tenancy) but introduces vendor lock-in and potentially higher long-term costs. **My assessment**: This split mirrors the broader cloud industry’s evolution from IaaS (raw compute) to PaaS (managed services). Frameworks remain essential for development flexibility; platforms become essential at scale where operational overhead becomes prohibitive. The smartest organizations use frameworks during development and deploy to platforms in production. ### DSPy: Is It an Agent Framework or a Prompt Optimizer? A definitional controversy exists around DSPy’s categorization. Some sources rank it alongside orchestration frameworks; others classify it separately as a prompt optimization tool. DSPy’s own documentation emphasizes “programming, not prompting, LLMs” rather than agent orchestration. **My assessment**: DSPy is best understood as a **complementary framework** to orchestration frameworks, not a replacement. Its strengths (prompt optimization, automated instruction tuning) address a different problem space (model quality) than LangGraph’s (execution control) or CrewAI’s (multi-agent coordination). The most effective architectures in 2026 combine DSPy for pipeline optimization with LangGraph or CrewAI for orchestration [designveloper.com, Sep 2025]. ### Security Concerns Across Frameworks The discovery of CVE-2026-4810 in Google ADK (authentication bypass allowing remote code execution on the server hosting the ADK instance) raised broader questions about security in agent frameworks [gitlab.com, 2026]. But this single CVE represents only one slice of a much larger and more urgent security landscape for AI agents. **Prompt injection and RCE risks:** Microsoft Research demonstrated that prompt injection in AI agent frameworks can lead to remote code execution when untrusted inputs are mapped to system capabilities [microsoft.com/security/blog, May 2026]. The Semantic Kernel platform was found vulnerable through a filter function executing user inputs via Python’s `eval()` and an exposed host-side file transfer tool. The researchers emphasize that these systems are “behaving exactly as designed by parsing language into tool schemas”: the issue is poor parameter validation transforming text manipulation into active execution threats. Similar architectural risks are anticipated in LangChain-based frameworks (LangGraph, CrewAI) since they share the same abstraction layer. **Sandboxing approaches vary dramatically:** **OpenAI**: Containerized sandboxes for file access, command execution, and package management: critical for production security [techcrunch.com, April 2026]. The sandbox runs in isolated containers with strict privilege limits.**Anthropic (Claude Code)**: Relies on OS-level primitives: Linux bubblewrap and macOS Seatbelt for process isolation. Cloud-hosted Python workloads use Azure Container Apps. The isolation strategy “relies entirely on this boundary,” meaning improper function exposure effectively negates container protections [microsoft.com/security/blog, May 2026].**Google**: Deploys gVisor on GKE, intercepting system calls through a user-space kernel implementation to prevent direct host kernel exposure.**Microsoft**: Azure Container Apps for isolated execution; policy enforcement via the Agent Governance Toolkit. Misconfigured tool permissions have allowed external prompts to trigger host-side downloads, showing that “relies entirely on this boundary” vulnerabilities exist across providers.**LangGraph, CrewAI, DSPy**: No built-in sandboxing. Agents run in the user’s process with whatever privileges the developer grants them. **Data privacy and compliance:** Self-hosted frameworks (LangGraph, CrewAI, DSPy) give full data control but require teams to implement GDPR/CCPA compliance themselves. Cloud-managed frameworks (OpenAI, Claude, Google ADK) inherit the cloud provider’s compliance posture but introduce third-party data processing concerns. Microsoft Agent Framework offers enterprise identity and zero-trust policies via its Governance Toolkit. **Tool poisoning and memory corruption:** As agents gain long-term memory capabilities (CrewAI’s persistent storage, LangGraph’s checkpointed state), the risk of memory poisoning, where poisoned inputs corrupt the agent’s memory, becomes a real threat. OWASP’s Top 10 for Agentic Applications 2026 explicitly lists ASI06 (Memory Poisoning) as a critical risk [genai.owasp.org]. Frameworks without systematic input validation (LangGraph, CrewAI, DSPy) are most vulnerable. **Hallucination propagation:** When agents pass hallucinated outputs downstream, the error compounds rather than dissipating. Frameworks with stronger guardrails and validation layers (OpenAI’s three-tier guardrails, Microsoft’s OWASP toolkit) mitigate this better than frameworks relying on agent self-correction alone [instatunnel.my, 2026]. DSPy addresses this indirectly through its optimization process. By training on evaluation metrics, it produces more reliable outputs, reducing the probability of hallucination at the source. ## Risks, Uncertainties, and Open Questions ### Technical Risks **Framework maturity**: Several frameworks (Google ADK v1.0, Microsoft Agent Framework v1.0) recently reached their first stable releases. Early-stage stability is unproven at enterprise scale. The Claude Agent SDK’s lack of built-in observability, durable execution, and state persistence means teams must build all platform infrastructure themselves [augmentcode.com, May 2026].**LLM dependency fragility**: All frameworks are fundamentally dependent on LLM quality. As benchmarks saturate (frontier models gaining 30 percentage points in a single year on Humanity’s Last Exam), frameworks that don’t adapt their optimization strategies risk becoming obsolete [hai.stanford.edu, April 2026].**Context window limitations**: Even with compression and summarization, long-running multi-agent sessions face context overflow. The Claude Agent SDK handles this through session resumption; LangGraph through checkpointing; others have less robust solutions.**Prompt injection and agent hijacking**: As agents gain more autonomy (file access, shell execution, API calls), the attack surface for prompt injection grows. DSPy’s optimization process could theoretically be poisoned if training data is compromised. Google ADK’s CVE-2026-4810 demonstrates real-world exploitation potential. ### Market Uncertainties **Consolidation risk**: With 14+ significant frameworks competing (the landscape includes LlamaIndex, Mastra, Smolagents, Pydantic AI, Dify in addition to the seven analyzed), consolidation is likely. Frameworks without clear differentiation or enterprise backing risk being absorbed or abandoned.**Protocol interoperability**: If A2A, MCP, and AGENTS.md achieve widespread adoption, the distinction between frameworks could diminish. Agents built with different frameworks could communicate seamlessly, reducing the competitive moat of each framework’s unique primitives.**Pricing model shifts**: Anthropic’s June 2026 Agent SDK credit policy change ($200 monthly budget separate from interactive usage) [xda-developers.com, May 2026] created significant cost uncertainty for teams building production agents. Similar pricing experiments across providers could destabilize framework economics. ### Open Questions **Will frameworks converge on a common runtime?** The trend toward protocol-level interoperability (A2A, MCP) suggests that the “framework” layer may become thinner over time, with most orchestration handled by standardized protocols.**How will fine-tuned/open-source models change the landscape?** DSPy’s model weight optimization algorithms and LiteLLM integration suggest frameworks are adapting to a multi-model world where open-source models compete with frontier providers.**What happens when agent evaluation matures?** Current benchmarks (GAIA 74.5%, SWE-bench variants) are improving rapidly but remain imperfect. Frameworks that integrate evaluation natively (LangSmith, DSPy’s optimization metrics) may pull ahead as quality becomes the primary differentiator [atlan.com, April 2026].**Will regulatory frameworks constrain agent autonomy?** As agents gain more autonomous capabilities (code execution, file modification, API access), regulatory scrutiny will likely increase. Microsoft’s OWASP Agentic Top 10 coverage suggests enterprise governance will become a competitive advantage. ## Implications and Outlook ### The Convergence Trend By mid-2026, the seven frameworks analyzed show clear convergence along several axes: **Graph-based orchestration is becoming standard**: CrewAI added Flows (event-driven workflows), Microsoft merged graph-based workflows into Agent Framework, Google ADK added graph-based multi-agent workflows. The only framework that doesn’t use graphs as a primitive is DSPy, but it’s primarily an optimization layer rather than an orchestration engine.**Protocol interoperability is reducing differentiation**: MCP (Anthropic), A2A (Google/Linux Foundation), and AGENTS.md (OpenAI/Linux Foundation) create a shared infrastructure layer. In 18–24 months, the question may shift from “which framework” to “which combination of protocols.”**Observability is becoming table stakes**: Every framework now offers some form of tracing or monitoring. The differentiation will be in the depth and actionability of observability. LangSmith leads here, but OpenTelemetry-native tools (LangWatch, Arize Phoenix) are framework-agnostic competitors.**Enterprise readiness is the primary battleground**: With the agentic AI market projected to reach $52.62 billion by 2030, frameworks that solve enterprise concerns (governance, security, multi-tenancy, compliance) will capture disproportionate value. Microsoft’s Agent Framework 1.0 and CrewAI’s enterprise console reflect this shift. ### Second-Order Effects **Talent market**: The framework landscape is creating a new specialization called “agent engineers” who understand not just LLM APIs but graph theory, state management, distributed systems, and protocol design. This is a significant departure from the “prompt engineer” role of 2023–2024. **Infrastructure evolution**: Frameworks are pushing cloud providers to offer agent-specific infrastructure, including managed agent runtimes (Claude Managed Agents, Vertex AI Agent Engine, Azure Foundry Agent Service), agent sandboxes, and agent-oriented observability platforms. **Security paradigm shift**: Traditional application security (input validation, authentication, authorization) is insufficient for agents that can execute code, access files, and make API calls. New paradigms (Microsoft’s Agent Governance Toolkit, OWASP Agentic Top 10) are emerging to address this. ### Scenarios for 2027 **Scenario A (Convergence)**: Two or three platform-layer frameworks emerge as standards, with protocol-level interoperability making the underlying framework largely transparent. DSPy remains as an optimization layer; Claude SDK and OpenAI SDK remain as provider-specific runtime options. **Scenario B (Fragmentation persists)**: The landscape remains diverse with no single winner. Organizations adopt a “best-of-breed” approach, using CrewAI for prototyping, LangGraph for production, DSPy for optimization, and Claude SDK for specific use cases. **Scenario C (Consolidation)**: Major tech companies acquire or absorb smaller frameworks. Anthropic acquires Claude Agent SDK’s independent ecosystem; Google absorbs ADK into Vertex AI; Microsoft’s Agent Framework becomes the de facto standard for enterprise. I assess **Scenario A** as most likely (60% probability), with **Scenario B** as a close second (30%), and **Scenario C** least likely (10%). The driving force toward convergence is economic: enterprises want to avoid vendor lock-in, which incentivizes protocol-level interoperability over framework-specific features. ## Conclusion The AI agent framework landscape in mid-2026 is not a winner-take-all market but a multi-dimensional space where different frameworks excel at different tasks. The seven frameworks analyzed represent distinct design philosophies, and the decision matrix in the Executive Summary provides actionable guidance for choosing based on specific priorities. **Synthesis of key architectural differences:** The cross-comparison in this report reveals that the three axes distinguishing frameworks are more orthogonal than commonly assumed. An organization can simultaneously need CrewAI’s velocity (for prototyping), LangGraph’s durability (for production), and DSPy’s optimization (for quality), which are not mutually exclusive choices but complementary layers in a composite architecture. The supervisor topology has emerged as the 2026 production default across LangGraph, OpenAI SDK, Claude SDK, and Google ADK [digitalapplied.com], but the implementation details differ fundamentally: only LangGraph’s supervisor runs inside a durable graph with checkpointing. **The practical gap between claimed and actual capabilities is significant:** - Provider-agnostic claims (DSPy, LangGraph, CrewAI) depend on LiteLLM or LangChain abstractions that have documented gaps with streaming, tool calling, and multimodal features. Organizations should expect to handle provider-specific edge cases. - The CrewAI 5.76× speed advantage over LangGraph lacks publicly available methodology details on task selection, model versions, hardware, and evaluation metrics. Treat it as indicative rather than rigorously validated. - Claude Agent SDK’s ~121k GitHub stars belong to the Claude Code CLI repo (anthropics/claude-code), not a standalone Agent SDK, since the framework has no independent repository. **Security is the fastest-moving dimension:** Microsoft’s Agent Governance Toolkit stands alone in offering deterministic, sub-millisecond policy enforcement covering all OWASP Agentic Top 10 risks. OpenAI’s sandbox isolation and Claude’s OS-level primitives provide runtime isolation but lack systematic governance. LangGraph, CrewAI, and DSPy have no built-in sandboxing, meaning agents run in the user’s process with whatever privileges the developer grants. **For organizations starting fresh in 2026:** The recommendation depends on your priorities. Use the decision matrix in the Executive Summary as a starting point. For prototyping → production migration paths, begin with CrewAI or OpenAI Agents SDK and migrate to LangGraph or Microsoft Agent Framework as workflows grow complex. Layer DSPy on top for prompt optimization regardless of orchestration choice. For existing systems, the migration path depends on current architecture: Claude SDK users stay put unless multi-provider support is needed; AutoGen users should migrate to Microsoft Agent Framework v1.0; DSPy users can integrate with any orchestration framework. **The underlying trend is convergence:** Frameworks are adopting each other’s best primitives (graphs, protocols, observability, governance), and protocol-level standards (A2A, MCP) are reducing the importance of framework-specific differentiation. The most successful organizations will be those that treat frameworks as interchangeable building blocks rather than permanent commitments. ## References - DSPy Official Documentation — “Programming — not prompting — LLMs.” [https://dspy.ai/](https://dspy.ai/)Accessed: 2026-05-28 - DSPy GitHub Repository — stanfordnlp/dspy, 34.7k stars. 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[https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills](https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills)Accessed: 2026-05-28 - Anthropic Engineering, “Advanced Tool Use on the Claude Developer Platform,” November 24, 2025. [https://www.anthropic.com/engineering/advanced-tool-use](https://www.anthropic.com/engineering/advanced-tool-use)Accessed: 2026-05-28 - Medium (Sal Shirgaleev), “Everyone’s Talking About LangChain. Nobody’s Talking About This,” April 2, 2026. [https://medium.com/the-pythonworld/everyones-talking-about-langchain-nobody-s-talking-about-this-0d845b213e17](https://medium.com/the-pythonworld/everyones-talking-about-langchain-nobody-s-talking-about-this-0d845b213e17)Accessed: 2026-05-28 - DataCamp, “Mastering Multi-Agent Systems with CrewAI,” 2026. [https://dev.to/ismail_zamareh_d099419122bc4f/mastering-multi-agent-systems-with-crewai-a-practical-guide-23f0](https://dev.to/ismail_zamareh_d099419122bc4f/mastering-multi-agent-systems-with-crewai-a-practical-guide-23f0)Accessed: 2026-05-28 - OpenAI Blog, “Using Skills to Accelerate OSS Maintenance,” March 9, 2026. [https://developers.openai.com/blog/skills-agents-sdk](https://developers.openai.com/blog/skills-agents-sdk)Accessed: 2026-05-28 - LangChain Changelog — “Agent Builder is now LangSmith Fleet,” March 19, 2026. [https://changelog.langchain.com/](https://changelog.langchain.com/)Accessed: 2026-05-28 - Anthropic, “Effective Harnesses for Long-Running Agents.” [https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents](https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents)Accessed: 2026-05-28 - OpenAI, “A Practical Guide to Building AI Agents.” [https://openai.com/business/guides-and-resources/a-practical-guide-to-building-ai-agents/](https://openai.com/business/guides-and-resources/a-practical-guide-to-building-ai-agents/)Accessed: 2026-05-28 - Microsoft Agent Framework v1.0 Announcement. [https://devblogs.microsoft.com/agent-framework/microsoft-agent-framework-version-1-0/](https://devblogs.microsoft.com/agent-framework/microsoft-agent-framework-version-1-0/)Accessed: 2026-05-28 - Google Cloud Next 2026 Wrap Up — ADK announcement. [https://cloud.google.com/blog/topics/google-cloud-next/google-cloud-next-2026-wrap-up](https://cloud.google.com/blog/topics/google-cloud-next/google-cloud-next-2026-wrap-up)Accessed: 2026-05-28 - Anthropic, “The Complete Guide to Building Skills for Claude” (PDF). [https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf](https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf)Accessed: 2026-05-28 - DSPy MIPROv2 Documentation. [https://aidoczh.com/dspy/api/optimizers/MIPROv2/MIPROv2.html](https://aidoczh.com/dspy/api/optimizers/MIPROv2/MIPROv2.html)Accessed: 2026-05-28 - DSPy GEPA Overview. [https://dspy.ai/api/optimizers/GEPA/overview/](https://dspy.ai/api/optimizers/GEPA/overview/)Accessed: 2026-05-28 - Pydantic AI Issue #3179 — “Add support for algorithmic optimizers (GEPA, TextGrad, MIPRO).” October 15, 2025. [https://github.com/pydantic/pydantic-ai/issues/3179](https://github.com/pydantic/pydantic-ai/issues/3179)Accessed: 2026-05-28 - DSPy Issue #8043 — “Is DSPy designed to allow export of optimized prompts?” April 2, 2025. [https://github.com/stanfordnlp/dspy/issues/8043](https://github.com/stanfordnlp/dspy/issues/8043)Accessed: 2026-05-28 - Bugcrowd, “Hacking AI Applications: In the Trenches with DSPy,” May 13, 2025. [https://www.bugcrowd.com/blog/hacking-llm-applications-in-the-trenches-with-dspy/](https://www.bugcrowd.com/blog/hacking-llm-applications-in-the-trenches-with-dspy/)Accessed: 2026-05-28 - Microsoft Security Blog, “When Prompts Become Shells: RCE Vulnerabilities in AI Agent Frameworks,” May 7, 2026. [https://www.microsoft.com/en-us/security/blog/2026/05/07/prompts-become-shells-rce-vulnerabilities-ai-agent-frameworks/](https://www.microsoft.com/en-us/security/blog/2026/05/07/prompts-become-shells-rce-vulnerabilities-ai-agent-frameworks/)Accessed: 2026-05-28 - Zscaler ThreatLabz, “Anthropic Claude Code Leak,” April 15, 2026. [https://www.zscaler.com/blogs/security-research/anthropic-claude-code-leak](https://www.zscaler.com/blogs/security-research/anthropic-claude-code-leak)Accessed: 2026-05-28 - Augment Code, “Claude Code Hits 121K GitHub Stars: Why Developers Are Skipping the IDE,” May 2026. [https://www.augmentcode.com/learn/claude-code-121k-stars](https://www.augmentcode.com/learn/claude-code-121k-stars)Accessed: 2026-05-28 - InfoQ, “Anthropic’s Code with Claude Announces Managed Agents, Proactive Workflows,” May 6, 2026. [https://www.infoq.com/news/2026/05/code-with-claude/](https://www.infoq.com/news/2026/05/code-with-claude/)Accessed: 2026-05-28 - Digital Applied, “Multi-Agent Orchestration: 5 Patterns That Work in 2026,” April 2026. [https://www.digitalapplied.com/blog/multi-agent-orchestration-5-patterns-that-work](https://www.digitalapplied.com/blog/multi-agent-orchestration-5-patterns-that-work)Accessed: 2026-05-28 - OWASP Gen AI Security, “OWASP Top 10 for Agentic Applications 2026.” [https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/](https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/)Accessed: 2026-05-28 - BerriAI/LiteLLM GitHub Issue #11433 — “litellm compatibility with ollama model and tool calling.” [https://github.com/BerriAI/litellm/issues/11433](https://github.com/BerriAI/litellm/issues/11433)Accessed: 2026-05-28 - BerriAI/LiteLLM GitHub Issue #9170 — “Is LiteLLM compatible with OpenAI agents SDK?” [https://github.com/BerriAI/litellm/issues/9170](https://github.com/BerriAI/litellm/issues/9170)Accessed: 2026-05-28 - TrueFoundry, “LiteLLM Review 2026: Features, Pricing, Pros and Cons,” February 26, 2026. [https://www.truefoundry.com/blog/a-detailed-litellm-review-features-pricing-pros-and-cons-2026](https://www.truefoundry.com/blog/a-detailed-litellm-review-features-pricing-pros-and-cons-2026)Accessed: 2026-05-28 - Agentic Engineering Jobs, “CrewAI Job Market 2026: Salaries, Stacks, Hiring Data,” April 19, 2026. [https://agentic-engineering-jobs.com/crewai-job-market-2026](https://agentic-engineering-jobs.com/crewai-job-market-2026)Accessed: 2026-05-28 - Atlan, “How Prompt Injection Attacks Compromise AI Agents in 2026.” [https://atlan.com/know/prompt-injection-attacks-ai-agents/](https://atlan.com/know/prompt-injection-attacks-ai-agents/)Accessed: 2026-05-28 - NVIDIA, “Practical Security Guidance for Sandboxing Agentic Workflows and Managing Execution Risk.” [https://developer.nvidia.com/blog/practical-security-guidance-for-sandboxing-agentic-workflows-and-managing-execution-risk/](https://developer.nvidia.com/blog/practical-security-guidance-for-sandboxing-agentic-workflows-and-managing-execution-risk/)Accessed: 2026-05-28 - Medium (Earlperry), “How Every Major Tech Company Is Sandboxing AI Agents Differently,” March 2026. [https://medium.com/@earlperry562/how-every-major-tech-company-is-sandboxing-ai-agents-differently-f41b65f14d8a](https://medium.com/@earlperry562/how-every-major-tech-company-is-sandboxing-ai-agents-differently-f41b65f14d8a)Accessed: 2026-05-28 - Firecrawl, “How to Build AI Agents for Beginners (2026).” [https://botpress.com/blog/build-ai-agent](https://botpress.com/blog/build-ai-agent)Accessed: 2026-05-28 - OpenAI, “Designing Agents to Resist Prompt Injection.” [https://openai.com/index/designing-agents-to-resist-prompt-injection/](https://openai.com/index/designing-agents-to-resist-prompt-injection/)Accessed: 2026-05-28 ## Methodology Note This report was compiled through extensive web research using multiple search engines (Bing, Brave, DuckDuckGo, Google, Yahoo, Yandex) to maximize coverage and minimize engine-specific bias. Primary sources were prioritized: official documentation (dspy.ai, platform.claude.com, developers.openai.com, docs.crewai.com, learn.microsoft.com/agent-framework, docs.langchain.com/langgraph, adk.dev), GitHub repositories, academic papers (ICLR 2024/2026 submissions), and vendor announcements. Comparative data points were cross-referenced across multiple independent sources (Alice Labs production ranking, Firecrawl framework comparison, MorphLLM agent framework analysis, JetThoughts benchmarks). Where sources disagreed on facts or rankings, the discrepancy was surfaced and assessed. The report distinguishes between established facts (documented features, version numbers), expert consensus (production readiness assessments), contested opinions (framework superiority claims), and independent inference (convergence predictions, scenario probabilities). Limitations: some frameworks (Claude Agent SDK) have limited public documentation due to private GitHub repositories; real-world production metrics are self-reported by vendors; benchmark results vary significantly based on task selection and evaluation methodology. ## Share this article ## Search Search by title, subtitle, tags, categories, authors, or body text.
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