HIEF: Launching The Missing Memory Layer For AI Coding Agents

Most AI coding demos look magical right up until day two.

My view is simple: reasoning quality is improving quickly, but operational memory and evaluation are still the weak link.

On day one, your agent can scaffold features, draft tests, and move fast. On day two, reality shows up:

It forgets what happened in the last session
It searches too shallowly and misses key parts of the codebase
It collides with other agents touching related files
It has no reliable quality baseline beyond “tests passed”

That gap is why I built HIEF.

Product Reveal

HIEF (Hybrid Intent-Evaluation Framework) is a local-first MCP sidecar for AI coding agents. It gives agents durable project memory, lightweight coordination, and quality guardrails without replacing the agent itself.

HIEF is not another agent. It is the memory and control plane around your existing agent workflow.

In one sentence

HIEF helps agents search better, coordinate safely, and verify output quality before humans review and merge.

What You Get

HIEF ships as a single Rust binary and provides:

Code Index: AST-aware chunking with keyword, structural, and semantic search
Intent Graph: lightweight DAG for task coordination and provenance
Evaluation Engine: golden-set scoring with regression detection
MCP Server: standard protocol interface for compatible agents
Documentation Scaffolding: spec and harness templates with drift checks

This is the workflow:

Search -> Intend -> Execute -> Verify -> Review

It keeps humans in the loop while making the agent much less fragile.

Why this architecture

I care about one boundary: sidecar, not agent.

HIEF has no LLM
HIEF does not execute your application code
HIEF does not make product decisions

The host agent does reasoning. HIEF provides memory, context, and enforceable quality checks.

That design keeps the system composable across Claude Code, Cursor, Copilot, Windsurf, Goose, and other MCP-compatible tools.

How to try HIEF

1) Build and install

git clone https://github.com/hiranp/hief.git
cd hief
cargo build --release

# Optional: make available on PATH
mkdir -p ~/bin
ln -sf "$(pwd)/target/release/hief" ~/bin/hief

2) Initialize in your project

cd /path/to/your/project
hief init
hief index build

3) Start the MCP server

hief serve

Or HTTP transport:

hief serve --transport http --port 3100

4) Connect your agent

For Claude Code or Claude Desktop MCP settings:

{
  "mcpServers": {
    "hief": {
      "command": "hief",
      "args": ["serve"]
    }
  }
}

5) Run your first quality loop

hief eval run
hief eval report

You now have a repeatable loop for context retrieval, intent coordination, and regression-aware evaluation.

Launch commitments

Local-first: your code stays on your machine
Open standards: MCP protocol and open source primitives
Minimal operations: one binary, no external services required
Auditable by default: provenance and score history are preserved
Adoption over mandate: add the pieces that help, skip the rest

Closing

AI coding agents are already useful. The missing part is not more generation; it is better memory and better feedback loops.

That is the job HIEF is built to do.

References

HIEF