getconsult 0.6.1

Multi-agent consensus system - LLM agents with different prompts analyze problems, critique each other, iterate to agreement

Consult

Multi-agent consensus system. Multiple LLM agents analyze your problem from different angles, critique each other's outputs, and iterate until they agree or an orchestrator resolves disagreements.

The value isn't individual agent outputs - it's the structured peer review that catches blind spots any single perspective would miss.

What This Does

1. Parallel analysis: N agents with domain-specific prompts analyze your problem simultaneously
2. Peer feedback: Each agent reviews the others' outputs with structured critique
3. Meta review: Separate pass catches integration issues the domain-focused agents miss
4. Iteration: Agents incorporate feedback and revise (configurable cycles)
5. Resolution: Either consensus is reached or an orchestrator synthesizes disagreements

┌─────────────────────────────────────────────────────────────────────────────┐
│                           CONSENSUS WORKFLOW                                 │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  1. PARALLEL ANALYSIS                                                       │
│     ┌──────────┐  ┌──────────┐  ┌──────────┐                               │
│     │ Agent A  │  │ Agent B  │  │ Agent C  │  ← Domain-specific            │
│     │ (DB)     │  │ (API)    │  │ (Infra)  │    system prompts             │
│     └────┬─────┘  └────┬─────┘  └────┬─────┘                               │
│          │             │             │                                      │
│  2. PEER REVIEW        ▼             ▼                                      │
│     Each agent critiques the others' solutions                              │
│                                                                             │
│  3. META REVIEW                                                             │
│     ┌─────────────────────────────────────┐                                │
│     │  Cross-cutting issues, gaps,        │                                │
│     │  integration problems               │                                │
│     └─────────────────────────────────────┘                                │
│                                                                             │
│  4. REVISION                                                                │
│     Agents incorporate feedback, revise their solutions                     │
│                                                                             │
│  5. APPROVAL VOTE                                                           │
│     Each agent: "Would I sign off on THEIR solution?"                       │
│     ├─ ≥80% approval → done                                                │
│     └─ <80% approval → iterate or orchestrator resolves                    │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

Installation

pip install getconsult

Configure API key:

mkdir -p ~/.consult
echo 'ANTHROPIC_API_KEY=sk-ant-...' > ~/.consult/.env
chmod 600 ~/.consult/.env

Or export directly:

export ANTHROPIC_API_KEY=sk-ant-...

Usage

# Interactive mode (Pro) - recommended for exploration
consult-tui

# CLI mode - for scripting and quick queries
consult -p "Design a real-time chat application database"

CLI Examples

Free Tier

Free tier: 2 experts max, 5 queries/day, 1 iteration.

# Use the essentials set (backend + frontend experts)
consult -p "Design a user authentication system" -e essentials

# Provider selection works on free tier
consult -p "Design a caching strategy" --provider openai -e essentials
consult -p "API rate limiting design" --provider google -e essentials

# Information commands (always free)
consult --status              # Check tier, limits, usage
consult --list-experts        # List all expert types and sets
consult --explain-consensus   # Understand how consensus works
consult --version             # Version info
consult --dry-run             # Validate setup without API calls

Pro Tier

Pro unlocks: unlimited experts, expert sets, team mode, sessions, attachments, export, multiple iterations.

Expert Sets (Pro)

# Predefined expert sets (3-4 experts each)
consult -p "Design a secure payment processing system" -e security_focused
consult -p "Evaluate microservices vs monolith for a startup MVP" -e architecture
consult -p "Design a recommendation engine for an e-commerce site" -e ai_system
consult -p "Build a real-time collaborative document editor" -e full_stack
consult -p "Optimize a high-traffic API handling 10k req/sec" -e performance
consult -p "Design a data pipeline for real-time analytics" -e data_platform
consult -p "Design onboarding flow for a mobile app" -e user_focused

Custom Expert Selection (Pro)

# Pick specific experts for your problem
consult -p "Design a secure, high-performance trading API" \
  -e "security_expert,performance_expert,backend_expert"

# Database-heavy analysis
consult -p "Schema design for multi-tenant SaaS" \
  -e "database_expert,software_architect,backend_expert"

# ML infrastructure
consult -p "Set up ML training pipeline with GPU clusters" \
  -e "ml_expert,infrastructure_expert,cloud_engineer"

Team Mode (Pro)

Run same experts across Anthropic, OpenAI, and Google simultaneously:

consult -p "Compare approaches to database sharding" -m team
consult -p "Evaluate authentication strategies" -m team -e security_focused

Multiple Iterations (Pro)

# Allow more revision cycles for complex problems
consult -p "Design a distributed transaction system" -i 3

# Stricter consensus requirement
consult -p "Security audit for financial API" -t 0.9 -e security_focused

Markdown Export (Pro)

consult -p "Document the architecture for a chat system" --markdown

# Custom output filename (appends for follow-ups)
consult -p "Initial architecture" --markdown --markdown-filename chat-system
consult -p "Add video calling support" --markdown --markdown-filename chat-system

Session Continuity (Pro)

# Start a session
consult -p "Design a social media feed algorithm" --memory-session feed-design

# Continue the conversation in same session
consult -p "How should we handle content ranking?" --memory-session feed-design
consult -p "Add support for sponsored posts" --memory-session feed-design

Attachments (Pro)

consult -p "Review this system design" -a diagram.png
consult -p "Analyze these API specs" -a openapi.pdf -a flow-diagram.png

Combining Options (Pro)

# Complex query with multiple Pro features
consult \
  -p "Design a real-time multiplayer game backend" \
  -e "backend_expert,infrastructure_expert,performance_expert" \
  -i 2 \
  -t 0.85 \
  --markdown \
  --markdown-filename game-backend

# Security-focused team analysis with attachments
consult \
  -p "Security audit for this payment system" \
  -m team \
  -e security_focused \
  -a payment-flow.pdf \
  --memory-session payment-audit

CLI Reference

consult [options]

Required:
  -p, --problem TEXT       Problem statement

Info:
  -v, --version            Show version
  -s, --status             Show tier, limits, usage
  --list-experts           Show agent configurations
  --explain-consensus      Explain the consensus mechanism
  --dry-run                Validate setup without API calls

Analysis:
  -m, --mode [single|team] Provider mode (default: single)
  --provider [anthropic|openai|google]
                           Provider for single mode (default: anthropic)
  -e, --experts TEXT       Agent set or comma-separated types
  -i, --max-iterations N   Max revision cycles (default: 1)
  -t, --consensus-threshold FLOAT
                           Agreement threshold 0.0-1.0 (default: 0.8)

Output:
  -c, --copy               Copy solution to clipboard (auto in TTY)
  --markdown               Save to ~/.consult/outputs/
  --markdown-filename TEXT Custom output filename

Context:
  --memory-session PATH    Session file for continuity
  -a, --attachments FILES  Image/PDF files to include

Agent Types

Each agent type has a domain-focused system prompt that shapes how it analyzes problems. The peer review mechanism means a security-focused agent will catch issues a performance-focused agent might overlook, and vice versa.

Agent Type	Focus
database_expert	Data modeling, queries, consistency, migrations
backend_expert	API design, service boundaries, error handling
infrastructure_expert	Deployment, scaling, monitoring, reliability
security_expert	Threat models, auth, input validation, compliance
performance_expert	Bottlenecks, caching, profiling, optimization
software_architect	System design, trade-offs, patterns
cloud_engineer	Cloud services, IaC, containers, DevOps
frontend_expert	UI architecture, state management, rendering
ml_expert	ML systems, training, inference, MLOps
data_expert	Pipelines, ETL, streaming, warehousing
ux_expert	User research, interaction design, accessibility

Predefined Expert Sets

Set	Experts	Tier
essentials	backend_expert, frontend_expert	Free
default	database_expert, backend_expert, infrastructure_expert	Pro
architecture	software_architect, database_expert, cloud_engineer	Pro
security_focused	security_expert, backend_expert, infrastructure_expert	Pro
full_stack	backend_expert, frontend_expert, database_expert, infrastructure_expert	Pro
performance	performance_expert, backend_expert, database_expert	Pro
data_platform	data_expert, backend_expert, infrastructure_expert	Pro
ai_system	ml_expert, backend_expert, data_expert, infrastructure_expert	Pro
user_focused	ux_expert, frontend_expert, backend_expert	Pro

Custom Expert Selection (Pro)

# Use full expert names when selecting custom experts
consult -p "..." -e "database_expert,security_expert,performance_expert"

How Consensus Works

Consensus isn't "do the outputs look similar" - it's "would each agent approve the others' solutions for production."

Approval Voting

Each agent reviews each OTHER agent's solution:

3 agents = 6 pairwise reviews:

Agent A → B's solution: APPROVE (1.0)
Agent A → C's solution: CONCERNS (0.7)
Agent B → A's solution: APPROVE (1.0)
Agent B → C's solution: OBJECT (0.0)
Agent C → A's solution: CONCERNS (0.7)
Agent C → B's solution: APPROVE (1.0)

Aggregate: (1.0 + 0.7 + 1.0 + 0.0 + 0.7 + 1.0) / 6 = 73%

Verdicts

Verdict	Score	Meaning
APPROVE	1.0	Production-ready
CONCERNS	0.7	Acceptable with noted issues
OBJECT	0.0	Fundamental problems

Resolution

≥80% approval → consensus reached → format output
<80% AND iterations left → revise and re-vote
<80% AND max iterations → orchestrator synthesizes

Tiers

BYOK model - you provide API keys, pay providers directly.

	Free	Pro ($9/mo)
Queries/day	5	100
Queries/hour	3	20
Max agents	2	Unlimited
Max iterations	1	Unlimited
Team mode	-	Yes
TUI	-	Yes
Sessions	-	Yes
Attachments	-	Yes
Export	-	Yes
Custom agents	-	Yes

License Keys

export CONSULT_LICENSE_KEY="CSL1_pro_..."
# or
echo "CSL1_pro_..." > ~/.consult/license

Check status:

consult --status

Configuration

# ~/.consult/.env

# API keys (at least one required)
ANTHROPIC_API_KEY=sk-ant-...
OPENAI_API_KEY=sk-...
GOOGLE_API_KEY=...

# Model overrides (defaults optimized for cost, Dec 2025)
ANTHROPIC_MODEL=claude-haiku-4-5-20251001
OPENAI_MODEL=gpt-4o-mini
GEMINI_MODEL=gemini-2.5-flash-lite

# For higher quality (and cost)
# ANTHROPIC_MODEL=claude-sonnet-4-20250514
# OPENAI_MODEL=gpt-4o

# Meta reviewer / orchestrator model
SOTA_MODEL=claude-opus-4-5-20251101

# Data directory
CONSULT_HOME=~/.consult

Data Directory

~/.consult/
├── sessions/     # Conversation state (Pro)
├── outputs/      # Markdown exports (Pro)
├── cache/        # Quota tracking
└── logs/         # Debug logs (API keys redacted)

Team Mode (Pro)

Runs the same agent configurations across multiple providers in parallel:

consult -p "Compare approaches to real-time sync" --mode team

Spawns agents on OpenAI, Anthropic, and Google simultaneously, then compares outputs across providers.

Programmatic Usage

from src.workflows import ConsensusWorkflow

workflow = ConsensusWorkflow(
    consensus_threshold=0.8,
    expert_config="architecture"
)

result = await workflow.solve_problem("Design microservices architecture")

print(f"Consensus: {result.consensus_achieved}")
print(f"Resolution: {result.resolution_method}")
print(result.final_solution)

Performance

Operation	Typical Time
3-agent consensus	120-180s
Team mode (9 agents)	180-300s

Dominated by LLM API latency.

Security

• API keys never written to logs, sessions, or outputs
• Sensitive data redacted before persistence
• Session files use hashed identifiers
• chmod 600 ~/.consult/.env

Development

git clone https://github.com/1x-eng/agentic-atlas.git
cd agentic-atlas
pip install -e ".[dev]"

Commits

Uses Conventional Commits:

git commit -m "fix: handle empty input"      # patch
git commit -m "feat: add CSV export"         # minor
git commit -m "feat!: rename --experts flag" # major

docs:, chore:, refactor:, test: don't trigger releases.

License

Proprietary. See LICENSE.

Permitted: Personal use, internal business use, contributing back.

Requires commercial license: SaaS offerings, commercial integration, redistribution.

Contact: Pruthvi | 1x-eng

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