Elva vs Freu: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Elva and Freu — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Elva
Theneo
Reads your repositories to discover every API, scores and governs them, then exposes them to developers and AI agents via hosted MCP servers.
Key features
- Spec-Free API Discovery: Elva scans repository code directly to find endpoints and generates OpenAPI 3.1 as output, so no existing spec is needed to start.
- Endpoint Scoring: Every collection is graded on design, developer experience, AI readiness, security and performance, with the weakest collection surfaced first.
- AI Fix Pass: A one-click agent writes missing descriptions from code, types response schemas and documents auth, then rescores the collection.
- API Contracts: Per-audience contracts pin the exact endpoints and fields a partner, internal team, public developer or MCP client receives, excluding PII and internal fields.
- Breaking Change Enforcement: Each commit is diffed against published contracts, showing the schema diff, affected consumers and tools, and blocking publish by policy.
- Hosted MCP Servers: Contracts generate MCP servers hosted behind Elva's gateway with OAuth2, scoped keys, per-tool authorization and exportable call logs.
- MCP Playground and Agent Feedback: Test the server with a live model, then read the complaints agents file about confusing or failing tools, scored back into the catalog.
- Multi-Target Publishing: One approved contract ships as OpenAPI spec, Theneo docs, MCP server, Postman collection and a typed TypeScript SDK in sync.
Best for
- API Inventory Audit: Discover undocumented or forgotten endpoints across a large codebase and get a ranked list of what to fix first.
- Agent Enablement: Expose an internal service to Claude, Cursor or ChatGPT as a governed MCP server instead of hand-writing tool wrappers.
- Partner Integration Safety: Publish a restricted contract to an external partner and have Elva block commits that would break their integration.
- PII Scoping: Keep customer emails and internal ops annotations out of a public or agent-facing surface while the same endpoints serve them internally.
- Zombie Endpoint Retirement: Prove no active consumer references an endpoint before deleting it, using contract and call-log evidence.
- Enterprise Security Review: Satisfy SOC 2, ISO 27001 and GDPR questions and wire agent access into an existing SSO and SCIM identity provider.
- Documentation Drift Control: Keep docs, SDKs and Postman collections regenerated from code on every merge instead of maintained by hand.
Freu
Freu AI (freu-ai)
Ahead-of-time web automation that records browser sessions and compiles them into reusable deterministic skill commands to reduce agent token use.
Key features
- Ahead-of-Time Compilation: Records a browser session once (via Chrome extension and CDP) and compiles it into a reusable JSON-based DSL skill that agents can execute deterministically.
- Token Usage Reduction: Offloads repeated visual and reasoning steps to compiled programs, reducing LLM/agent token consumption (repo claims up to ~90% savings) and lowering recurring inference costs.
- Chrome Extension + CDP Runner: Captures user interaction and driving Chrome DevTools Protocol commands for precise, reproducible playback and capture of complex UI flows.
- Skill DSL & Artifacts: Emits human- and agent-readable artifacts (SKILL.md and <Cmd>.json steps) that document the workflow, provide structured muscle memory, and enable auditing and reuse.
- Local HTTP Bridge: Runs a Python HTTP service (default 127.0.0.1:8787) to serve skills to agents and orchestrate learn/run cycles programmatically.
- Deterministic Execution: Converts volatile DOM parsing and visual reasoning into stable, deterministic commands so agents can skip expensive visual interpretation.
- Extensibility Toward Desktop: Roadmap includes an OS-level Computer Use Agent (CUA) and vision-based desktop automation to extend AOT pipeline beyond browsers.
- Record browser sessions via a Chrome extension and CDP command runner
- Compile recorded sessions into reusable, deterministic JSON skill files (DSL)
- Local Python HTTP bridge (freu-cli) that communicates with the Chrome extension (default 127.0.0.1:8787)
- Outputs SKILL.md and <Cmd>.json structured steps suitable for agent consumption
- Reduces LLM token usage by delegating repeated deterministic actions to compiled skills
- Logging and intermediate artifact generation during learn/run workflows
- CLI-based workflow: learn (capture + LLM) and run (execute compiled skill)
- Planned extension to OS-level vision-based desktop automation (Computer Use Agent)
Best for
- Automating repetitive web workflows (form submission, navigation, multi-step interactions) by recording once and replaying as a compiled skill.
- Reducing LLM/agent operating costs by converting expensive, repeated web reasoning into deterministic skills loaded into the agent context.
- Building stable enterprise automation where auditability and reproducibility are required—SKILL.md and JSON steps provide documented, inspectable workflows.
- Integrating precompiled skills into agent pipelines via the local HTTP bridge to coordinate when and how skills are executed programmatically.
- Creating reusable automation libraries across teams: capture a complex sales/CRM flow once and share the compiled skill for consistent execution.
- Translating human-performed browser sessions into structured automation artifacts for testing, monitoring, and regression checks.
- Stabilize and accelerate complex enterprise web workflows by precompiling repetitive tasks
- Reduce costs for LLM-enabled agents by offloading deterministic UI interactions to compiled skills
- Build reusable skill libraries for agent-driven automation and RPA-like tasks
- Automated end-to-end test recording and deterministic playback for web applications
- Integrate precompiled web skills into agent context windows to avoid DOM re-parsing