AEVS vs InsForge: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of AEVS and InsForge — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
AEVS
Fetch.ai
Open-source SDK that creates tamper-evident, cryptographically signed receipts for every tool call an AI agent makes.
Key features
- Signed Receipts: Records every tool call and seals it with an ECDSA P-256 signature backed by KMS.
- Hash-Chained Logs: Links each receipt to the previous one so tampering or skipped steps are detectable.
- Independent Verification: Confirms signatures via a public API or explorer using only a reference ID.
- Drop-In SDK: Installs with pip and wraps existing tools without changing them.
- Framework Auto-Detection: Automatically integrates with LangChain and MCP-based agents.
- Open Source: Released as fetchai/AEVS-sdk for Python 3.10–3.13.
Best for
- Agent Auditing: Keep a verifiable record of exactly what an agent did and when.
- High-Stakes Actions: Prove execution of sensitive operations such as payments or refunds.
- Compliance Evidence: Provide tamper-evident logs for regulated or accountable workflows.
- Debugging Agents: Inspect tool inputs, outputs, timing, and errors for each call.
- Third-Party Verification: Let external parties confirm an action occurred without sharing source code.
InsForge
InsForge
Backend platform built for AI-assisted development, providing auth, database, storage, functions, and agent-focused AI integrations.
Key features
- Authentication & Authorization: Managed user auth system to add sign-up, login, and role-based access controls quickly, enabling secure agent and user interactions without custom auth development.
- Managed Database: Hosted relational database capabilities (Supabase-like) designed for agent-driven schemas and operations, allowing agents to read, write, and migrate data programmatically.
- Object Storage: Built-in storage for files and assets with APIs for upload, download, and access control, simplifying how agents handle media and persistent artifacts.
- Serverless Functions: Deployable functions to run custom business logic and glue code, enabling agents to invoke or extend backend behavior with server-side code.
- AI Agent Integrations: Native connectors and integration points to link any AI agent to backend services, allowing agents to orchestrate data, storage, and functions autonomously.
- Agent-Native Tooling: Developer SDKs and APIs optimized for agent workflows, enabling rapid connection of agents to backend resources and simplifying agent-driven app development.
- Rapid Provisioning: Ability to add authentication, database, storage, functions, and AI integrations to apps in seconds, accelerating prototyping and deployments for agent-enabled products.