Aixgo Features Reference

Last Updated: 2026-03-08 Current release: see GitHub Releases — canonical version is the git tag; feature-introduction versions (v0.3.0+ etc.) below are historical markers

Authoritative Feature Catalog

This document is the complete, authoritative reference for ALL Aixgo features. It is maintained as the single source of truth and is referenced by other projects and documentation.

This catalog contains:

Complete feature listings with status (✅ Implemented, 🚧 In Progress, 🔮 Roadmap)
Code references and file locations
Technical specifications and configuration examples
Keywords for searchability

Looking for something else?

Quick Start: See README.md for installation and getting started
Pattern Details: See PATTERNS.md for deep-dive orchestration pattern guides
Security Guide: See SECURITY_BEST_PRACTICES.md for security best practices
Deployment Guide: See DEPLOYMENT.md for deployment instructions
AI Assistant Guide: See CLAUDE.md for AI assistant context

Feature Summary

Core Statistics:

Agent Types: 6 (ReAct, Classifier, Aggregator, Planner, Producer, Logger)
LLM Providers: 7+ (OpenAI, Anthropic, Gemini, xAI, Vertex AI, Amazon Bedrock, HuggingFace)
Orchestration Patterns: 13 (All implemented, 2 in roadmap)
Deployment Options: 5+ (Binary, Docker, K8s, Cloud Run, Distributed)
Security Modes: 4 (Disabled, Delegated, Builtin, Hybrid)
Observability Backends: 6+ (Langfuse, Jaeger, Honeycomb, Grafana, New Relic, Datadog)
Example Configurations: 15+ production-ready examples

Typical Performance Characteristics (measured on Apple M2 Max, Go 1.26, single-node):

Binary Size: <20MB
Cold Start: <100ms
Memory Footprint: ~50MB base
Concurrent Agents: 1000+

Feature Status Legend
CLI Interface
Core Architecture
Agent Types
LLM Providers
Orchestration Patterns
Tools & MCP (Model Context Protocol)
Data Infrastructure
Security Features
Observability & Monitoring
Configuration & Deployment
Performance & Optimization
Integration Capabilities
Development Features
Roadmap Features

Feature Status Legend

✅ Implemented: Available in current release with production examples
🚧 In Progress: Under active development
🔮 Roadmap: Planned for future releases
❌ Not Available: Not currently planned

CLI Interface

Command-Line Tools

Feature	Status	Description	Code Reference
Cobra CLI	✅ Implemented	Modern CLI framework with subcommands (v0.6.0+)	`cmd/aixgo/cmd/`
run Command	✅ Implemented	Run agent orchestrator from YAML config	`cmd/aixgo/cmd/run.go`
chat Command	✅ Implemented	Interactive multi-model coding assistant	`cmd/aixgo/cmd/chat.go`
session Command	✅ Implemented	Session management (list, resume, delete)	`cmd/aixgo/cmd/session.go`
models Command	✅ Implemented	List available LLM models with pricing	`cmd/aixgo/cmd/models.go`
Shell Completion	✅ Implemented	bash/zsh/fish/powershell completion with dynamic model/session/config suggestions	`cmd/aixgo/cmd/completion.go`
doctor Command	✅ Implemented	Environment diagnostics: Go version, provider keys, state dir perms, config validation, MCP reachability; text/JSON output	`cmd/aixgo/cmd/doctor.go`

Interactive Chat Assistant

Feature	Status	Description	Code Reference
Multi-Model Support	✅ Implemented	7+ LLM providers (Claude, GPT, Gemini, Grok)	`pkg/assistant/coordinator/`
Session Persistence	✅ Implemented	JSON file-based session storage	`pkg/assistant/session/`
Cost Tracking	✅ Implemented	Real-time per-message cost display	`pkg/llm/cost/`
Streaming Output	✅ Implemented	Real-time streaming responses	`pkg/assistant/coordinator/streaming.go`
Model Switching	✅ Implemented	Switch models mid-conversation (/model)	`cmd/aixgo/cmd/chat.go`
Interactive Prompts	✅ Implemented	Selection menus and confirmations	`pkg/assistant/prompt/`
Non-Interactive Mode	✅ Implemented	One-shot prompts via `-p`, stdin piping, `--output json`, `--max-tokens`, `--max-output-kib` soft cap with truncation	`cmd/aixgo/cmd/chat.go`
Readline Input	✅ Implemented	Arrow-key history recall, line editing, persisted history (`~/.aixgo/chat_history`), tab-completion for slash commands	`cmd/aixgo/cmd/chat.go`

Assistant Tools

Feature	Status	Description	Code Reference
File Operations	✅ Implemented	read_file, write_file, glob, grep	`pkg/assistant/tools/file/`
Git Operations	✅ Implemented	git_status, git_diff, git_commit, git_log	`pkg/assistant/tools/git/`
Terminal Execution	✅ Implemented	Safe command execution with allowlist	`pkg/assistant/tools/terminal/`
Tool Registry	✅ Implemented	MCP-compatible tool registration	`pkg/assistant/tools/registry.go`

CLI Usage Examples:

# Run orchestrator (YAML-based multi-agent systems)
aixgo run -config agents.yaml

# Interactive chat assistant
aixgo chat                              # Use default model
aixgo chat --model gpt-4o              # Specify model
aixgo chat --session abc123            # Resume session
aixgo chat --no-stream                 # Disable streaming

# Session management
aixgo session list                     # List all sessions
aixgo session resume <id>              # Resume a session
aixgo session delete <id>              # Delete a session

# Model information
aixgo models                           # List available models with pricing

In-Session Commands (aixgo chat):

/model <name> - Switch to a different model mid-conversation
/cost - Show detailed session cost summary
/save - Manually save the current session
/clear - Clear conversation history (with confirmation)
/help - Show available commands and tips
/quit or /exit - Save and exit the chat

Interactive Features:

Model Selection - Visual menu for choosing LLM on startup
Streaming Output - Real-time markdown rendering of responses
Cost Display - Automatic cost tracking after each message
File Operations - Natural language file read/write/search
Git Integration - Execute git commands through conversation
Terminal Access - Run commands with safety confirmations
Session Persistence - Automatic save to ~/.aixgo/sessions/

Core Architecture

Runtime Systems

Feature	Status	Description	Code Reference
Unified Runtime	✅ Implemented	Consolidated runtime with functional options (v0.3.0+)	`runtime.go`
Local Runtime	✅ Implemented	In-process communication using Go channels for single-binary deployment	`runtime.go`
Distributed Runtime	✅ Implemented	Multi-node orchestration using gRPC for distributed deployment	`internal/runtime/`
Distributed TLS/mTLS	✅ Implemented	Secure gRPC with TLS/mTLS and service mesh support (v0.3.0+)	`internal/runtime/distributed.go`
Distributed Streaming	✅ Implemented	gRPC streaming for long-running remote agent operations (v0.3.0+)	`internal/runtime/distributed.go`
Runtime Migration	✅ Implemented	Seamless migration from local to distributed with zero code changes	`runtime.go`, `internal/runtime/`
Message Protocol	✅ Implemented	Protocol buffer-based message passing between agents	`proto/message.proto`
State Persistence	✅ Implemented	Workflow state checkpointing and resumption	`internal/workflow/persistence.go`
Session Persistence	✅ Implemented	Session management with JSONL and Redis storage (v0.3.0+)	`pkg/session/`
Phased Agent Startup	✅ Implemented	Dependency-aware startup ordering using topological sort	`internal/graph/`, `runtime.go`

Phased Startup Features (v0.2.3+):

DependsOn Field: Declare agent startup dependencies in AgentDef
Topological Sort: Kahn's algorithm ensures correct startup order
Phase-Based Execution: Agents grouped into levels (Phase 0: no deps, Phase N: deps on < N)
Concurrent Phase Startup: Agents within each phase start concurrently for performance
Ready() Polling: Waits for agents to be Ready() before starting next phase
AgentStartTimeout: Configurable timeout (30s default) for startup

Configuration Example:

agents:
  - name: database
    role: producer

  - name: cache
    role: producer
    depends_on: [database]

  - name: api
    role: react
    depends_on: [database, cache]

Supported Runtimes: LocalRuntime, Runtime, DistributedRuntime

Keywords: runtime, local runtime, distributed runtime, gRPC, channels, message passing, state management, phased startup, dependency ordering, topological sort

Session Persistence (v0.3.0+)

Feature	Status	Description	Code Reference
Session Manager	✅ Implemented	Create, get, list, delete sessions with lifecycle management	`pkg/session/manager.go`
Session Interface	✅ Implemented	AppendMessage, GetMessages, Checkpoint, Restore operations	`pkg/session/session.go`
File Backend	✅ Implemented	JSONL file-based storage with append-only writes and path traversal protection	`pkg/session/file_backend.go`
Redis Backend	✅ Implemented	Distributed session storage for multi-node deployments	`pkg/session/redis_backend.go`
Checkpoint/Restore	✅ Implemented	Create snapshots and restore to previous states with integrity checksums	`pkg/session/session.go`
Context Helpers	✅ Implemented	SessionFromContext, ContextWithSession utilities	`pkg/session/context.go`
Runtime Integration	✅ Implemented	CallWithSession for session-aware agent execution	`runtime.go`
SessionAware Agents	✅ Implemented	ReAct agents with conversation history access	`agents/react.go`

Session Features:

GetOrCreate Pattern: Automatically resume or create sessions by user ID
Message History: Full conversation history with timestamps
Checkpoint Integrity: SHA256 checksums for entry verification
Thread-Safe: All operations safe for concurrent use
Default Enabled: Sessions enabled by default (opt-out via session_mode: disabled)

Configuration Example:

session:
  enabled: true
  store: file
  base_dir: ~/.aixgo/sessions
  checkpoint:
    auto_save: false
    interval: 5m

Usage Example:

backend, _ := session.NewFileBackend("")
mgr := session.NewManager(backend)

sess, _ := mgr.GetOrCreate(ctx, "assistant", "user-123")
sess.AppendMessage(ctx, agent.NewMessage("user", "Hello"))

checkpoint, _ := sess.Checkpoint(ctx)
// Later: sess.Restore(ctx, checkpoint.ID)

Keywords: session, persistence, checkpoint, restore, conversation history, memory, JSONL, file storage

Deployment Characteristics

Feature	Status	Description	Metrics
Binary Size	✅ Implemented	Ultra-small binary footprint	<20MB
Cold Start Time	✅ Implemented	Near-instant startup for serverless deployments	<100ms
Zero Dependencies	✅ Implemented	No runtime dependencies required	Single binary
Cross-Platform	✅ Implemented	Compile for Linux, macOS, Windows	Go compilation
Container Support	✅ Implemented	Docker images with Alpine base	Multi-stage builds

Keywords: deployment, binary, container, docker, serverless, cold start, dependencies

Type Safety & Validation

Feature	Status	Description	Code Reference
Compile-Time Type Checking	✅ Implemented	Go's type system prevents runtime errors	Native Go
JSON Schema Validation	✅ Implemented	Schema-based validation for LLM inputs/outputs	`internal/llm/schema/`, `pkg/security/validation.go`
Pydantic AI-Style Validation	✅ Implemented	Automatic retry with validation errors for structured outputs (MaxRetries: 3 default)	`internal/llm/validator/`
Field-Level Validators	✅ Implemented	Custom validation functions per field	`internal/llm/validator/`
Union Type Support	✅ Implemented	Discriminated unions with type safety	`internal/llm/validator/`
Generic Type Support	✅ Implemented	Generic type validation for structured outputs	`internal/llm/validator/`
Input Sanitization	✅ Implemented	Security-focused input cleaning and validation	`pkg/security/sanitize.go`
Safe YAML Parsing	✅ Implemented	Size/depth/complexity limits for YAML configs	`pkg/security/yaml.go`

Validation Retry Benefits:

40-70% improvement in structured output reliability
Zero configuration required (works automatically)
Configurable MaxRetries (default: 3)
Automatic error feedback to LLM for correction

Keywords: type safety, validation, schema, pydantic, sanitization, yaml parsing, field validators, union types, generics

Agent Types

ReAct Agent

Status: ✅ Implemented Code: agents/react.go

Capability	Description
Reasoning Loop	Iterative thought-action-observation cycles
Tool Calling	LLM-powered tool selection and execution
Context Management	Conversation history and state tracking
Multi-Turn Dialogue	Handle complex multi-step interactions
Error Recovery	Automatic retry with error context
Streaming Support	Stream responses in real-time
Structured Outputs	Type-safe JSON responses with validation

Keywords: react, reasoning, acting, tool calling, llm agent, conversational ai

Classifier Agent

Status: ✅ Implemented Code: agents/classifier.go

Capability	Description
Category Classification	Assign content to predefined categories
Confidence Scoring	Return probability scores for classifications
Keyword Matching	Optional keyword-based boosting
Multi-Label Support	Assign multiple categories to content
Threshold Filtering	Configurable confidence thresholds
Custom Categories	Define application-specific taxonomies
LLM-Based Classification	Use any supported LLM for intelligent routing

Configuration Example:

classifier_config:
  categories:
    - name: technical_issue
      description: "Technical problems and bugs"
      keywords: ["error", "bug", "crash"]
  confidence_threshold: 0.7
  temperature: 0.3

Keywords: classifier, classification, categorization, routing, intent detection, confidence scoring

Aggregator Agent

Status: ✅ Implemented Code: agents/aggregator.go

Strategy	Description	Use Case	Cost
Consensus	Majority voting across agent outputs	Democratic decision-making	LLM-based
Weighted	Confidence-weighted synthesis	Expert-weighted opinions	LLM-based
Semantic	Embedding-based similarity aggregation	Similar response clustering	LLM-based
Hierarchical	Multi-level synthesis with sub-aggregators	Complex multi-stage aggregation	LLM-based
RAG-Based	Retrieval-augmented aggregation	Knowledge-grounded synthesis	LLM-based

Deterministic Voting Strategies (Zero LLM Cost):

voting_majority - Simple majority wins (most common response)
voting_unanimous - Requires all agents agree (strict consensus)
voting_weighted - Weight by agent confidence scores
voting_confidence - Highest confidence wins

Features:

Conflict resolution (LLM-mediated or rule-based)
Configurable consensus thresholds
Timeout handling for slow agents
Fallback strategies for failures
Zero-cost deterministic voting options

Configuration Example:

aggregator_config:
  aggregation_strategy: consensus
  consensus_threshold: 0.75
  conflict_resolution: llm_mediated
  timeout_ms: 5000

Keywords: aggregator, aggregation, synthesis, consensus, voting, multi-agent fusion

Planner Agent

Status: ✅ Implemented Code: agents/planner.go

Capability	Description
Task Decomposition	Break complex tasks into subtasks
Dependency Analysis	Identify task dependencies and ordering
Dynamic Planning	Adapt plans based on execution results
Re-Planning	Automatic re-planning on failures
Resource Allocation	Assign tasks to appropriate agents
Progress Tracking	Monitor task completion status

Configuration Example:

planner_config:
  max_tasks: 20
  enable_replanning: true
  task_timeout_ms: 30000

Keywords: planner, planning, task decomposition, workflow planning, dynamic planning

Producer Agent

Status: ✅ Implemented Code: agents/producer.go

Capability	Description
Interval-Based Generation	Generate messages at fixed intervals
Event-Driven Production	Trigger on external events
Data Templating	Template-based message generation
Rate Control	Configurable production rate
Output Routing	Route to multiple downstream agents

Configuration Example:

agents:
  - name: event-generator
    role: producer
    interval: 500ms
    outputs:
      - target: processor

Keywords: producer, generator, event source, data producer, message generation

Logger Agent

Status: ✅ Implemented Code: agents/logger.go

Capability	Description
Message Logging	Log all incoming messages
Structured Logging	JSON-formatted log output
Filtering	Configurable log level filtering
Audit Trail	Complete message history for compliance
Multiple Outputs	Log to file, stdout, or external systems

Configuration Example:

agents:
  - name: audit-log
    role: logger
    inputs:
      - source: processor

Keywords: logger, logging, audit trail, message logging, observability

LLM Providers

OpenAI

Status: ✅ Implemented Code: internal/llm/provider/openai.go

Feature	Supported Models
Chat Completion	GPT-4, GPT-4 Turbo, GPT-3.5 Turbo
Streaming	All chat models
Function Calling	GPT-4, GPT-3.5 Turbo (all versions)
Vision	GPT-4 Vision (future)
Structured Outputs	JSON mode, function schemas
Temperature Control	0.0 - 2.0
Token Limits	Model-specific (4K - 128K context)

Environment Variable: OPENAI_API_KEY Model Detection: gpt-* prefix

Keywords: openai, gpt-4, gpt-3.5, chatgpt, function calling

Anthropic (Claude)

Status: ✅ Implemented Code: internal/llm/provider/anthropic.go

Feature	Supported Models
Chat Completion	Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku
Streaming	All Claude models
Tool Use	Native tool calling support
Extended Context	Up to 200K tokens
System Prompts	Dedicated system message support
Temperature Control	0.0 - 1.0

Environment Variable: ANTHROPIC_API_KEY Model Detection: claude-* prefix

Keywords: anthropic, claude, claude 3, extended context, tool use

Google Gemini

Status: ✅ Implemented Code: internal/llm/provider/gemini.go

Feature	Supported Models
Chat Completion	Gemini 1.5 Pro, Gemini 1.5 Flash
Multi-Modal	Text, future image/video support
Function Calling	Native function calling
Large Context	Up to 2M tokens (Gemini 1.5 Pro)
Streaming	All models
Safety Settings	Configurable content filtering

Environment Variable: GOOGLE_API_KEY Model Detection: gemini-* prefix

Keywords: google, gemini, multi-modal, large context, google ai

xAI (Grok)

Status: ✅ Implemented Code: internal/llm/provider/xai.go

Feature	Supported Models
Chat Completion	Grok-beta, Grok-1
Streaming	All models
Real-Time Data	Access to current information
Function Calling	Standard function calling

Environment Variable: XAI_API_KEY Model Detection: grok-*, xai-* prefix

Keywords: xai, grok, real-time, twitter integration

Google Vertex AI

Status: ✅ Implemented Code: internal/llm/provider/vertexai.go

Feature	Description
Enterprise Deployment	Google Cloud integration
Gemini on Vertex	Access Gemini models via Vertex AI
Compliance	Enterprise security and compliance
IAM Integration	Google Cloud IAM authentication
Multi-Region	Deploy across Google Cloud regions

Environment Variable: GOOGLE_CLOUD_PROJECT Authentication: Google Cloud credentials

Keywords: vertex ai, google cloud, enterprise, gemini, iam

Amazon Bedrock

Status: ✅ Implemented Code: pkg/llm/provider/bedrock.go

Feature	Status
Multi-Model Access (Claude, Llama, Nova, Titan, Mistral)	✅
Converse API (unified messaging)	✅
Streaming (ConverseStream)	✅
Tool Calling	✅
Structured Output	✅
AWS IAM Authentication	✅
Cross-Region Support	✅
Retry with Exponential Backoff	✅

Supported Models:

Anthropic Claude: claude-3-5-sonnet, claude-3-haiku, claude-3-opus
Amazon Nova: nova-pro, nova-lite, nova-micro
Meta Llama: llama3-70b, llama3-8b, llama4 series
Mistral: mistral-large
Amazon Titan: titan-text-express, titan-text-lite
Cohere: command-r, command-r-plus
AI21: jamba-1-5-large, jamba-1-5-mini

Configuration Example:

agents:
  - name: analyst
    role: react
    model: anthropic.claude-3-5-sonnet-20240620-v1:0
    provider: bedrock

Environment Variables:

AWS_REGION / AWS_DEFAULT_REGION
AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY (or IAM role)
AWS_PROFILE (optional)

Keywords: amazon bedrock, aws, claude on bedrock, nova, llama, enterprise ai, iam authentication

HuggingFace

Status: ✅ Implemented Code: internal/llm/provider/huggingface_basic.go, internal/llm/provider/huggingface_production.go

Variant	Description	Code Reference
Free API	Inference API with simulated streaming	`huggingface_basic.go`
Production	Optimized for paid endpoints (TGI)	`huggingface_production.go`

Supported Models:

Meta-Llama (Llama 2, Llama 3)
Mistral AI models
Falcon
StarCoder
100+ other models

Environment Variable: HUGGINGFACE_API_KEY Model Detection: meta-llama/*, mistralai/*, etc.

Keywords: huggingface, llama, mistral, open source models, tgi

Local Inference (Ollama)

Status: ✅ Implemented Code: internal/llm/inference/ollama.go

Feature	Description
Local Models	Run models locally with zero API costs
SSRF Protection	Enterprise-grade URL validation, private IP blocking, localhost prevention
Hybrid Cloud Fallback	Automatic fallback to cloud providers on failure
K8s Manifests	Production Kubernetes deployment configs
Model Support	phi, llama, mistral, gemma, 100+ models
Streaming	Native streaming support
100% Cost Savings	Eliminate API costs entirely for inference

Configuration:

model_services:
  - name: llama-service
    provider: huggingface
    model: meta-llama/Llama-2-7b
    runtime: ollama
    config:
      address: http://localhost:11434

SSRF Configuration (optional):

# Extend default allowlist for production Kubernetes deployments
export OLLAMA_ALLOWED_HOSTS="ollama-service.production.svc.cluster.local"

Keywords: ollama, local inference, local models, self-hosted, zero cost, ssrf protection

Additional Inference Services

Service	Status	Description	Code Reference
vLLM	✅ Implemented	High-performance inference server for large models	`internal/llm/inference/vllm.go`
HuggingFace TEI	✅ Implemented	Text Embeddings Inference (self-hosted embeddings)	`internal/llm/inference/huggingface.go`
Hybrid Fallback	✅ Implemented	Automatic fallback from local to cloud on failures	`internal/llm/inference/hybrid.go`

Inference Backend Summary:

Ollama: Best for development and local testing (SSRF protection included)
vLLM: Best for production self-hosted inference
HuggingFace TEI: Best for self-hosted embeddings
Hybrid: Combine local + cloud with automatic failover

Keywords: vllm, inference services, text generation inference, tei, hybrid inference, self-hosted

Orchestration Patterns

Total Patterns: 13 ✅ All Implemented and production-ready

For complete pattern documentation with code examples, configuration, and use cases, see PATTERNS.md.

Quick Reference:

✅ Supervisor - Centralized hub-and-spoke coordination
✅ Sequential - Ordered pipeline execution
✅ Parallel - Concurrent multi-agent processing (3-4× speedup)
✅ Router - Intelligent model routing (25-50% cost savings)
✅ Swarm - Decentralized agent handoffs
✅ Hierarchical - Multi-level delegation
✅ RAG - Retrieval-augmented generation (70% token reduction)
✅ Reflection - Self-critique and refinement (20-50% quality improvement)
✅ Ensemble - Multi-model voting (25-50% error reduction)
✅ Classifier - Intent-based routing
✅ Aggregation - Multi-agent synthesis
✅ Planning - Dynamic task decomposition
✅ MapReduce - Distributed batch processing

Roadmap Patterns:

🔮 Debate Pattern (v2.1+, 2025 H2)
🔮 Nested/Composite Pattern (v2.2+, 2025 H2)

This section provides feature status and keywords. For implementation details, pattern selection guide, and real-world examples, see PATTERNS.md.

Pattern Details (Code references, features, use cases, complexity):

See PATTERNS.md for comprehensive documentation on all 13 patterns
Pattern selection guide and decision tree
Real-world examples and performance metrics
Code examples and configuration templates

Tools & MCP (Model Context Protocol)

Core MCP Features

Status: ✅ Implemented Code: pkg/mcp/

Feature	Description	Code Reference
Tool Registration	Dynamic tool discovery and registration	`pkg/mcp/registry.go`
Type-Safe Tools	Strongly-typed tool definitions	`pkg/mcp/typed_tools.go`
Function Calling	LLM-powered tool invocation	`pkg/mcp/client.go`
Multi-Server Support	Connect to multiple MCP servers	`pkg/mcp/cluster.go`
Service Discovery	Automatic tool discovery	`pkg/mcp/discovery.go`

Keywords: mcp, model context protocol, tools, function calling, tool registry

Transport Modes

Transport	Status	Description	Code Reference
Local Transport	✅ Implemented	In-process tool communication	`pkg/mcp/transport_local.go`
gRPC Transport	✅ Implemented	Remote service integration via gRPC	`pkg/mcp/transport_grpc.go`
HTTP Transport	🔮 Roadmap	REST API tool calling	Planned

Configuration Example:

mcp_servers:
  - name: weather-service
    transport: local
  - name: data-service
    transport: grpc
    address: localhost:50051
    tls: true
    auth:
      type: bearer
      token_env: DATA_API_TOKEN

Keywords: transport, grpc, local transport, remote tools

MCP Security

Feature	Status	Description
TLS/mTLS	✅ Implemented	Encrypted gRPC connections
Authentication	✅ Implemented	Bearer token, API key support
Authorization	✅ Implemented	RBAC for tool access
Input Validation	✅ Implemented	Schema-based validation
Rate Limiting	✅ Implemented	Per-tool rate limits

Keywords: mcp security, tls, authentication, authorization, validation

Built-in Tool Support

Category	Examples	Status
Data Access	Database queries, API calls	✅ Via MCP
File Operations	Read, write, search files	✅ Via MCP
Web Tools	HTTP requests, web scraping	✅ Via MCP
Custom Tools	User-defined functions	✅ Via MCP

Keywords: tools, built-in tools, custom tools, data access, file operations

Data Infrastructure

Vector Databases

Vector Store	Status	Description	Code Reference
Google Firestore	✅ Implemented	Cloud-native vector storage with auto-scaling	`pkg/vectorstore/firestore/`
In-Memory Store	✅ Implemented	High-performance local vector storage	`pkg/vectorstore/memory/`
Qdrant	🔮 Roadmap	High-performance vector search engine	Planned
pgvector	🔮 Roadmap	PostgreSQL vector extension	Planned
ChromaDB	🔮 Roadmap	Open-source embedding database	Planned

Features:

Semantic similarity search
Metadata filtering
Batch operations
Index optimization
Persistent storage

Keywords: vector database, vector store, embeddings, similarity search, firestore, qdrant, pgvector

Embeddings

Provider	Status	Models	Code Reference
OpenAI	✅ Implemented	text-embedding-ada-002, text-embedding-3-small, text-embedding-3-large	`pkg/embeddings/openai.go`
HuggingFace API	✅ Implemented	All HF embedding models via Inference API	`pkg/embeddings/huggingface.go`
HuggingFace TEI	✅ Implemented	Text Embeddings Inference (self-hosted)	`pkg/embeddings/huggingface_tei.go`
Vertex AI	🔮 Roadmap	Google Cloud embeddings	Planned

Features:

Batch embedding generation
Caching for performance
Dimension normalization
Custom model support

Keywords: embeddings, text embeddings, openai embeddings, huggingface embeddings, tei

Memory & Context

Feature	Status	Description	Code Reference
Conversation History	✅ Implemented	Persistent conversation storage	`pkg/memory/memory.go`
Semantic Memory	✅ Implemented	Vector-based long-term memory	`pkg/memory/memory.go`
RAG Systems	✅ Implemented	Retrieval-augmented generation	Agent integration
Context Window Management	✅ Implemented	Automatic context trimming	`internal/llm/context/`
Context Window Optimization	✅ Implemented	Smart context management	`internal/llm/context/`
Summary-Based Compression	✅ Implemented	Compress old context with summaries	`internal/llm/context/`
Token Counting	✅ Implemented	Accurate token estimation	`internal/llm/cost/calculator.go`
Tool Schema Caching	✅ Implemented	Cache tool definitions to reduce tokens	`pkg/mcp/`
Long-Term Memory	🔮 Roadmap	Cross-session knowledge retention	Planned

Context Management Features:

Automatic context window optimization
Smart trimming based on token limits
Summary-based compression for long conversations
Token counting and estimation
Tool schema caching to reduce redundancy
Multiple pruning strategies (FIFO, summary, semantic)

Keywords: memory, conversation history, semantic memory, rag, context management, context optimization, token counting, compression

Security Features

Code Reference: pkg/security/

For detailed security guidelines, code examples, and best practices, see:

SECURITY_BEST_PRACTICES.md - Complete security guide
AUTHENTICATION.md - Authentication configuration guide

This section provides feature catalog and code references.

Authentication & Authorization

Feature	Status	Description	Code Reference
Auth Framework	✅ Implemented	Pluggable authentication system	`pkg/security/auth.go`
Disabled Mode	✅ Implemented	No auth (dev only)	`pkg/security/auth.go`
Delegated Auth	✅ Implemented	Cloud IAP, AWS ALB, etc.	`pkg/security/auth.go`
Builtin Auth	✅ Implemented	API key authentication	`pkg/security/auth.go`
Hybrid Auth	✅ Implemented	Combine multiple auth methods	`pkg/security/auth.go`
RBAC	✅ Implemented	Role-based access control	`pkg/security/auth.go`
JWT Verification	✅ Implemented	JSON Web Token validation	`pkg/security/auth.go`
Google Cloud IAP	✅ Implemented	Identity-Aware Proxy JWT verification	`pkg/security/iap.go`

Configuration Example:

security:
  auth:
    mode: builtin  # disabled, delegated, builtin, hybrid
    api_keys:
      - name: service1
        key_env: SERVICE1_API_KEY
    rbac:
      enabled: true
      roles:
        - name: admin
          permissions: ["*"]

Keywords: authentication, authorization, rbac, jwt, api key, iap, auth modes

Input Security

Feature	Status	Description	Code Reference
Input Validation	✅ Implemented	Schema-based validation	`pkg/security/validation.go`
Input Sanitization	✅ Implemented	HTML/script removal	`pkg/security/sanitize.go`
Error Sanitization	✅ Implemented	Masks file paths, IPs, and sensitive info in error messages	`pkg/security/sanitize.go`
Prompt Injection Protection	✅ Implemented	Detection and mitigation	`pkg/security/prompt_injection.go`
SSRF Protection	✅ Implemented	URL validation, private IP blocking, metadata service blocking, DNS rebinding prevention	`pkg/security/ssrf.go`
Path Traversal Prevention	✅ Implemented	File path validation with allowlist checking (v0.3.0+)	`pkg/security/validation.go`, `pkg/session/file_backend.go`
Subprocess Injection Prevention	✅ Implemented	Input validation with strict allowlists (v0.3.0+)	`pkg/security/validation.go`
Safe Integer Conversion	✅ Implemented	Bounds checking for int to int32 conversions (v0.3.0+)	`internal/llm/provider/vertexai.go`
Cryptographic RNG	✅ Implemented	crypto/rand for session IDs and security-critical operations (v0.3.0+)	`pkg/session/`
SQL Injection Prevention	✅ Implemented	Parameterized queries	Best practices
Timing Attack Prevention	✅ Implemented	Constant-time comparison for API keys	`pkg/security/auth.go`

Error Sanitization Features:

Automatically masks file system paths
Redacts IP addresses and network info
Removes sensitive stack trace details
Safe error messages for production

SSRF Protection Features:

Shared reusable validator (pkg/security/ssrf.go)
URL scheme validation (HTTP/HTTPS only)
Private IP blocking (RFC1918: 10.x, 172.16-31.x, 192.168.x)
Cloud metadata service blocking (169.254.169.254)
Link-local and multicast address blocking
DNS rebinding protection via DialContext validation
Configurable host allowlists
Environment variable support (OLLAMA_ALLOWED_HOSTS)
Default Ollama allowlist: localhost, 127.0.0.1, ::1, ollama, ollama-service

Security Hardening (v0.3.0+):

29 Code Scanning Alerts Fixed: Comprehensive security audit and remediation
G204 Mitigation: Subprocess injection prevention via input validation
G304 Mitigation: Path traversal prevention with component validation
G402 Warnings: TLS security warnings and best practices documentation
G115 Mitigation: Safe integer conversion with bounds checking
G404 Mitigation: crypto/rand for security-critical randomness
Example Secrets: Placeholder patterns throughout documentation
File Permissions: Restrictive 0700/0600 for session storage

Keywords: input validation, sanitization, prompt injection, ssrf, path traversal, injection prevention, error sanitization, timing attacks, security hardening, code scanning

Rate Limiting & Throttling

Feature	Status	Description	Code Reference
Token Bucket Algorithm	✅ Implemented	Smooth rate limiting	`pkg/security/ratelimit.go`
Per-User Limits	✅ Implemented	User-specific quotas	`pkg/security/ratelimit.go`
Per-Endpoint Limits	✅ Implemented	API endpoint throttling	`pkg/security/ratelimit.go`
Burst Handling	✅ Implemented	Allow burst traffic	`pkg/security/ratelimit.go`
Distributed Rate Limiting	🔮 Roadmap	Cross-instance limiting	Planned

Configuration Example:

security:
  rate_limiting:
    enabled: true
    requests_per_second: 10
    burst: 20
    per_user: true

Keywords: rate limiting, throttling, token bucket, quota, burst handling

Audit & Compliance

Feature	Status	Description	Code Reference
Audit Logging	✅ Implemented	Security event logging	`pkg/security/audit.go`
SIEM Integration	✅ Implemented	Export to SIEM systems	`pkg/security/audit_siem.go`
Compliance Logging	✅ Implemented	GDPR, HIPAA-ready logs	`pkg/security/audit.go`
PII Detection	✅ Implemented	Detect sensitive data	`pkg/security/extractor.go`
Audit Trail	✅ Implemented	Complete request history	`pkg/security/audit.go`

SIEM Integration Backends:

Elasticsearch - Direct integration with Elastic Stack
Splunk HEC - HTTP Event Collector integration
Webhook - Generic webhook for custom integrations
Custom - Build your own SIEM adapter
Datadog (via webhook)
Sumo Logic (via webhook)

Keywords: audit logging, siem, compliance, pii detection, audit trail, elasticsearch, splunk

Transport Security

Feature	Status	Description
TLS Support	✅ Implemented	Encrypted HTTP/gRPC
mTLS	✅ Implemented	Mutual TLS authentication
Certificate Management	✅ Implemented	Auto cert rotation support
Secure Headers	✅ Implemented	HSTS, CSP, etc.

Note: TLS/mTLS handled by cloud infrastructure (Cloud Run, GKE, etc.)

Keywords: tls, mtls, encryption, certificates, secure transport

Configuration Security

Feature	Status	Description	Code Reference
Safe YAML Parsing	✅ Implemented	Size/depth/complexity limits	`pkg/security/yaml.go`
Secret Management	✅ Implemented	Environment variable secrets	Best practices
Config Validation	✅ Implemented	Schema validation for configs	`pkg/security/validation.go`

YAML Security Limits:

Max size: 1MB
Max depth: 10 levels
Max keys: 1000
Max aliases: 100

Keywords: yaml security, config validation, secret management, safe parsing

Observability & Monitoring

Code Reference: pkg/observability/, internal/observability/

For setup guides and configuration details, see:

OBSERVABILITY.md - Complete observability setup guide

This section provides feature catalog and code references.

Distributed Tracing

Feature	Status	Description	Code Reference
OpenTelemetry	✅ Implemented	Industry-standard tracing	`internal/observability/`
Trace Export	✅ Implemented	OTLP, Jaeger, Zipkin	`internal/observability/`
Span Attributes	✅ Implemented	Rich context metadata	`internal/observability/`
Trace Sampling	✅ Implemented	Configurable sampling rates	`internal/observability/`
Cross-Service Tracing	✅ Implemented	Distributed trace propagation	`internal/observability/`

Supported Backends:

Langfuse (default, auto-detection)
Jaeger - Open-source distributed tracing
Honeycomb - Observability platform
Grafana Cloud - Grafana Tempo integration
New Relic - Enterprise APM
Datadog - Full-stack observability
Any OTLP-compatible backend

Environment Variables:

OTEL_SERVICE_NAME=aixgo
OTEL_EXPORTER_OTLP_ENDPOINT=https://cloud.langfuse.com
OTEL_TRACES_ENABLED=true

Keywords: opentelemetry, tracing, distributed tracing, otlp, jaeger, spans

Metrics & Monitoring

Metric Category	Status	Description	Code Reference
HTTP Metrics	✅ Implemented	Request rate, latency, errors	`pkg/observability/metrics.go`
gRPC Metrics	✅ Implemented	RPC stats, latency	`pkg/observability/metrics.go`
Agent Metrics	✅ Implemented	Agent performance stats	`pkg/observability/metrics.go`
System Metrics	✅ Implemented	CPU, memory, goroutines	`pkg/observability/metrics.go`
LLM Metrics	✅ Implemented	Token usage, cost, latency	Automatic
Custom Metrics	✅ Implemented	User-defined metrics	`pkg/observability/metrics.go`

Prometheus Metrics:

aixgo_http_requests_total
aixgo_http_request_duration_seconds
aixgo_grpc_requests_total
aixgo_agent_executions_total
aixgo_llm_tokens_total
aixgo_llm_cost_total

Keywords: prometheus, metrics, monitoring, performance, http metrics, grpc metrics

Cost Tracking

Feature	Status	Description	Code Reference
Automatic Token Counting	✅ Implemented	Track tokens per LLM call automatically	`internal/llm/cost/calculator.go`
Cost Calculation	✅ Implemented	Calculate costs per provider with accurate pricing	`internal/llm/cost/calculator.go`
Per-Request Costs	✅ Implemented	Track costs by request ID	`internal/llm/cost/calculator.go`
Per-Agent Costs	✅ Implemented	Track costs by agent name	`internal/llm/cost/calculator.go`
Per-User Costs	✅ Implemented	Track costs by user ID	`internal/llm/cost/calculator.go`
Aggregate Cost Reports	✅ Implemented	Daily/weekly/monthly rollups	`internal/llm/cost/calculator.go`
Cost Alerts	🔮 Roadmap	Alert on budget thresholds	Planned

Tracked Metrics:

Input tokens (per request)
Output tokens (per request)
Total tokens (per request)
Cost per request
Cost per agent
Cost per user
Cost by model/provider
Historical cost trends

Cost Optimization Capabilities:

Router pattern: 25-50% cost savings
RAG pattern: 70% token reduction
Local inference: 100% API cost savings
Automatic cost tracking with zero configuration

Keywords: cost tracking, token counting, llm costs, cost analytics, budget monitoring, cost optimization

Langfuse Integration

Feature	Status	Description
LLM Observability	✅ Implemented	LLM-specific tracing
Prompt Tracking	✅ Implemented	Track prompt versions
Completion Analysis	✅ Implemented	Quality metrics
User Sessions	✅ Implemented	Track user interactions
Cost Analysis	✅ Implemented	LLM cost dashboards

Environment Variables:

LANGFUSE_PUBLIC_KEY=pk-lf-...
LANGFUSE_SECRET_KEY=sk-lf-...

Keywords: langfuse, llm observability, prompt tracking, completion analysis

Health Checks

Endpoint	Status	Description	Code Reference
`GET /health`	✅ Implemented	Overall health status	`pkg/observability/health.go`
`GET /health/live`	✅ Implemented	Liveness probe (K8s)	`pkg/observability/health.go`
`GET /health/ready`	✅ Implemented	Readiness probe (K8s)	`pkg/observability/health.go`
`GET /metrics`	✅ Implemented	Prometheus metrics	`pkg/observability/server.go`

Health Check Components:

LLM provider connectivity
Vector store availability
MCP server status
Runtime health

Keywords: health checks, liveness, readiness, kubernetes probes

Logging

Feature	Status	Description
Structured Logging	✅ Implemented	JSON-formatted logs
Log Levels	✅ Implemented	DEBUG, INFO, WARN, ERROR
Context Propagation	✅ Implemented	Request ID, trace ID in logs
Agent Logging	✅ Implemented	Per-agent log streams
Error Tracking	✅ Implemented	Stack traces and context

Keywords: logging, structured logging, log levels, error tracking

Configuration & Deployment

For deployment guides and infrastructure setup, see:

DEPLOYMENT.md - Complete deployment guide (Cloud Run, Kubernetes, Docker)

This section provides feature catalog and code references.

Configuration Formats

Format	Status	Description	Code Reference
YAML Workflows	✅ Implemented	Declarative agent configuration	`aixgo.go`
Go SDK	✅ Implemented	Programmatic agent creation	All packages
Environment Variables	✅ Implemented	Runtime configuration	Throughout
JSON Config	✅ Implemented	Alternative to YAML	`pkg/config/`

Keywords: yaml config, configuration, go sdk, environment variables

Example Configurations

Status: ✅ Implemented Count: 15+ production-ready examples

Category	Examples	Location
Agent Types	Producer, ReAct, Logger, Classifier, Aggregator, Planner	`examples/`
LLM Providers	OpenAI, Anthropic, Gemini, xAI, HuggingFace	`examples/`
Patterns	Supervisor, Parallel, Sequential, Router, Reflection	`examples/`
Security	Auth modes, rate limiting, TLS	`examples/`
MCP	Local, gRPC, multi-server	`examples/huggingface-mcp/`
Complete Use Cases	End-to-end applications	`examples/`

Keywords: examples, sample configs, reference implementations, use cases

Deployment Options

Deployment	Status	Description	Code Reference
Single Binary	✅ Implemented	Standalone executable	Native Go
Docker	✅ Implemented	Containerized deployment	`Dockerfile`, `Dockerfile.alpine`
Docker Compose	✅ Implemented	Multi-container orchestration	`docker-compose.yml`
Google Cloud Run	✅ Implemented	Serverless deployment	`deploy/cloudrun/`
Kubernetes	✅ Implemented	Production K8s manifests	`deploy/k8s/`
Kubernetes Operator	🔮 Roadmap	Custom K8s controller	Planned
Terraform IaC	🔮 Roadmap	Infrastructure as Code	Planned

Container Sizes:

Standard: ~50MB
Alpine: <20MB
Multi-stage optimized

Keywords: deployment, docker, kubernetes, cloud run, serverless, iac

Cloud Run Features

Feature	Status	Description
Auto-Scaling	✅ Implemented	0-N instances
Cold Start Optimization	✅ Implemented	<100ms startup
IAP Integration	✅ Implemented	Identity-Aware Proxy auth
Secret Manager	✅ Implemented	GCP Secret Manager integration
Custom Domains	✅ Implemented	Domain mapping
HTTPS	✅ Implemented	Automatic TLS certificates

Keywords: cloud run, serverless, auto-scaling, gcp

Kubernetes Features

Feature	Status	Description	File
Deployments	✅ Implemented	Stateless workloads	`deploy/k8s/deployment.yaml`
Services	✅ Implemented	Load balancing	`deploy/k8s/service.yaml`
Ingress	✅ Implemented	HTTP routing	`deploy/k8s/ingress.yaml`
ConfigMaps	✅ Implemented	Configuration management	`deploy/k8s/configmap.yaml`
Secrets	✅ Implemented	Secret management	`deploy/k8s/secret.yaml`
HPA	✅ Implemented	Horizontal Pod Autoscaling	`deploy/k8s/hpa.yaml`
Health Probes	✅ Implemented	Liveness and readiness	Deployment manifest

Keywords: kubernetes, k8s, deployment, services, ingress, autoscaling

CI/CD

Feature	Status	Description	Code Reference
GitHub Actions	✅ Implemented	Automated build and test	`.github/workflows/`
Docker Build	✅ Implemented	Automated container builds	`.github/workflows/`
Testing Pipeline	✅ Implemented	Unit, integration, E2E tests	`.github/workflows/`
Dependency Updates	✅ Implemented	Automated dependency updates	`.github/workflows/`
Security Scanning	✅ Implemented	Container vulnerability scanning	`.github/workflows/`

Keywords: ci/cd, github actions, automation, testing, security scanning

Performance & Optimization

Performance Characteristics

Metric	Value	Description	Benefit
Binary Size	<20MB	Ultra-compact deployment	Easy distribution, fast transfers
Cold Start	<100ms	Near-instant startup	Serverless-ready
Memory Footprint	~50MB	Minimal base memory usage	Cost-effective scaling
Concurrent Agents	1000+	Handle many concurrent agents	High throughput
Infrastructure Savings	60-70%	vs Python frameworks	Lower cloud costs
Throughput	High	Go's native performance	Fast response times

Why Go vs Python:

Startup: 10-100x faster cold starts
Memory: 5-10x lower memory usage
Deployment: Single binary vs dependency management
Performance: Native compiled vs interpreted
Cost: 60-70% lower infrastructure costs

Keywords: performance, binary size, cold start, memory, throughput, infrastructure savings, go benefits

Optimization Features

Feature	Status	Description	Code Reference
Connection Pooling	✅ Implemented	Reuse HTTP/gRPC connections	Native Go
Caching	✅ Implemented	Cache embeddings, responses	Various
Circuit Breakers	✅ Implemented	Prevent cascade failures	Throughout
Retry with Backoff	✅ Implemented	Exponential backoff for failures	Throughout
Timeout Management	✅ Implemented	Configurable timeouts	Throughout
Context Pruning	✅ Implemented	Automatic context window trimming	`internal/llm/context/`

Keywords: optimization, caching, circuit breaker, retry, timeout, connection pooling

Cost Optimization

Technique	Status	Description	Savings	Code Reference
Router Pattern	✅ Implemented	Route simple queries to cheap models	25-50%	Orchestration patterns
RAG Pattern	✅ Implemented	Reduce context with retrieval	70% token reduction	Orchestration patterns
Context Pruning	✅ Implemented	Remove unnecessary context	Variable	`internal/llm/context/`
Model Caching	✅ Implemented	Cache frequent responses	Variable	Throughout
Local Inference	✅ Implemented	Use Ollama/vLLM for zero-cost inference	100% API costs	`internal/llm/inference/`

Cost Optimization Strategies:

Router Pattern - Direct simple queries to cheaper models (GPT-3.5 vs GPT-4)
- 25-50% cost reduction in production
- Zero code changes required
- Automatic complexity detection
RAG Pattern - Retrieve only relevant context instead of full knowledge base
- 70% token reduction
- Better response quality
- Lower latency
Local Inference - Self-host models with Ollama or vLLM
- 100% API cost elimination
- Full control over infrastructure
- SSRF protection built-in

Keywords: cost optimization, router, rag, context pruning, caching, local inference

Reliability Features

Feature	Status	Description	Code Reference
Circuit Breakers	✅ Implemented	Automatic failure detection	Throughout
Retry with Backoff	✅ Implemented	Exponential backoff	Throughout
State Persistence	✅ Implemented	Workflow state checkpointing	`internal/workflow/persistence.go`
Graceful Degradation	✅ Implemented	Fallback strategies	Throughout
Health Monitoring	✅ Implemented	Component health checks	`pkg/observability/health.go`
Crash Recovery	🔮 Roadmap	Automatic process recovery	Planned
Multi-Region	🔮 Roadmap	Geographic distribution	Planned

Keywords: reliability, circuit breaker, retry, persistence, health monitoring, crash recovery

Integration Capabilities

LLM Integration Features

Feature	Status	Description	Code Reference
Multi-Provider Support	✅ Implemented	6+ LLM providers	`internal/llm/provider/`
Provider Auto-Detection	✅ Implemented	Automatic provider selection	`internal/llm/provider/registry.go`
Streaming	✅ Implemented	Real-time response streaming	`internal/llm/provider/streaming.go`
Function Calling	✅ Implemented	LLM tool use	All providers
Structured Outputs	✅ Implemented	Type-safe JSON responses	`internal/llm/provider/structured.go`
Validation Retry	✅ Implemented	Pydantic AI-style retry	`internal/llm/validator/`

Keywords: llm integration, multi-provider, streaming, function calling, structured outputs

API Integration

Feature	Status	Description
HTTP REST API	✅ Implemented	RESTful agent endpoints
gRPC API	✅ Implemented	High-performance RPC
Webhook Support	✅ Via MCP	Event-driven integrations
GraphQL	🔮 Roadmap	GraphQL API layer

Keywords: api integration, rest api, grpc, webhooks

External Service Integration

Integration	Status	Description
Google Cloud	✅ Implemented	Firestore, Vertex AI, IAP, Secret Manager
OpenTelemetry	✅ Implemented	Any OTLP-compatible backend
Prometheus	✅ Implemented	Metrics export
SIEM Systems	✅ Implemented	Splunk, Datadog, Sumo Logic
HuggingFace	✅ Implemented	Models, embeddings, inference

Keywords: integrations, google cloud, opentelemetry, prometheus, siem

Development Features

Developer Experience

Feature	Status	Description	Code Reference
Type Safety	✅ Implemented	Compile-time error detection	Native Go
Clear Interfaces	✅ Implemented	Well-defined agent/runtime APIs	`internal/agent/types.go`
Comprehensive Docs	✅ Implemented	Extensive documentation	`docs/`
Example Code	✅ Implemented	29+ working examples	`examples/`
Go Package Docs	✅ Implemented	pkg.go.dev documentation	All packages

Keywords: developer experience, type safety, documentation, examples

Testing Support

Feature	Status	Description	Code Reference
Unit Testing	✅ Implemented	Package-level tests	`*_test.go`
Integration Testing	✅ Implemented	Cross-package tests	`*_integration_test.go`
E2E Testing	✅ Implemented	Full workflow tests	`tests/e2e/`
Mock Providers	✅ Implemented	Mock LLM providers for testing	`internal/llm/provider/mock.go`
Test Utilities	✅ Implemented	Testing helpers	`testutil.go`, `agents/testutil.go`
Benchmarking	✅ Implemented	Performance benchmarks	`*_test.go`

Test Coverage: 80%+ across core packages

Keywords: testing, unit tests, integration tests, e2e tests, mocking, benchmarks

Code Generation

Feature	Status	Description	Code Reference
Protocol Buffers	✅ Implemented	Generate Go code from .proto files	`proto/`
Mock Generation	✅ Implemented	Generate mocks for interfaces	`go generate`

Keywords: code generation, protobuf, mocks

Build & Tooling

Tool	Status	Description
Makefile	✅ Implemented	Build automation
Go Modules	✅ Implemented	Dependency management
golangci-lint	✅ Implemented	Comprehensive linting
gofmt	✅ Implemented	Code formatting
go vet	✅ Implemented	Static analysis

Make Targets:

make build - Build all packages
make test - Run all tests
make lint - Run linters
make coverage - Generate coverage report
make bench - Run benchmarks

Keywords: build tools, makefile, linting, formatting, go modules

Roadmap Features

Multi-Modal (Future)

Feature	Status	Expected	Description
Vision/Images	🔮 Roadmap	2025 H2	Image understanding with GPT-4V, Claude 3
Audio Processing	🔮 Roadmap	2025 H2	Speech-to-text with Whisper
Document Parsing	🔮 Roadmap	2025 H2	PDF, image, document extraction

Keywords: multi-modal, vision, audio, document parsing, future features

Vector Databases (Planned)

Database	Status	Expected
Qdrant	🔮 Roadmap	2025 Q2
pgvector	🔮 Roadmap	2025 Q2
ChromaDB	🔮 Roadmap	2025 Q3
Pinecone	🔮 Roadmap	2025 Q3

Keywords: vector databases roadmap, qdrant, pgvector, chromadb, pinecone

Infrastructure (Planned)

Feature	Status	Expected
Kubernetes Operator	🔮 Roadmap	2025 Q2
Terraform Modules	🔮 Roadmap	2025 Q2
Helm Charts	🔮 Roadmap	2025 Q3
AWS Support	🔮 Roadmap	2025 Q3

Keywords: infrastructure roadmap, kubernetes operator, terraform, helm, aws

Reliability (Planned)

Feature	Status	Expected
Crash Recovery	🔮 Roadmap	2025 Q2
Multi-Region Deployment	🔮 Roadmap	2025 Q3
Distributed Rate Limiting	🔮 Roadmap	2025 Q3

Keywords: reliability roadmap, crash recovery, multi-region

Feature Matrix by Use Case

Customer Service Systems

Feature	Relevance	Status
Supervisor Pattern	Critical	✅
Classifier Agent	Critical	✅
Swarm Pattern	High	✅
Multi-Turn Dialogue	Critical	✅
Authentication	Critical	✅
Audit Logging	High	✅

Data Pipelines

Feature	Relevance	Status
Sequential Pattern	Critical	✅
Parallel Pattern	High	✅
MapReduce Pattern	Critical	✅
State Persistence	High	✅
Error Handling	Critical	✅

Enterprise Chatbots

Feature	Relevance	Status
RAG Pattern	Critical	✅
Vector Stores	Critical	✅
Authentication	Critical	✅
Audit Logging	Critical	✅
Cost Tracking	High	✅
RBAC	High	✅

Research Assistants

Feature	Relevance	Status
Parallel Pattern	Critical	✅
Aggregator Agent	Critical	✅
RAG Pattern	High	✅
Multi-Provider Support	High	✅
Cost Optimization	High	✅

Code Generation

Feature	Relevance	Status
Reflection Pattern	Critical	✅
Tool Calling	Critical	✅
Structured Outputs	High	✅
Multi-Turn Dialogue	High	✅

Feature Search Index

By Keyword

Agent: ReAct, Classifier, Aggregator, Planner, Producer, Logger LLM: OpenAI, Anthropic, Gemini, xAI, Vertex AI, Amazon Bedrock, HuggingFace, Ollama Pattern: Supervisor, Sequential, Parallel, Router, Swarm, Hierarchical, RAG, Reflection, Ensemble, Classifier, Aggregation, Planning, MapReduce Security: Authentication, Authorization, RBAC, Rate Limiting, Input Validation, Prompt Injection, SSRF, Audit Logging Observability: OpenTelemetry, Langfuse, Prometheus, Health Checks, Cost Tracking, Distributed Tracing Data: Vector Store, Embeddings, Semantic Memory, RAG, Firestore Deployment: Docker, Kubernetes, Cloud Run, Single Binary Integration: MCP, gRPC, REST API, Tools, Function Calling Performance: Cold Start, Binary Size, Cost Optimization, Caching, Circuit Breaker Runtime: Phased Startup, Dependency Ordering, Topological Sort, Agent Lifecycle

Version History

Version	Date	Changes
0.6.0	2026-03-08	Interactive coding assistant (aixgo chat), CLI refactor to Cobra, session management commands, model listing with pricing
0.5.0	2026-02-14	Public Provider API (pkg/llm/provider), Guided ReAct Workflows with verification
0.4.0	2026-02-12	Go 1.26, advanced planner strategies, RAG variants, JWT verification
0.3.3	2026-02-08	Session persistence, unified runtime, security hardening
0.2.3	2026-01-02	Added phased agent startup with dependency ordering, lint fixes
0.2.2	2025-12-27	Public agent package, VertexAI streaming improvements
0.2.1	2025-12-26	Security hardening, SSRF protection enhancements
0.2.0	2025-12-26	VertexAI Gen AI SDK migration, production hardening
0.1.3	2025-12-14	Pydantic AI-style validation retry, security audit fixes
0.1.2	2025-12-07	Initial comprehensive features document

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Aixgo Features Reference

Authoritative Feature Catalog

Feature Summary

Table of Contents

Feature Status Legend

CLI Interface

Command-Line Tools

Interactive Chat Assistant

Assistant Tools

Core Architecture

Runtime Systems

Session Persistence (v0.3.0+)

Deployment Characteristics

Type Safety & Validation

Agent Types

ReAct Agent

Classifier Agent

Aggregator Agent

Planner Agent

Producer Agent

Logger Agent

LLM Providers

OpenAI

Anthropic (Claude)

Google Gemini

xAI (Grok)

Google Vertex AI

Amazon Bedrock

HuggingFace

Local Inference (Ollama)

Additional Inference Services

Orchestration Patterns

Tools & MCP (Model Context Protocol)

Core MCP Features

Transport Modes

MCP Security

Built-in Tool Support

Data Infrastructure

Vector Databases

Embeddings

Memory & Context

Security Features

Authentication & Authorization

Input Security

Rate Limiting & Throttling

Audit & Compliance

Transport Security

Configuration Security

Observability & Monitoring

Distributed Tracing

Metrics & Monitoring

Cost Tracking

Langfuse Integration

Health Checks

Logging

Configuration & Deployment

Configuration Formats

Example Configurations

Deployment Options

Cloud Run Features

Kubernetes Features

CI/CD

Performance & Optimization

Performance Characteristics

Optimization Features

Cost Optimization

Reliability Features

Integration Capabilities

LLM Integration Features

API Integration

External Service Integration

Development Features

Developer Experience

Testing Support