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FuzzForge AI Module

FuzzForge AI is the multi-agent layer that lets you operate the FuzzForge security platform through natural language. It orchestrates local tooling, registered Agent-to-Agent (A2A) peers, and the Temporal-powered backend while keeping long-running context in memory and project knowledge graphs.

Quick Start​

  1. Initialise a project 
    cd /path/to/project
    fuzzforge init
  2. Review environment settings – copy .fuzzforge/.env.template to .fuzzforge/.env, then edit the values to match your provider. The template ships with commented defaults for OpenAI-style usage and placeholders for Cognee keys. 
    LLM_PROVIDER=openai
    LITELLM_MODEL=gpt-5-mini
    OPENAI_API_KEY=sk-your-key
    FUZZFORGE_MCP_URL=http://localhost:8010/mcp
    SESSION_PERSISTENCE=sqlite
    Optional flags you may want to enable early: 
    MEMORY_SERVICE=inmemory
    AGENTOPS_API_KEY=sk-your-agentops-key   # Enable hosted tracing
    LOG_LEVEL=INFO                          # CLI / server log level
  3. Populate the knowledge graph 
    fuzzforge ingest --path . --recursive
    # alias: fuzzforge rag ingest --path . --recursive
  4. Launch the agent shell 
    fuzzforge ai agent
    Keep the backend running (Temporal API at FUZZFORGE_MCP_URL) so workflow commands succeed.

Everyday Workflow​

  • Run fuzzforge ai agent and start with list available fuzzforge workflows or /memory status to confirm everything is wired.
  • Use natural prompts for automation (run fuzzforge workflow …, search project knowledge for …) and fall back to slash commands for precision (/recall, /sendfile).
  • Keep /memory datasets handy to see which Cognee datasets are available after each ingest.
  • Start the HTTP surface with python -m fuzzforge_ai when external agents need access to artifacts or graph queries. The CLI stays usable at the same time.
  • Refresh the knowledge graph regularly: fuzzforge ingest --path . --recursive --force keeps responses aligned with recent code changes.

What the Agent Can Do​

  • Route requests – automatically selects the right local tool or remote agent using the A2A capability registry.
  • Run security workflows – list, submit, and monitor FuzzForge workflows via MCP wrappers.
  • Manage artifacts – create downloadable files for reports, code edits, and shared attachments.
  • Maintain context – stores session history, semantic recall, and Cognee project graphs.
  • Serve over HTTP – expose the same agent as an A2A server using python -m fuzzforge_ai.

Essential Commands​

Inside fuzzforge ai agent you can mix slash commands and free-form prompts:

/list                     # Show registered A2A agents
/register http://:10201   # Add a remote agent
/artifacts                 # List generated files
/sendfile SecurityAgent src/report.md "Please review"
You> route_to SecurityAnalyzer: scan ./backend for secrets
You> run fuzzforge workflow static_analysis_scan on ./test_projects/demo
You> search project knowledge for "temporal status" using INSIGHTS

Artifacts created during the conversation are served from .fuzzforge/artifacts/ and exposed through the A2A HTTP API.

Memory & Knowledge​

The module layers three storage systems:

  • Session persistence (SQLite or in-memory) for chat transcripts.
  • Semantic recall via the ADK memory service for fuzzy search.
  • Cognee graphs for project-wide knowledge built from ingestion runs.

Re-run ingestion after major code changes to keep graph answers relevant. If Cognee variables are not set, graph-specific tools automatically respond with a polite "not configured" message.

Sample Prompts​

Use these to validate the setup once the agent shell is running:

  • list available fuzzforge workflows
  • run fuzzforge workflow static_analysis_scan on ./backend with target_branch=main
  • show findings for that run once it finishes
  • refresh the project knowledge graph for ./backend
  • search project knowledge for "temporal readiness" using INSIGHTS
  • /recall terraform secrets
  • /memory status
  • ROUTE_TO SecurityAnalyzer: audit infrastructure_vulnerable

Need More Detail?​

Dive into the dedicated guides in this category :

  • Architecture – High-level architecture with diagrams and component breakdowns.
  • Ingestion – Command options, Cognee persistence, and prompt examples.
  • Configuration – LLM provider matrix, local model setup, and tracing options.
  • Prompts – Slash commands, workflow prompts, and routing tips.
  • A2A Services – HTTP endpoints, agent card, and collaboration flow.
  • Memory Persistence – Deep dive on memory storage, datasets, and how /memory status inspects them.

Development Notes​

  • Entry point for the CLI: ai/src/fuzzforge_ai/cli.py
  • A2A HTTP server: ai/src/fuzzforge_ai/a2a_server.py
  • Tool routing & workflow glue: ai/src/fuzzforge_ai/agent_executor.py
  • Ingestion helpers: ai/src/fuzzforge_ai/ingest_utils.py

Install the module in editable mode (pip install -e ai) while iterating so CLI changes are picked up immediately.