agent · ai-coding-agents
Goose vs Warp AI
55 fields both were evaluated on, 29 of them different — including where one discloses something the other does not. Every value links to the document it came from. 26 further fields are disclosed by neither and are listed at the end rather than tabled.
Assurance
| Field | Goose | Warp AI |
|---|---|---|
| Audit trail· | no public information goose-docs.ai · checked Aug 1, 2026 | Each agent run captures a screenshot or video recording, giving a reviewable record before a PR ships. “every factory agent captures a screenshot or video so you can verify its work before shipping a PR.”warp.dev · checked Sep 10, 2026 |
| Explainability· | no public information goose-docs.ai · checked Aug 1, 2026 | A worked example shows the agent narrating a root-cause fix and opening a PR, illustrating that its actions come with a stated rationale rather than being opaque. “Found it — the testimonials section was hidden by collapsing divs. Patched, PR #436 is up for review.”warp.dev · checked Sep 10, 2026 |
| Runtime governance· | “🔒 Security Prompt injection detection, tool permission controls, sandbox mode, and an adversary reviewer that watches for unsafe actions.”goose-docs.ai · checked Aug 1, 2026 | Positions itself around giving customers continuous governance and security controls over their coding agents by default. “Control your coding agent chaos continuous improvement, better governance and security, by default.”warp.dev · checked Sep 10, 2026 |
| Permission scopes· | “🔒 Security Prompt injection detection, tool permission controls, sandbox mode, and an adversary reviewer that watches for unsafe actions.”goose-docs.ai · checked Aug 1, 2026 | Access controls are centrally configured and centrally managed at the org level. “Run agents confidently with guardrails in place, centrally configured agent access, and centrally managed permissions.”warp.dev · checked Sep 10, 2026 |
| Evaluation coverage· | no public information goose-docs.ai · checked Aug 1, 2026 | States that evaluation and benchmarking are built-in, core features rather than an add-on. “evals, benchmarks, and self-improvement built in.”warp.dev · checked Sep 10, 2026 |
Agency
| Field | Goose | Warp AI |
|---|---|---|
| Autonomy level· | no public information goose-docs.ai · checked Aug 1, 2026 | States most organizations start with 20-30% of pull requests fully automated, rising over time as the system self-improves — i.e. partial rather than full autonomy initially. “most orgs start around 20-30% of PRs fully automated, starting with simple tasks. over time this goes up as models improve and your factory self-improves.”warp.dev · checked Sep 10, 2026 |
| Human oversight· | no public information goose-docs.ai · checked Aug 1, 2026 | Lets a human watch, steer, or take over an agent run from any surface (web, mobile, terminal, or IDE). “any surface watch, steer, and hand off runs from web, mobile, terminal, or IDE.”warp.dev · checked Sep 10, 2026 |
| Goal complexity· | no public information goose-docs.ai · checked Aug 1, 2026 | Positions its scope broadly as automating the entire software development lifecycle, beyond just CI/CD, at scale. “Beyond CI/CD to automating the whole SDLC defined in code, built for scale, and easy to deploy.”warp.dev · checked Sep 10, 2026 |
| Action space· | no public information goose-docs.ai · checked Aug 1, 2026 | Agents are triggered by an issue, chat message or schedule and carry work through triage, review, and up to a mergeable pull request. “a fleet of agents wired to your SDLC — triggered by an issue, a slack message, or a schedule, and coordinated by warp factories from triage through review to a mergeable PR.”warp.dev · checked Sep 10, 2026 |
| Operating environment· | “goose is a general-purpose AI agent that runs on your machine.”goose-docs.ai · checked Aug 1, 2026 | Marketed as an agentic development platform meant to work across wherever and however a developer works. “Warp is an open agentic development platform that was built to work wherever and however you work.”warp.dev · checked Sep 10, 2026 |
| Initiative· | no public information goose-docs.ai · checked Aug 1, 2026 | Agents proactively pull a human back in mid-run when they get stuck, rather than only waiting to be asked. “the factory agents loop you in proactively when they need help.”warp.dev · checked Sep 10, 2026 |
Safety
| Field | Goose | Warp AI |
|---|---|---|
| Safety policy· | “🔒 Security Prompt injection detection, tool permission controls, sandbox mode, and an adversary reviewer that watches for unsafe actions.”goose-docs.ai · checked Aug 1, 2026 | Advertises guardrails, centralized agent-access configuration, and centrally managed permissions as enterprise controls. “Run agents confidently with guardrails in place, centrally configured agent access, and centrally managed permissions.”warp.dev · checked Sep 10, 2026 |
| Data handling· | no public information goose-docs.ai · checked Aug 1, 2026 | Data can be stored in Warp's cloud or fully self-hosted in the customer's own VPC, under the customer's existing retention/compliance rules. “wherever you choose — warp's cloud, or fully self-hosted inside your own VPC, under your existing retention and compliance rules.”warp.dev · checked Sep 10, 2026 |
Practicality
| Field | Goose | Warp AI |
|---|---|---|
| Pricing model· | no public information goose-docs.ai · checked Aug 1, 2026 | Priced on a usage basis per agent run, with $10k of free usage offered to qualifying orgs during closed early access. “usage-based, priced per agent run. qualifying orgs get $10k of factory usage during closed early access.”warp.dev · checked Sep 10, 2026 |
| Price point· | no public information goose-docs.ai · checked Aug 1, 2026 | Offers up to $10,000 of free usage credit for qualifying organizations during early access. “get up to $10,000 in free factory usage”warp.dev · checked Sep 10, 2026 |
| Availability· | “Download the desktop app Available for macOS, Linux, and Windows”goose-docs.ai · checked Aug 1, 2026 | no public information warp.dev · checked Sep 10, 2026 |
| Deployment options· | Runs either on Warp's own hosted cloud or fully self-hosted inside the customer's own VPC. “warp cloud self-hosted warp's cloud, or self-hosted in your own VPC.”warp.dev · checked Sep 10, 2026 | |
| Integrations· | “Connect to 70+ extensions — databases, APIs, browsers, GitHub, Google Drive, and more — via the Model Context Protocol open standard.”goose-docs.ai · checked Aug 1, 2026 | Work enters the system from chat apps, ticketing tools and source control, with status reported back to the same origin. “integrations work flows in from chat, tickets, and source control; status flows back to where it started.”warp.dev · checked Sep 10, 2026 |
Foundation models
| Field | Goose | Warp AI |
|---|---|---|
| Base models· | no public information goose-docs.ai · checked Aug 1, 2026 | Its own benchmark table names specific models it runs and compares, including Claude Fable 5, GPT-5.6 Sol, and Gemini 3.6 Flash. “best 1.00x Claude Fable 5 pass 1.00x $0.53 GPT-5.6 Sol pass 0.94x $0.41 Gemini 3.6 Flash fail 0.89x $0.32”warp.dev · checked Sep 10, 2026 |
| Model provider· | “Works with 15+ providers — Anthropic, OpenAI, Google, Ollama, OpenRouter, Azure, Bedrock, and more.”goose-docs.ai · checked Aug 1, 2026 | Is provider-agnostic: customers bring their own model or coding harness rather than being locked to one model vendor. “bring your own model or harness (e.g. claude code or codex) — warp factories works with whatever your team prefers”warp.dev · checked Sep 10, 2026 |
| Model swappable· | “Use API keys or your existing Claude, ChatGPT, or Gemini subscriptions via ACP .”goose-docs.ai · checked Aug 1, 2026 | Model choice is not locked in — customers can bring their own model or harness rather than being tied to one. “bring your own model or harness (e.g. claude code or codex) — warp factories works with whatever your team prefers”warp.dev · checked Sep 10, 2026 |
| Open weights· | no public information goose-docs.ai · checked Aug 1, 2026 | Supports open-weight models as an option alongside frontier models, selectable per pipeline stage. “frontier open-weight frontier or open-weight, chosen per pipeline stage.”warp.dev · checked Sep 10, 2026 |
Ecosystem
| Field | Goose | Warp AI |
|---|---|---|
| Protocols supported· | “Model Context Protocol MCP is the open standard for connecting AI agents to tools and data sources.”goose-docs.ai · checked Aug 1, 2026 | Exposes itself as a platform via API, CLI, SDK and MCP rather than a single vertical product. “API, CLI, SDK and MCP built as a platform, not a vertical product or AI teammate.”warp.dev · checked Sep 10, 2026 |
| Tool use· | “Connect to 70+ extensions — databases, APIs, browsers, GitHub, Google Drive, and more — via the Model Context Protocol open standard.”goose-docs.ai · checked Aug 1, 2026 | Work is invoked through its API, CLI, SDK and MCP surfaces. “API, CLI, SDK and MCP built as a platform, not a vertical product or AI teammate.”warp.dev · checked Sep 10, 2026 |
| Multi-agent· | “Subagents Spawn independent subagents to handle tasks in parallel — code review, research, file processing — keeping the main conversation clean.”goose-docs.ai · checked Aug 1, 2026 | A single customer example config is applied across as many as 112 agents at once, indicating multi-agent orchestration at scale. “factory.yaml applied to all 112 agents”warp.dev · checked Sep 10, 2026 |
| API access· | “Desktop app, CLI, and API — for code, workflows, and everything in between.”goose-docs.ai · checked Aug 1, 2026 | Exposes programmatic access as part of an API, CLI, SDK and MCP platform surface. “API, CLI, SDK and MCP built as a platform, not a vertical product or AI teammate.”warp.dev · checked Sep 10, 2026 |
| Open source· | Its terminal product is open source. “An open-source terminal built for AI-assisted software development.”warp.dev · checked Sep 10, 2026 |
Impact
| Field | Goose | Warp AI |
|---|---|---|
| User base· | Claims over 800,000 developers and thousands of engineering teams as users. “Trusted by over 800,000 developers and thousands of engineering teams at leading companies”warp.dev · checked Sep 10, 2026 | |
| Deployment scale· | Claims 800,000+ developers as its adoption-scale headline. “trusted by 800k+ devs at SDLC coverage”warp.dev · checked Sep 10, 2026 |
Disclosed by neither
Both Goose and Warp AI publish nothing on these 26 fields. That is a finding about the category rather than a difference between them, so it is recorded here instead of as 26 identical table rows. Each is shown with its source check on the individual profiles.
- Target sectors
- High-risk domains
- Documented incidents
- Supported regions
- Support model
- Fine-tuning
- Context window
- Marketplace presence
- Safety evaluations
- Red teaming
- Usage restrictions
- Model or system card
- Incident reporting
- Third-party evaluations
- Insurance available
- Insurance carriers
- Coverage limits
- Indemnification
- Liability cap
- Tamper-evident log
- Compliance certifications
- Regulatory alignment
- Outcome-based pricing
- Settlement mechanism
- Dispute process
- SLA terms
Questions
- How do Goose and Warp AI compare on autonomy level?
- Goose: no public information disclosed. Warp AI: States most organizations start with 20-30% of pull requests fully automated, rising over time as the system self-improves — i.e. partial rather than full autonomy initially..
- How do Goose and Warp AI compare on human oversight?
- Goose: no public information disclosed. Warp AI: Lets a human watch, steer, or take over an agent run from any surface (web, mobile, terminal, or IDE)..
- How do Goose and Warp AI compare on goal complexity?
- Goose: no public information disclosed. Warp AI: Positions its scope broadly as automating the entire software development lifecycle, beyond just CI/CD, at scale..
- How do Goose and Warp AI compare on action space?
- Goose: no public information disclosed. Warp AI: Agents are triggered by an issue, chat message or schedule and carry work through triage, review, and up to a mergeable pull request..
- How do Goose and Warp AI compare on initiative?
- Goose: no public information disclosed. Warp AI: Agents proactively pull a human back in mid-run when they get stuck, rather than only waiting to be asked..
- How do Goose and Warp AI compare on pricing model?
- Goose: no public information disclosed. Warp AI: Priced on a usage basis per agent run, with $10k of free usage offered to qualifying orgs during closed early access..