Augment Code vs Devin (Cognition)
Augment Code vs Devin (Cognition): side-by-side comparison of two coding-agent memory systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Cost & capability
| Augment Code | Devin (Cognition) | |
|---|---|---|
| Capability band | competent | competent |
| Capability composite | 70 | 72 |
| Cost tier | free | free |
| $/Mtok input | 0 | 0 |
| $/Mtok output | 0 | 0 |
| Use cases | Code Generation Focused, Long Running Session | Code Generation Focused, Long Running Session, Multi Agent Coordination |
Where they differ (9)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Augment Code | Devin (Cognition) | |
|---|---|---|
| Capability composite | 70 | 72 |
| Use cases | Code Generation Focused, Long Running Session | Code Generation Focused, Long Running Session, Multi Agent Coordination |
| Type | Real-time semantic Context Engine + curated Agent Memory | Server-side Knowledge + auto-Repo Knowledge |
| Created | 2023-01 | 2024-03 |
| Funding | $252M total $977M val Series B · 2024-04 | $1.1B total $10.2B val Series E · 2025-09 |
| Multi-tenancy | Proof-of-Possession API architecture: each request includes hardware-backed proof of codebase ownership; eliminates cross-tenant contamination by design | Enterprise dedicated SaaS keeps customer data in customer tenant; on-prem and VPC deployment options available |
| MCP | via official adapter — Augment MCP | via official adapter — Devin supports MCP servers |
| Optimised for | real-time semantic Context Engine across repos / commits / CI | autonomous SWE work + structured Knowledge |
| Anti-fit | not for non-coding tasks | not for non-coding tasks; high cost per session |
At a glance
| Augment Code | Devin (Cognition) | |
|---|---|---|
| Section | Coding-agent memory | Coding-agent memory |
| Tier | T1 | T1 |
| Type | Real-time semantic Context Engine + curated Agent Memory | Server-side Knowledge + auto-Repo Knowledge |
| Created | 2023-01 | 2024-03 |
| Pricing | Free + paid | Free + paid |
| Funding | $252M total $977M val Series B · 2024-04 | $1.1B total $10.2B val Series E · 2025-09 |
| Backend storage | custom | custom |
| Deployment | Both | Both |
| API surface | — | REST, Slack integration |
| 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 | Enterprise dedicated SaaS keeps customer data in customer tenant; on-prem and VPC deployment options available |
| MCP | via official adapter — Augment MCP | via official adapter — Devin supports MCP servers |
| 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 | autonomous SWE work + structured Knowledge |
| Anti-fit | not for non-coding tasks | not for non-coding tasks; high cost per session |
Taxonomy
| Axis | Augment Code | Devin (Cognition) |
|---|---|---|
| storage | graph | kv |
| retrieval | graph-traversal | injection |
| persistence | long-term | cross-session |
| update | extraction | agent-controlled |
| unit | document | fact |
| 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.
Devin (Cognition)
Pros: Most aggressive memory model in the coding-agent space — explicit knowledge base + file-backed task notes + persistent terminal sessions.
Cons: Memory features only available inside the Devin product; not portable to other agents.