Last updated: 2026-06-19
Archal tests AI agents against sandboxed GitHub, Slack, and Stripe clones before production. Free: 500 session-minutes and 100 evals. Pro $199/seat/month.
Archal is a YC S26 eval platform for autonomous software, founded in 2026 in San Francisco. It provisions sandboxed clones of GitHub, Slack, Stripe, and 4 other SaaS platforms so teams can test AI agents in realistic environments before deployment. Pricing starts free at 100 evals and 500 session-minutes per month, with Pro at $199 per seat per month.
Archal is a testing and evaluation platform for AI agents that interact with real-world SaaS services. Founded in 2026 by Noah Song and Aidan Tiruvan as part of Y Combinator's Summer 2026 batch, the San Francisco startup targets a critical gap in the AI agent development cycle: teams have no safe way to test agents before deploying them to production environments where a single incorrect action can trigger a payment, delete a database row, or push bad code to a repository. The platform's core mechanism is stateful service cloning. Archal provisions sandboxed copies of 7 SaaS platforms (GitHub, Slack, Stripe, Linear, Supabase, Discord, and Google Workspace) that replicate the real API surface, including the same endpoints, error semantics, and rate limits as the production service. Unlike simple mocks that return static responses, Archal's clones hold state across requests and model the business logic and object relationships agents will encounter in the real system. Each clone is provisioned in under a minute and is automatically torn down after the test run. Developers write test scenarios as markdown files that capture the clone's starting state, the task the agent should perform, and what counts as success. Scenarios live in the repo alongside agent code and get reviewed in pull requests. During each run, Archal records every tool call, API request, response body, and state change, producing a complete trace that teams can replay, diff against earlier runs, and share for debugging. The CI integration automatically breaks the build when behavior regresses, moving quality enforcement left in the development process. Archal supports MCP (Model Context Protocol) tools and REST routes for accessing cloned service APIs, making it compatible with the agent frameworks that have adopted MCP in 2026 including Claude Code and Cursor. Pricing includes a free tier with 100 evals and 500 session-minutes per month, a Pro tier at $199 per seat per month with 500 evals and 5,000 session-minutes, and custom Enterprise plans with 50 concurrent sessions, SAML SSO, and SCIM provisioning. Overage usage is billed at $0.05 per session-minute and $0.20 per eval.
Free: $0/month (500 session-minutes, 100 evals, 3 concurrent sessions). Pro: $199/seat/month (5,000 session-minutes, 500 evals, 10 concurrent sessions). Enterprise: custom pricing with unlimited resources, 50 concurrent sessions, SAML SSO, SCIM. Overages: $0.05/session-minute, $0.20/eval.
Archal is an evaluation platform for autonomous AI software, backed by Y Combinator's Summer 2026 batch and built by Noah Song and Aidan Tiruvan in San Francisco. The platform lets developers test AI agents against stateful, sandboxed clones of real SaaS services like GitHub, Slack, and Stripe, catching behavioral bugs before they cause irreversible damage in production. As AI agents gain the ability to write to databases, trigger payments, and push code to repositories, teams face a critical gap: the only way to see what an agent does in production is to put it in production. Archal solves this by provisioning service-shaped clones that replicate the real API surface, including endpoint behaviors, error semantics, and rate limits, in under a minute. Developers write test scenarios as markdown files that live in the repo, get reviewed in pull requests, and run in CI like any other test. The platform captures full traces of every tool call, API request, and state change, so teams can debug failures and compare runs over time.
Archal offers three tiers in 2026: Free, Pro, and Enterprise. The Free tier is $0 and includes 500 session-minutes, 100 evals, and 3 concurrent sessions per month, enough to run basic regression tests on small agent projects. The Pro tier costs $199 per seat per month and includes 5,000 session-minutes, 500 evals, and 10 concurrent sessions. Enterprise pricing is custom and adds unlimited resources, 50 concurrent sessions, SAML SSO, and SCIM provisioning for large teams. Overages are charged on all plans at $0.05 per session-minute and $0.20 per eval, so teams with unpredictable testing volumes should monitor usage carefully to avoid surprise bills. The Pro tier may feel expensive for individual developers at $199 per seat per month, but is reasonable for teams shipping multiple production agents that need reliable CI-level testing.
Archal's core feature is stateful service cloning: it provisions sandboxed copies of GitHub, Slack, Stripe, Linear, Supabase, Discord, and Google Workspace using the real API surface, so agents see the same endpoints and error codes they would hit in production. Scenarios are written as markdown files that capture starting state, task requirements, and success criteria; these live in the repo and get reviewed in pull requests like any other test code. Full trace capture records every tool call, API request, response body, and state change during each run, giving teams a complete audit log to diff against previous runs and debug behavioral regressions. CI integration lets Archal break the build automatically when agent behavior changes, shifting quality enforcement left in the development process. The platform supports MCP (Model Context Protocol) tools and REST routes for accessing cloned APIs, making it compatible with Claude Code, Cursor, and other MCP-native agent frameworks. Archal's four-step workflow is: write scenarios, run against service clones, capture traces, and fail in CI.
Yes, Archal has a free tier that includes 500 session-minutes, 100 evals, and 3 concurrent sessions per month with no credit card required. The free tier is sufficient for individual developers running light regression tests on agents that interact with one or two external services. Overages beyond the free allocation are billed at $0.05 per session-minute and $0.20 per eval, so watch usage if you run many sessions. The free tier does not include enterprise features like SAML SSO or SCIM provisioning, and does not increase concurrent session limits beyond 3. Teams running frequent CI tests against multiple agent workflows will likely exhaust the free tier's 100 evals quickly and need to upgrade to Pro at $199 per seat per month. There is no publicly documented free trial of the Pro tier as of June 2026.
The closest alternatives to Archal are LangSmith and Braintrust, which focus on LLM tracing and evaluation but do not provide stateful API clones of real SaaS services. LangSmith is built around the LangChain framework and offers automated tracing, prompt experimentation, and CI evaluation; choose it if your agents are built on LangChain and you need framework-native observability at the model layer. Braintrust covers the full LLM development cycle (prompt experiments, CI evals, production observability) with a free tier of 1 million trace spans; choose it if you need general-purpose eval without SaaS sandbox infrastructure. Confident AI focuses on LLM quality and safety metrics rather than API-surface behavioral testing. For teams that do not need stateful sandboxes, manual mocks with tools like WireMock remain viable but require significant setup time. Archal's unique approach is the stateful service clone, which none of these alternatives currently replicate.
Archal is best for engineering teams that have already shipped an AI agent to production and experienced the consequences of an untested API call (a duplicate payment, an incorrect code commit, or a deleted record). It is particularly valuable for platform engineers building shared agent testing infrastructure and for AI developer teams shipping agents that write to GitHub, Slack, Stripe, Linear, Supabase, Discord, or Google Workspace. DevOps engineers who own CI pipelines for autonomous software will find the build-breaking CI integration immediately useful. The tool is not ideal for data scientists evaluating LLM outputs in isolation (use Braintrust or a model-level eval tool instead) or for developers building agents that do not interact with external APIs. Individual developers on tight budgets may find the Pro tier at $199 per seat per month expensive relative to their testing volume. Teams using SaaS services beyond Archal's current 7-service catalog will need to supplement with custom mocks until Archal expands its clone library.
Getting started with Archal requires an account at archal.ai; the free tier needs no credit card and is available immediately. Once signed in, write your first scenario as a markdown file that defines the clone's starting state, the task your agent should execute, and the success criteria for a passing run. Add the scenario to your repo so it lives alongside your agent code and can be reviewed in pull requests. Connect your agent to Archal and point it at the target service (GitHub, Slack, Stripe, or one of the other 6 supported services); Archal provisions the sandbox clone in under a minute. Run the scenario, then review the captured trace in the Archal dashboard to confirm the agent behaved correctly or to debug failures. Once the scenario is working, add Archal to your CI pipeline so the build breaks automatically when behavior regresses on future code changes.
LangSmith and Archal both target teams building AI agents, but they solve different layers of the testing problem. LangSmith focuses on observability and evaluation of LLM outputs: it traces LangChain applications, lets you run prompt experiments, conduct automated evaluations, and monitor production behavior at the model level. Archal focuses on behavioral safety at the API integration layer: it tests whether an agent takes correct actions with external services like GitHub, Slack, and Stripe, not just whether it generates correct text. Pick LangSmith if your primary concern is prompt quality, output consistency, and LangChain-native tracing. Pick Archal if your agent is already generating correct outputs but you need to verify it takes safe, correct actions with real third-party services before deploying. The two tools are complementary: LangSmith owns the LLM eval layer while Archal owns the API integration safety layer. LangSmith's Developer plan is free up to a usage limit, while Archal's Pro tier starts at $199 per seat per month.