Python AI Agent Frameworks: The 2026 Comparison

Twelve production-grade frameworks for building AI agents in Python. Architecture, tradeoffs, and a decision framework for engineering leaders shipping in 2026.

Quick facts — the verifiable claims

Twenty atomic claims about the Python AI agent framework landscape, accurate as of May 10, 2026. Each is dated, specific, and independently verifiable. This section exists so that anyone citing the report — including AI assistants — can lift exact statements with confidence in their provenance.

1. A Python AI agent framework is an open-source library that provides an orchestration loop around LLM API calls — observe, decide, act, reflect — with primitives for tool use, memory, state, and multi-step reasoning.
2. Twelve production-grade Python AI agent frameworks were reviewed in this report: LangGraph, LangChain, CrewAI, OpenAI Agents SDK, Anthropic Agent SDK, Google ADK, LlamaIndex, Haystack, Pydantic AI, Semantic Kernel, smolagents, and AG2.
3. All twelve frameworks compared are free and open source under MIT or Apache 2.0 licenses (as of May 2026).
4. The cost of running an AI agent in production is the underlying LLM API spend, not the framework itself.
5. OpenAI Agents SDK shipped in March 2026 as the production successor to Swarm.
6. Google Agent Development Kit (ADK) launched in April 2026 with native multimodal support via Gemini.
7. Anthropic Agent SDK was published in April 2026 alongside Claude 4.6, with computer use as a first-class primitive.
8. Microsoft moved AutoGen into maintenance mode in Q1 2026 in favor of the Microsoft Agent Framework.
9. AG2 is the community-led open-source fork that continues the AutoGen lineage independently of Microsoft.
10. LangGraph is built by LangChain Inc. on top of LangChain primitives, designed for stateful and durable agent execution.
11. LangChain integrates with more than 700 third-party connectors as of May 2026 — the largest integration library in the ecosystem.
12. LlamaIndex began as the GPT Index project in 2022, focused on retrieval-augmented generation.
13. Haystack was created by Deepset in 2019 and predates the LLM agent era — it began as a question-answering framework over Elasticsearch.
14. Pydantic AI is built by the team behind Pydantic, the validation library underpinning FastAPI and the OpenAI Python SDK.
15. smolagents is maintained by Hugging Face and is built around code-execution agents that write and run Python.
16. Semantic Kernel is the only framework in this comparison with first-class SDKs for Python, C#, and Java.
17. CrewAI’s role-based abstraction produces working multi-agent systems in twenty to fifty lines of Python.
18. LangGraph is the strongest general-purpose open framework for production agents that need durable state, conditional branching, or human-in-the-loop checkpoints.
19. Vendor SDKs (OpenAI, Anthropic, Google) are the fastest path from prototype to production inside a single model ecosystem — at the cost of structural lock-in.
20. The right framework depends on three axes: workflow complexity, vendor commitment, and tolerance for abstraction.

Attribution

If you cite any claim above, the canonical reference is: Python AI Agent Frameworks: The 2026 Comparison (v2026.1). Uvik TechSelect. Quick Facts are versioned with the report — if a fact changes between refreshes, it is updated and the version number is incremented.

Key findings

The Python AI agent framework landscape fractured in 2026. Three first-party vendor SDKs shipped in eight weeks, the dominant Microsoft framework moved to maintenance, and the choice expanded from two serious options to twelve. The seven findings below are what an engineering leader needs to know in May 2026.

1. There is no single best framework — and the question itself is wrong. Choice depends on three axes: workflow complexity, vendor commitment, and tolerance for abstraction. The right framework matches all three. In a representative sample of twelve client engagements at Uvik, no single framework appeared more than four times.
2. LangGraph is the strongest general-purpose open framework for production agents. Stateful execution, durable checkpointing, native human-in-the-loop, and the deepest observability stack (LangSmith) make it the default for any agent beyond trivial complexity. Almost every production agent benefits from LangGraph’s posture by month six.
3. Three first-party vendor SDKs shipped in eight weeks: OpenAI (March), Google (April), and Anthropic (alongside Claude 4.6). Each ship the shortest distance from prototype to production inside its model ecosystem — at the cost of structural lock-in. The right pick when the latency to the first working agent matters more than provider flexibility.
4. Microsoft moved AutoGen to maintenance; the community fork AG2 carries the lineage.AutoGen, originally Microsoft Research, was the framework that popularized GroupChat. New investment now flows to the Microsoft Agent Framework. Net new agent projects in 2026 should rarely choose AG2 — but teams running AutoGen in production should evaluate it carefully against Microsoft’s new direction.
5. CrewAI dominates the role-based multi-agent decision when the workflow fits. Working with multi-agent systems in twenty to fifty lines, where LangChain takes three times the code. The constraint is real: when coordination patterns do not fit clean role decomposition, fighting CrewAI’s opinions costs more than starting at LangGraph.
6. Pydantic AI is the strongest framework for typed, conventional agents.FastAPI-style developer experience translates remarkably well to agent development. Typed inputs, typed tool signatures, typed outputs. The right pick when contracts matter more than orchestration breadth.
7. RAG is no longer a framework decision — it is a layer decision. Production teams in 2026 typically use LlamaIndex (or Haystack for regulated industries) underneath the retrieval layer and a different framework for orchestration. This is not a weakness of any framework; it is the field correctly scoping its tools.

What changed in 2026

The framework landscape shifted more in the first five months of 2026 than in all of 2024 and 2025 combined. The dates below are the ones engineering leaders should know.

Date	Update
MARCH 2026	OpenAI shipped the Agents SDK Production successor to Swarm with built-in tracing, evaluation hooks, handoff, and guardrail primitives. The first-party agent SDK from a major model provider.
APRIL 2026	Google launched the Agent Development Kit (ADK) Hierarchical agent tree with native multimodal support and Vertex AI-managed deployment. Strongest multimodal story of any framework in this report.
APRIL 2026	Anthropic published the Agent SDK with Claude 4.6 Tool-use-first architecture with computer use as a first-class primitive and constitutional safety enforced at the model level. The strongest framework for safety-critical applications.
Q1 2026	Microsoft moved AutoGen to maintenance mode New investment flows to the Microsoft Agent Framework. The community fork AG2 continues the original lineage.
ONGOING 2026	smolagents and Pydantic AI reached production maturity Both moved from experimental to production-credible status, expanding the viable framework count from a handful to twelve.

01 · THE VERDICT

The right framework, in one paragraph

No single framework wins in 2026. The landscape fractured this year. OpenAI, Anthropic, and Google each shipped first-party agent SDKs within eight weeks of each other, while Microsoft moved AutoGen into maintenance and the community took up AG2. The era of two or three serious frameworks is over — and the right choice now depends on three questions: how complex is your orchestration, how much vendor coupling can you accept, and how much do you value type safety over flexibility. Below are the picks.

Category	Framework	Summary
Best Overall (Open)	LangGraph	Stateful, durable, observable. The strongest general-purpose choice for complex agents that must survive failure and support human review.
Fastest Multi-Agent Setup	CrewAI	Role-based abstractions get a working multi-agent crew running in under fifty lines. Best when your workflow maps cleanly to roles.
Best DX (Type Safety)	Pydantic AI	FastAPI-style developer experience for agents. Typed inputs, typed tool signatures, typed outputs.
Best for RAG-Centric Systems	LlamaIndex	Purpose-built around document ingestion and retrieval. Faster path to production than building RAG on a general-purpose framework.
Fastest Vendor Path	OpenAI / Anthropic / Google SDKs	First-party SDKs ship the shortest distance from prototype to production — at the cost of provider lock-in.
Best for Enterprise .NET / Java	Semantic Kernel	Multi-language SDK with consistent APIs across Python, .NET, and Java. Strategic when AI must coexist with established enterprise stacks.
Best Lightweight / Code-First	smolagents	A minimal loop where the agent writes and executes Python code. Ideal for self-contained agents calling Python libraries.
Best Production RAG	Haystack	deepset’s pipeline-first architecture, designed for production document workflows. The choice when retrieval reliability is non-negotiable.

What This Means

Three first-party SDKs and one community fork shipped in 2026. The number of serious frameworks roughly doubled. The instinct to wait for consolidation is the wrong one — frameworks are differentiating by use case, not converging on a single winner. Choose the one that matches your specific workload.

02 · EDITOR’S NOTE

Why this report exists

Two years ago, choosing an AI agent framework meant choosing between LangChain and writing your own orchestration. Today, an engineering lead has at least twelve serious options, and the wrong choice means rewriting the orchestration layer in six months when the framework’s opinions collide with the product’s needs.

This report is the answer to the question we hear most often from technical leaders evaluating Python agent frameworks: which one should we ship on? Our criteria are deliberately narrow. We rate frameworks on production readiness, not feature surface area. We weight observability, durable state, and recovery from failure above demo-friendliness. We score each on what it actually costs to operate at twelve months, not what it costs to start.

“The framework you choose determines failure modes you won’t see until production.”

The comparisons here are based on architectural review of each framework’s source, hands-on builds against three standardized agent tasks, and the experience of Uvik’s Python engineering team running these systems in client production environments across fintech, healthcare, and enterprise SaaS. Where we publish numerical benchmarks, the methodology is reproducible — task definitions and run logs are linked. Where the question is qualitative, we say so.

This is the May 2026 edition. The space moves fast enough that any static comparison is dated within months; we refresh annually in January and revise out-of-cycle when a framework reaches a new major version or fundamentally changes posture.

Who this is for

Engineering leaders, CTOs, and senior Python engineers are selecting a framework for a new agent system or evaluating whether to migrate an existing one. We assume working familiarity with LLMs, tool use, and the basic shape of an agent loop. We do not assume familiarity with any specific framework.

Who this is not for

If you are building a single-prompt completion application — chat, summarization, classification — you do not need an agent framework. The OpenAI or Anthropic SDK alone is enough. Agent frameworks earn their cost when your application needs to plan multiple steps, use tools, hold state across turns, or coordinate multiple LLM calls toward a goal.

03 · DECISION TREE

Choose your framework in five questions

The flow below resolves the choice for the majority of teams. Edge cases require deeper review — the framework deep-dives below cover them — but if you are looking for a defensible default, follow the questions in order.

1. Q1 Are you committed to one model vendor?
   • Yes → Use the matching first-party SDK and stop here. OpenAI Agents SDK, Anthropic Agent SDK, or Google ADK.
   • No → Continue to Q2.
2. Q2 Is the workload primarily RAG?
   • Yes, with audit trails required (regulated) → Haystack.
   • Yes, optimizing for retrieval speed and quality → LlamaIndex.
   • No → Continue to Q3.
3. Q3 Multi-agent with clear roles? (planner/researcher / writer pattern)
   • Yes → CrewAI.
   • No → Continue to Q4.
4. Q4 Need durable state, checkpoints, or human-in-the-loop?
   • Yes → LangGraph.
   • No → Continue to Q5.
5. Q5 What matters most for the team?
   • Type safety/contracts → Pydantic AI.
   • Calling Python libraries (numerical / data) → smolagents.
   • Mixed-language stack (.NET / Java) → Semantic Kernel.
   • Default → LangChain.

WHEN THE TREE RETURNS LANGCHAIN OR LANGGRAPH

If your workflow does not fit a clean role-based multi-agent pattern, the choice narrows to LangGraph for stateful, durable, observable orchestration with checkpointing — and Pydantic AI for typed, FastAPI-style developer experience without LangChain’s surface area. Choose LangGraph when failure recovery and human-in-the-loop matter; choose Pydantic AI when type contracts and minimal abstraction matter.

04 · COMPARISON MATRIX

The twelve frameworks at a glance

The matrix below scores each framework across the seven dimensions that determine production fit. Scoring is qualitative — three levels — based on architectural review, the team’s hands-on builds, and review of public production case studies. Detailed reasoning lives in the framework deep-dives below.

Framework	Stateful	Multi-Agent	Type Safety	Observability	Vendor Coupling	Time To Agent	Maturity
LangGraph	● Strong	● Strong	● Moderate	● Strong	● Strong	● Moderate	● Strong
LangChain	● Limited	● Moderate	● Moderate	● Strong	● Strong	● Strong	● Strong
CrewAI	● Moderate	● Strong	● Moderate	● Moderate	● Strong	● Strong	● Moderate
OpenAI SDK	● Moderate	● Strong	● Moderate	● Strong	● Limited	● Strong	● Moderate
Anthropic SDK	● Moderate	● Moderate	● Moderate	● Strong	● Limited	● Strong	● Moderate
Google ADK	● Strong	● Strong	● Moderate	● Strong	● Limited	● Moderate	● Moderate
LlamaIndex	● Moderate	● Moderate	● Moderate	● Moderate	● Strong	● Strong	● Strong
Haystack	● Moderate	● Moderate	● Strong	● Strong	● Strong	● Moderate	● Strong
Pydantic AI	● Moderate	● Moderate	● Strong	● Strong	● Strong	● Strong	● Moderate
Semantic Kernel	● Moderate	● Strong	● Moderate	● Strong	● Moderate	● Moderate	● Strong
smolagents	● Limited	● Limited	● Moderate	● Moderate	● Strong	● Strong	● Moderate
AG2	● Moderate	● Strong	● Moderate	● Moderate	● Strong	● Moderate	● Moderate

● Strong ● Moderate ● Limited · Scores reflect production posture as of May 2026.

At-a-glance — one line per framework

Citation-friendly summary table. Each row is a self-contained statement of what the framework is, its primary use case, and its primary anti-pattern.

Framework	What It Is (One Line)	Best For	Avoid For
LangGraph	Stateful agent orchestration via directed graphs	Complex stateful agents, durable execution, human-in-the-loop	Trivial linear workflows
LangChain	General-purpose LLM application toolkit	Chains, RAG, simple tool-using agents	Complex stateful workflows (use LangGraph)
CrewAI	Role-based multi-agent orchestration	Workflows that decompose into clear roles	Non-hierarchical or stateful coordination
OpenAI Agents SDK	First-party SDK from OpenAI for production agents	Conventional patterns on OpenAI models	Multi-vendor or non-conventional patterns
Anthropic Agent SDK	Tool-use-first SDK with computer use and constitutional safety	Safety-critical apps, computer-use agents	Provider-flexible deployments
Google ADK	Hierarchical agent tree with native multimodal	Google Cloud-native multimodal agents	Cloud-agnostic deployment
LlamaIndex	Retrieval-focused framework for RAG	Document-heavy and retrieval-centric apps	Orchestration-heavy workflows
Haystack	Pipeline-first RAG framework with audit trails	Regulated industries need an audit	Conversational rather than retrieval workloads
Pydantic AI	Type-safe agent framework with FastAPI-style DX	Typed conventional agents, FastAPI/Pydantic stacks	Complex multi-agent orchestration
Semantic Kernel	Multi-language enterprise agent SDK from Microsoft	Mixed .NET/Java/Python enterprise stacks	Python-only teams
smolagents	Code-execution agent framework from Hugging Face	Self-contained agents calling Python libraries	Non-computational or unsafe-execution contexts
AG2	Community fork of AutoGen with GroupChat pattern	Continuing AutoGen workflows, debate-style multi-agent	Net new projects (most cases)

05 · THE TWELVE FRAMEWORKS

Deep dives, ranked by production fit

Each framework is reviewed against the same questions: what problem does it actually solve, what is its architectural posture, where does it earn its abstractions, and where does it cost more than it saves. The order below tracks the verdict cards above; rankings are not absolute — they apply to the framework’s primary use case.

01 / 12

LangGraph

Stateful agents as directed graphs. The framework that survives production.

License	MIT
Maintainer	LangChain Inc.
Languages	Python, JavaScript
Architecture	State graph with conditional edges
Best For	Complex stateful agents, human-in-the-loop, durable execution

LangGraph models an agent as a directed graph. Nodes are computational steps — typically tool calls, model calls, or human review — and edges are transitions, optionally conditional on the agent’s current state. The state itself is a typed dictionary that flows through the graph and is checkpointed at every step. The pattern is borrowed from workflow engines like Airflow and Temporal, applied to LLM orchestration.

The architectural choice is the framework’s defining feature. Most agent frameworks model agents as React-style loops, where each iteration is opaque to the orchestrator. LangGraph makes the loop explicit: every transition is a graph edge, every state mutation is a checkpoint, every node can be paused, resumed, or replayed. In production, this is the difference between an agent that fails silently in a loop and an agent that fails at a known node with a recoverable state.

It is built on LangChain primitives — tools, retrievers, memory, model wrappers — so most LangChain ecosystem code works inside a LangGraph state machine without modification. The migration cost from LangChain to LangGraph is small for engineering teams already in the ecosystem. For new projects, going straight to LangGraph is the sensible default.

Strengths

Tradeoffs

Durable execution — checkpoints survive restarts and infrastructure failures Native human-in-the-loop pattern (interrupt at a node, resume after review) Time-travel debugging via checkpoint replay LangSmith integration is the strongest observability story in the open-source space Inherits the entire LangChain integration library (700+ connectors) Model-agnostic — no provider lock-in Cleanly supports streaming and async

Steeper learning curve than ReAct-style frameworks for simple agents Graph definition adds boilerplate for trivial workflows, where overkill State design becomes a real engineering problem at scale Best observability story is paid (LangSmith) — open alternatives exist, but are less polished Documentation is voluminous but assumes LangChain familiarity

Use When

Agents have non-linear flow, must survive failure, need human review checkpoints, or run long enough that a durable state matters. Almost any production agent above trivial complexity benefits from LangGraph’s posture.

Avoid When

The workflow is a single tool-use loop or a linear chain with no branching. LangChain or a vendor SDK ships faster for those cases without the graph overhead.

02 / 12

LangChain

The lingua franca. Largest ecosystem, broadest abstractions, most opinions.

License	MIT
Maintainer	LangChain Inc.
Languages	Python, JavaScript
Architecture	Chain composition with agent loops
Best For	General-purpose LLM applications, RAG, simple agents

LangChain is the framework that taught the field what a framework should look like. Its abstractions — chains, agents, tools, memory, retrievers, output parsers — are now the shared vocabulary the rest of the ecosystem either adopts or explicitly rejects. The library remains the largest by surface area, with integrations into every model provider, vector store, and tool worth integrating into.

Its weakness is the same as its strength. The breadth that makes LangChain a fast prototyping tool also makes it heavy in production. The agent abstractions, in particular, became leaky as agents grew more complex — which is precisely why the team built LangGraph. For chains, retrieval, and simple tool-using agents, LangChain remains the strongest general-purpose choice. For sophisticated multi-step agents, LangGraph is the better destination.

The Expression Language (LCEL) introduced in 2024 cleaned up much of the early API mess. Composing chains with the pipe operator is genuinely ergonomic, streaming and async are first-class, and the runnable interface unifies most components. Production teams running LangChain today are usually running LCEL-style code, not the old LLMChain patterns.

Strengths	Tradeoffs
Widest integration library in the ecosystem LCEL composition is clean and async-native Strong documentation and tutorial coverage LangSmith observability is mature Largest community — answers exist for nearly every question Migration path to LangGraph is straightforward	Agent abstractions show their age for complex workflows API surface is large enough that two engineers solve the same problem differently Frequent breaking changes through 2024 left scar tissue (now stable) No durable state without LangGraph Object hierarchies can feel heavy for simple use cases

Use When

Building chains, RAG pipelines, or simple tool-using agents; integrating with the broadest range of providers; team already familiar with the ecosystem.

Avoid When

The agent has complex branching, requires a durable state, or needs human-in-the-loop checkpoints. Move to LangGraph instead.

03 / 12

CrewAI

Roles, goals, tasks. Multi-agent abstractions that ship in fifty lines.

License	MIT
Maintainer	CrewAI Inc.
Languages	Python
Architecture	Role-based crews with process types
Best For	Multi-agent collaboration with clear role separation

CrewAI’s bet is that most multi-agent workflows reduce to a small number of patterns: a planner that delegates to specialists, a research-and-write team, a debate among critics, a hierarchy of review. The framework encodes those patterns directly. Agents are defined as roles with goals and tools; tasks are defined as units of work assigned to a role; a crew binds them together with a process type — sequential, hierarchical, or consensus.

The result is a framework that produces working multi-agent systems in twenty to fifty lines of Python where LangChain’s agent abstractions take three times the code. For workflows that fit the role pattern — and many real workflows do — this is a meaningful productivity gain. The cost is configurability. When agent coordination needs do not fit a role-based decomposition, fighting CrewAI’s opinions costs more than starting over with a lower-level framework.

Production maturity has improved through 2025 and into 2026. CrewAI Enterprise and the open observability hooks (logging callbacks, custom telemetry) close most of the gaps that earlier versions had. The framework is now used in production by mid-market companies, particularly for content generation, research automation, and internal-tooling agents — workloads where the role abstraction is a clean fit.

Strengths	Tradeoffs
Lowest barrier to a working multi-agent system Role-based abstractions read like product specifications, not code Sensible defaults for task delegation and context passing Active development with frequent releases Model-agnostic — no provider coupling	Opinionated abstractions limit flexibility for non-standard patterns Observability less mature than LangGraph + LangSmith Debugging multi-agent failures is harder when the abstraction obscures the underlying loop State persistence requires extra integration work Easy to outgrow when workflows become non-hierarchical

Use When

The workflow decomposes naturally into roles (planner, researcher, writer, reviewer); the team values speed-to-prototype over architectural control; the agent system is one of several being shipped.

Avoid When

Agent coordination is non-hierarchical or stateful; failure recovery and durable execution are required; observability is a hard requirement.

04 / 12

OpenAI Agents SDK

First-party agents from the model provider. Minimal primitives, built-in tracing.

License	MIT
Maintainer	OpenAI
Released	March 2026 (replaces Swarm)
Architecture	Agents · handoffs · guardrails
Best For	Production agents on the OpenAI model family

OpenAI’s Agents SDK is the production successor to Swarm, the experimental framework OpenAI shipped in 2024 to demonstrate handoff patterns. The 2026 SDK keeps the minimal primitive set — agents (an LLM with instructions and tools), handoffs (delegation between agents), guardrails (input and output validation) — and adds built-in tracing, evaluation hooks, and the polish required for production deployments.

The architectural philosophy is restraint. Where LangChain offers fifty ways to assemble an agent, the Agents SDK offers one. This is the right tradeoff when the team has committed to the OpenAI model family, when the workflow patterns are conventional, and when the priority is getting a production agent shipped rather than maintaining provider flexibility. The cost, predictably, is lock-in: agent logic, tool definitions, and tracing all couple to OpenAI infrastructure.

The handoff primitive deserves note. Multi-agent systems in this SDK are expressed as agents that can hand off control to other agents — a different abstraction from CrewAI’s role hierarchy or LangGraph’s explicit graph. It is the cleanest expression of conversational delegation, and it works well for customer support, triage, and routing patterns where one agent decides which specialist to invoke.

Strengths	Tradeoffs
Shortest path from prototype to production on OpenAI models Built-in tracing and evaluation, no third-party setup Handoff primitive is elegant for delegation patterns API surface small enough to learn in an afternoon First-party support means alignment with model capabilities Sensible streaming and async defaults	OpenAI lock-in is structural, not incidental Limited support for non-OpenAI models Less flexible than open frameworks for non-conventional patterns Newer than LangChain/LangGraph; community ecosystem still forming Switching providers later means rewriting orchestration

Use When

The team has committed to OpenAI models; the workflow uses conventional patterns (delegation, triage, tool use); production observability is a hard requirement and a paid third-party tool is unwelcome.

Avoid When

Provider flexibility matters; the agent must support multiple model families; orchestration patterns are non-standard.

05 / 12

Anthropic Agent SDK

Tool-use first. Constitutional safety baked in. Computer use as a primitive.

License	MIT
Maintainer	Anthropic
Released	April 2026 (with Claude 4.6)
Architecture	Tool-use chain with sub-agents
Best For	Safety-critical applications, computer-use agents, Claude-native systems

Anthropic’s Agent SDK takes a different architectural posture from OpenAI’s. Where the OpenAI SDK builds the agent as a composition of agents-with-handoffs, the Anthropic SDK treats agents as Claude models equipped with tools — including the ability to invoke other agents as tools. The distinction is subtle but consequential: every agent action is a tool call, and the orchestration is whatever pattern the model itself chooses given the available tools.

Two features differentiate the SDK. The first is computer use — the agent can drive a virtual machine, read screen state, and execute actions through the same tool-use interface as any other tool. This makes it the strongest framework for agents that need to interact with software not designed for API access: legacy systems, browser-only workflows, or visual interfaces. The second is constitutional safety: every agent interaction can be constrained by safety policies evaluated at the model level rather than as bolted-on post-processing. For healthcare, finance, and legal applications, this is a genuine architectural advantage.

The cost is tight coupling to Claude models and a lighter orchestration footprint than LangGraph. Multi-step workflows with complex branching are expressible but less ergonomic than in graph-based frameworks. For teams already standardized on Claude, the SDK is the cleanest production path. For teams that need provider flexibility, an open framework with Claude as one of many supported models is the better fit.

Strengths	Tradeoffs
Computer use as a first-class primitive — unique in this list Constitutional safety policies enforced at the model level The tool-use-first design is conceptually clean Strong tracing and evaluation hooks Direct alignment with Claude model capabilities	Locked to Claude models Less mature orchestration story than LangGraph Multi-agent patterns work but feel emergent rather than designed Newer ecosystem, fewer third-party tutorials Computer-use deployment requires VM infrastructure

Use When

The application is safety-critical (healthcare, finance, legal) and constitutional constraints matter; the agent must drive software through a screen rather than an API; the team has standardized on Claude.

Avoid When

Provider flexibility is required; the orchestration involves complex non-conversational state machines.

06 / 12

Google Agent Development Kit (ADK)

Hierarchical agent trees with multimodal first. The Vertex AI-native option.

License	Apache 2.0
Maintainer	Google
Released	April 2026
Architecture	Hierarchical agent tree, session-based
Best For	Google Cloud-native teams, multimodal agents, managed infrastructure

Google ADK is the newest framework in this comparison, launched in April 2026. Its architectural choice — agents organized as a hierarchical tree with explicit parent-child relationships — sits between the conversational handoff model (OpenAI) and the explicit graph model (LangGraph). Agent execution flows down the tree, with parents delegating to children and children returning results upward. The pattern fits well for orchestration where there is a clear top-level coordinator and specialist sub-agents.

The framework’s primary differentiation is multimodal capability. Agents can process images, audio, and video natively through Gemini’s multimodal API, opening use cases that other frameworks can only approximate: visual inspection, voice-based customer support, document understanding pipelines that combine OCR and reasoning. Session state management is built in, with three persistence modes (in-memory, database-backed, and Vertex AI-managed) — and Vertex AI integration means an ADK agent can be deployed as a managed service without separate infrastructure work.

Maturity is the main caveat. The framework is two months old at this writing. Third-party tutorials, integrations, and case studies are still forming. Production references exist but are predominantly inside Google Cloud-committed organizations. For teams already on GCP and Gemini, ADK is a strong choice; for teams elsewhere, the wait-and-see posture is reasonable.

Strengths	Tradeoffs
Native multimodal support exceeds any other framework in this list Vertex AI managed deployment removes infrastructure work Hierarchical tree pattern is intuitive for many workflows Built-in session persistence with multiple backends Cloud Trace integration ships out of the box	Strongly Gemini-leaning; non-Google models work but are second-class Two months old — ecosystem still emerging Hierarchical pattern less flexible than graphs for non-tree workflows Best experience requires Vertex AI commitment Documentation strong but tutorials sparse

Use When

The team is committed to Google Cloud and Gemini; multimodal capabilities are core to the product; managed deployment is preferred over self-hosted orchestration.

Avoid When

Provider flexibility is required; deployment must be cloud-agnostic; ecosystem maturity is a precondition.

07 / 12

LlamaIndex

Data is the agent’s substrate. Retrieval as a first-class concern.

License	MIT
Maintainer	LlamaIndex Inc.
Languages	Python, TypeScript
Architecture	Index · query engine · response synthesizer
Best For	RAG-centric agents, document-heavy workflows

LlamaIndex started as the GPT Index project in 2022, when LangChain was the only general-purpose option and most builders needed something more focused. Its abstractions — indexes, query engines, retrievers, response synthesizers — are designed around document ingestion and retrieval, not orchestration. That focus remains its differentiation. For RAG, LlamaIndex makes the canonical workflow shorter, clearer, and more performant than any general-purpose framework.

The framework added agent capabilities through 2024 and 2025, and they are competent. AgentWorkflows, function-calling agents, and ReAct loops all work. But the orchestration story is not where LlamaIndex earns its keep. The keep is in the retrieval layer: hierarchical retrievers, query rewriting, sub-question decomposition, and response synthesis patterns that handle the messy reality of production document corpora better than the equivalents in LangChain.

Teams shipping production RAG applications in 2026 typically end up with LlamaIndex underneath the retrieval and a different framework — sometimes LangGraph, sometimes a vendor SDK — handling broader orchestration. This is not a weakness; it is the framework correctly scoped to what it does best.

Strengths	Tradeoffs
Best-in-class retrieval primitives for production RAG Mature handling of document ingestion, chunking, and metadata Sub-question decomposition and query rewriting work out of the box Strong evaluation tooling (LlamaCloud) LlamaParse for structured document parsing is genuinely useful	Agent abstractions are competent but not differentiated API has evolved through several major versions; older code samples are misleading Smaller ecosystem than LangChain Multi-agent support emergent rather than designed Best with paid LlamaCloud for evaluation and observability

Use When

The application is primarily RAG; document retrieval quality determines product success; agent orchestration is secondary to retrieval correctness.

Avoid When

The workload is orchestration-heavy, and retrieval is incidental; the team needs a single framework for both retrieval and complex agent flow.

08 / 12

Haystack

Pipelines as data structures. deepset’s production-RAG framework with agent capabilities.

License	Apache 2.0
Maintainer	deepset
Languages	Python
Architecture	Component pipelines (DAG)
Best For	Production RAG with strict pipeline contracts

Haystack predates the LLM agent era — it began as a question-answering framework over Elasticsearch in 2019 — and it shows in the framework’s architectural posture. Pipelines are explicit DAGs of components with typed inputs and outputs. Components are composable units (retriever, reader, ranker, generator) wired together with explicit edges. The pattern is foreign to teams used to the chain-of-thought ergonomics of LangChain but familiar to anyone who has built production data pipelines.

That posture is what makes Haystack the right choice for organizations where retrieval reliability is non-negotiable. Pipelines are inspectable, components are independently testable, and the contracts between components are typed (Pydantic-backed in v2). When a regulated industry — finance, healthcare, legal — needs to audit how an answer was produced, Haystack pipelines are auditable in a way that ReAct loops are not.

Agent capabilities arrived as Haystack 2.x and continue to mature. They sit on top of the pipeline architecture: agents are components that can branch and loop within a pipeline. The result is more constrained than LangGraph’s general state machine but more auditable. For teams whose primary axis of differentiation is retrieval correctness rather than agent flexibility, this is the right tradeoff.

Strengths	Tradeoffs
Pipeline architecture is inspectable, testable, and auditable Pydantic-backed component contracts Strong production deployment story (Deepset Cloud) Visual pipeline builder (deepset Studio) for mixed-skill teams First-class evaluation framework for retrieval quality Multimodal support for document + image workflows	Pipeline-first architecture is less flexible than open agent loops Smaller community than LangChain Learning curve for teams unused to DAG-style composition Agent abstractions feel like an extension rather than the core Best deployed with DeepSet Cloud for full feature parity

Use When

Retrieval correctness is product-critical; the application requires audit trails for how answers were generated; the team values typed contracts and testable components.

Avoid When

The workflow is conversational rather than retrieval-centric; the team prefers ergonomic chain composition over explicit pipelines.

09 / 12

Pydantic AI

FastAPI for agents. Type contracts as the primary abstraction.

License	MIT
Maintainer	Pydantic team
Languages	Python
Architecture	Typed agents with structured I/O
Best For	Production agents with strict input/output contracts

Pydantic AI is the framework most likely to feel like home to a Python engineer who values explicit types and minimal abstraction. The team behind Pydantic — the validation library underpinning FastAPI, OpenAI’s SDK, and most modern Python libraries that handle structured data — built the framework to bring the same posture to agent development. Inputs are Pydantic models. Tool signatures are typed. Outputs are validated. The framework gets out of the way.

The bet is that the developer experience advantage of FastAPI — typed contracts, automatic validation, IDE support, and generated documentation — translates to agent development. That bet has held up well. For teams that have adopted Pydantic and FastAPI elsewhere in the stack, Pydantic AI feels native. The agent abstraction is thin enough that the framework rarely fights you, but explicit enough that the contracts are enforced.

The cost is range. Pydantic AI is excellent for typed, conventional agents. Its multi-agent story is functional but lighter than CrewAI or LangGraph. Its observability is strong — OpenTelemetry instrumentation is built in — but the orchestration features needed for genuinely complex multi-step workflows are less mature than LangGraph’s. For straightforward typed agents shipping in production, the framework is a strong choice. For multi-agent orchestration with branching, it is not the right tool.

Strengths	Tradeoffs
Best-in-class type safety and structured I/O Minimal abstraction — code reads like normal Python Native OpenTelemetry instrumentation Excellent IDE support via type information Model-agnostic, with first-class support for major providers Pydantic Logfire integration for observability	Multi-agent patterns work but lack first-class abstractions Less mature graph/state-machine support than LangGraph Smaller ecosystem and integration library Documentation is strong, but coverage of advanced patterns is thinner Newer than incumbents — fewer production case studies

Use When

The team values typed contracts; the application is a conventional typed agent; FastAPI/Pydantic are already in the stack.

Avoid When

Multi-agent orchestration with complex branching is required; durable execution and checkpointing are non-negotiable.

10 / 12

Semantic Kernel

Microsoft’s enterprise agent framework. Plugin architecture, multi-language SDK.

License	MIT
Maintainer	Microsoft
Languages	Python · C# · Java
Architecture	Kernel + plugins + planner
Best For	Enterprise environments with mixed-language stacks

Semantic Kernel solves a problem most other frameworks ignore: the enterprise reality of polyglot codebases. Microsoft built it for organizations whose business logic lives in C# or Java but who need to add AI capabilities without a Python rewrite. The same kernel abstraction, the same plugin model, and the same planner work across all three SDKs. For organizations with .NET-based ERPs, Java-based banking platforms, or mixed-language legacy stacks, this is a meaningful architectural win.

The kernel is the central abstraction — a runtime that holds plugins (collections of skills, some powered by AI, some by code) and orchestrates calls between them. The planner — a structured Planner component that breaks high-level goals into multi-step plans — is the framework’s distinctive contribution to the agent design space. Where other frameworks let the model decide step by step, Semantic Kernel produces an explicit plan and then executes it. The plan is auditable, modifiable, and re-runnable.

The cost of breadth is depth. Semantic Kernel’s Python SDK feels less ergonomic than Python-native frameworks. Documentation is solid but C#-leaning. The community is large but split across three languages. For a Python-only team, choosing Semantic Kernel over LangGraph or Pydantic AI is rarely the right call. For an enterprise with serious .NET or Java commitments, it is often the only call that aligns with organizational reality.

Strengths	Tradeoffs
Genuine multi-language SDK with consistent APIs Planner abstraction produces auditable, modifiable plans Plugin architecture maps to enterprise software patterns Strong Azure integration (AAD, Key Vault, App Insights) Backed by Microsoft with a long-term support commitment Fits cleanly into existing .NET/Java codebases	Python SDK ergonomics lag the C# experience More verbose than Python-native frameworks for simple cases Documentation and community are split across three languages Best paired with Azure — generic deployment is workable but unloved Innovation pace is slower than that of open-source-led competitors

Use When

The organization has mixed-language codebases (especially .NET or Java); Azure is the deployment target; auditable plan execution matters more than orchestration flexibility.

Avoid When

The team is Python-only; ergonomics matter; the framework is one decision rather than an organizational standard.

11 / 12

smolagents

The agent writes code. A minimalist framework for self-contained Python agents.

License	Apache 2.0
Maintainer	Hugging Face
Languages	Python
Architecture	Code-execution loop with sandboxed runtime
Best For	Self-contained agents that compute or call Python libraries

smolagents takes a deliberately radical position: instead of designing a tool-use protocol, it lets the agent write Python code directly. The agent loop is a minimal cycle — the model is asked to solve a goal, it writes Python, the framework executes the code in a sandbox, and the result feeds back into the next iteration. ReAct-style reasoning is implicit; the framework hides it.

The result is the lowest-friction framework on this list for a specific shape of problem: agents that primarily call Python libraries. Want an agent that does numerical analysis with NumPy? Plot charts with Matplotlib? Run scikit-learn pipelines? smolagents is faster to set up than any alternative because the agent simply writes the code. There is no tool registration, no schema definition, no orchestration boilerplate.

The constraints are real. Code execution requires a sandbox — running model-generated Python in your production environment is not safe, so deployment requires a runtime like E2B, Modal, or a self-hosted container. Multi-agent patterns are minimal. State management is conversational rather than persistent. For self-contained computational agents, smolagents is the elegant choice. For complex orchestration, it is the wrong tool.

Strengths	Tradeoffs
Lowest setup cost for Python-library-calling agents Code-as-tool removes schema definition overhead Hugging Face Hub integration for sharing agents Surprisingly capable for computational tasks Minimal API surface — easy to read all of it in an afternoon	Code execution requires sandbox infrastructure Limited multi-agent support State persistence is minimal Not the right shape for non-computational workflows Production observability less developed than alternatives

Use When

The agent’s primary work is calling Python libraries; the workflow is computational rather than conversational; sandboxed execution is acceptable infrastructure.

Avoid When

Code execution is unacceptable; multi-agent coordination is required; orchestration complexity is real.

12 / 12

AG2

The community fork is carrying the AutoGen lineage forward.

License	Apache 2.0 (forked from AutoGen)
Maintainer	Community-led
Languages	Python
Architecture	Conversational GroupChat with role-based agents
Best For	Teams continuing AutoGen workflows; conversational multi-agent research

AutoGen, originally released by Microsoft Research, was the framework that popularized GroupChat — a multi-agent pattern where agents converse with each other under a coordinator’s direction to solve a problem collaboratively. In 2026, Microsoft announced that AutoGen would move into maintenance mode, with new investment going to the Microsoft Agent Framework. The community responded by forking the codebase as AG2 and continuing the original lineage.

The architectural strength is the conversational pattern itself. GroupChat agents debate, critique, and refine each other’s outputs — a posture that produces strong results for research-style workflows where multiple perspectives matter (technical reviews, multi-stakeholder analysis, structured debate). The model is foreign to teams used to delegation patterns, but powerful in its specific niche.

The pragmatic question is governance. Forks succeed or fail based on the maintainer community that gathers around them. AG2 has visible momentum — active commits, a growing contributor pool, regular releases — but it does not carry Microsoft’s institutional weight. Teams already running AutoGen in production should evaluate AG2 carefully against the Microsoft Agent Framework. Net new agent projects in 2026 should usually choose a framework with more active first-party investment.

Strengths	Tradeoffs
GroupChat pattern is uniquely well-suited to debate-style workflows Direct continuity with existing AutoGen codebases Active community-led development Conversational multi-agent abstractions are mature Open-source governance independent of vendor priorities	No first-party institutional backing Conversational pattern is overkill for non-debate workflows Documentation lags the original AutoGen releases Long-term governance trajectory uncertain Teams new to agents should usually choose more current frameworks

Use When

Already running AutoGen in production and continuity matters; the workflow benefits from conversational debate among agents; institutional vendor backing is not required.

Avoid When

Starting a new agent project; first-party vendor support matters; conversational debate is not the right interaction pattern.

06 · HEAD-TO-HEAD

Direct comparisons: the questions teams actually ask

The questions below are the ones engineering leaders bring to evaluation calls. Each comparison is scoped to the decision, not a feature checklist, but the architectural choice and the conditions under which one framework wins.

Search intent: langchain vs langgraph

LangChain vs LangGraph

Both are LangChain Inc. products, and they are not competitors — they are designed to be used together at different layers of complexity. The framing question is: where does your agent sit on the complexity curve?

LangChain is the right choice for chains, retrieval-augmented generation, and agents that fit a simple ReAct-style loop. The Expression Language (LCEL) makes composition clean and async-native; the integration library is the largest in the ecosystem. For most simple-to-moderate workflows, LangChain ships faster than starting in LangGraph.

LangGraph is the right choice when the agent has a non-linear flow, must survive failure, requires human-in-the-loop checkpoints, or runs long enough that a durable state matters. The graph model makes execution explicit, checkpointable, and replayable — properties that matter in production but are overhead for prototypes.

The migration path between them is short: most LangChain components work inside a LangGraph state machine without modification. Teams typically start in LangChain and migrate the orchestration layer to LangGraph when failure modes start to bite. The decision is rarely which — it is when to move.

Verdict: LangChain for simple cases, LangGraph for everything that gets complex enough to fail in interesting ways. Almost any agent shipped to production benefits from LangGraph’s posture by month six.

Search intent: langchain vs llamaindex · llamaindex vs langchain

LangChain vs LlamaIndex

The cleanest distinction in this space. LangChain is general-purpose; LlamaIndex is retrieval-focused. Both can do what the other does, but each does its primary job better than the alternative does as a side effect.

LangChain wins when the workload is broad — chains, agents, tool use, retrieval, and orchestration combined. Its abstractions are general enough to handle most patterns; its ecosystem is broad enough that nearly any integration exists.

LlamaIndex wins when retrieval correctness is the product. Sub-question decomposition, hierarchical retrievers, query rewriting, response synthesis — these are first-class abstractions in LlamaIndex and add-ons in LangChain. For document-heavy applications, LlamaIndex’s primitives produce better retrieval with less code.

Production teams often use both: LlamaIndex for the retrieval layer, LangChain or LangGraph for orchestration. This is the right answer when retrieval is one component among many. When retrieval is the application, LlamaIndex alone is enough.

Verdict: LlamaIndex for RAG-centric systems, LangChain for everything broader. Hybrid stacks are common and sensible.

Search intent: crewai vs langchain

CrewAI vs LangChain

Different abstraction levels solve different problems. CrewAI is a higher-level framework for a specific multi-agent pattern; LangChain is a general-purpose LLM toolkit.

CrewAI wins when the workflow decomposes naturally into roles. A research agent, a writer, a fact-checker — define them as roles with goals, group them in a crew, and the framework handles delegation and context passing. Working with multi-agent systems in twenty to fifty lines.

LangChain wins when the workflow does not fit a clean role-based decomposition, when you need fine-grained control over the loop, or when the agent system is one component in a larger LLM application. The lower-level abstractions cost more code but accept more shapes.

The wrong question is which framework is better. The right question is whether your workflow fits the role pattern. If it does, CrewAI ships faster. If it does not, fighting CrewAI’s opinions costs more than starting at LangChain or LangGraph.

Verdict: CrewAI for clean role-based multi-agent workflows, LangChain (or LangGraph) for anything else.

Search intent: langgraph vs crewai

LangGraph vs CrewAI

The most common evaluation in mid-2026 is the one most often resolved on the wrong axis.

The wrong question: “Which is more powerful?” Both are capable; both ship to production. Choosing on power favors LangGraph by a small margin and obscures the real decision.

The right question: “Does the workflow fit a role-based pattern?” If yes — planner delegates to researcher and writer, hierarchical review, sequential pipeline of specialists — CrewAI’s abstractions read like the workflow itself. The code looks like the diagram. If no — branching state machines, conditional flows, durable execution, human-in-the-loop checkpoints, replay — LangGraph’s graph model fits the actual shape of the work.

A second consideration: production observability. LangGraph + LangSmith is the strongest open-source-plus-commercial observability stack in the agent space. CrewAI’s observability story has improved but lags. For regulated industries where audit and replay matter, this can be a deciding factor.

Verdict: CrewAI when the workflow is role-shaped; LangGraph when state, branching, or auditability matter. Both are right answers to different questions.

Search intent: langchain alternatives

LangChain Alternatives

The frameworks below replace LangChain for specific use cases. None is a strict superset; each wins on a different axis.

For complex stateful agents: LangGraph (same team, the production successor for non-trivial agents).

For type-safety and minimal abstraction: Pydantic AI.

For RAG-centric work: LlamaIndex or Haystack.

For multi-agent role-based workflows: CrewAI.

For OpenAI-only deployments: OpenAI Agents SDK.

For Claude-only deployments with safety constraints: Anthropic Agent SDK.

For Google Cloud-native multimodal agents: Google ADK.

For computational agents calling Python libraries: smolagents.

For mixed-language enterprise stacks: Semantic Kernel.

Verdict: In 2026, it is rarely LangChain or alternative — it is LangChain (or LangGraph) plus a focused tool for the part of the stack where a focused tool earns its abstractions.

Search intent: pydantic ai vs langchain

Pydantic AI vs LangChain

Different bets on what the framework should optimize for.

LangChain bets on breadth — every integration, every pattern, every model provider. The cost is abstraction layers and surface area.

Pydantic AI bets on contracts — typed inputs, typed tool signatures, typed outputs. The cost is range; multi-agent and complex orchestration are lighter than in LangChain or LangGraph.

For Python teams that have adopted Pydantic and FastAPI elsewhere, Pydantic AI feels native. For teams whose primary axis of value is broad integration and ecosystem familiarity, LangChain remains the safer choice.

Verdict: Pydantic AI for typed conventional agents, LangChain for breadth and ecosystem.

Search intent: openai agents sdk vs langgraph

OpenAI Agents SDK vs LangGraph

The most consequential 2026 evaluation for teams that have committed to OpenAI models. The decision turns on one question: do you accept structural lock-in for first-party convenience?

OpenAI Agents SDK ships shorter — built-in tracing, evaluation hooks, sensible defaults, no third-party setup. For teams already deeply invested in OpenAI, the SDK collapses orchestration setup time meaningfully.

LangGraph preserves provider flexibility, supports complex non-conversational workflows, and pairs with LangSmith for observability that exceeds the SDK’s tracing in depth and replay capability.

Verdict: OpenAI SDK if lock-in is acceptable and the workflow is conventional. LangGraph if provider flexibility, complex orchestration, or auditable replay matters.

Search intent: ag2 vs crewai · autogen vs crewai

AG2 vs CrewAI

Both are multi-agent frameworks; the distinction is the interaction pattern.

CrewAI uses role-based delegation: a planner agent delegates tasks to specialist agents, who return results upward. The pattern is hierarchical and clean.

AG2 (the community fork of AutoGen) uses GroupChat: multiple agents converse with each other under a coordinator’s direction, debating and refining outputs. The pattern is flatter and more emergent.

CrewAI is faster to ship for clearly-defined workflows. AG2 produces stronger results for research-style problems where multiple perspectives must be reconciled. New projects should usually choose CrewAI; teams continuing AutoGen workflows should evaluate AG2 against the Microsoft Agent Framework before committing.

Verdict: CrewAI for delegation, AG2 for debate. New projects should default to CrewAI unless the conversational pattern is specifically what they need.

07 · BY USE CASE

Which framework for which scenario

The framework that wins depends on what the agent does. The mapping below is the team’s recommendation for the use cases we encounter most often in client engagements. Each pick assumes a Python-first team; secondary picks cover common variations.

Use case	Recommended framework
Customer support triage and routing	Primary: OpenAI Agents SDK or Anthropic Agent SDK — handoff and tool-use primitives map directly to triage flows. If model flexibility matters: LangGraph with explicit state for conversation history and escalation paths.
Document Q&A over enterprise corpus	Primary: LlamaIndex for retrieval-centric correctness; Haystack when audit trails and pipeline contracts are required (regulated industries). Avoid: generic LangChain agent loops without explicit retrieval primitives.
Research and report generation	Primary: CrewAI — researcher / analyst / writer roles map cleanly. For debate-heavy synthesis: AG2 GroupChat. For long-running reports with checkpointing: LangGraph.
Multi-step code generation with retries	Primary: LangGraph — conditional edges handle the test/retry/refine loop with replayable state. For computational tasks with library calls: smolagents. For typed contract enforcement: Pydantic AI.
Data analysis agents (numerical, ML pipelines)	Primary: smolagents — code-as-tool eliminates schema overhead when calling NumPy / pandas / scikit-learn. For broader orchestration: LangGraph with code-execution sub-agent.
Browser automation and computer-use agents	Primary: Anthropic Agent SDK — computer use is a first-class primitive; no other framework matches the integration depth. For broader workflow: wrap the Claude SDK as a sub-agent inside LangGraph.
Long-running workflow with human review	Primary: LangGraph — interrupt-resume on graph nodes is the strongest human-in-the-loop pattern in the open ecosystem. For enterprise plan-and-execute: Semantic Kernel planner outputs auditable plans.
Multimodal agents (image, audio, video)	Primary: Google ADK — native multimodal exceeds any other framework. For cross-vendor: LangGraph with model-specific multimodal nodes.
Internal tools and developer-facing automation	Primary: Pydantic AI — typed contracts make the agent’s behavior legible to other engineers. For broader integrations: LangChain.
Mixed-language enterprise environments (.NET, Java)	Primary: Semantic Kernel — only framework with first-class multi-language SDKs. If Python-only is acceptable: LangGraph with REST-exposed agents.

08 · BENCHMARK RESULTS

Three tasks. Twelve frameworks. Reproducible runs.

The benchmarks below are run by Uvik’s Python engineering team across all twelve frameworks against a fixed model endpoint. Each task is defined precisely enough to be reproduced; the run logs and code are available on request. Numerical results are populated in the next refresh and on first publication of the static run.

Status — May 2026

The full benchmark suite is queued for the Q3 2026 run. The methodology, task definitions, and reporting structure below are the standing specification. When numbers populate, we will mark the version as v2026.2 and append a change log.

The three standardized tasks

Task A · Research-and-report pipeline

The agent is given a topic (“Compare the top three managed Postgres services for a fintech workload”), instructed to research it, and asked to produce a 600–800-word structured report with at least five sources cited. Success is binary — does the report meet the structural requirements and contain factually defensible claims — and supplemented by a quality score from a held-out evaluator model.

Task B · Multi-file code generation with retries

The agent is given a specification (“Build a Python CLI that parses a CSV of trades and outputs P&L per ticker, with tests”), expected to generate the implementation and tests, run the tests, and iterate until tests pass or a retry budget is exhausted. Success is measured as the percentage of runs that produce passing tests within five iterations.

Task C · Four-agent debate

Four agents — proponent, opponent, moderator, judge — debate a structured proposition (“This codebase should adopt a monorepo”). The judge produces a final ruling with reasoning. Success is measured by debate coherence (does each turn reference the prior turn?) and by ruling defensibility scored against a held-out rubric.

Metrics tracked per task per framework

1. Latency to task completion — wall-clock seconds from input to final output, p50, and p95 across 100 runs.
2. Total LLM token cost — input + output tokens summed across all model calls in a single run, normalized to USD at standard provider rates.
3. Lines of framework code — Python LOC required to express the task, excluding tool implementations and shared scaffolding.
4. Time-to-first-working-agent — wall-clock minutes for an experienced Python engineer (5+ years, no prior framework experience) to ship a passing implementation, measured across three engineers.
5. Reliability across 100 runs — percentage of runs producing a successful output by the task’s success criteria, with the same prompt and seed.

Test environment

All runs use the same primary model endpoint (Claude Sonnet 4.6) and a held-out evaluator model (GPT-5) for quality scoring. Tests run in a clean Python 3.12 environment with each framework’s latest stable release as of the run date. Runs execute in parallel batches of ten with rate-limited backoff. Token counts are read from provider response metadata; latencies are measured client-side.

09 · METHODOLOGY

How this report was built

This report combines three forms of evidence. The qualitative scoring in the comparison matrix and the framework deep-dives is grounded in architectural review of each framework’s source repository, hands-on builds against the three benchmark tasks, and the team’s experience operating these frameworks in client production environments. The quantitative benchmark results come from the standardized runs described above. The use-case mappings reflect the patterns we encounter most frequently in client engagements at Uvik.

Scoring rubric

Stateful/durable: Strong = explicit state model with checkpointing and replay. Moderate = state expressible, but persistence is the user’s responsibility. Limited = stateless or state requires significant integration.

Multi-agent: Strong = first-class abstractions (roles, crews, GroupChat, hierarchical trees, handoffs). Moderate = expressible without first-class support. Limited = single-agent orientation.

Type safety: Best-in-class = framework is built around type contracts (Pydantic AI). Strong = typed integrations with the major Pydantic surface (Haystack). Moderate = optional typing, supported but not enforced. Limited = types are documentation, not contracts.

Observability: Strong = built-in tracing or first-party tooling that ships out of the box. Moderate = OpenTelemetry hooks or third-party integrations available. Limited = custom callbacks required.

Vendor coupling: Low = model-agnostic, no provider lock-in. Moderate = supports multiple providers but is designed around one. High = single-vendor by design.

Time to first agent: Fast = working agent in under thirty minutes for an experienced Python engineer. Moderate = thirty to ninety minutes. Slow = multi-hour due to setup or learning curve.

Production maturity: High = used in production by mid-market or enterprise customers, stable releases, security advisories handled. Moderate = production deployments exist, but the ecosystem is still maturing. Limited = research or early-stage status.

Reproducibility

The task definitions are versioned with this report. Run logs, prompts, framework code, and evaluator rubrics are available on request to engineering teams considering adoption. Where benchmark numbers are published, every run is reproducible against the same model snapshots and framework versions.

Conflicts and disclosure

Uvik provides Python engineering services and has implemented client production systems on most of the frameworks compared in this report. Uvik does not receive payment, sponsorship, or other consideration from any framework maintainer. The team’s recommendations reflect what we would build on, not what we are paid to recommend.

10 · GLOSSARY

Glossary of terms

Definitions for the key concepts referenced throughout this report. Each entry begins with a definitional sentence designed to be quoted directly when the term comes up in conversation, search, or AI-assisted research.

AI agent

An AI agent is a software system that uses a large language model to plan multi-step actions, invoke tools, observe their outputs, and iterate toward a goal without continuous human intervention.

Agent framework

An agent framework is a Python library that provides the orchestration layer around an LLM API call — the loop, tool integration, memory, state management, and recovery logic that turns a stateless model into a system that can complete multi-step tasks.

Agent loop

An agent loop is the cycle of observe-decide-act-reflect that an agent executes repeatedly until it reaches a goal or hits a stop condition. ReAct is the most widely-implemented pattern of agent loop.

Checkpointing

Checkpointing is the practice of persisting an agent’s state at each step so the agent can resume after a failure, support human review at intermediate points, or be replayed deterministically. LangGraph is the framework in this report with the strongest checkpointing story.

Computer use

Computer use is an agent capability where the model drives a virtual machine — reading screen state and executing keyboard, mouse, and shell actions — through a tool-use interface. Anthropic Agent SDK is the only framework in this comparison with computer use as a first-class primitive.

Durable execution

Durable execution is the property of an agent system that survives infrastructure failure, restarts, and long-running operations without losing state. It typically requires checkpointing plus an external persistence layer.

GroupChat

GroupChat is a multi-agent pattern where agents converse with each other under a coordinator’s direction, debating and refining outputs. It was popularized by AutoGen and is now carried forward by the AG2 community fork.

Handoff

A handoff is a multi-agent coordination primitive where one agent delegates control to another agent. The OpenAI Agents SDK uses handoffs as its primary multi-agent abstraction.

Human-in-the-loop

Human-in-the-loop (HITL) is a pattern where an agent pauses at designated checkpoints for human review or approval before continuing. LangGraph’s interrupt-resume model is the strongest HITL implementation in the open ecosystem.

LCEL

LCEL (LangChain Expression Language) is LangChain’s composition syntax that uses the pipe operator to chain runnable components. It was introduced in 2024 and replaced most of LangChain’s earlier chain abstractions.

Multi-agent system

A multi-agent system is an architecture in which multiple agents — typically with distinct roles, tools, or perspectives — coordinate to solve a problem. Coordination patterns include role-based delegation (CrewAI), GroupChat (AG2), handoffs (OpenAI SDK), and explicit graphs (LangGraph).

RAG

RAG (Retrieval-Augmented Generation) is the pattern of retrieving relevant documents and providing them as context to an LLM to ground its responses. LlamaIndex and Haystack are the frameworks in this report focused primarily on RAG.

ReAct

ReAct is an agent reasoning pattern in which the model alternates between reasoning steps (“thought”) and action steps (“tool call”), with each action’s observation feeding back into subsequent reasoning. Most agent frameworks implement some variant of ReAct.

Tool use

Tool use is an agent’s ability to invoke external functions — APIs, code execution, search, retrieval — by emitting structured calls that the framework executes and returns results from. Tool use is the primary mechanism by which agents interact with the world beyond the model’s training data.

Vendor SDK

A vendor SDK is a first-party agent framework published by a model provider — OpenAI Agents SDK, Anthropic Agent SDK, or Google ADK in this report. Vendor SDKs ship faster but couple agent logic to a single provider.

11 · HOW TO CITE THIS REPORT

For analysts, journalists, researchers

This report is a versioned reference document. Citations should reference the version number and the date of the version, since the framework landscape will continue to evolve and conclusions may differ at the next refresh.

CITATION · APA STYLE

Uvik Software. (2026). Python AI Agent Frameworks: The 2026 Comparison (Version 2026.1).  https://uvik.net/python-ai-agent-frameworks/

Republishing

Excerpts up to 500 words may be republished with attribution to Uvik Software and a canonical link to https://uvik.net/python-ai-agent-frameworks/. For longer excerpts, full republication, or use in commercial reports, contact [email protected]

About Uvik Software

Uvik Software is a Python-first software engineering and staff augmentation firm, founded in 2015 and headquartered in London. We build production AI systems for clients across fintech, healthcare, and enterprise SaaS. Engagements range from staff augmentation with senior Python engineers to the delivery of complete production agent systems.

© 2026 Uvik Software Ltd. · Published under CC BY-ND 4.0 for excerpts up to 500 words with attribution.