Open source AI agent frameworks are becoming the control layer for software that can reason through tasks, call tools, coordinate specialised agents and carry work across multiple steps. For enterprises, the decision is no longer just which large language model to use. It is which framework gives engineering teams the right balance of execution control, data access, reliability and governance.
- What is an open-source AI agent framework?
- Open source AI agent frameworks compared
- 1. LangGraph: when control over the workflow matters
- 2. CrewAI: when work can be divided across specialist agents
- 3. Google ADK: a code-first route into agent development
- 4. Microsoft Agent Framework: the strategic path for Microsoft environments
- 5. Pydantic AI: when typed outputs and validation are central
- 6. LlamaIndex Workflows: when enterprise knowledge is the hard part
- 7. Hugging Face smolagents: a lightweight option for code-first agents
- What about Microsoft AutoGen?
- How should enterprises choose an AI agent framework?
- A practical enterprise evaluation: build one workflow, not seven demos
- Open source does not mean governance is optional
- The FutureIsNow take
- Frequently asked questions
A convincing prototype and a dependable enterprise system are not the same thing. A framework can connect an agent to tools; it cannot, by itself, make those tools safe, the outputs correct or the workflow resilient.
This guide compares seven open-source options enterprise technology teams should evaluate in 2026: LangGraph, CrewAI, Google Agent Development Kit (ADK), Microsoft Agent Framework, Pydantic AI, LlamaIndex Workflows and Hugging Face smolagents. It also explains why Microsoft AutoGen should now be treated as a migration consideration rather than a default choice for new deployments.
What is an open-source AI agent framework?
An AI agent framework is a software layer that helps developers build applications in which a model can select tools, take actions, manage state and work towards a goal over multiple steps. Depending on the framework, it may provide an agent loop, tool interfaces, workflow orchestration, memory, human approval, tracing, evaluation or deployment integrations.
Open source generally gives teams access to source code and the ability to inspect, modify and self-host it under its licence. It does not mean every connected service is open source, model inference is free, or operating costs disappear. Managed observability, hosted runtimes, premium connectors and model APIs may still carry commercial terms.
For the terminology, start with our explainer on agentic AI vs AI agents. For the broader operating model, read how agentic AI is changing enterprise workflows.
Open source AI agent frameworks compared
These frameworks are not interchangeable products. Some are low-level orchestration runtimes; others offer higher-level abstractions, typed agent interfaces or data-centric workflow components. The right choice depends on the problem, the team’s stack and how much execution control the organisation needs.
| Framework | Best-fit use case | Core strength | What to evaluate |
|---|---|---|---|
| LangGraph | Long-running, stateful workflows | Explicit graph control, persistence and human-in-the-loop patterns | Design complexity and observability |
| CrewAI | Role-based multi-agent collaboration | Readable agent, task, crew and flow abstractions | Whether multiple agents are actually needed |
| Google ADK | Code-first agent systems, especially Google Cloud environments | Structured agent development and composition | Provider fit, deployment and portability |
| Microsoft Agent Framework | Microsoft-oriented enterprise environments | Agent and workflow development across Python and .NET | Identity, Azure and .NET integration |
| Pydantic AI | Typed Python applications | Validation, structured results and provider flexibility | Runtime and orchestration responsibilities |
| LlamaIndex Workflows | Document-heavy, retrieval-intensive systems | Event-driven workflows close to enterprise data | Retrieval quality and permissions |
| Hugging Face smolagents | Lightweight, code-first agents | Minimal abstractions and compact developer experience | Sandboxing and production controls |
1. LangGraph: when control over the workflow matters
LangGraph is a low-level orchestration framework for stateful agents and workflows. It models execution as a graph, making it useful when teams need to define the steps an agent can take, preserve state and recover from interruptions.
Its strengths become relevant when an agent must pause for a human decision, resume after a failure or follow a controlled sequence rather than improvise every step. LangGraph documents durable execution and human-in-the-loop capabilities, including inspecting or modifying state during a run.
Enterprise fit: complex approval workflows, multi-step operations and processes that must be auditable. Watch-out: flexibility comes with design responsibility. Teams must model state, retries, idempotency, permissions and observability. A graph does not remove the need for sound systems engineering.
Explore the LangGraph documentation.
2. CrewAI: when work can be divided across specialist agents
CrewAI organises agent systems around roles, tasks, crews and flows. It is designed for applications where specialised agents collaborate—for example, a research agent gathers evidence, an analyst evaluates it and a reviewer checks the result.
The role-based model can make multi-agent designs easier to communicate to product teams. Its flow abstractions help developers structure larger processes rather than relying on free-form agent conversations.
Enterprise fit: research and analysis pipelines, content operations and structured knowledge work. Watch-out: more agents do not automatically mean better outcomes. Each additional agent can add latency, model cost, failure points and coordination overhead. Start with one agent and introduce a crew only when specialisation improves the result.
Explore the CrewAI documentation.
3. Google ADK: a code-first route into agent development
Google’s Agent Development Kit (ADK) provides a structured way to build, test and compose agents in code. It is relevant to organisations evaluating Google’s AI and cloud ecosystem.
ADK supports agent composition and tool-oriented workflows. Teams should assess language support, model-provider integrations, deployment options and how its conventions fit their architecture. A framework’s strong integration with one cloud does not mean every component must live there.
Enterprise fit: organisations building agent applications with Google Cloud or Gemini in the stack. Watch-out: test portability and operational dependencies early. Confirm which capabilities are in the framework and which rely on managed services.
Explore the Google ADK documentation.
4. Microsoft Agent Framework: the strategic path for Microsoft environments
Microsoft Agent Framework brings together Microsoft’s agent and workflow development direction for Python and .NET teams. It is relevant to enterprises with significant Microsoft investments, where integration with existing applications, cloud services and development practices can matter as much as the agent abstraction itself.
One important ecosystem change: Microsoft has archived AutoGen and now directs new users towards Microsoft Agent Framework. Existing AutoGen deployments should be assessed against Microsoft’s migration guidance rather than assumed to be on the active development path.
Enterprise fit: organisations with .NET estates, Azure-based application platforms and teams seeking a Microsoft-aligned framework. Watch-out: do not choose it solely because the enterprise already uses Microsoft software. Test identity, data access, monitoring and deployment requirements in a real proof of concept.
Explore the Microsoft Agent Framework documentation and AutoGen project status.
5. Pydantic AI: when typed outputs and validation are central
Pydantic AI is a Python framework built around typed agent development. It is a strong candidate when an application needs structured outputs, explicit dependencies and validation as part of the application contract.
A model response is often not the final product. It may become an API payload, a workflow instruction or a record written into another system. Typed validation can catch malformed outputs before they move downstream. The project also documents production concerns such as durable execution, usage limits, human approval and OpenTelemetry-based tracing.
Enterprise fit: Python services that depend on validated structured data, provider flexibility and integration with existing application code. Watch-out: distinguish the core agent library from optional capabilities and external infrastructure. Teams still need authentication, authorisation, persistence, deployment and monitoring.
Explore the Pydantic AI documentation.
6. LlamaIndex Workflows: when enterprise knowledge is the hard part
LlamaIndex is closely associated with connecting language models to data. Its Workflows capability is relevant to applications where agents retrieve, process and reason over enterprise documents or other knowledge sources as part of a larger process.
For many organisations, the challenge is not getting an agent to call a tool. It is ensuring that it retrieves the right information, respects document-level permissions, identifies stale material and provides traceable evidence. A data-centric framework can help structure that work, but retrieval quality and access control still need to be engineered.
Enterprise fit: document-intensive workflows, knowledge assistants, research systems and applications where retrieval and data connectors are central. Watch-out: retrieval-augmented generation is not a substitute for access governance. Test permission inheritance, source freshness, citation quality and behaviour when evidence is missing.
Explore the LlamaIndex documentation.
7. Hugging Face smolagents: a lightweight option for code-first agents
smolagents is a compact framework from Hugging Face for building agents with a relatively small abstraction layer. Its code-oriented approach can be useful for developers who want to experiment with agent loops and tool use without adopting a larger orchestration system.
Enterprise fit: research prototypes, internal developer tools and narrowly scoped agents where the team wants to understand and control the implementation. Watch-out: code execution creates a security boundary. Do not expose an agent that can execute generated code to sensitive systems without sandboxing, strict permissions, resource limits, logging and human approval for consequential actions.
Explore the smolagents documentation.
What about Microsoft AutoGen?
AutoGen remains important to the history of multi-agent development, and existing deployments may still be in use. But Microsoft’s repository now marks AutoGen as archived and no longer maintained, and recommends that new users start with Microsoft Agent Framework. Enterprises should distinguish between maintaining an existing AutoGen workload and selecting a framework for a new strategic system.
Before migrating, inventory agents, tools, dependencies, conversation patterns and integrations. Map those requirements to the successor framework, test behavioural parity and migrate one bounded workflow before changing business-critical systems.
How should enterprises choose an AI agent framework?
Do not select a framework based on popularity or a feature checklist. Evaluate it against the work the agent must perform and the controls the organisation needs to retain.
| Decision area | Questions to ask |
|---|---|
| Workflow control | Can the system pause, resume, retry and recover without repeating unsafe actions? |
| Data and permissions | Can it enforce the same access rights as the systems and records it connects to? |
| Human oversight | Can people review, approve, reject or reverse consequential actions? |
| Observability | Can the team trace model decisions, tool calls, errors, latency and cost? |
| Evaluation | Can the organisation test task success, tool selection, groundedness and policy compliance? |
| Portability | Can the application change models or hosting environments without a full rewrite? |
| Security | Are secrets protected, tools scoped and code execution isolated? |
| Total cost | What will inference, infrastructure, observability, engineering and evaluation cost at production scale? |
A practical enterprise evaluation: build one workflow, not seven demos
Compare shortlisted frameworks using the same representative business task. For example, an internal procurement agent could retrieve approved supplier information, compare it with policy, prepare a recommendation and stop for human approval before creating a purchase request.
- Define the boundary. Specify what the agent may read, what it may propose and which actions it must never execute without approval.
- Use the same task and test data. Give each framework identical inputs, tools, model access and success criteria.
- Measure the full workflow. Record completion rate, incorrect tool calls, recovery after failure, latency, token use and human intervention.
- Test adverse conditions. Include missing data, conflicting instructions, permission-denied records, unavailable tools and prompt-injection attempts.
- Review the operating model. Assess deployment, logging, secrets, version upgrades, incident response and post-launch ownership.
- Run a limited pilot. Start with read-only or recommendation-only access, then expand permissions only when evidence supports it.
This is not merely a framework selection exercise. It is a test of whether the enterprise can operate an autonomous system safely. Our analysis of why enterprise agentic AI projects fail to reach production explores the gap between promising pilots and reliable deployments. For governance, see our guide to AI governance in India.
Open source does not mean governance is optional
Open-source frameworks can improve transparency and reduce dependence on a single vendor’s agent runtime. They do not remove the enterprise’s responsibility for the system it builds. The organisation remains accountable for model selection, data handling, tool permissions, user consent, audit trails, security testing and the consequences of automated actions.
Every production agent should have a defined owner, documented purpose, least-privilege tool access, limits on spending and execution, a way to stop or roll back actions, and monitoring that can reconstruct what happened. Sensitive actions—such as moving money, changing customer records, approving access or sending external communications—should have explicit approval and verification controls.
The FutureIsNow take
The agent framework market is not converging on one universal winner. It is separating into different architectural choices: graph-based orchestration, role-based collaboration, typed agent interfaces, cloud-aligned development kits and data-centric workflows. That is a sign of a maturing engineering category, not a reason to choose the framework with the longest feature list.
For enterprise leaders, the better question is not “Which framework is best?” It is “Which framework gives our teams the right balance of control, portability, reliability and governance for this specific workflow?” Start with the operating requirement, run a comparable proof of concept and treat security and observability as design requirements—not later additions.
Signals over noise. Intelligence for what comes next.
Frequently asked questions
Which is the best open-source AI agent framework?
There is no single best option for every enterprise. LangGraph suits explicit stateful workflows, CrewAI role-based multi-agent patterns, Google ADK and Microsoft Agent Framework their respective ecosystems, Pydantic AI typed Python applications, LlamaIndex data-intensive workflows and smolagents lightweight code-first agents. Choose based on the workload’s control, security and operating requirements.
Are open-source AI agent frameworks free for commercial use?
Many popular frameworks use permissive licences, but enterprises should verify the current licence and notices for the exact version and every dependency. Hosted services, model APIs, support and premium features may have separate fees or terms. This article is not legal advice.
Can enterprises run AI agent frameworks on their own infrastructure?
Many frameworks can be self-hosted or run inside an organisation’s application environment. Self-hosting does not automatically make a deployment secure: teams still need identity controls, network boundaries, secrets management, logging, patching, model access policies and operational support.
Is Microsoft AutoGen still recommended for new projects?
Microsoft has placed AutoGen in maintenance mode and directs new users to Microsoft Agent Framework. Existing AutoGen applications should be reviewed against migration guidance and their support requirements.
What should an enterprise test before deploying an AI agent?
Test task completion, tool-call accuracy, data permissions, prompt-injection resistance, failure recovery, auditability, latency, cost and human approval paths. Begin with a narrow workflow and limited permissions before expanding the agent’s authority.
