agent-fde

A methodology-driven consulting agent toolkit that accumulates experience. FDE manages the process and deliverables; the memory system manages experience reuse.

agent-fde fuses two ideas into one clean, tested, open-source Python package:

FDE (Frontline Deployment Engineering) — a 5-phase pipeline that turns an enterprise AI/automation rollout into executable, file-based deliverables.
Layered memory + skill crystallization — the agent remembers what worked and recalls it automatically the next time, like muscle memory.

It ships as both an MCP server (for host agents such as Claude) and a CLI.

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Why

Most agent tooling either has a strong workflow but no memory, or accumulates memory but has no methodology. agent-fde combines both: a disciplined 5-phase engagement process and a memory loop that crystallizes each finished engagement into a reusable skill.

Key design: inversion of control

The engine never calls an LLM. Instead:

old (stub):   engine → calls LLM API → generates deliverable    ❌ needs API key, vendor lock-in
new (inverted): host LLM → does its own analysis → submits structured result
                → fde_submit_analysis(...) → engine validates (pydantic) + renders  ✅

Result: the package has zero LLM dependency — no API key, no vendor lock-in. Methodology travels with the package as SKILL.md prompts that guide the host agent through each phase. Pure-CLI use falls back to heuristic templates.

Install

pip install agent-fde              # core (MCP + FDE + memory)
pip install "agent-fde[browser]"   # + optional Playwright browser tools
pip install "agent-fde[dev]"       # + test/lint tooling

Usage

As an MCP server

agent-fde serve

{
  "mcpServers": {
    "agent-fde": { "command": "agent-fde", "args": ["serve"] }
  }
}

As a CLI

agent-fde fde new --client "Acme Clinic" --industry "Healthcare" --project "Chart AI"
agent-fde fde list
agent-fde fde run <engagement_id> --phase 1
agent-fde fde run-all <engagement_id> --input "Doctors spend 2h/day on chart entry"
agent-fde fde deliverable <engagement_id> --phase 3

The 5 phases

Phase	Name	Deliverable
1	Discovery	Pain-point matrix
2	Assessment	Automation opportunity list + ROI
3	Architecture	Tech design + Mermaid diagram + milestones
4	Prototype	Prototype file skeleton + test cases + setup guide
5	Handoff	Evidence package + project index

On Phase 5 completion the tools prompt the host agent to call memory_crystallize, closing the experience loop.

Layered memory (L0–L4)

Layer	Role
L0	Meta rules (behavioural red lines)
L1	Insight index (≤25 navigation pointers)
L2	Global facts (user preferences, environment)
L3	Skill library (crystallized reusable SOPs)
L4	Session archive

Stored under ~/.agent-fde/ (override with AGENT_FDE_HOME), decoupled from code. Engagements live under ~/.agent-fde/engagements/<id>/.

Muscle-memory hook

hooks/skill_reflex.py is a zero-dependency Claude Code UserPromptSubmit hook. On each prompt it fuzzy-matches your crystallized L3 skills (Chinese-friendly) and injects the matching SOP as context — no manual retrieval. It shares the exact matcher used by the skill_find MCP tool (single source of truth) and fails safe (any error → silent exit 0).

Development

pip install -e ".[dev,browser]"
pytest
ruff check .

License

MIT