Best Merge.dev Alternatives for AI Agent Connectivity (2026)

Merge earned its position. Thousands of B2B SaaS teams use Merge Unified API to stop reinventing HRIS, ATS, CRM, and ticketing integrations - one API, 220+ providers, maintained by someone else. The enterprise brand is real. The support is strong.

The question isn't whether Merge is good at what it was built for. It's whether what it was built for - normalized, deterministic API integration for SaaS products, is the right foundation for the agent use cases now landing on those same teams.

The architecture mismatch worth understanding

Merge earned its position. Thousands of B2B SaaS teams use Merge Unified API to stop reinventing HRIS, ATS, CRM, and ticketing integrations — one API, 220+ providers, maintained by someone else. The enterprise brand is real. The support is strong.

The question isn't whether Merge is good at what it was built for. It's whether what it was built for — normalized, deterministic API integration for SaaS products — is the right foundation for the agent use cases now landing on those same teams.

When you're building deterministic SaaS integrations, normalization is a feature. One schema for "Employee" across Workday, BambooHR, and Personio means you write integration logic once. The variation is abstracted away. That's the unified API value proposition and it's legitimate.

Agent tool calling has different requirements:

• LLMs need semantic richness, not normalized abstractions. An agent working with Salesforce needs Salesforce concepts: pipeline stages, opportunity fields, activity types. When those are normalized into a generic CRM schema, the specificity the model needs to reason correctly gets compressed out. The result isn't just a less elegant tool definition — it's a model that calls tools with wrong parameters, misunderstands field semantics, or falls back to asking clarifying questions that shouldn't be necessary.
• Agents operate probabilistically, not deterministically. A SaaS integration that runs on a schedule with fixed inputs can tolerate schema ambiguity. An agent deciding dynamically which tool to call, with which arguments, based on natural language input cannot. The tool definition is the contract between the model and the outside world. Imprecision in that contract shows up as agent behavior failures.
• Authorization for agents is a different problem from authorization for integration code. Merge's auth model was designed for developer-initiated API calls; your application connecting to your customer's Workday. Agent auth requires per-user delegation: the agent acts as a specific person, with that person's credentials, limited to what they authorized. These are structurally different.
• Multi-tenant tooling needs per-tenant configuration. Different enterprise customers have different field structures, different permission hierarchies, different requirements for what agents should and shouldn't be able to do. Unified API normalization, by design, applies identically across all tenants.

What Merge gets right

• 220+ integrations across HRIS, ATS, CRM, ticketing, accounting, file storage: deep category coverage with real maintenance behind it
• Normalized data models: significant value for teams building deterministic integration logic across many providers
• Enterprise compliance: SOC 2 Type II, ISO 27001, HIPAA, GDPR; battle-tested, not marketing
• White-glove support: post-sales teams that understand the integrations, not just the platform
• Merge Agent Handler: launched October 2025, adds governed agent access with DLP, PII scanning, rule enforcement, tool packs, and audit logs

For teams whose agent use cases map to shallow reads and writes across many providers, Agent Handler is a credible extension of existing infrastructure.

Where Merge hits its limits for agent use cases

• Pricing at agent scale. $650/month for 10 production linked accounts, then $65 per linked account. In a multi-tenant agent product with hundreds of enterprise customers, each connecting multiple systems, this compounds into a significant infrastructure cost.
• Tool definitions aren't configurable per tenant. Every customer gets the same tool behavior. No per-org scope configuration, no tenant-specific field mappings, no custom validation per customer.
• No custom tool authoring. Merge's pre-built tools are maintained by Merge. If a tool doesn't cover the operation your agent needs at the right granularity, you can't modify it.
• The iPaaS-to-agent retrofit gap. Merge Agent Handler is well-built for what it is: a governance and DLP layer on top of an existing Unified API. The agent-specific concerns — LLM-optimized tool schemas, per-user OAuth delegation, authz enforcement at the tool level — weren't the foundation the product was built from.

The broader pattern: iPaaS coming to agents

Merge, Nango, and Paragon all built serious integration infrastructure for a specific problem: SaaS product teams that need their customers to connect third-party systems reliably. That problem is real and they solved it well.

Agent connectivity is adjacent but not identical. The infrastructure underneath a deterministic integration — schedule-based sync, fixed schema, developer-initiated — differs in meaningful ways from what an agent needs at tool-call time: per-user delegation, scope enforcement per operation, LLM-optimized schema, sub-second execution with retries.

The question when evaluating any of these products for agent use cases: how much of what they've built reflects the agent execution model natively, and how much is capable integration infrastructure with an agent API on top?

Merge alternatives for agentic tool calling

1. Scalekit

Scalekit was designed from the start around the agent execution model, not as an extension to an existing integration product but as infrastructure specifically for agents acting on behalf of users in multi-tenant environments.

The execution model:

• Credential resolved from an AES-256 encrypted per-tenant vault; no raw token in agent code
• Scope verified against per-connector, per-org configuration — is this agent permitted to call this operation for this org, right now
• API executed with auto-refreshed tokens; retries and rate limiting handled transparently
• Full delegation chain logged, i.e., agent, connector, scope, tenant; result, SIEM-exportable

Per-connector scope configuration means different orgs can have different permission profiles for the same integration. Enforcement happens before the API is touched. An agent attempting an operation outside its configured scope doesn't receive an API error — the request never leaves the platform.

What teams can build on top: Beyond pre-built connectors, teams can bring custom tools, custom API endpoints, and custom MCP servers into the same auth and authz framework. The credential management and scope enforcement apply uniformly — pre-built and custom integrations treated identically by the security layer.

Developer experience:

• Frameworks: Native adapters for LangChain, Google ADK, Anthropic, OpenAI, Vercel AI SDK, Mastra, Claude Managed Agents, OpenClaw — 8 frameworks, tool schemas in each framework's native format, no reshaping
• MCP server: Per-user authenticated MCP URLs generated by the platform; any connected tool exposed over MCP without additional configuration
• 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})
• SDK: Python and Node.js

Connector catalog: ~300 and growing toward 400, built depth-first. Each connector covers the operations agents actually complete in production, not a surface-level wrapper that passes an integration count audit.

From Von's engineering team:

Von's agents act across Salesforce, Gong, HubSpot, Google Drive, and more on behalf of individual sales team members. The identity challenge was running across four credential surfaces simultaneously, per user, with enterprise-grade auditability.

"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 integration Von added inherited the same auth and authz pattern. New connector, same layer. Enterprise security reviews answered by default.

One note for teams building user-facing products alongside agents: the same team also builds enterprise identity infrastructure — SSO, SCIM, user and org management. Running both on the same platform is an option for B2B products where "who is this user" and "what can their agent touch" need to be coherent answers from the same system.

Best fit: Production-grade agent products where per-tenant authz, depth of connector coverage, and enterprise-grade audit infrastructure matter from the start.

2. Nango

The most compelling alternative on customization and pricing. Nango is open source; tool definitions are TypeScript functions in your repo; pricing is usage-based rather than per-linked-account.

The observability story is the strongest in this comparison: full API request/response logs, custom log messages, OpenTelemetry export. 700+ APIs. Code-first: write a TypeScript function, deploy to Nango's runtime, iterate with Claude Code or Cursor directly in your repo.

The trade-off: you're authoring integration logic rather than consuming pre-built tools. The ceiling is higher; so is the floor.

Best fit: Teams that need to own their integration code, have high observability requirements, and want pricing that doesn't scale with customer count.

3. Composio

Composio is the agent-native option with the largest pre-built catalog: 1,000+ integrations, framework adapters across LangChain, CrewAI, OpenAI Agents SDK, and more. Time-to-first-tool-call is measurable in minutes.

The gap relative to Merge: enterprise compliance and governance features are less mature. Closed-source tools, limited observability, no per-tenant configuration. The auth model handles token storage and refresh; it doesn't enforce authz at the tool level.

Best fit: Teams coming from Merge who want agent-native breadth and are willing to accept weaker governance in exchange for faster integration coverage.

4. Arcade

Arcade is the most principled auth-first option in this list. The Okta founding team DNA shows: agents act as users through proper OAuth delegation, not service accounts, not bot tokens. The tool evaluation framework is a genuine capability gap-filler that Merge Agent Handler lacks.

Limitations: ~112 first-party integrations. Closed-source runtime. Enterprise governance still developing.

Best fit: Teams where auth model correctness is the primary driver and current integration requirements fit within Arcade's catalog.

5. Paragon (ActionKit)

Paragon rebuilt around AI in 2025. ActionKit is a single API returning 1,000+ integration tools, with a white-labeled Connect Portal that's as polished as anything in this space. For teams moving from Merge because of pricing at scale, Paragon tends to be more favorable at growth-stage customer counts.

The constraint: similar per-tenant customization limitations as Merge — pre-built tools, no per-org configuration.

Best fit: ISVs moving from Merge who need similar integration breadth with a better pricing model at scale and for whom end-user connection UX is a product requirement.

How to choose

The honest summary

The teams most likely to be evaluating Merge alternatives for agent use cases fall into two groups.

The first is already on Merge and evaluating whether Agent Handler is the right path. For standard use cases that map to Merge's existing catalog and don't require per-tenant tool configuration, Agent Handler is a reasonable extension. For anything requiring deeper authz control, custom tool behavior, or agent-native architecture, it's worth evaluating from scratch.

The second group is building agent products and considering Merge because of the enterprise brand and compliance story. The compliance story is real. The question is whether the unified API abstraction serves your agent use case — or whether you're optimizing for a familiar brand when the architecture isn't the right fit.

The evaluation question in both cases: does your agent need to act on behalf of users with their own credentials, scoped to their permissions, with audit evidence per action? If yes, that requirement should drive the selection — not which product has the longest integration list.