Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/existential-birds/beagle/deepagents-architecturenpx skills add existential-birds/beagle --skill deepagents-architecturegit clone --depth 1 https://github.com/existential-birds/beagleWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/existential-birds/beagle/deepagents-architecture)<a href="https://agentmods.dev/skills/existential-birds/beagle/deepagents-architecture"><img src="https://agentmods.dev/badge/skills/existential-birds/beagle/deepagents-architecture.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00037 | $0.01916 |
| Opus 5 | $0.00018 | $0.00958 |
| Sonnet 5 | $0.00007 | $0.00383 |
| Haiku 4.5 | $0.00004 | $0.00192 |
Grade A, and why
deepagents-architecture scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 229 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Agents Architecture Decisions
When to Use Deep Agents
Use Deep Agents When You Need:
- Long-horizon tasks - Complex workflows spanning dozens of tool calls
- Planning capabilities - Task decomposition before execution
- Filesystem operations - Reading, writing, and editing files
- Subagent delegation - Isolated task execution with separate context windows
- Persistent memory - Long-term storage across conversations
- Human-in-the-loop - Approval gates for sensitive operations
- Context management - Auto-summarization for long conversations
Consider Alternatives When:
| Scenario | Alternative | Why |
|---|---|---|
| Single LLM call | Direct API call | Deep Agents overhead not justified |
| Simple RAG pipeline | LangChain LCEL | Simpler abstraction |
| Custom graph control flow | LangGraph directly | More flexibility |
| No file operations needed | create_react_agent |
Lighter weight |
| Stateless tool use | Function calling | No middleware needed |
Backend Selection
Backend Comparison
| Backend | Persistence | Use Case | Requires |
|---|---|---|---|
StateBackend |
Ephemeral (per-thread) | Working files, temp data | Nothing (default) |
FilesystemBackend |
Disk | Local development, real files | root_dir path |
StoreBackend |
Cross-thread | User preferences, knowledge bases | LangGraph store |
CompositeBackend |
Mixed | Hybrid memory patterns | Multiple backends |
Backend Decision Tree
Need real disk access?
├─ Yes → FilesystemBackend(root_dir="/path")
└─ No
└─ Need persistence across conversations?
├─ Yes → Need mixed ephemeral + persistent?
│ ├─ Yes → CompositeBackend
│ └─ No → StoreBackend
└─ No → StateBackend (default)
CompositeBackend Routing
Route different paths to different storage backends:
from deepagents import create_deep_agent
from deepagents.backends import CompositeBackend, StateBackend, StoreBackend
agent = create_deep_agent(
backend=CompositeBackend(
default=StateBackend(), # Working files (ephemeral)
routes={
"/memories/": StoreBackend(store=store), # Persistent
"/preferences/": StoreBackend(store=store), # Persistent
},
),
)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 229 lines · 37 tokens per session scan A 4c7d19214cfa
deepagents-architecture is a skill published in the GitHub repository existential-birds/beagle (79 stars, last pushed 26d ago), licensed Apache-2.0. It adds 37 tokens to every session and 1,916 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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