Forsy vs LoopX: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Forsy and LoopX — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
%20(1200%20x%20404%20px)%20(21).png){kind=small}
Forsy
Forsy (Forsy-AI)
A platform and open trace format for AI agents to capture, share, and learn from structured real-world work experience.
Key features
- Structured Trace Capture: Records complete agent workflows as structured trajectory data including task context, timestamps, step traces, and tool invocations to make processes inspectable and reproducible.
- Annotated Reasoning Signals: Captures intermediate reasoning artifacts (observations, thoughts, decisions) so researchers and developers can analyze agent cognition and debugging points.
- Tool and Artifact Logging: Logs concrete tool usage, generated artifacts, and outputs from external systems to connect actions with outcomes for audit and post-hoc analysis.
- Human Feedback & Failure Signals: Annotates human corrections, feedback, retries, failures and recovery steps to support supervised fine-tuning, evaluation, and safety analysis.
- Open Skill Format & SDKs: Provides an open, shareable trace schema and skill implementations (e.g., npm / Python components) to integrate with different agent frameworks and pipelines.
- Dataset & Research Support: Enables creation of labeled, inspectable datasets from real agent runs to support evaluation benchmarks, training data, and reproducible experiments.
- Structured trace format capturing agent task context and full step-by-step trajectories
- Records tool usage, observations, internal reasoning signals, and human feedback
- Logs failures, retries, artifacts, and final outcomes for workflows
- Provides a schema directory and example datasets for standardized trace representation
- Published as an open-source repository with MIT license
- Distributed via GitHub with package.json (npm) metadata for integration
- Includes docs, examples, scripts, and dataset folders to support adoption
- Designed to support evaluation, post-training, and research workflows
Best for
- Agent Training Data Generation: Converting completed agent workflows into structured traces to create supervised datasets for fine-tuning or imitation learning.
- Post-Training Evaluation and Auditing: Inspecting step-level reasoning, tool usage, and failures to evaluate agent reliability, reproducibility, and compliance.
- Knowledge Transfer Between Agents: Sharing high-quality workflow traces so specialized agents can learn proven procedures, templates, and tool chains from others' experience.
- Debugging and Root-Cause Analysis: Tracing tool calls and intermediate reasoning signals to reproduce bugs, identify failure modes, and implement targeted fixes.
- Research on Agent Behavior: Providing annotated trajectories for academic or internal research into agent decision-making, emergent behaviors, and safety interventions.
- Reusable Workflow Components: Extracting and packaging repeatable sub-workflows and skills from traced runs to speed development of new agent automations.
- Creating reproducible datasets of agent behavior for academic or internal research
- Evaluating and benchmarking agent workflows and tool use with structured traces
- Collecting process-level data to support post-training, fine-tuning, or RLHF
- Auditing and explainability of agent decision paths and failures
- Sharing reusable agent experience or skills across teams or systems
L
LoopX
huangruiteng
Provider-neutral state kernel and local-first control plane for governing long-running AI agent loops across Codex, Claude Code, Cursor, and peer teams.
Key features
- Loop-Engineering State Kernel: A compact durable-state layer that keeps objectives, gates, todos, evidence, quotas, and handoffs consistent across many bounded turns.
- Runtime-Agnostic: Governs work executed by any coding-agent runtime — Codex, Claude Code, Cursor, or your own — without replacing them.
- Peer-Agent Model: Registered agents are peers; claims, leases, capabilities, and typed continuation decide who acts next, with no durable leader identity.
- Kanban-Style Control Plane: Cards carry identity, authority, evidence, and continuation; moves are validated operators (claim, gate, monitor, writeback).
- Local-First: The control plane runs locally by default — the public/private boundary is explicit, so private data and code stay on your machine.
- Auto-Wake and Quotas: Quota-aware auto-wake keeps agents progressing on long-running goals without a runaway scheduler.
- Evidence & Continuation: 200+ hour example loops preserve decision lineage, evidence branches, and invalid experiments across turns.
- Human-In-Command: Dangerous permissions, publishing, and production writes remain gated to the human owner — not autonomous.
Best for
- Multi-Day SWE Loops: Drive week-long engineering objectives across many bounded agent turns while keeping scope and review state intact.
- PR/Issue Automation: Preserve review state, evidence, and reviewer preferences across a PR that touches multiple turns and agents.
- Auto-ML Experiments: Keep hypotheses, matched evidence, invalid lineages, and promote/stop gates visible in a single graph over hundreds of hours.
- Multi-Agent Coordination: Coordinate a peer team of Codex + Claude Code + Cursor agents on the same objective with typed handoffs.
- Recurring Monitors: Run heartbeat or monitoring loops with owner-visible gates and evidence trails.
- Creator/Research Workflows: Give non-engineering owners a legible board of long-running work with human sign-off at each gate.