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agentmemory
Pricing
- Model
- Free
Summary
You asked three different coding agents to implement the same feature and got three different answers, zero consistent evidence, and no way to know which one actually passed your test suite — Orbit exists to fix that.
Orbit is an open-source agent orchestration harness that wraps coding agent runs in bounded, dependency-ordered tasks, then gates task completion on real validation: tests, lint, and type checks must pass before an orbit closes. Every run produces structured JSON artifacts — agent output, rubric scores, accept/iterate/stop recommendations, and a human-readable progress log — so you have a trail to review, not just a diff to guess at. It runs against Claude, Codex, Cursor, or any agent that speaks JSON over CLI. The demo runs without an API key, which matters when you're evaluating whether it even fits your workflow. Where it strains: teams who need a web UI, multi-agent parallelism, or cloud-managed infrastructure will hit the limits of an intentionally small CLI harness fast.
Bottom line: Orbit earns its place in a team building repeatable, auditable agent workflows from a local terminal — it breaks down when your project needs parallel agent execution, a managed control plane, or anything beyond what a JSON-speaking CLI adapter can express.
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Pros
Sign in to edit- Validation gates tied to your actual test suite and linter — not a model's self-report — which means a task cannot be marked complete when the code still breaks your build.
- Structured JSON artifacts on every run (agent output, rubric scores, review recommendation, progress log), so you have inspectable evidence for human review instead of reconstructing what the agent did from a diff.
- Agent-neutral adapter contract, so you can run the same task through Claude and Codex and compare the resulting evaluation files directly — replacing 'I think this model is better' with a logged side-by-side.
- Dependency-ordered backlog execution that advances one verified task at a time, which means you avoid the common failure mode where an agent skips ahead and builds on work that never actually passed.
- MIT licensed and self-hostable with no API key required to run the replay demo, so you can validate the harness fits your workflow before wiring it to any external service.
Cons
Sign in to edit- Orbit has no web UI and no managed control plane — non-engineers who need to review agent progress or trigger runs without touching a terminal cannot use it without a wrapper built on top, and building that wrapper puts the maintenance burden on your team.
- Task execution is sequential and single-agent per orbit: one task, one agent, one validation loop at a time. Teams that need agents running tasks in parallel — or coordinating across multiple agents on a shared codebase — hit this architectural ceiling immediately and move to a heavier orchestration framework.
- The adapter layer requires each coding agent to speak JSON over a CLI interface; agents without a scriptable CLI or JSON output format require a custom adapter, which the docs flag as a contribution opportunity but which in practice means engineering time before the harness is usable with those agents.
- There is no cloud execution or hosted option — everything runs locally or on infrastructure you manage. Teams under compliance requirements that mandate audit trails stored in a vendor-controlled environment, rather than self-managed storage, will need a different tool.
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About
- Platforms
- Linux, macOS, Windows (Python 3.6+)
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-06-07T13:54:41.433Z
Best For
Who it's for
- Teams experimenting with multiple AI coding agents
- Projects requiring auditable, repeatable agent workflows
- Development teams who want deterministic replay and validation gates
- Organizations building internal agent orchestration infrastructure
- Teams needing structured evidence and review artifacts for compliance or human oversight
What it does well
- Testing and validating AI agent code generation against test suites and lint rules
- Orchestrating agent work through dependency-ordered task backlogs
- Comparing different coding agents (Claude vs. Codex vs. Cursor) using artifacts instead of anecdotes
- Building self-healing codebases where agents must fix failing tests before marking work complete
- Creating durable audit trails and progress logs of agent execution for human review
Integrations
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Frequently Asked Questions
- Is agentmemory free?
- Yes — agentmemory is fully free to use. There is no paid tier.
- Is agentmemory open source?
- Yes. agentmemory is open source.
- Can I self-host agentmemory?
- Yes. agentmemory supports self-hosting on your own infrastructure.
- What platforms does agentmemory support?
- agentmemory is available on: Linux, macOS, Windows (Python 3.6+).
Hours Saved & ROI Stories Community
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Curated lists that include this category
Most agent runs leave behind a changed file and a feeling. Orbit replaces the feeling with evidence. The harness selects tasks from a dependency-ordered backlog, hands one task at a time to a coding agent, runs your actual test suite and linter against the result, and only closes the task if validation passes. What remains is a set of JSON artifacts: the agent’s raw output, a rubric-scored evaluation, an accept/iterate/stop recommendation, and a markdown progress log written for a human reviewer. The vendor describes this as ‘bounded, validated, auditable loops’ — the key word is bounded, because scope drift is where most agent runs fall apart.
The defining architectural decision is agent neutrality. Orbit talks to Claude, Codex, Cursor, or any agent that exposes a JSON-speaking CLI interface through a swappable adapter layer. That means you can run the same task through two different agents and compare their evaluation artifacts directly — artifacts instead of anecdotes, as the vendor puts it. This turns agent selection from a gut call into a logged experiment.
Orbit fits teams in the ‘messy middle’ of agentic development: past the proof-of-concept stage, not yet at the point where they need a managed platform. Self-healing repo workflows — where the agent must fix failing tests before a task closes — are explicitly supported. Compliance and oversight use cases benefit from the durable audit trail. Where it breaks: Orbit is intentionally small, CLI-first, and local. Teams that need a visual interface for non-engineers, cloud-hosted execution, or agents running tasks in parallel will find themselves outside what the harness is designed to do, and at that point the path forward is either a heavier orchestration framework or building infrastructure around Orbit themselves.
The deterministic replay demo requires no API key and runs in minutes after cloning — a concrete way to verify the harness behaves as described before committing anything to it. The project is MIT licensed and accepts contributions in the form of adapters, demo missions, and mission templates, with the stated contribution goal of making the harness easier to verify and replay.