Developer Frameworks: 9 Agent Frameworks Compared to Nexus

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The real question is not "can we build it?" It is "should we?"

Most engineering teams evaluating LangGraph, CrewAI, AutoGen, or Haystack already know they can build an AI agent system. The frameworks are genuinely powerful. The open-source communities are strong. The documentation is solid. Given enough engineering time, your team could build almost anything.

That is not the question.

The question is what happens after the prototype works. Getting an agent from "it works on my machine" to "it reliably handles 12,000 enterprise interactions daily with full compliance, audit trails, and governance" is where the real cost lives. That gap between prototype and production is where most enterprise AI agent projects stall, and it is where Gartner's prediction that over 40% of agentic AI projects will be canceled by 2027 comes from. Not because the models fail, but because organizations struggle to operationalize them.

Developer frameworks give you tools. Enterprise agent platforms give you outcomes. And the difference between those two things is a service layer that most build-vs-buy analyses miss entirely: Forward Deployed Engineers embedded with your team, change management support, and ongoing optimization. Nexus is a solution (platform + service), not just software.

This page covers the full landscape: 9 frameworks compared against Nexus across every dimension that matters for enterprise deployment. When each framework makes sense. What the real trade-offs are. And why a $4B AI company with world-class engineers chose to buy instead of build.

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The production gap: what frameworks leave to you

Frameworks are development tools. They help you build AI agents. They do not help you run AI agents at enterprise scale. The gap between a working prototype and a production system is where most of the engineering effort, cost, and timeline lives.

According to LangChain's 2025 State of Agent Engineering report (surveying 1,300+ professionals), 57% of respondents have agents in production. But quality remains the top barrier, cited by 32% as the biggest challenge. For enterprises with 2,000+ employees, security is the second-largest concern (24.9%), followed by latency (20%) as agents move into customer-facing use cases.

Here is what your engineering team owns when building on any agent framework:

Infrastructure and deployment. Hosting, scaling, load balancing, failure recovery, and durable execution for long-running agents. Many agent tasks run in the background on schedules or in response to triggers, making them prone to mid-task failures that require specialized infrastructure to handle gracefully.

Monitoring and observability. LangChain's report found that 94% of organizations with agents in production have some form of observability. Building tracing, logging, performance dashboards, and alerting from scratch is a significant engineering project on its own.

Enterprise governance. Audit trails, decision traceability, role-based access controls, compliance certifications (SOC 2, ISO 27001, GDPR). These are not features you add after launch. For regulated industries, they are prerequisites.

Integration maintenance. Every system the agent connects to (CRM, ERP, communication tools, ticketing systems) is an integration your team builds and maintains. When those systems update their APIs, your team fixes the breakage.

Exception handling at scale. The prototype handles the happy path. Production handles everything else: edge cases, malformed data, system timeouts, unexpected user behavior. At enterprise volume, exceptions are not rare. They are constant.

Ongoing maintenance. Framework updates, dependency management, breaking changes, security patches. This is not a one-time build. It is a permanent engineering commitment.

None of this is a criticism of the frameworks themselves. They are excellent at what they do: giving developers powerful building blocks to build AI agents. The question is whether building and maintaining all the layers above those building blocks is the highest-value use of your engineering team's time.

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Category comparison: all 9 frameworks vs Nexus

This table compares every developer framework and agent toolkit we cover against Nexus across the dimensions that matter most for enterprise deployment. Use it to quickly identify which frameworks fit your team's situation, then read the detailed comparison for the ones on your shortlist.

Dimension	LangChain	LangGraph	CrewAI	AutoGen	AutoGPT	Haystack	Microsoft Agent Framework	Google Vertex AI Agent Builder	OpenClaw	Nexus
Category	LLM application framework	Graph-based agent orchestration	Multi-agent framework	Multi-agent conversation framework	Autonomous agent framework	RAG/search pipeline framework	Enterprise developer SDK	Cloud developer toolkit	AI coding/automation agent	Enterprise platform + service
GitHub stars	127K+	24K+	57K+	55K+	180K+	21K+	N/A (combined SDK)	N/A (cloud product)	145K+	N/A (commercial)
Who builds	Engineers (Python/JS)	Engineers (Python)	Engineers (Python)	Engineers (Python/C#)	Developers (Docker/Python)	Engineers (Python)	Engineers (Python/C#/Java)	Engineers (Python/Java)	Developers (terminal)	Business teams + FDEs
Core strength	LLM app building, chains, RAG	Stateful graph orchestration	Role-based multi-agent	Multi-agent conversations	Autonomous goal decomposition	Retrieval, search, RAG pipelines	Azure ecosystem + multi-lang	GCP-native, Gemini models	Personal automation, coding	End-to-end workflow completion
Time to production	Weeks to months	Weeks to months	Weeks to months	Weeks to months	Highly variable (beta)	Weeks to months	Weeks to months	Weeks to months	Per-agent (individual effort)	Days to weeks
Production readiness	Building blocks; you build infra	Building blocks; you build infra	Building blocks; AMP adds hosting	No managed production path	Beta; known reliability issues	Building blocks; Enterprise Platform adds hosting	Azure AI Foundry for hosting	Agent Engine for managed runtime	Not enterprise-designed	Production-ready from day one
Enterprise governance	Build your own	Build your own	Build your own; AMP adds some	None built in	None; recommends sandbox	Build your own; Enterprise Platform adds some	Inherits Azure security; agent-level is custom	Inherits GCP security; agent-level is custom	None; documented security risks	SOC 2 Type II, ISO 27001, ISO 42001, GDPR
Integrations	Community-built, variable quality	Build your own	Build your own; MCP connectors	Build your own	Community skills (100+)	90+ (model providers, doc stores)	Azure/M365 native; others custom	100+ connectors; GCP-native	100+ community skills	4,000+ native enterprise integrations
Exception handling	Code it yourself	Code it yourself	Code it yourself	Code conversation patterns	Prone to loops, hallucinations	Code it yourself	Code it yourself	Code it yourself	Requires human supervision	Intelligent escalation with full context
Maintenance burden	Frequent breaking changes noted	Your team, permanently	Your team, permanently	Major breaking changes (0.2 to 0.4); merging into Agent Framework	Beta; evolving	Your team, permanently	Framework in transition to GA	Your team + GCP infra	Each agent maintained individually	Platform-managed; agents adapt
Ecosystem lock-in	Open-source	Open-source (LangChain ecosystem)	Open-source	Transitioning to Microsoft Agent Framework	Open-source	Open-source	Azure/Microsoft ecosystem	GCP ecosystem	Open-source	System-agnostic; any cloud, any vendor
Support model	Community, paid LangSmith plans	Community, paid LangSmith plans	Community, paid AMP plans	Community only (no enterprise tier)	Community (GitHub, Discord)	Community, Enterprise Starter (4 hrs/month)	Microsoft support tiers	Google Cloud support tiers	Community (GitHub, Discord)	Forward Deployed Engineers embedded with your team
Service layer	None	None	None	None	None	None	None	None	None	FDEs, change management, ongoing optimization
Pricing model	Free framework + LangSmith costs	Free framework + LangSmith/Platform costs	Free framework; Cloud: $99-$120K/yr	Free framework; all infra costs yours	Free; API costs can escalate	Free framework; Enterprise Platform custom	Free framework; Azure compute costs	Usage-based (Agent Engine + Gemini + connectors)	Free; $5-30/month API costs	Per-agent, tied to value delivered
Best for	Product-facing LLM features	Custom agent architectures	Multi-agent prototyping	Research, multi-agent conversations	Experimentation, personal automation	RAG-first products, search	Microsoft-native enterprises	GCP-native enterprises	Developer personal automation	Enterprise business workflow automation

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Quick decision guide

Choose a framework if:

• The agent system is part of your core product, customer-facing and central to what you sell
• You have a dedicated AI engineering team with available capacity (not competing with core product work)
• The use case is highly specialized, novel, or research-oriented
• You want full architectural control over every design decision
• You are prototyping or doing R&D with low initial commitment

Choose Nexus if:

• Business teams need to own the agents, not wait for engineering
• Your engineering team has higher-value work on your core product
• Speed to production is a priority (days to weeks, not months to quarters)
• You need enterprise governance from day one (SOC 2, ISO 27001, GDPR)
• Your workflows span multiple enterprise systems and channels
• You want a partner (Forward Deployed Engineers, change management, ongoing optimization), not just software
• You have already tried building and experienced the gap between prototype and production

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All 9 comparisons: detailed breakdowns

Established orchestration frameworks

These are the most widely adopted frameworks for building AI agents from scratch. Each gives engineering teams powerful building blocks, but leaves production infrastructure, governance, and maintenance to you.

Nexus vs LangChain The most popular LLM framework (127K+ GitHub stars, $1.25B valuation). Strong ecosystem with LangGraph and LangSmith (plus the DataStax-owned LangFlow visual builder). Best for teams building product-facing LLM features. The trade-off: ecosystem complexity and a permanent engineering commitment for internal business workflows.

Nexus vs LangGraph Graph-based agent orchestration for developers who want precise control over state, routing, and execution flow. Part of the LangChain ecosystem with 24K+ GitHub stars. Best for custom, highly stateful agent architectures. Production typically takes 6-18 weeks per agent for well-resourced teams.

Nexus vs CrewAI Role-based multi-agent framework with 57K+ GitHub stars. Intuitive mental model for orchestrating specialized agent roles. CrewAI AMP adds a hosted platform layer. Best for multi-agent prototyping. Teams report scaling challenges as requirements grow beyond sequential or hierarchical patterns, sometimes requiring rewrites 6-12 months in.

Nexus vs AutoGen Microsoft Research's multi-agent conversation framework (55K+ GitHub stars). Pioneered agents-as-conversation. Important caveat: AutoGen is in transition. Original creators forked to AG2; Microsoft is merging AutoGen with Semantic Kernel into Microsoft Agent Framework (1.0 GA targeted Q1 2026). AutoGen is now in maintenance mode. Teams face three paths with an unclear future.

Nexus vs Haystack RAG and search pipeline framework by deepset (21K+ GitHub stars, $45.6M+ funding). Clean component-based pipeline architecture with strong retrieval capabilities. Enterprise customers include Airbus and Siemens. Best for retrieval-first use cases. Enterprise workflows that go beyond search (collecting data, validating, routing, escalating) require significant custom engineering on top.

Cloud platform toolkits

These are developer toolkits offered by major cloud providers. They combine agent-building capabilities with cloud-native infrastructure, but tie you to their ecosystem.

Nexus vs Microsoft Agent Framework Microsoft's unified SDK merging AutoGen and Semantic Kernel. Multi-language support (Python, C#, Java), Azure AI Foundry for deployment, deep M365 and Dynamics integration. Strong choice for Microsoft-native organizations with dedicated AI engineering teams. Trade-off: Microsoft ecosystem dependency for non-Microsoft systems, and your team still owns the full build, governance, and organizational change challenge.

Nexus vs Google Vertex AI Agent Builder Google Cloud's developer platform including Agent Development Kit (ADK), Agent Engine, and Gemini Enterprise ($30/user/month). 100+ connectors, strongest within GCP. Best for teams already on Google Cloud building product-facing agents. Trade-off: GCP ecosystem pull, self-serve model with no embedded engineering support, and most enterprise workflows cross vendor boundaries.

Open-source autonomous agents

These are open-source projects that popularized the idea of autonomous AI agents. Powerful for experimentation and personal use, but not designed for enterprise deployment.

Nexus vs AutoGPT The project that started the AI agent conversation (180K+ GitHub stars). Demonstrated GPT-4 breaking goals into subtasks autonomously. Now evolving into AutoGPT Platform with a visual builder (still in beta). Important for its historical significance. Known issues with execution loops, hallucinations, and cost escalation. No enterprise compliance certifications, no dedicated support, no production reliability guarantees.

Nexus vs OpenClaw Open-source AI coding/automation agent (145K+ GitHub stars). Connects messaging platforms to LLMs for personal task automation. Powerful for individual developers. The enterprise challenge: every agent built differently, no unified governance, documented critical security vulnerabilities (CVE-2026-25253, supply chain poisoning), and Gartner characterized it as "high utility with unacceptable cybersecurity risk." Not designed for organizational-scale deployment.

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What enterprises experienced: Lambda chose to buy instead of build

This is the proof point that matters most for anyone evaluating developer frameworks.

Lambda is a $4B AI cloud infrastructure company. Approximately 600 employees. World-class AI engineers. If any company could build AI agents internally using LangChain, LangGraph, AutoGen, or any other framework, it is Lambda. AI is literally their core business.

Lambda considered building internally. Their leadership weighed the option seriously. They had engineers who could work with any framework.

They chose to buy.

The reason was opportunity cost. Every hour their engineers spent building internal sales intelligence agents was an hour not spent on Lambda's core AI infrastructure product, the product that generates their $500M+ ARR.

Joaquin Paz, Lambda's Head of Sales Intelligence, built the agent himself, without engineering support:

"I'm not an engineer. I built this in days. With the automation tools we looked at before, I would have needed to spec everything out and wait months for development."

Before finding Nexus, Lambda tried two other approaches. Open-ended AI agents (like ChatGPT Deep Search) were intelligent but inconsistent: same question, different answer every time. Traditional automation platforms were reliable but rigid: heavy hard-coding, brittle integrations, no ability to reason about what mattered.

"We looked at open-ended AI agents; they were smart but inconsistent. We looked at traditional automation; it was reliable but felt heavy, lots of hard coding. With Nexus, we got both: intelligent and consistent."

The results: $4B+ in pipeline identified, 24,000+ hours of research capacity added annually (equivalent to 12 full-time analysts), 12,000+ enterprise accounts analyzed with deep intelligence. Deployment took days.

Lambda has since expanded from a single agent to building an entire agent fleet across sales and marketing, with anticipated value exceeding $7M by 2026. Each new agent deploys in days and builds on the same foundation.

"We're not building separate automations. We're building an intelligent layer that understands how Lambda works. Each agent we add makes the foundation stronger."

-- Joaquin Paz, Head of Sales Intelligence, Lambda

If a $4B AI company with every reason to build (world-class AI engineers, technical expertise, infrastructure) chose to buy because the math on opportunity cost did not justify tying up engineering resources, the question for most enterprises becomes: what is the opportunity cost of having your engineers build internal agents instead of working on your core product?

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Worth exploring?

If your team has been evaluating developer frameworks for internal agent use cases, or has already started building and experienced the gap between prototype and production, it might be worth seeing how other engineering leaders have navigated this decision.

Lambda, a $4B AI company with world-class engineers, chose to buy instead of build. Their Head of Sales Intelligence, with no engineering background, built a production agent in days that now monitors 12,000+ enterprise accounts annually. Orange Group, with 120,000+ employees and every resource available, deployed through their business team in 4 weeks, achieving 50% conversion improvement and $4M+ incremental yearly revenue.

Every engagement starts with a 3-month proof of concept tied to specific outcomes. Forward Deployed Engineers work alongside your team from day one. You can exit anytime.

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