DeepAgents' orchestration capabilities are extremely powerful, but as a CLI tool, it lacks management of orchestration results, visual monitoring of Agent behavior, and support for multi-project parallel orchestration. In production environments, orchestrating tasks is only the first step — managing the results is what sustains ongoing productivity.
Multi-threaded parallelism: Let DeepAgents orchestrate multiple complex tasks simultaneously
DeepAgents can do a lot inside one session, but real delivery rarely happens one task at a time.
HagiCode lets you run multiple DeepAgents sessions in parallel, each with isolated context, a clear responsibility boundary, and independent progress.
That turns AI assistance from a single conversation into a parallel engineering workbench that matches how teams actually build software.
- Thread A is refining backend API endpoints;
- Thread B is refactoring frontend components;
- Thread C is writing unit tests;
- Thread D is auditing code for security vulnerabilities.
OpenSpec proposal sessions: Pair DeepAgents' orchestration results with decision traceability
In daily development, the most common chaos isn't being unable to write code — it's making a bunch of changes and then forgetting why you made them, what you changed, and how those changes relate to each other.
HagiCode's built-in OpenSpec proposal workflow fundamentally solves this problem. Every development task launches as a proposal:
This think-first-then-act approach lets DeepAgents's reasoning power shine even brighter, and lets you quickly understand the decision context months later when looking back at the code.
- First, write down what problem this task solves and why this approach makes sense;
- Discuss the technical approach in depth with DeepAgents within the proposal framework — all conversations and decisions are recorded in the proposal context;
- Once the approach is confirmed, DeepAgents implements the code within the proposal's constraints;
- The final proposal document, discussion records, and code changes form a complete trace chain.
AI commits: Standardize commits for every sub-task DeepAgents produces
Writing commit messages after finishing code is a form of mental friction for many developers. Too casual and you can't find key commits later; too formal and it feels like wasted time.
HagiCode's AI commit feature hands this entirely to DeepAgents: it analyzes your code changes, understands the intent and scope of impact, then automatically generates clearly structured, semantically accurate commit messages. More importantly, during the AI commit process, HagiCode automatically locks the repository to prevent state conflicts from concurrent operations, ensuring safe and reliable commits.
Save your attention for creation — let DeepAgents handle the commit paperwork.
Code Server browser editor: Make DeepAgents' orchestration process visible
After DeepAgents analyzes code, identifies the problematic file, and pinpoints the exact line number, the familiar awkward moment occurs: you need to leave the AI conversation window, return to your IDE, find the file again, and manually jump to the right location. This analysis-to-editing context break not only interrupts your flow but keeps AI's value at 'telling you where the problem is' rather than 'helping you directly enter editing mode.'
HagiCode's built-in Code Server-based browser editor is designed specifically to solve this break:
The Code Server integration makes HagiCode not just a frontend page that can analyze code, but a complete workstation where DeepAgents's analysis can directly land as editing actions. It minimizes the tool-switching cost between AI analysis and hands-on modification.
- One-click from analysis to editing: Once DeepAgents identifies the file needing changes in a proposal, HagiCode can directly open that file in the workbench for editing. You don't need to switch tools or re-locate the file — the distance from analysis result to hands-on modification is zero.
- Local, container, remote — full coverage: Whether your project runs on a local machine, in a Docker container, or on a remote server, HagiCode's Code Server can open the project directory through the browser for editing. You're no longer constrained by 'this project can only be edited in one specific environment.'
- Vault direct editing: Code reference libraries and learning projects registered in your Vault can also be opened and browsed directly through Code Server. When DeepAgents references example code, you can jump in anytime for deeper study or practice.
Preset Task: Templatize DeepAgents' complex orchestration capabilities
DeepAgents's capabilities are strong, but manually typing out requirements each time isn't efficient. More importantly, the community has already produced a wealth of high-quality Skills — from code review templates to full-stack CRUD generators, from automatic documentation generation to test case orchestration. These proven practices are scattered everywhere with no unified place to host and invoke them.
HagiCode's Preset Task mechanism is designed to solve this problem. It's more than just shortcut commands — it's an extensible Skills integration platform:
Preset Task upgrades your collaboration with DeepAgents from 'communicating from scratch every time' to 'standing on the community's shoulders, one-click invocation of mature workflows.' You pick the template, DeepAgents executes — this is true workflow automation, and the whole process is visually delightful.
- Community Skills ready to use: HagiCode supports importing popular community Skills as Preset Tasks. You don't need to craft complex prompts from scratch — the community has already accumulated a wealth of high-quality task templates. Import and use immediately — new CRUD modules, comprehensive code reviews, API documentation generation — all have ready-made solutions.
- Extensible Skills system: If you have unique project requirements or team conventions, HagiCode lets you customize and compose on top of community Skills. You can adjust checklists, add team coding standards, or even chain multiple Skills into a complete development pipeline, building your own team's task template library.
- Visual operations, goodbye to the tedium of plain text: This is the fundamental difference between HagiCode and pure command-line tools. Choosing a Preset Task doesn't require typing commands and assembling parameters in the terminal — it's done through a carefully designed visual interface. Click to select tasks, dropdown menus to switch parameters, drag-and-drop to reorder tasks — every step has clear visual feedback and status indicators. The human-machine interaction shifts from 'writing code to invoke AI' to 'using a UI to direct AI.'
Gamified interface: Make DeepAgents' complex orchestration intuitive and perceptible
Programming itself can be tedious, but it can also be fun. HagiCode's gamified interface design breaks away from the cold experience of command-line tools:
DeepAgents provides intelligence, HagiCode provides experience — together, they transform AI programming from a 'productivity tool' into a 'development environment you actually want to open.'
- Clear visual feedback: Each session's running status, progress, and results are presented through intuitive interface elements — no need to frantically scroll through terminal output.
- Achievement and progress visualization: Task completion, code commits, proposal approvals — these milestones are wrapped as visible achievements, giving the development process a sense of rhythm and accomplishment.
- Low barrier to entry: The combination of mouse clicks, drag-and-drop, and keyboard shortcuts lets developers unaccustomed to pure terminal workflows easily harness DeepAgents's full capabilities.
Agents management: Turn DeepAgents' multiple orchestration instances into a manageable Agent fleet
Parallel sessions are useful, but once you have several of them running, coordination becomes the next bottleneck.
HagiCode's Agents management layer turns each DeepAgents worker into a named, schedulable Agent with visible status, assigned scope, and independent configuration.
You move from 'a lot of terminals are open' to 'a small AI team is being coordinated in one place.'
- Agent identity and status visualization: see which Agent is running, waiting, blocked, or ready for archive at a glance.
- Task-to-Agent binding: proposals, reviews, refactors, and test work can each be assigned to a dedicated Agent.
- Independent Agent configuration: different Agents can use different model routes, Skills, and context scopes without interfering with each other.
Monospecs multi-repo management: Give DeepAgents a complete repo view for orchestration
In real projects, code rarely lives in a single repository. Frontend, backend, documentation, shared libraries are spread across different repos, and a single feature change may need to touch several repos simultaneously. For DeepAgents, single-repo mode works, but it has no native understanding of cross-repo relationships — you have to manually tell it in every conversation that 'this change also needs to sync to two other repos,' which is clearly inefficient.
HagiCode's Monospecs mechanism is the structured solution designed for multi-repo scenarios. Through the .hagicode/monospecs.yaml configuration file, it declares all sub-repo addresses, names, and relationships in the project group, giving DeepAgents an automatic cross-repo map when launching proposals:
Monospecs essentially eliminates DeepAgents's cognitive blind spots in cross-repo collaboration. DeepAgents's reasoning is powerful, but it still needs an accurate map to locate the scope of changes — and Monospecs is that systematically managed project map.
- Automatic repo relationship awareness: When creating a development proposal, DeepAgents can directly read the sub-repo list from the Monospecs config, knowing 'this change's frontend code is in repos/frontend, API definitions in repos/backend, documentation in repos/docs' — you no longer need to manually list which repos are involved each time.
- Cross-repo change tracking: When a proposal spans multiple sub-repos, the OpenSpec proposal directory stays in the main repo, with sub-repos only carrying code changes. This keeps specs and code separated, sub-repos stay clean, but the entire change's decision chain and discussion records are centralized in one place. On archive, commit_when_archive can also auto-commit specs to the main repo, saving manual version control headaches.
- AI commit smart repo targeting: When doing AI commits, HagiCode analyzes your changes against the Monospecs config and automatically suggests which target repo the commit should go to. You don't need to cd into each directory in the terminal — HagiCode handles the judgment of which repo this code belongs to.
- Per-repo AGENTS.md: Each sub-repo can have its own AGENTS.md telling DeepAgents about that repo's tech stack, coding standards, and development conventions. DeepAgents automatically reads the corresponding guidance when operating on different repos, always behaving in line with your team's standards.
Vault cross-project knowledge base: Provide knowledge support for DeepAgents' orchestration
DeepAgents's context window is large, but every new session starts from scratch — the experience accumulated in the previous round of proposals, the project structures analyzed, the best practices discussed — all are 'forgotten' when the session ends. In pure conversation tools, you can only accept this, but in HagiCode, the Vault system changes the game.
Vault is HagiCode's cross-project persistent knowledge storage layer. Its core design philosophy is 'register once, reuse everywhere':
If Monospecs lets DeepAgents understand where projects are, Vault lets DeepAgents remember what we've learned before. The former expands DeepAgents's spatial vision, the latter extends DeepAgents's temporal memory — together, DeepAgents is no longer a stranger reintroducing themselves every time, but a long-term partner who truly understands your project landscape and knowledge accumulation.
- Multi-type knowledge containers: Vault supports four types — folder (general file directories), coderef (specifically for studying open-source projects, automatically initializing standardized directory structures), obsidian (directly connecting your existing Obsidian notebook), and system-managed (system-automated project config and prompt template libraries). You can register code repositories, study notes, and design documents scattered across different places into Vault — DeepAgents automatically perceives the existence of these knowledge resources in any proposal.
- Automatic AI context injection: Every time a new proposal starts, HagiCode automatically injects your registered Vault information into DeepAgents's context. You don't need to manually copy code fragments or re-explain project backgrounds — DeepAgents receives the proposal while already knowing what learning resources and reference projects you have available, and can start working directly from existing knowledge.
- Fine-grained access control: Each Vault can be marked as reference (read-only) or editable. Reference code libraries for open-source projects are set to read-only — DeepAgents can read and analyze but not modify, preventing accidental changes. Your own project Vaults set to editable let DeepAgents help you write code directly. This boundary keeps AI's freedom always controllable.
- Cross-project knowledge reuse: Register a 'design patterns reference' Vault while analyzing Project A, and DeepAgents can access it in any subsequent proposal. You don't need to repeatedly build knowledge bases — Vault turns your learning outcomes and reference resources into inheritable long-term assets.
OmniRoute model routing: Give DeepAgents' orchestration the optimal model support
Teams should be able to keep the workflow they like without locking model access to one vendor or one subscription path.
OmniRoute separates the interaction layer from the model-routing layer, so HagiCode can keep DeepAgents in the workflow while switching model sources underneath.
That gives you better cost control, better task-to-model fit, and less workflow churn when model pricing or availability changes.
- Keep the CLI or interaction style you already prefer while changing only the model route underneath.
- Share one routing strategy across multiple Agents and multiple AI tools connected to HagiCode.
- Tune different model routes for different workloads, such as fast coding, deep review, or architecture planning.
- Adjust cost and capability once at the routing layer instead of reworking each individual workflow.
