u/Funny-Anything-791

We shipped ChunkHound v5.0 + v5.1 recently and forgot to post about 5.0, so here’s the combined update.

ChunkHound is a code search / code research tool for AI coding workflows, especially MCP-based setups with OpenCode, Claude Code, Codex-style agents, VS Code, etc.

The big 5.x themes:

- Multi-client MCP daemon: multiple MCP clients can share one DuckDB connection instead of fighting over locks

- MCP search now returns token efficient markdown instead of JSON

- Nee language support: Elixir, Dart, Lua, SQL, HTML/CSS/SCSS, and more

- Better deep research support: OpenAI Responses API, Anthropic structured outputs, Grok, reasoning-effort controls

- Safer indexing: global gitignore support, embedded SQL detection, disk usage limits, .env exclusion, and better handling of unknown file types

A bunch of stability fixes around HNSW, WAL validation, DuckDB paths, MCP startup, Windows unicode, and parser install hints

The goal is to make codebase context more reliable for real agent workflows: less lock contention, fewer indexing surprises, better search output for LLMs, and broader language coverage.

Thank you so much for everyone who worked hard, reported bugs, and contributed to the project in one way or another. It wouldn't have been possible without you 🙏

I built pi-agenticoding because CLI coding agents shouldn’t rely on humans to keep them in their optimal working zone.

Today, a lot of agentic coding still depends on the user deciding what to preserve, what to summarize, when to compact, when to restart, and when the session has become too noisy.

That feels backwards.

The agent should carry more of that cognitive load itself.

pi-agenticoding gives agents primitives to manage their own context: spawning focused sub-agents, writing durable notes to a ledger, and handing off work into a clean new session when the current one drifts out of shape.

The goal isn’t just a bigger context window.

It’s keeping the agent operating where it works best: focused, grounded, and not overloaded by its own history.

Repo: https://github.com/agenticoding/pi-agenticoding

Super interested in hearing what this group has to say about this approach and if anyone else finds it useful 🙏

ChunkHound v5.1

Automating context management