Augment Code vs OpenAI Codex (cloud agent)
Augment Code vs OpenAI Codex (cloud agent): side-by-side comparison of two coding-agent memory systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Cost & capability
| Augment Code | OpenAI Codex (cloud agent) | |
|---|---|---|
| Capability band | competent | frontier |
| Capability composite | 70 | 78 |
| Cost tier | free | — |
| $/Mtok input | 0 | — |
| $/Mtok output | 0 | — |
| Use cases | Code Generation Focused, Long Running Session | Code Generation Focused, Long Running Session |
Where they differ (10)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Augment Code | OpenAI Codex (cloud agent) | |
|---|---|---|
| Capability band | competent | frontier |
| Capability composite | 70 | 78 |
| Type | Real-time semantic Context Engine + curated Agent Memory | File-backed ~/.codex/memories + Chronicle |
| Created | 2023-01 | searched not found |
| Pricing | Standard $20/mo (incl. $20 usage, up to 50 seats); Business $100/mo (incl. $100 usage); Enterprise custom | Included in ChatGPT Plus/Pro/Business/Edu/Enterprise plans (memory feature) |
| Funding | Acquired by Harness (2026-10-08; select assets incl. Cosmos, Auggie CLI, Context Engine); previously $252M total $977M val Series B · 2024-04 | total n/a; latest round $122B committed at $852B post-money (2026-04) |
| Deployment | Both | Both (cloud agent via ChatGPT platform + CLI tool local) |
| Multi-tenancy | Proof-of-Possession API architecture: each request includes hardware-backed proof of codebase ownership; eliminates cross-tenant contamination by design | hard-isolation |
| MCP | yes — Augment Agent is an MCP client (Easy MCP, settings panel, JSON import) | native (first-party) |
| Optimised for | real-time semantic Context Engine across repos / commits / CI | file-backed memory under ~/.codex/memories |
At a glance
| Augment Code | OpenAI Codex (cloud agent) | |
|---|---|---|
| Section | Coding-agent memory | Coding-agent memory |
| Tier | T1 | T1 |
| Type | Real-time semantic Context Engine + curated Agent Memory | File-backed ~/.codex/memories + Chronicle |
| Created | 2023-01 | searched not found |
| Latest release | — | 0.165.0 (2026-10-27) (Codex CLI) |
| License | — | Apache-2.0 (Codex CLI; cloud agent proprietary) |
| GitHub | — | openai/codex — ~128k stars, last commit 2026-10 |
| Pricing | Standard $20/mo (incl. $20 usage, up to 50 seats); Business $100/mo (incl. $100 usage); Enterprise custom | Included in ChatGPT Plus/Pro/Business/Edu/Enterprise plans (memory feature) |
| Funding | Acquired by Harness (2026-10-08; select assets incl. Cosmos, Auggie CLI, Context Engine); previously $252M total $977M val Series B · 2024-04 | total n/a; latest round $122B committed at $852B post-money (2026-04) |
| Backend storage | custom | custom |
| Deployment | Both | Both (cloud agent via ChatGPT platform + CLI tool local) |
| API surface | — | REST (OpenAI Responses API; Codex CLI wraps it) |
| Embedding | locked | locked |
| Multi-tenancy | Proof-of-Possession API architecture: each request includes hardware-backed proof of codebase ownership; eliminates cross-tenant contamination by design | hard-isolation |
| MCP | yes — Augment Agent is an MCP client (Easy MCP, settings panel, JSON import) | native (first-party) |
| A2A | searched not found | searched not found |
| OpenTelemetry | searched not found | searched not found |
| Optimised for | real-time semantic Context Engine across repos / commits / CI | file-backed memory under ~/.codex/memories |
| Anti-fit | not for non-coding tasks | not for non-coding tasks |
Taxonomy
| Axis | Augment Code | OpenAI Codex (cloud agent) |
|---|---|---|
| storage | graph | file |
| retrieval | graph-traversal | injection |
| persistence | long-term | long-term |
| update | extraction | consolidation |
| unit | document | summary |
| governance | opaque | inspectable |
| conflict | none | none |
Pros & cons
Augment Code
Pros: Codebase-level semantic graph (Context Engine) gives precise navigation across very large repos that confuse generic file-context tools.
Cons: Heavyweight indexing makes initial setup slow on large codebases; pricing is enterprise-tier.
OpenAI Codex (cloud agent)
Pros: Tight integration with the OpenAI model + tool-use roadmap; benefits directly from frontier model improvements.
Cons: Memory is shallow — primarily session-scoped; no long-running project memory yet.