synthpanel 0.7.1

Run synthetic focus groups and user research panels using AI personas. CLI tool, Python library, any LLM.

synthpanel

Open-source synthetic focus groups. Any LLM. Your terminal or your agent's tool call.

Define personas in YAML. Define your research instrument in YAML. Run against any LLM — from your terminal, from a pipeline, or from an AI agent's MCP tool call. Get structured, reproducible output with full cost transparency.

pip install synthpanel
synthpanel panel run --personas personas.yaml --instrument survey.yaml

# For MCP server support (Claude Code, Cursor, Windsurf, etc.)
pip install synthpanel[mcp]

Why

Traditional focus groups cost $5,000-$15,000 and take weeks. Synthetic panels cost pennies and take seconds. They don't replace real user research, but they're excellent for:

• Pre-screening survey instruments before spending budget on real participants
• Rapid iteration on product names, copy, and positioning
• Hypothesis generation across demographic segments
• Concept testing at the speed of thought

Quick Start

# Install from PyPI
pip install synthpanel

# For MCP server support (agent integration)
pip install synthpanel[mcp]

# Or install from source for the latest unreleased changes
pip install git+https://github.com/DataViking-Tech/synthpanel.git@main

# Set your API key (Claude, OpenAI, Gemini, xAI, or any OpenAI-compatible provider)
export ANTHROPIC_API_KEY="sk-..."

# Run a single prompt
synthpanel prompt "What do you think of the name Traitprint for a career app?"

# Run a full panel
synthpanel panel run \
  --personas examples/personas.yaml \
  --instrument examples/survey.yaml

What You Get

============================================================
Persona: Sarah Chen (Product Manager, 34)
============================================================
  Q: What is the most frustrating part of your workflow?
  A: Version control on documents that aren't in a proper system...

  Cost: $0.0779

============================================================
Persona: Marcus Johnson (Small Business Owner, 52)
============================================================
  Q: What is the most frustrating part of your workflow?
  A: I'll send my manager a menu update in an email, she makes
     her changes, sends it back...

  Cost: $0.0761

============================================================
Total: estimated_cost=$0.2360

Each persona responds in character with distinct voice, concerns, and perspective. Cost is tracked and printed per-panelist and in aggregate.

Defining Personas

# personas.yaml
personas:
  - name: Sarah Chen
    age: 34
    occupation: Product Manager
    background: >
      Works at a mid-size SaaS company. 8 years in tech,
      previously a software engineer. Manages a team of 5.
    personality_traits:
      - analytical
      - pragmatic
      - detail-oriented

  - name: Marcus Johnson
    age: 52
    occupation: Small Business Owner
    background: >
      Runs a family-owned restaurant chain with 3 locations.
      Not tech-savvy but recognizes the need for digital tools.
    personality_traits:
      - practical
      - skeptical of technology
      - values personal relationships

Defining Instruments

# survey.yaml
instrument:
  questions:
    - text: >
        What is the most frustrating part of your current
        workflow when collaborating with others?
      response_schema:
        type: text
      follow_ups:
        - "Can you describe a specific recent example?"

    - text: >
        If you could fix one thing about how you work with
        technology daily, what would it be?
      response_schema:
        type: text

Adaptive Research (0.5.0): Branching Instruments

A v3 instrument is a small DAG of rounds. After each round, a routing predicate decides which round runs next based on the synthesizer's themes and recommendation. The panel chooses its own probe path — no human in the loop, no hand-coded conditional flows.

# The Show HN demo: ~$0.20, one command, the panel decides
# whether to dig into pain, pricing, or alternatives.
synthpanel panel run \
  --personas examples/personas.yaml \
  --instrument pricing-discovery

pricing-discovery is one of five bundled v3 packs (pricing-discovery, name-test, feature-prioritization, landing-page-comprehension, churn-diagnosis). List them with synthpanel instruments list.

The output now carries a path array recording the routing decisions that actually fired:

discovery -> probe[themes contains price] -> probe_pricing -> validation

Render the DAG of any instrument:

synthpanel instruments graph pricing-discovery --format mermaid

Predicate Reference

route_when is a list of clauses evaluated in order. The first matching clause wins; an else clause is mandatory as the last entry.

route_when:
  - if: { field: themes, op: contains, value: price }
    goto: probe_pricing
  - if: { field: recommendation, op: matches, value: "(?i)wait|delay" }
    goto: probe_objections
  - else: __end__

Field	Source
themes	SynthesisResult.themes (list, substring match)
recommendation	SynthesisResult.recommendation (string)
disagreements, agreements, surprises	SynthesisResult (lists)
summary	SynthesisResult.summary (string)

Op	Meaning
contains	Substring match against any list entry or the string
equals	Exact string match
matches	Python regex match (use (?i) for case-insensitive)

The reserved target __end__ terminates the run; the path so far feeds final synthesis.

Theme Matching: The R3 Caveat

Predicates match against the synthesizer's exact theme strings.

themes contains price only fires if the synthesizer actually emitted a theme containing the substring price. LLM synthesizers paraphrase — "cost concerns" or "sticker shock" will not match. The bundled packs mitigate this with a comment block at the top of the instrument that hints at the canonical theme tags the synthesizer should prefer:

# Synthesizer guidance: when emitting `themes`, prefer the short
# canonical tags below so route_when predicates match reliably:
#   - "pain"   (workflow pain, frustration, broken status quo)
#   - "price"  (cost concerns, perceived value, sticker shock)
#   - "alternative" (existing tools, workarounds, competitors)

When you author your own v3 packs, always add a similar tag-hint block. The synthesizer reads it and tends to use the canonical tags; your contains predicates then route reliably. If you skip this step, expect routes to silently fall through to else because the synthesizer's prose theme labels won't match your predicate values.

Prefer short, lowercase, single-token tags (price, pain, confusion) over long phrases. contains does substring matching, so price will also match pricing, priced, etc.

instruments Subcommand

synthpanel instruments list                       # bundled + installed packs
synthpanel instruments show pricing-discovery     # full YAML body
synthpanel instruments install ./my-pack.yaml     # add a local pack
synthpanel instruments graph pricing-discovery    # text DAG
synthpanel instruments graph pricing-discovery \
  --format mermaid                                 # mermaid flowchart

The unified instrument resolver (used by panel run --instrument) accepts either a YAML path or an installed pack name, so you can iterate on a local file and then install it once it's stable.

LLM Provider Support

synthpanel works with any LLM provider. Set the appropriate environment variable:

Provider	Environment Variable	Model Flag
Anthropic (Claude)	ANTHROPIC_API_KEY	--model sonnet
Google (Gemini)	GOOGLE_API_KEY or GEMINI_API_KEY	--model gemini
OpenAI	OPENAI_API_KEY	--model gpt-4o
OpenRouter	OPENROUTER_API_KEY	--model openrouter/anthropic/claude-haiku-4-5
xAI (Grok)	XAI_API_KEY	--model grok
Any OpenAI-compatible	OPENAI_API_KEY + OPENAI_BASE_URL	--model <model-id>

# Use Claude (default)
synthpanel panel run --personas p.yaml --instrument s.yaml

# Use GPT-4o
synthpanel panel run --personas p.yaml --instrument s.yaml --model gpt-4o

# Use a local model via Ollama
OPENAI_BASE_URL=http://localhost:11434/v1 \
synthpanel panel run --personas p.yaml --instrument s.yaml --model llama3

Model Aliases

synthpanel ships with short aliases (sonnet, opus, haiku, grok, gemini, gemini-pro) that map to canonical model identifiers. You can override or extend these without changing code:

Resolution order (highest priority wins):

1. SYNTHPANEL_MODEL_ALIASES env var — JSON string of alias→model pairs
2. ~/.synthpanel/aliases.yaml — YAML file
3. Hardcoded defaults — built into the package

# Override via env var (JSON)
export SYNTHPANEL_MODEL_ALIASES='{"sonnet": "claude-sonnet-4-6-20250414", "fast": "claude-haiku-4-5-20251001"}'
synthpanel prompt "Hello" --model fast

# ~/.synthpanel/aliases.yaml
aliases:
  fast: claude-haiku-4-5-20251001
  smart: claude-opus-4-6
  sonnet: claude-sonnet-4-6-20250414

Env var entries override file entries, which override hardcoded defaults. Aliases from all tiers are merged, so you only need to specify the ones you want to add or change.

Architecture

synthpanel is a research harness, not an LLM wrapper. It orchestrates the research workflow:

personas.yaml ──┐
                 ├──> Orchestrator ──> Panelist 1 ──> LLM ──> Response
instrument.yaml ─┘                 ├──> Panelist 2 ──> LLM ──> Response
                                   └──> Panelist N ──> LLM ──> Response
                                                                  │
                                              Aggregated Report <──┘

Components

Module	Purpose
llm/	Provider-agnostic LLM client (Anthropic, Google, OpenAI, xAI)
runtime.py	Agent session loop (turns, tool calls, compaction)
orchestrator.py	Parallel panelist execution with worker state tracking
structured/	Schema-validated responses via tool-use forcing
cost.py	Token tracking, model-specific pricing, budget enforcement
persistence.py	Session save/load/fork (JSON + JSONL)
plugins/	Manifest-based extension system with lifecycle hooks
mcp/	MCP server for agent-native invocation (stdio transport)
cli/	CLI framework with slash commands, output formatting

Design Principles

• Minimal dependencies — Python 3.10+ with httpx for HTTP and pyyaml for YAML parsing. Optional: mcp for the MCP server
• Agent-native — invoke from your terminal or from an AI agent's MCP tool call
• Provider agnostic — swap LLMs without changing research definitions
• Cost transparent — every API call is tracked and priced
• Reproducible — same personas + same instrument = comparable output
• Structured by default — responses conform to declared schemas

MCP Server (Agent Integration)

synthpanel includes an MCP server so AI agents can run panels as tool calls:

synthpanel mcp-serve

Add to your editor's MCP config (Claude Code, Cursor, Windsurf, etc.):

{
  "mcpServers": {
    "synth_panel": {
      "command": "synthpanel",
      "args": ["mcp-serve"],
      "env": { "ANTHROPIC_API_KEY": "sk-..." }
    }
  }
}

Tools exposed (12): run_prompt, run_panel, run_quick_poll, extend_panel, list_persona_packs, get_persona_pack, save_persona_pack, list_instrument_packs, get_instrument_pack, save_instrument_pack, list_panel_results, get_panel_result.

run_panel accepts an inline instrument dict or an instrument_pack name, so an agent can offload research-design judgment in a single tool call. v3 responses include rounds, path, terminal_round, and warnings alongside the back-compat results array.

extend_panel appends a single ad-hoc round to a saved panel result — it is not a re-entry into the authored DAG. Use it for follow-up probes that the original instrument didn't anticipate.

Output Formats

# Human-readable (default)
synthpanel panel run --personas p.yaml --instrument s.yaml

# JSON (pipe to jq, store in database)
synthpanel panel run --personas p.yaml --instrument s.yaml --output-format json

# NDJSON (streaming, one event per line)
synthpanel panel run --personas p.yaml --instrument s.yaml --output-format ndjson

Budget Control

# Set a dollar budget for the panel
synthpanel panel run --personas p.yaml --instrument s.yaml --config budget.yaml

The cost tracker enforces soft budget limits — the current panelist completes, but no new panelists start if the budget is exceeded.

Persona Prompt Template Variants

The templates/ directory contains four prompt template variants for benchmarking how persona prompt construction affects response quality:

Template	File	Fields	Purpose
Current	templates/current.txt	name, age, occupation, background, personality_traits	Control — documents the default prompt style
Demo	templates/demo.txt	name, age, occupation, education_level, income_bracket, urban_rural, political_leaning, background	Demographic-enriched — adds SubPOP/OpinionsQA stratification axes
Values	templates/values.txt	name, age, occupation, background, core_values, decision_style	Values-enriched — adds belief and decision-making context
Minimal	templates/minimal.txt	name, age, occupation	Ablation control — tests how much narrative matters

Usage:

synthpanel panel run --personas personas.yaml --instrument survey.yaml --prompt-template templates/demo.txt

Templates use Python format-string syntax ({field_name}). Missing persona fields are left as literal {field_name} in the output.

Methodology Notes

Synthetic research is useful for exploration, hypothesis generation, and rapid iteration. It is not a replacement for talking to real humans.

Known limitations:

• Synthetic responses tend to cluster around means
• LLMs exhibit sycophancy (tendency to please)
• Cultural and demographic representation has blind spots
• Higher-order correlations between variables are poorly replicated

Use synthpanel to pre-screen and iterate, then validate with real participants.

Multi-Model Ensemble (0.7.0)

Run the same panel through multiple models and blend their response distributions for higher-fidelity results. SynthBench experiments show 3-model ensembles improve human-parity scores by +5-7 points over any single model.

# Run 3 models with equal weights and blend distributions
synthpanel panel run \
  --models haiku:0.33,gemini:0.33,gpt-4o-mini:0.34 \
  --blend \
  --personas personas.yaml \
  --instrument survey.yaml

# Each persona is interviewed by all 3 models independently.
# The --blend flag averages response distributions across models,
# producing more representative synthetic survey data.

The blended output includes per-model distributions and the weighted ensemble distribution, letting you inspect both individual model perspectives and the consensus view.

Versions

Version	Highlights
0.7.0	Multi-model ensemble blending (--blend), OpenRouter provider support, temperature/top_p controls, prompt template customization
0.6.0	--models weighted model spec, --temperature/--top_p flags, persona prompt templates, pack generation, domain templates, MCP improvements
0.5.0	v3 branching instruments, router predicates, 5 bundled instrument packs, instruments subcommand (list/show/install/graph), MCP *_instrument_pack tools, rounds-shaped panel output, extend_panel ad-hoc round tool
0.4.0	--var KEY=VALUE and --vars-file for instrument templates, fail-loud on all-provider errors, default --model respects available credentials, pack show <id> alias, publish workflow fix
0.3.0	Structured output via tool-use forcing, cost tracking, MCP server (stdio), persona-pack persistence

See CHANGELOG.md for detailed release notes.

Contributing

See CONTRIBUTING.md for development setup, testing, and how to submit changes.

MCP Server Documentation

For detailed MCP server documentation (all 12 tools, 4 resources, 3 prompt templates), see docs/mcp.md.

License

MIT