Best Arcade.dev Alternatives for AI Agent Tool Calling (2026)

Arcade made a bet early that the rest of the category is still catching up to - that agent tool calling is fundamentally an authorization problem, and that solving it properly through OAuth delegation rather than service accounts and bot tokens is what separates production agents from demos.

That bet was right. The question worth asking now is whether Arcade has built enough around that conviction to serve your use case, or whether a different tool fits better.

Why agent authorization deserves its own infrastructure layer

When developers first wire an LLM to external tools, credentials are usually an afterthought: environment variables, a shared API key, a bot token with admin access. This works until it doesn't.

The problems that arrive in production are predictable:

• Acting as a bot isn't the same as acting as a user. When an agent updates a Salesforce record using a service account, the change is attributed to a bot. Audit logs show machine activity. Approval workflows that expect a human identity break. The action may succeed technically and fail operationally.
• Scope creep is invisible until it causes harm. A credential with broad access doesn't announce what it's doing with that access. In a multi-customer deployment, the risk isn't just a single misconfigured call - it's that one credential can be misused across an entire tenant base before anyone notices.
• Authorization and authentication are different problems. Authn answers: does this agent have a valid credential? Authz answers: is this agent permitted to perform this specific operation, for this specific tenant, in this specific context? Most early-stage agent infrastructure handles authn. Production systems need both.
• Tool schema quality is an execution problem. A well-authenticated agent calling a poorly defined tool still fails. Tool definitions shape how the LLM decides what to call and what arguments to pass. Poorly scoped parameters, ambiguous descriptions, missing field constraints; all of these cause silent failures that look like model problems but are actually schema problems.

This is the problem space Arcade is explicitly trying to own. Worth understanding before comparing alternatives.

What Arcade gets right

Arcade was founded by executives from Okta - people whose entire professional context was enterprise authorization done correctly. That lineage is visible throughout the product.

• OAuth delegation as the core model. Agents act as users through proper OAuth flows; not bot tokens, not service accounts. The agent calls a tool; Arcade verifies the user's delegation, checks their permission scope, executes with exactly what that user authorized.
• MCP-native runtime. Arcade operates as an MCP runtime - tool execution is standardized, reproducible, and decoupled from the orchestration layer.
• Tool evaluation framework. CI/CD-style rubrics for testing tool calling behavior before deployment. This is underbuilt across the category and Arcade's implementation is genuinely useful.
• Custom tool and MCP server SDK. Teams can build tools and entire MCP servers that run on Arcade's runtime, not just consume the pre-built catalog.
• Clean developer experience. The local dev loop is fast. The CLI is well designed.

Where Arcade has gaps

• Catalog coverage is limited. ~112 first-party integrations as of early 2026. For teams with broad integration requirements, this creates real coverage gaps.
• Closed-source runtime. The execution engine is a closed-source binary distributed as a Docker container. You can inspect the SDK and the tools, but you can't inspect what actually runs your tool calls.
• Enterprise governance is still developing. Arcade handles per-user OAuth delegation well. Org-level credential hierarchies, SIEM-exportable audit logs, and per-connector per-org authz configuration are not comprehensive in the current product.
• No data sync or webhooks. Tool calls only. Teams that need continuous context from external systems for RAG pipelines, or event-driven agent triggers, need a second platform.
• Production evidence at enterprise scale is limited. Arcade is building fast and the architecture is sound. The track record of large-scale, multi-tenant enterprise deployments is still accumulating.

Arcade alternatives for 2026

1. Scalekit

Scalekit and Arcade share the same foundational conviction: auth and authz are the right starting point for agent connectivity, not the last thing you add. The difference is where that conviction gets applied and at what scale.

Arcade solves the per-user delegation problem elegantly. Scalekit solves that and the org-level authorization problem that emerges when you're running agents across thousands of enterprise customers, each with different permission requirements and compliance expectations.

Where Scalekit extends the Arcade model:

The authz layer goes deeper. Per-connector scope configuration means you define, per integration, what each org's agents are permitted to do, and that enforcement happens at the infrastructure layer, before the API is touched, regardless of what the agent requests. An agent attempting an operation outside its configured scope doesn't get an API error. It never reaches the API.

Credential isolation is per-tenant by design. Each org's tokens live in an isolated vault. Cross-tenant credential access isn't a misconfiguration risk -it's not architecturally possible.

Developer experience:

• Frameworks: Native adapters for LangChain, Google ADK, Anthropic, OpenAI, Vercel AI SDK, Mastra, Claude Managed Agents, OpenClaw; 8 frameworks with tool schemas returned in each framework's native format
• MCP server: Per-user authenticated MCP URLs, generated by the platform; expose any connected tool over MCP without additional config
• Coding agent plugins: One-command install for Claude Code, GitHub Copilot CLI, Codex, and 40+ agents via Skills CLI (^{npx skills add scalekit-inc/skills}). Full API awareness in your coding agent from the first line
• SDK: Python and Node.js with full type coverage
• Custom connectors: Build custom tools, custom API endpoints, and custom MCP servers - the auth and authz framework applies uniformly across pre-built and custom integrations

Connector catalog: ~300 and growing, built depth-first. Each connector covers the operations agents actually need to complete tasks, not just the most accessible API surface.

The Von case:

Von's agents act inside Salesforce, Gong, HubSpot, Google Drive on behalf of individual sales team members. Every tool call needed: a valid scoped token for that specific user, kept current without human intervention, with no standing agent credentials, producing an audit trail that could survive enterprise procurement review.

"Von touches identity in four places: user auth, embedded SSO, token store for integrations, and an AI tool calling proxy. Having all of that managed by Scalekit behind the scenes is what let us ship fast without stitching together parallel systems."

~ Venu Madhav Kattagoni, Head of Engineering, Von

Each new connector Von added inherited the same auth and authz pattern. The identity layer didn't change. The team spent their time on revenue intelligence, not credential plumbing.

Best fit: Production-grade agent products where the Arcade model is right in principle but needs to scale across many enterprise customers with different permission requirements, compliance expectations, and audit demands.

2. Composio

The trade-off from Arcade is clear: Composio has roughly 10x the integration catalog and dramatically faster time-to-first-tool-call. The auth model is weaker - it works, but it doesn't implement proper OAuth delegation - and the closed-source tools mean you're consuming, not owning, your tool definitions. There's also no per-tenant authorization layer.

Best fit: Prototyping and single-tenant use cases where integration breadth matters more than auth model correctness. For a full breakdown, see Composio alternatives.

3. Nango

Nango approaches the tool calling problem from the integration build layer rather than the auth layer. Tool definitions are TypeScript functions in your repo - you write them (or have a coding agent write them), deploy to Nango's runtime, and Nango handles execution, auth, retries, and rate limiting.

The observability story is the strongest in this comparison: full API request/response visibility, custom log messages, OpenTelemetry export. 700+ APIs supported. Open source. Usage-based pricing that doesn't scale with customer count.

Best fit: Teams that want code-level ownership of their integration logic alongside a managed execution runtime.

4. Merge Agent Handler

Merge's governance and DLP features are mature: PII scanning on request/response, rule enforcement per tool pack, granular audit logs. The compliance story is credible and battle-tested.

The structural constraint: Merge Agent Handler is a layer on top of a Unified API designed for deterministic SaaS integration code. The normalized data schemas strip out API-specific semantics agents need for reliable tool use. Tool definitions aren't configurable per tenant.

The auth model is also meaningfully different from Arcade's: Merge's credential management was designed for developer-initiated integration code, not per-user OAuth delegation for agent-initiated actions.

Best fit: Teams already invested in the Merge ecosystem. Not a natural fit for teams who chose Arcade specifically for its auth model.

5. Paragon (ActionKit)

ActionKit gives agents 1,000+ tools via a single API call. The embedded Connect Portal for end-user authorization is polished. The constraint relative to Arcade: Paragon's auth model reflects its embedded iPaaS heritage. Per-tenant authz enforcement and audit infrastructure are less developed.

Best fit: ISVs where integration breadth and embedded auth UX matter more than auth model correctness.

How to choose

The actual question

The teams most likely to be evaluating Arcade alternatives are not questioning the auth-first premise, they're testing whether Arcade has built enough around that premise for their specific use case.

The gaps that surface most often: catalog coverage that doesn't reach their integration requirements, enterprise governance features that aren't comprehensive enough for their customers' security reviews, or production confidence that comes from track record rather than architecture alone.

The alternatives above each make a different trade-off around the same core problem. The right choice is the one that maps to your actual constraints in production, not the one that wins the feature checklist.