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Always keep your codebases ready for Agents. Improve any coding workflow by atleast 2x by maintaing a live, pluggable context layer per repo that creates and maintains Agents.md
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unoplat/unoplat-code-confluence
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Always keep your agents ready with all the context required per repository.
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What's in the demo: automaticAGENTS.md generation per repo and an org index that gives any coding agent a precise source of truth.
AI coding agents excel atgreenfield projects (new codebases built from scratch) but struggle withbrownfield codebases (mature, production systems with existing code).
Why? They burn most of their context window on exploration—searching files, tracing flows, connecting dots—leaving little capacity for actual implementation. By the time they're ready to code, they've hit the "dumb zone" where performance degrades sharply. And since they lack long-term memory, this cycle repeats with every conversation.
Multi-repo complexity makes it worse. When code is split across connected repositories, the agent exhausts its context just mapping dependencies between codebases—often before writing a single line.
Internal dependencies present another failure mode. The agent has no onboarding to proprietary systems, so it hallucinates usage patterns. Worse, when internal documentation has drifted from actual implementation, the agent trusts those "lies" and produces code that doesn't work.
The end result:slop code requiring heavy rework.
Unoplat Code Confluence is thecontext engine for application development, organizing precise, up-to-date knowledge of your data models, entry points, endpoints, and more—so coding agents can deliver and maintain features2–3x faster with higher quality.
Auto-generates machine-readableAGENTS.md files per repo to give coding agents aprecise source of truth:
- Engineering Workflow — Canonical install/build/dev/test/lint/type_check commands plus key config files and their responsibilities
- Business Logic — Core application logic, domain entities, and database entities
- Entry Points & Interfaces — Main entry points, API endpoints, and external interfaces
- External Dependencies — Roles and responsibilities of external libraries
- Extensible Language Support: Modular Tree-sitter based grammar extraction delivers consistent, accurate code context across all programming languages
- Extensible Framework-Aware Parsing: Specialized grammar engines recognize framework and library-specific patterns based on project dependencies
- All important metadata about application—dependencies, inbound/outbound interfaces, domain models, and data store models—are identified and their relationships preserved
- Scalable, auditable and reliable processing powered by workflow orchestrator
Ready to enhance your development workflow?
Check out ourQuick Start Guide.
We're actively developing Unoplat Code Confluence. Currently supportsPython andTypeScript codebases.
For detailed roadmap, language support status, and planned features, see ourProduct Roadmap.
| Jay Ghiya Contact: jayghiya@unoplat.io |
Book a call with me -Cal Link
Unoplat Code Confluence is in alpha. We’re building for our own daily use first, prioritizing stability and bug fixes. We’re collecting feedback now and will act on it once the core is solid. Early adopters welcome. Expect rapid changes and rough edges.
- Discord: Join ourcommunity channel
- GitHub Issues:Create an issue for bug reports or feature requests
- GitHub Discussions:Start a discussion for broader conversations
Your feedback is invaluable as we work toward production readiness and helps us prioritize our roadmap to better serve the developer community.
Unoplat-CodeConfluence is licensed under theGNU Affero General Public License v3.0 (AGPL-3.0) + COMMONS CLAUSE.
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Always keep your codebases ready for Agents. Improve any coding workflow by atleast 2x by maintaing a live, pluggable context layer per repo that creates and maintains Agents.md
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