Skip to content
The Agents Index

Compare AI agents

Pick two to four agents and see their prices and features side by side.

175 agents34 head-to-head reviewsupdated 18 October 2026

Build your shortlist

Choose 1 more agent to compare.

Explore this section

Head-to-heads by agent

Head to head

In-depth comparisons

  • Comparison 01

    Gemini Code Assist vs Juggler

    Gemini Code Assist suits Google Cloud teams that want a managed IDE assistant with SSO; Juggler suits developers who self-host and pick their own model.

    Verdict Pick Gemini Code Assist if your team works in VS Code or JetBrains, already runs on Google Cloud and needs single sign-on and an audit log from the vendor. Be ready to stay on Gemini models. If multi-file agent mode is the reason you are buying, it is on both editions but still a preview. Pick Juggler if you want to choose and switch models yourself, including Claude or open models on your own hardware, and want to see every tool call the agent makes. It suits a developer comfortable running alpha software on their own machine, beside the editor rather than inside it, under an AGPLv3 app license.

    Read the verdict →

  • Comparison 02

    LangGraph vs Mastra (2026): Agent Framework for Your Stack

    For developer or technical buyers choosing between LangGraph and Mastra, two open-source agent frameworks that meet at the TypeScript-Python line.

    Verdict Choose LangGraph if your team is standardized on Python, you are building a long-running production agent that has to survive restarts and replay from prior checkpoints, and you want the LangSmith deployment and tracing layer behind it. Choose Mastra if you are a full-stack TypeScript or JavaScript team shipping on Next.js, Node or serverless, and you want agents, durable workflows, memory and observability unified in one framework without a separate Python service.

    Read the verdict →

  • Comparison 03

    LangChain vs LangGraph

    LangChain vs LangGraph: same company, one layer built on the other. When to use create_agent and when to drop down to the graph directly, compared live.

    Verdict LangChain, specifically create_agent, for most people building a standard tool-calling agent. You get a working agent in a few lines, the largest integration ecosystem in the space, and you are not giving up any of LangGraph's durability, persistence or human-in-the-loop support in the trade, since create_agent runs on that engine already. If your workflow is a model, some tools, a system prompt, maybe some middleware, this is the layer to start on, and most teams never need to leave it. Pick LangGraph directly if you are building something the harness cannot express: a workflow that mixes deterministic steps with agentic ones in a specific order, a custom state schema or checkpoint strategy, several coordinated agents with a topology create_agent does not offer as a preset, or infrastructure you intend to expose to other developers the way create_agent itself was built on this engine. Writing the graph by hand costs more code, and it buys the control that a pre-wired harness intentionally trades away. The mistake runs in both directions. Starting on raw LangGraph for a standard tool-calling agent means hand-building durability, checkpointing and human-in-the-loop wiring that create_agent already gives you for free, for no control you end up using. Staying on create_agent once your workflow needs a graph topology, state shape or deterministic-plus-agentic sequencing the harness was not built to express means fighting the abstraction instead of dropping one layer down to the tool built for exactly that job.

    Read the verdict →

  • Comparison 04

    Microsoft Agent Framework vs Google ADK

    Microsoft Agent Framework vs Google Agent Development Kit: the two major-lab, protocol-forward agent SDKs, compared on language spread, production tooling and pricing, with real 2026 GitHub stats and a clear pick for your cloud.

    Verdict Already standardized on Azure or Microsoft Foundry, or migrating an existing AutoGen or Semantic Kernel codebase, pick Microsoft Agent Framework: it has a documented first-party migration path and is the supported successor rather than an ambiguous third option. Already on Google Cloud or Gemini, or building a genuinely polyglot system across Python, Go, Java and TypeScript, pick Google ADK: it is the only framework in this category shipping four production-grade language SDKs. Need an agent that can safely act inside a real environment today, with shell access, filesystem access and human approval, backed by a measured performance benchmark: MAF's Agent Harness and CodeAct are further along than anything ADK ships natively. Need a strong built-in evaluation loop before trusting an agent in production: ADK's `adk eval` and ADK Web are purpose-built for exactly that, ahead of MAF's current tooling. Neither commits a team to its parent cloud. MAF reaches Anthropic Claude and local Ollama models alongside Azure OpenAI; ADK reaches OpenAI, Claude, Cohere and local models via LiteLLM alongside Gemini. The honest deciding factor is which hyperscaler ecosystem, and which language line-up, a team is already committed to.

    Read the verdict →

  • Comparison 05

    Microsoft AutoGen vs Microsoft Agent Framework

    Microsoft AutoGen vs Agent Framework: why AutoGen is now frozen, what its official successor adds (Agent Harness, CodeAct, Foundry Hosted Agents), and how to plan a 2026 migration.

    Verdict Already running AutoGen in production and it's doing its job, with no near-term need for the newer capabilities → keep it; it still receives security fixes, and Microsoft's own migration guide means you aren't locked in if that changes. Actively extending an AutoGen codebase, or want to keep its exact API without a rewrite → look at the AG2 fork first: it's shipping real commits weekly and controls the original PyPI packages. Starting any new multi-agent project, or need production-ops features AutoGen never shipped (guarded shell/filesystem access, human-in-the-loop approval, managed hosting, native MCP/A2A support) → Microsoft Agent Framework; it's the stack Microsoft is now putting new engineering hours into. Running a large, mature AutoGen deployment with no urgent trigger to move → plan a deliberate migration using Microsoft's own from-AutoGen guide rather than a rewrite. Frozen is not the same as abandoned, and there's no cost pressure forcing an immediate switch.

    Read the verdict →

  • Comparison 06

    Glean vs Moveworks

    Glean vs Moveworks: an independent, model-agnostic Work AI platform against a ServiceNow-owned assistant. Ownership, models and pricing read live in August 2026.

    Verdict Glean, for the buyer who is not already standardised on ServiceNow. It is the neutral choice in a decision that has stopped being neutral: an independent company whose roadmap answers to its own board, a model layer you control from a console rather than inherit, and a core asset (a permission-aware index of everything your company knows) that stays useful no matter which assistant, agent or platform you put in front of it next year. Buying your knowledge layer from a company that also wants to sell you the workflow platform underneath it is a bet on that platform, and if you have not otherwise made that bet, this is a poor way to make it by accident. Pick Moveworks in three cases, and the first is close to decisive. First, if you already run ServiceNow: Moveworks is now first-party, ServiceNow disclosed 250 customers already running both technologies when the deal closed, and EmployeeWorks is a supported path from an employee’s question to a ServiceNow workflow that no independent vendor can match on paper. For this buyer the acquisition is not a risk to price in: it is the reason to buy, and it is worth more than any feature difference on this page. Second, if the problem you are funding is ticket volume rather than knowledge discovery: Moveworks is built to close IT, HR and finance requests end to end, and it is the only side of this pair that publishes deflection as its headline evidence, including CVS Health at a 50% reduction in live agent chats in under 30 days. A service desk drowning in password resets and PTO questions should buy the tool whose customers publish that number. Third, if you are a regulated enterprise buying on counterparty risk as much as capability: a vendor owned outright by a public company carries a different risk profile into a procurement review than a private one at a $7.2 billion paper valuation, and for some risk committees that difference ends the conversation before the features start. Stated as the two mistakes. Buying Moveworks while running Okta, Jira and Slack with no ServiceNow anywhere means paying for an integration advantage you will never collect, on a roadmap now set by a platform vendor you are not otherwise a customer of. Buying Glean because it is the independent one, when your real problem is forty thousand password-reset and PTO-balance tickets a year and your service desk already runs on ServiceNow, means buying an excellent index and still having the tickets.

    Read the verdict →

  • Comparison 07

    Ada vs Decagon (2026): Compare the Two Enterprise AI Support Agents

    Two of the largest AI customer service platforms, side by side. This page lays out Ada and Decagon's pricing, configuration model and developer surface so an enterprise support team can choose the right one.

    Verdict Choose Ada if you want a public API and MCP server, a $72,000/year median contract, and an established customer base, and if you are willing to live with structured Playbooks and to weigh the Trustpilot end-user warning. Choose Decagon if you want a newer, better-funded product with plain-English AOP configuration, deep named-customer case studies and the budget for a roughly five-times-higher median contract.

    Read the verdict →

  • Comparison 08

    Ada vs Fin (2026): enterprise platform vs $0.99 per resolution

    A head-to-head for enterprise support leaders choosing between Ada's custom-quoted platform and Fin's per-resolution self-serve agent, settled on procurement, billing shape and ownership of the helpdesk, not on which model is "smarter."

    Verdict Choose Ada if you want a single enterprise platform with a 10-year track record, can run a sales-led procurement with custom contracts in the six figures, and want the helpdesk, the agent and the improvement loop on one bill. Choose Fin if you already run a major helpdesk, want transparent per-resolution pricing and a 14-day trial, and can tolerate the open question of what happens to non-Salesforce customers when the Salesforce deal closes.

    Read the verdict →
All 34 comparisons

Suggested

The top two in each category

  1. 01 · Coding agents

    Orbi vs Modurian

    All 30 agents in Coding agents →

  2. 02 · Support agents

    Mixdesk vs AiZipZap

    All 14 agents in Support agents →

  3. 03 · Sales & marketing agents

    Copy.ai vs Gong

    All 21 agents in Sales & marketing agents →

  4. 04 · Research & data agents

    SpecStream vs jobfinder-ai

    All 14 agents in Research & data agents →

  5. 05 · Data analysis agents

    ThoughtSpot vs Hex

    All 4 agents in Data analysis agents →

  6. 06 · Industry agents

    EximAgent vs GC AI

    All 2 agents in Industry agents →

  7. 07 · Voice agents

    VoiceFleet vs Kalyvox

    All 23 agents in Voice agents →

  8. 08 · Agent frameworks

    Langflow vs LangGraph

    All 16 agents in Agent frameworks →

  9. 09 · Agent platforms

    Blits.ai Agentic AI vs Novastart

    All 34 agents in Agent platforms →

  10. 10 · Agent tools & infrastructure

    API.market vs ProofFetch

    All 13 agents in Agent tools & infrastructure →

  11. 11 · AI workflow automation

    Make vs Zapier

    All 11 agents in AI workflow automation →

  12. 12 · Agencies & build partners

    Markovate vs Azumo

    All 14 agents in Agencies & build partners →

  13. 14 · MCP servers

    Context7 vs SkaleAgents

    All 4 agents in MCP servers →

  14. 15 · AI content & video tools

    Midjourney vs Descript

    All 5 agents in AI content & video tools →

  15. 16 · AI apps & tools

    Midjourney vs Runway

    All 2 agents in AI apps & tools →

Missing a comparison? Tell us which two agents to compare, or list your own.

Request a comparison →Submit it free →