
Planning & Spec Decomposition
Ingests requirements from Linear/Jira or natural language, analyzes repository dependencies, and builds an atomic step DAG.
Do you want to coordinate autonomous AI agent swarms directly inside your codebase — turning specifications into verified, production pull requests?


Traditional AI tools treat software like a chat window. Hynah treats it like real engineering: with full repo understanding, typed AST graphs, sandboxed tests, and human-gated reviews.
Agents don't guess your architecture from 10 snippets. They index every symbol, type, and dependency in a local-first graph.
From database schema migrations to backend handlers and frontend UI components, changes are authored coherently in one pass.
When a test fails, the agent parses the stack trace, adjusts the logic in the sandbox, and verifies passing state before you even review.
Not one overwhelmed model doing everything. Hynah assigns specialized autonomous agents with bounded execution scopes, coordinated by a central orchestrator.
Writes idiomatic TypeScript/Go/Rust code matching existing codebase patterns
auth/service.go, token.go (+84 lines, -12 lines)
AST verification: 100% typed · 0 hallucinated imports
From task specification to verified pull request, every stage is observable, sandboxed, and governed by strict engineering standards.

Ingests requirements from Linear/Jira or natural language, analyzes repository dependencies, and builds an atomic step DAG.

Writes and modifies backend handlers, schemas, database migrations, and frontend components in parallel without import errors.

Creates complete unit, integration, and property test suites. Executes them inside a fast, isolated local sandbox.

When compilation or runtime tests break, the agent inspects the stack trace, patches the defect, and reruns until green.

Validates security boundaries, scans against OWASP standards, and presents clear, interactive diffs for human approval.

Packages docker artifacts, runs canary integration tests, commits signed git branches, and publishes changelogs.
Hynah is not a runaway script. Every synthesized file, test run, and branch commit requires explicit verification, rollback snapshots, and team visibility.
Every agent step creates a local git tree snapshot. At any point, one click restores your workspace to the exact state before an agent modified a single line.
Engineered for production codebases with thousands of files. No toy demos, no vendor lock-in, and full architectural transparency.
Route different agent roles to optimal models. Use high-reasoning models for planning and ultra-fast models for syntax synthesis.
Indexes your codebase using native Tree-Sitter parsers. Builds accurate symbol call hierarchies instead of naive vector embeddings.
Tests run inside local isolated containers. No secret leakage, no network egress without explicit policy permissions.
Agents work in clean background Git worktrees. Your active editor buffer is never overwritten while you work.
Use your own Anthropic, OpenAI, or Google API keys, or connect to private on-premise LLM inference clusters.
Native desktop binaries for Windows (x64), macOS (Apple Silicon / Intel), and Linux (AppImage / deb).
Download Hynah IDE for your desktop. Bring autonomous multi-agent workflows directly into your codebase with complete local privacy.