🤖 Agents Framework

Universal MCP Server for AI Agent Roles, Skills & Cognitive Implants

A semantic router that dynamically loads specialized agent personas, domain skills, and cognitive reasoning implants based on user queries. Works with any MCP-compatible client (Claude Code, Cursor, Windsurf, and others).

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🚀 Quick Start

After Cloning

git clone <repository-url>
cd Agents

# Run initialization script
./scripts/init_repo.sh

The script will:

• ✅ Create Python virtual environment (.venv/)
• ✅ Install all dependencies
• ✅ Create .env configuration file
• ✅ Validate MCP server configuration

Manual Setup

# Create and activate virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp env.example .env
# Edit .env with your API keys

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⚙️ Configuration

Required Environment Variables

Create .env file with:

LANGFUSE_PUBLIC_KEY=pk-lf-... # Optional: observability
LANGFUSE_SECRET_KEY=sk-lf-... # Optional: observability
LANGFUSE_HOST=https://cloud.langfuse.com
ANTHROPIC_API_KEY=sk-ant-...  # Optional: for document OCR
AGENTS_DEBUG=0                # Set to 1 for JSON debug logging in logs/

Note: Embeddings are handled locally by fastembed (ONNX Runtime). Model is selected during setup — no external API key is required for core routing.

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🎯 How It Works

The server exposes MCP tools that any compatible client can call:

Tool	Purpose
route_and_load(query)	Semantic routing — finds the best agent, enriches its prompt with relevant skills & implants
get_agent_context(agent_name, query)	Direct agent loading when the target is already known
load_implants(query|task_type)	Load cognitive reasoning strategies by semantic query or preset bundle
list_agents()	Enumerate all available agents with metadata
log_interaction(agent_name, query, response_content, intent?, action?, outcome?, files?, tags?)	End-of-turn logger — appends to history.md (deduped by content hash) and, if configured, sends a Langfuse generation trace
clear_session_cache()	Reset session cache
describe_repo(force_refresh=False)	One-shot repo bootstrap — writes a structured summary into the managed Repository Memory section of CLAUDE.md
read_history(limit?, since?, query?)	Recent entries or lazy semantic recall over the action log

Routing Flow

1. route_and_load(query) → Single-hop routing via semantic cache
2. Meta Detection → Greetings/short queries auto-route to universal_agent
3. Cache Hit → Returns enriched prompt (SUCCESS) or sampled response (SUCCESS_SAMPLED)
4. Cache Miss → Returns ROUTE_REQUIRED with agent candidates for client selection
5. Tier-Based Enrichment → lite (no extras) / standard (2 skills + 2 implants) / deep (4+ skills + 3 implants)
6. Multi-Turn → context_hash enables delta optimization on follow-up queries

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🏗️ Architecture

Agents/
├── agents/               # Agent personas (system prompts, 38 agents)
│   ├── software_engineer/
│   │   └── system_prompt.mdc
│   ├── common/           # Shared agent resources
│   ├── capabilities/     # Capability compositions (registry.yaml)
│   └── schemas/          # Validation schemas
├── skills/               # Reusable knowledge chunks (RAG)
│   └── skill-*.mdc
├── implants/             # Cognitive reasoning strategies (RAG)
│   └── implant-*.mdc
├── src/
│   ├── server.py         # MCP Server entrypoint (FastMCP)
│   ├── engine/
│   │   ├── router.py     # Semantic routing (cache-first)
│   │   ├── skills.py     # Skill retrieval (vector search)
│   │   ├── implants.py   # Implant retrieval (vector search)
│   │   ├── config.py     # Centralized configuration
│   │   ├── embedder.py   # FastEmbed wrapper (ONNX Runtime)
│   │   ├── vector_store.py # NumPy-based vector store
│   │   ├── enrichment.py # Tier-based context enrichment
│   │   ├── capabilities.py # Capability registry resolution
│   │   ├── context.py    # Context retrieval (history formatting)
│   │   └── language.py   # Language detection
│   └── utils/
│       ├── prompt_loader.py
│       ├── debug_logger.py     # Optional JSON debug logging
│       └── langfuse_compat.py  # Optional Langfuse layer
├── data/                 # Vector store cache (auto-initialized)
├── mcp.json              # MCP server configuration
├── pyproject.toml        # Python project metadata
└── requirements.txt

Key Components

Component	Description
Agents	Specialized personas with unique system prompts
Skills	Domain-specific knowledge chunks (retrieved via RAG)
Implants	Cognitive patterns & reasoning strategies
Router	Semantic matching + caching for fast agent selection

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🔌 MCP Client Configuration

Claude Code (.mcp.json in project root)

{
  "mcpServers": {
    "Agents-Core": {
      "command": ".venv/bin/python",
      "args": ["src/server.py"]
    }
  }
}

Cursor (mcp.json in project root)

{
  "mcpServers": {
    "Agents-Core": {
      "command": ".venv/bin/python",
      "args": ["src/server.py"]
    }
  }
}

Generic stdio

source .venv/bin/activate
python src/server.py
# Server communicates via stdin/stdout using MCP protocol

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🧠 Creating New Agents

1. Create directory: agents/<agent_name>/
2. Create system_prompt.mdc with frontmatter:

---
identity:
  name: "my_agent"
  display_name: "My Agent"
  role: "Expert in X"
  tone: "Professional, Clear"
routing:
  domain_keywords: ["keyword1", "keyword2"]
  trigger_command: "/my_command"
---
# My Agent System Prompt

## Identity
You are an expert in X...

The agent will be auto-discovered by the MCP server on next startup.

Capabilities System

Instead of listing skills per agent, you can declare high-level capabilities:

capabilities: [development, dev-security]

The enrichment pipeline resolves capabilities to skill bundles via agents/capabilities/registry.yaml. Available capabilities: critical-analysis, content-structure, development, dense-summary, trust-weighted-research, bio-health, tech-documentation, dev-security, consultative-intake, creative-writing, psychology, 3d-printing, data-investigation, epistemic-analysis, code-review, decision-making, product-thinking, temporal-research, performance-engineering, prompt-design, prompt-security, roblox-development, dev-tools, blender-scripting, health-optimization, consumer-research, visualization, child-psychology.

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🧠 Repository Memory

The server ships with a per-repo memory subsystem so each new Claude session does not have to re-explore the codebase from scratch:

• describe_repo — generates a compressed, LLM-consumable repo overview via MCP sampling and writes it into the managed Repository Memory section of CLAUDE.md. Idempotent: re-runs are no-ops unless the repo manifest changes or force_refresh=True.
• log_interaction — end-of-turn logger. Appends intent / action / outcome entries (with optional files and tags) to history.md at the repo root; deduplicated by content hash; rotated to history/YYYY-MM.md when the file exceeds 512 KB. Also sends a Langfuse generation trace if keys are configured.
• read_history — returns recent entries by recency/since filter, or runs a lazy semantic search backed by the same NumpyVectorStore used for routing.

The full design and step-by-step rationale lives in docs/memory-subsystem-spec.md.

⚠️ Privacy warning — history.md captures raw prompts and responses. If you paste secrets (API keys, tokens, credentials) into Claude, they will land in this file. It is gitignored by default to keep them out of git history; if you want the action log visible in PRs, remove history.md / history/ from .gitignore and review entries before pushing.

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📊 Observability

The framework integrates with LangFuse for tracing:

• All tool calls are automatically traced
• Routing decisions are logged
• Cache hits/misses are tracked

Configure LangFuse in .env or leave blank for local-only operation.

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🛠️ Development

Running Server Manually

source .venv/bin/activate
python src/server.py

Debug Logging

Enable detailed per-call JSON logging:

AGENTS_DEBUG=1 python src/server.py

Logs are written to logs/{YYYY-MM-DD}/{HH-MM-SS.fff}_{tool}_{direction}.json. Zero overhead when disabled.

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📝 License

MIT