magi-core 0.4.1

Multi-Agent Group Intelligence: orchestrate a council of LLMs to deliberate on complex questions

MAGI: Multi-Agent Group Intelligence

What if you could consult a council of diverse artificial minds before making a tough decision?

MAGI is a decision-support system inspired by the MAGI supercomputer from Neon Genesis Evangelion. In the anime, three distinct AI personas deliberate to govern Tokyo-3. Similarly, this project orchestrates a council of multiple LLMs to deliberate, vote, and reason about complex moral, ethical, and practical questions. By aggregating diverse AI perspectives, MAGI simulates a more human-like deliberation process — revealing consensus, surfacing minority risks, and synthesising collective wisdom.

How It Works

MAGI sends a question to all configured models in parallel. Each model responds in a structured JSON format (answer + reasoning + confidence score). A Rapporteur — the most confident model — then synthesises the group's findings into a final report. An optional Deliberative Round lets agents read each other's initial responses before finalising their own, mimicking human group deliberation. During deliberation, each model is assigned a random anonymous ID (e.g. Participant X7K2) to reduce brand bias in peer review.

Decision Modes

Mode	What it does
VoteYesNo	Democratic Yes / No / Abstain vote
VoteOptions	Vote on a custom set of options
Majority	Summarises the prevailing opinion
Consensus	Finds common ground across all views
Minority	Surfaces dissenting and overlooked perspectives
Probability	Estimates the likelihood of a statement being true
Compose	Generates content and ranks it via blind peer review
Synthesis	Comprehensively combines all perspectives into one unified response

Synthesis is the most inclusive mode — unlike Majority (which amplifies the dominant view) or Consensus (which finds the lowest common denominator), Synthesis instructs the rapporteur to weave every argument, nuance, and disagreement into a single coherent narrative.

Decision Flows

Standard flow (all methods)

Every deliberation follows this pipeline regardless of mode:

  ┌──────────────────────────────────────────────────┐
  │                    User Prompt                   │
  └────────────────────────┬─────────────────────────┘
                           │  dispatched in parallel
           ┌───────────────┼───────────────┐
           ▼               ▼               ▼
  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐
  │   LLM  1    │  │   LLM  2    │  │   LLM  N    │
  │  response   │  │  response   │  │  response   │
  │  reason     │  │  reason     │  │  reason     │
  │  confidence │  │  confidence │  │  confidence │
  └──────┬──────┘  └──────┬──────┘  └──────┬──────┘
         └────────────────┼────────────────┘
                          │  results collected
                          ▼
             ┌────────────────────────┐
             │       Aggregate        │
             │   (method-dependent)   │
             └────────────┬───────────┘
                          │
                          ▼
             ┌────────────────────────┐
             │  Rapporteur selected   │
             │  (by confidence score) │
             └────────────┬───────────┘
                          │
                          ▼
             ┌────────────────────────┐
             │      Final Report      │
             │   text  │  JSON        │
             └────────────────────────┘

Aggregation by method

The "Aggregate" step is what distinguishes each mode:

  Responses collected
         │
         ├─ VoteYesNo / VoteOptions ──► tally votes ──► declare winner (if > threshold)
         │                                                       │
         │                                           rapporteur summarises vote
         │
         ├─ Majority ──────────────────────────────► highest-confidence model
         │                                           summarises prevailing view
         │
         ├─ Consensus ─────────────────────────────► highest-confidence model
         │                                           identifies common ground
         │
         ├─ Minority ──────────────────────────────► lowest-confidence model
         │                                           surfaces dissent and gaps
         │
         ├─ Probability ───────────────────────────► compute average / median score
         │                                           median model writes analysis
         │
         ├─ Compose ───────────────────────────────► generate texts (Round 1)
         │                                                │
         │                                           blind peer rating (Round 2)
         │                                                │
         │                                           ranked output, no rapporteur
         │
         └─ Synthesis ─────────────────────────────► highest-confidence model
                                                     weaves ALL views into one narrative

Deliberative mode (--deliberative)

An optional second round where each agent reads its peers' anonymous responses before finalising:

  ┌──────────────────────────────────────────────────────────────┐
  │  Round 1                                                     │
  │                                                              │
  │  Prompt ──► LLM 1 ──► response1                              │
  │         ──► LLM 2 ──► response2                              │
  │         ──► LLM N ──► responseN                              │
  │                   │                                          │
  │          Aggregate + Rapporteur ──► Pre-Deliberation Report  │
  └──────────────────────────────────────────────────────────────┘
                      │  responses shared anonymously
                      ▼  (agents see peers' views, not their names)
  ┌──────────────────────────────────────────────────────────────┐
  │  Round 2                                                     │
  │                                                              │
  │  Prompt + peers' Round 1 responses                           │
  │         ──► LLM 1  (sees 2..N)   ──► response1'              │
  │         ──► LLM 2  (sees 1,3..N) ──► response2'              │
  │         ──► LLM N  (sees 1..N-1) ──► responseN'              │
  │                   │                                          │
  │          Aggregate + Rapporteur ──► Post-Deliberation Report │
  └──────────────────────────────────────────────────────────────┘

Installation

From PyPI

pip install magi-core

From source

git clone https://github.com/jason-chao/magi
cd magi
pip install -e .

Configuration

API Keys

You only need a key for each provider you actually use.

CLI: The magi command automatically loads a .env file from your working directory. Copy .env.example and fill in your keys:

cp .env.example .env
# then edit .env with your actual keys

Or set them directly as environment variables:

export OPENAI_API_KEY=your-key-here
export ANTHROPIC_API_KEY=your-key-here
export GEMINI_API_KEY=your-key-here

Package: API keys must be set as environment variables before calling run() or run_structured(). The package does not auto-load .env — call load_dotenv() yourself if needed:

from dotenv import load_dotenv
load_dotenv()  # reads .env from the current directory

config.yaml

CLI: Required (unless you pass --llms on the command line). Place it in your working directory:

llms:
  - openai/gpt-4.1
  - anthropic/claude-haiku-4-5-20251001
  - gemini/gemini-2.5-flash

defaults:
  max_retries: 2
  min_models: 2       # abort if fewer than this many models respond
  request_timeout: 60 # seconds before an individual LLM call is abandoned

Fallback chains are also supported — if the primary model fails, MAGI automatically tries the next:

llms:
  - - openai/gpt-4.1
    - openai/gpt-4o          # fallback if gpt-4.1 is unavailable
  - anthropic/claude-haiku-4-5-20251001

Package: Not required — pass the model list directly as a Python dict. No file needed:

config = {
    "llms": [
        "openai/gpt-4.1",
        "anthropic/claude-haiku-4-5-20251001",
        "gemini/gemini-2.5-flash",
    ]
}

magi_core/prompts.yaml

The bundled prompt templates are used by default and work out of the box. Override them only if you want to customise system prompts or method instructions.

CLI Usage

After installation, the magi command is available:

magi "Your question here" --method Synthesis

Or run directly from the repository:

python magi-cli.py "Your question here" --method VoteYesNo

Examples

1. VoteYesNo — The Self-Driving Car Dilemma

magi "A self-driving car's brakes have failed. It will kill five pedestrians unless its AI swerves onto the pavement, killing the single passenger inside. Should the AI be programmed to sacrifice its passenger?" \
  --method VoteYesNo

2. VoteOptions — Organ Allocation

magi "A hospital has one donor heart. Who should receive it?" \
  --method VoteOptions \
  --options "A 10-year-old child with decades ahead,A 45-year-old surgeon who saves hundreds of lives per year,The patient who has waited longest on the list,Whoever has the highest chance of survival post-transplant"

3. Majority — Capital Punishment After Mass Atrocity

magi "Should capital punishment be re-introduced for terrorist attacks that cause mass civilian casualties, even knowing that wrongful convictions are statistically inevitable?" \
  --method Majority

4. Consensus — Abortion

magi "At what point, if any, does terminating a pregnancy become morally impermissible, and who — if anyone — has the authority to enforce that line?" \
  --method Consensus

5. Minority — The Demanding Conclusion of Effective Altruism

magi "Anyone who spends money on luxuries while children die of preventable diseases is morally equivalent to letting a drowning child die. Is this argument sound?" \
  --method Minority

6. Probability — Moral Luck

magi "Two drivers drink the same amount and drive home. One kills a pedestrian by chance; the other arrives safely. The lucky driver deserves the same moral blame and legal punishment as the unlucky one." \
  --method Probability

7. Compose — Steel-Manning Open Borders

magi "Write the strongest possible moral argument for the claim that wealthy nations have an absolute obligation to accept unlimited refugees, regardless of cultural, economic, or security consequences." \
  --method Compose

8. Synthesis — Parfit's Repugnant Conclusion

magi "Derek Parfit's Repugnant Conclusion: a world of a trillion people living lives barely worth living is morally preferable to ten billion living very happy lives. Is this repugnant, unavoidable, or does it reveal a flaw in utilitarian reasoning?" \
  --method Synthesis

9. Deliberative Round — Capital Punishment

magi "Should capital punishment be re-introduced for terrorist attacks that cause mass civilian casualties, even knowing that wrongful convictions are statistically inevitable?" \
  --method VoteYesNo --deliberative

10. JSON output — pipe into other tools

magi "Should you pull the lever?" --method VoteYesNo --output-format json | jq '.rounds[0].aggregate'

CLI Reference

Argument	Description
prompt	The question or issue to deliberate on
--method	VoteYesNo (default), VoteOptions, Majority, Consensus, Minority, Probability, Compose, Synthesis
--llms	Comma-separated model names (overrides config.yaml)
--options	Custom options for VoteOptions
--vote-threshold	Fraction of votes to declare a winner (default: 0.5)
--no-abstain	Disallow abstaining in VoteYesNo / Probability
--deliberative	Enable deliberative second round
--rapporteur-prompt	Additional instructions for the rapporteur
--system-prompt	Context prepended to every agent's system prompt
--output-format	text (default) or json
--config	Path to a custom config.yaml
--prompts	Path to a custom prompts.yaml
--check-models	Probe each model with a live API call and report availability, then exit

JSON Output

Both the CLI and the Python package support structured JSON output, designed for integration with UIs and downstream applications.

CLI

magi "Your question" --method VoteYesNo --output-format json

Package

result = await magi.run_structured(
    user_prompt="Your question",
    method="VoteYesNo",
)
import json
print(json.dumps(result, indent=2))

JSON Schema

{
  "schema_version": "1.0",
  "method": "VoteYesNo",
  "prompt": "Should you pull the lever?",
  "system_prompt": null,
  "deliberative": false,
  "models": ["openai/gpt-4.1", "anthropic/claude-haiku-4-5-20251001"],
  "rounds": [
    {
      "round": 1,
      "responses": [
        {
          "model": "openai/gpt-4.1",
          "pseudonym": "Participant X7K2",
          "response": "yes",
          "reason": "Utilitarian reasoning: saving five outweighs saving one.",
          "confidence_score": 0.9,
          "fallback_for": null
        }
      ],
      "errors": [
        {
          "model": "gemini/gemini-2.5-flash",
          "error": "Model not found",
          "error_category": "not_found",
          "attempted_fallbacks": ["gemini/gemini-2.5-flash", "gemini/gemini-1.5-pro"]
        }
      ],
      "aggregate": { },
      "rapporteur": {
        "model": "openai/gpt-4.1",
        "summary": "The council voted yes by a clear majority..."
      }
    }
  ]
}

aggregate by method

Method	aggregate fields
VoteYesNo, VoteOptions	votes (object), winner (string), threshold (float)
Probability	average, median, min, max (all floats), abstained_count (int)
Compose	ranked_candidates (array — see below)
Majority, Consensus, Minority, Synthesis	null — result is in rapporteur.summary

ranked_candidates (Compose)

[
  {
    "rank": 1,
    "model": "openai/gpt-4.1",
    "pseudonym": "Participant X7K2",
    "average_score": 8.5,
    "text": "The composed paragraph text...",
    "peer_reviews": [
      {
        "reviewer_model": "anthropic/claude-haiku-4-5-20251001",
        "score": 8.0,
        "justification": "Well-structured and compelling."
      }
    ]
  }
]

Error responses

When deliberation cannot proceed (empty prompt, no models, quorum failure), run_structured() returns a dict with a top-level error key instead of rounds:

{
  "error": "Quorum not met — 1 of 3 model(s) responded (minimum required: 2).",
  "failed_models": [
    { "model": "gemini/gemini-2.5-flash", "error_category": "not_found", "error": "Model not found" }
  ]
}

run() converts these to a plain "Error: ..." string.

Package API

No config.yaml file needed — pass models as a dict. API keys can be set as environment variables (or loaded via load_dotenv()), or passed per-call via the api_keys parameter for multi-tenant services.

import asyncio
from magi_core import Magi
from magi_core.utils import load_yaml, get_default_prompts_path

config = {
    "llms": [
        "openai/gpt-4.1",
        "anthropic/claude-haiku-4-5-20251001",
        "gemini/gemini-2.5-flash",
    ]
}
prompts = load_yaml(get_default_prompts_path())  # bundled prompts, no file needed

magi = Magi(config, prompts)

# Text output
result = asyncio.run(magi.run(
    user_prompt="A runaway trolley will kill five people. Should you pull the lever?",
    method="Synthesis",
    deliberative=True,
))
print(result)

# Structured JSON output
import json
result = asyncio.run(magi.run_structured(
    user_prompt="A runaway trolley will kill five people. Should you pull the lever?",
    method="VoteYesNo",
))
print(json.dumps(result, indent=2))

Magi(config, prompts)

Parameter	Type	Description
config	dict	Configuration dict with llms list and optional defaults
prompts	dict	Prompt templates — load from bundled file via load_yaml(get_default_prompts_path())

config keys:

Key	Type	Default	Description
llms	list	—	Model names or fallback lists
defaults.max_retries	int	3	Retries per model on transient errors
defaults.min_models	int	1	Minimum responding models before aborting
defaults.request_timeout	float	60	Seconds before an LLM call is abandoned
defaults.vote_threshold	float	0.5	Minimum vote fraction for a winner
litellm_debug_mode	bool	false	Enable verbose litellm logging

Config is validated at construction time — invalid values raise ValueError immediately.

await magi.run(...) / await magi.run_structured(...)

Both methods accept identical parameters:

Parameter	Type	Default	Description
user_prompt	str	—	The question or statement to deliberate on
system_prompt	str | None	None	Extra context prepended to the system prompt
selected_llms	list | None	config value	Model names or fallback lists to use
method	str	"VoteYesNo"	One of the eight methods listed above
method_options	dict	{}	Method-specific options (see below)
deliberative	bool	False	Enable a second round where agents review peer responses
api_keys	dict[str, str] | None	None	Per-call API keys keyed by provider (e.g. {"openai": "sk-...", "anthropic": "sk-ant-..."}) — overrides env vars

method_options keys:

Key	Type	Description
vote_threshold	float	Minimum fraction of votes to declare a winner (default 0.5)
allow_abstain	bool	Allow abstaining in VoteYesNo / -1.0 probability in Probability (default True)
options	list[str]	Choices for VoteOptions
rapporteur_prompt	str	Additional instructions appended to the rapporteur prompt

Returns:

• run() → str — Markdown-formatted report
• run_structured() → dict — JSON-serialisable structured result (see JSON Schema)

Method Quick Reference

# Vote — trolley problem
await magi.run("Should you pull the lever?", method="VoteYesNo")

# Custom options — pandemic triage
await magi.run(
    "Who should receive the last ventilator?",
    method="VoteOptions",
    method_options={"options": ["Young Child", "Frontline Doctor", "Elderly Patient", "Random Lottery"]},
)

# Synthesis — comprehensive view of all perspectives
await magi.run(
    "Is it ever justified to lie to protect someone's feelings?",
    method="Synthesis",
)

# Probability — simulation hypothesis
await magi.run("We are living in a computer simulation.", method="Probability")

# Compose — generate and peer-review ethical arguments
await magi.run(
    "Write a paragraph arguing that lying is sometimes morally permissible.",
    method="Compose",
)

# Structured output for downstream use
result = await magi.run_structured("Should you pull the lever?", method="VoteYesNo")
winner = result["rounds"][0]["aggregate"]["winner"]

# Per-call API keys — each user passes their own keys (ideal for multi-tenant services)
result = await magi.run_structured(
    "Should we proceed with the merger?",
    method="VoteYesNo",
    api_keys={
        "openai": user_openai_key,
        "anthropic": user_anthropic_key,
    },
)

Architecture

magi_core/
  __init__.py      Public API (Magi class, load_yaml, get_default_prompts_path)
  core.py          Orchestration, aggregation, rapporteur logic, and renderers
  cli.py           Console entry point (installed as `magi` command)
  utils.py         Pseudonym generation and YAML loading helpers
  prompts.yaml     System prompts and per-method instructions
config.yaml        Default model selection

Key design decisions:

• All LLM calls are async (asyncio.gather) — models are queried in parallel.
• Agents use random anonymous IDs (Participant X7K2) during deliberation to reduce brand bias.
• The rapporteur is selected by confidence score; ties are broken randomly.
• Synthesis uses the same rapporteur selection as Majority but with a prompt that mandates comprehensive inclusion of all perspectives.
• run() and run_structured() share a single _deliberate() engine; the only difference is whether the result dict is rendered to Markdown or returned as-is.
• Fallback chains trigger on permanent errors (model not found, deprecated, auth) and rate limits; timeouts and unknown errors retry the primary only.

Fallback chain

Each slot in config.yaml can be a list; MAGI walks the list when a model is permanently unavailable:

  Slot: [primary, fallback-1, fallback-2, …]

  ┌───────────┐  deprecated /     ┌─────────────┐  error again  ┌─────────────┐
  │  Primary  │  not found /  ──► │  Fallback 1 │  ──────────►  │  Fallback 2 │
  │  Model    │  auth error /     │             │               │             │
  └───────────┘  rate limit       └─────────────┘               └──────┬──────┘
       │ ok                            │ ok                             │ all failed
       ▼                               ▼                                ▼
  result used                     result used +                error logged in
                                  fallback noted               round errors[]
                                  in report

Timeout and unknown errors retry the primary only — they do not burn through the fallback chain.

Security

litellm<1.44.12 is affected by CVE-2024-8938 (arbitrary code execution via eval()). This package pins litellm>=1.44.12. Keep your dependencies up to date.

Testing

Unit tests (no API keys required)

pytest tests/test_core.py -v

Integration tests (live LLM calls — incurs API costs)

pytest tests/test_integration_matrix.py -v

Integration test outputs are saved as Markdown in test_results/.

License

MIT — see LICENSE.