conclave

A bring-your-own-keys multi-model council — a CLI and Python library that fans a prompt out to several foundation models concurrently (each via your own API keys) and merges their answers into one consolidated response.

Built on conclave's own provider highway — an httpx async transport behind a per-provider adapter registry, so there is no LLM-SDK dependency — plus asyncio for concurrent fan-out, rich for output, and pydantic for config.

It is library-first (the CLI is a thin shell over the same Council you import), returns structured results (per-model latency, token usage, and error capture), and is partial-failure resilient — one provider erroring never aborts the run. Keys are bring-your-own, referenced by environment-variable name only — never stored or logged. It ships four modes: synthesize (merge answers into one), raw (no merge), debate (multi-round, members revise after seeing peers' anonymized answers), and adversarial (propose → refute → verdict). conclave is intentionally lightweight — a small council primitive, not an agent framework.

The v1.1 wedge — the auditable council. Every run also yields a multi-model council verdict you can act on — structured, scored for agreement, fully auditable: a CouncilVerdict exposing agreement, conflicts, minority_reports, and provider_votes; a deterministic consensus_score (arithmetic over the model's clustering, never an LLM-emitted number); and a redacted ModelHarnessManifest recording how the run executed and which model produced the disagreement analysis. The verdict is default-on. (A vote mode was once on the roadmap; it is absorbed by provider_votes — see below.)

See the canonical spec and design docs:

docs/PRODUCT_DESIGN_DOCUMENT.md — canonical product spec, council modes, security model, roadmap, positioning (the authority doc).
SYSTEM_CONTEXT_DIAGRAM.md — system context diagram.
DOCUMENTATION_INDEX.md — master index of all docs + source.

Install

pip install conclave-cli

Name split (read this once). The PyPI distribution name is conclave-cli — the name conclave on PyPI is an unrelated project (a blockchain client, not this one). Everything else stays conclave: the CLI command you type is conclave, the package you import is conclave, and the repo is conclave. So: pip install conclave-cli → run conclave ... / from conclave import Council.

From a source checkout (for development), install it editable instead:

# from the repo root
pip install -e .
# or with dev/test extras
pip install -e ".[dev]"

Requires Python 3.11+.

Bring your own keys

conclave never stores or hardcodes keys. It reads them from the environment using each provider's standard variable name:

Provider	Friendly name	Default model id	Env var(s)
xAI	`grok`	`xai/grok-4.3`	`XAI_API_KEY`
Google	`gemini`	`gemini/gemini-2.5-pro`	`GEMINI_API_KEY` / `GOOGLE_API_KEY`
Anthropic	`claude`	`anthropic/claude-sonnet-4-6`	`ANTHROPIC_API_KEY`
Perplexity	`perplexity`	`perplexity/sonar-pro`	`PERPLEXITY_API_KEY`
OpenAI	`openai`	`openai/gpt-4.1`	`OPENAI_API_KEY`
Groq	`groq`	`groq/llama-3.3-70b-versatile`	`GROQ_API_KEY`
DeepSeek	`deepseek`	`deepseek/deepseek-chat`	`DEEPSEEK_API_KEY`
Mistral	`mistral`	`mistral/mistral-large-latest`	`MISTRAL_API_KEY`
Together	`together`	`together/meta-llama/Llama-3.3-70B-Instruct-Turbo`	`TOGETHER_API_KEY`

Every first-class provider above is a direct vendor key to a direct vendor endpoint — conclave never routes through an aggregator. Any other OpenAI-compatible vendor (including aggregators/routers, which are deliberately not first-class) remains usable config-only via an endpoints: entry.

Set whichever you have:

export XAI_API_KEY=...
export PERPLEXITY_API_KEY=...

Any requested provider whose key is absent is skipped with a warning — the council runs with whoever is available. One provider erroring (network/auth) never kills the run; you still get partial results plus a synthesis of the survivors.

Quickstart (CLI)

# Which providers have a key right now? (never prints key values)
conclave providers

# Fan out to a council and synthesize
conclave ask "Explain CRDTs in two sentences." \
  --council grok,gemini,claude,perplexity --mode synthesize

# Pick the synthesizer explicitly
conclave ask "Compare gRPC vs REST." -c grok,perplexity -s claude

# Raw answers only, no synthesis
conclave ask "Name three sorting algorithms." -c grok,perplexity --mode raw

# Debate: members revise over N rounds after seeing peers' anonymized answers
conclave ask "Is a service mesh worth it for 8 services?" \
  -c grok,gemini,claude --mode debate --rounds 3

# Debate with early-stop: stop before --rounds once answers stop changing
conclave ask "Is a service mesh worth it for 8 services?" \
  -c grok,gemini,claude --mode debate --rounds 5 --converge-threshold 0.95

# Adversarial: one model proposes, the rest refute, the synthesizer judges
conclave ask "Defend event sourcing for this ledger." \
  -c grok,gemini,perplexity --mode adversarial --proposer grok

# Machine-readable output (works for every mode; carries rounds/adversarial too)
conclave ask "..." -c grok,perplexity --mode debate --json

Mode flags at a glance: --mode synthesize|raw|debate|adversarial. --rounds N (default 2) is the maximum round count for debate; --converge-threshold FLOAT (or --converge/--no-converge) optionally stops a debate early once answers stabilize round-over-round (off by default — --rounds runs in full). --proposer NAME (default: first member) applies to adversarial. --synthesizer/-s overrides the synthesizer and the adversarial judge.

--council accepts either a comma-separated list of friendly names or the name of a council defined in your config (see below). The built-in default council is all known providers.

Add --stream to render member (and synthesizer) tokens live as they arrive (synthesize/raw modes only):

conclave ask "Explain CRDTs in two paragraphs." -c grok,gemini,claude --stream

Streaming and the non-streaming default produce the same final CouncilResult; --stream only changes how output is rendered. It is ignored with --json (which always emits the full structured payload), and on a cache hit the cached text is rendered in one shot rather than as a fake token stream.

Quickstart (library)

from conclave import Council

council = Council(models=["grok", "perplexity"], synthesizer="claude")

# sync
result = council.ask_sync("What is the capital of France?")

# or async
# result = await council.ask("What is the capital of France?")

for answer in result.answers:
    print(answer.name, answer.latency_s, answer.error or answer.answer[:80])

print("SYNTHESIS:\n", result.synthesis)

Auditable verdict

On a decision- or review-style prompt with at least two responding members, conclave adjudicates the answers into a structured, agreement-scored, auditable verdict. It is default-on — no flag needed.

From the CLI, a decision prompt renders a green VERDICT panel:

$ conclave ask "Should we adopt a service mesh for 8 services?" -c grok,gemini,claude

╭─ VERDICT (decision) ─────────────────────────────────────────────────────────╮
│ A service mesh is not yet justified for 8 services.                            │
│                                                                                │
│ Start with library-level retries/timeouts and centralized metrics; revisit a   │
│ mesh past ~20 services or when mTLS/traffic-splitting become hard requirements. │
│                                                                                │
│ consensus: strong (0.75) — heuristic: position_cluster_ratio_v1                │
╰────────────────────────────────────────────────────────────────────────────────╯
Conflicts
  • operational cost: not-yet-worth-it (grok, claude) vs worth-it (gemini)
Minority reports
  • gemini: mesh pays off early if you need mTLS everywhere

The consensus line is framed as a heuristic, never authoritative: the number is plain arithmetic over the model's clustering of the answers, never a value the model emitted.

The same structure is on the result object:

from conclave import Council

council = Council(models=["grok", "gemini", "claude"])   # verdict is default-on
result = council.ask_sync("Should we adopt a service mesh for 8 services?")

if result.verdict is not None:
    print(result.verdict.verdict_type)        # "decision"
    print(result.verdict.headline)
    print(result.verdict.recommendation)
    print(result.consensus_label, result.consensus_score)   # e.g. "strong" 0.75

    for conflict in result.conflicts:
        print("conflict:", conflict.topic, conflict.position_labels)
    for position in result.verdict.positions:
        print(position.label, "backed by answers:", position.evidence_answer_ids)
    for vote in result.provider_votes:
        print(vote.provider, "->", vote.position_label)     # who voted for what
    for mr in result.minority_reports:
        print("minority:", mr.providers, mr.claim)
else:
    # open-ended / fewer-than-2 members / extraction failure: synthesis is still returned
    print("no verdict:", result.manifest.verdict_absent_reason)
    print(result.synthesis)

# the execution manifest rides on every result — first-class, secret-free
print(result.manifest.verdict_extraction.model_id)   # which model produced the clustering
print(result.manifest.secret_safety)                 # "verified_no_secrets"

To opt out (e.g. for cost-sensitive runs — the verdict adds one extra synthesizer call per run), construct with Council(extract_verdict=False); then result.verdict stays None. conclave ask ... --json already carries the full structured verdict and manifest.

How the consensus number stays honest. The single LLM-assisted step is clustering the members' stances; consensus_score is then deterministic arithmetic over that clustering (largest cluster / members with a position — no text-similarity, never model-emitted). Every cluster cites the member answer_ids backing it (evidence_answer_ids), and the manifest records which model + prompt version did the clustering, so the score is reproducible and traceable rather than a number to take on faith.

The verdict is optional. It is absent — result.verdict is None, with the synthesis and member answers still returned — for an open-ended/creative prompt, for fewer than two responding members, or if extraction fails schema validation; the exact reason is on result.manifest.verdict_absent_reason.

Streaming (synthesize/raw)

Council.ask_stream is an async generator that yields incremental StreamEvents as member (and synthesizer) tokens arrive, then a terminal done event carrying the full CouncilResult — the same shape ask() returns, so downstream code is unaffected:

async for event in council.ask_stream("What is the capital of France?"):
    if event.type in ("member_delta", "synthesis_delta"):
        print(event.text, end="", flush=True)        # live token
    elif event.type == "done":
        result = event.result                          # full CouncilResult

Event types: member_delta / member_done (per member, interleaved), synthesis_delta / synthesis_done (when synthesizing), and the final done. A member that cannot stream, or any mid-stream failure, degrades gracefully — partial text is preserved and the error lands on that member's ModelAnswer, never raising (the same never-raises contract as ask). Streaming applies to synthesize/raw only; debate/adversarial are not streamed.

Debate and adversarial modes

council = Council(models=["grok", "gemini", "claude"], synthesizer="claude")

# debate: multi-round, anonymized peers, partial-failure resilient
debate = council.debate_sync("Is P=NP likely false?", rounds=3)   # or: await council.debate(...)
for rnd in debate.rounds:
    print("round", rnd.round_number, [a.name for a in rnd.successful_answers])
print("FINAL:\n", debate.synthesis)

# debate with optional early-stop: stop before `rounds` once answers converge
quick = council.debate_sync("Is P=NP likely false?", rounds=5, converge_threshold=0.95)
print("ran", len(quick.rounds), "rounds; converged:", quick.converged, quick.convergence_score)

# adversarial: propose -> refute -> verdict
adv = council.adversarial_sync("Defend CRDTs for offline-first apps.", proposer="grok")
print("PROPOSAL by", adv.adversarial.proposer, "->", adv.adversarial.proposal.answer)
for crit in adv.adversarial.critiques:
    print("CRITIQUE", crit.name, ":", crit.error or crit.answer[:80])
print("VERDICT:\n", adv.adversarial.verdict)   # also mirrored to adv.synthesis

CouncilResult exposes mode, answers (per-model ModelAnswer with model_id, latency_s, usage, error), synthesis, synthesizer, skipped, plus successful_answers / failed_answers helpers. For debate it also carries rounds (a list of DebateRound, each with per-member answers) plus converged/convergence_score (set when an early-stop fired); for adversarial it carries adversarial (an AdversarialResult with proposer, proposal, critiques, verdict). For debate the final round is mirrored into answers and the synthesis into synthesis; for adversarial the proposal + critiques populate answers and the verdict mirrors into synthesis — so code written against the v0.1 surface keeps working across every mode.

Synthesizer behavior

The synthesizer is the single model that merges the council's answers (and is the judge in adversarial mode and the final consolidator in debate). It is chosen by this precedence, highest first:

the synthesizer= argument to Council (CLI: --synthesizer/-s);
the synthesizer: key in ~/.conclave/config.yml;
the built-in default — claude (anthropic/claude-sonnet-4-6).

conclave ask "..." --council grok,gemini --synthesizer openai   # override per run

Degradation is observable, never silent. Synthesis is skipped — and the reason is always surfaced on the result — in three cases:

Situation	What happens
No usable member answers (all errored/skipped)	`synthesis = None`, `synthesis_error = "no successful member answers…"`
Synthesizer has no API key	`synthesis = None`, `synthesis_error = "…has no API key; returning raw answers only"`; member answers preserved
Synthesizer call fails	`synthesis = None`, `synthesis_error =` the provider error

In every case the member answers are returned intact and a warning is logged, so a caller can reliably detect a non-synthesis with result.synthesis is None and result.synthesis_error is not None. There is no path where concatenated or partial output is silently returned as if it were a synthesis. In adversarial mode the same signal lands on adversarial.verdict_error (mirrored to synthesis_error).

The synthesis prompt is a versioned constant. The synthesize-mode system prompt is fixed in code (not built per call); the debate/judge prompts live in conclave.prompts. The whole prompt set carries a version tag, conclave.prompts.SYNTHESIS_PROMPT_VERSION, stamped onto every CouncilResult as result.prompt_version. A downstream eval or regression suite can compare it across runs to detect that the synthesis wording changed, instead of silently attributing the shift to model drift. The test suite pins both the prompt text and the version, so changing one without the other fails CI.

Config (optional)

Create ~/.conclave/config.yml to add models, define named councils, and set a default synthesizer. It references providers by name only — never keys.

models:
  grok: xai/grok-4.3
  claude: anthropic/claude-sonnet-4-6
councils:
  default: [grok, gemini, claude, perplexity]
  fast: [grok, perplexity]
synthesizer: claude

Then: conclave ask "..." --council fast.

Result cache (optional, off by default)

Repeated or eval runs can be served from an on-disk cache instead of re-calling the providers. It is off by default and never persists API keys — the cache key is a SHA-256 over the normalized prompt, the ordered council members (friendly name + resolved model id), the mode, the synthesizer/judge identity, and the mode params (temperature, debate rounds + converge_threshold, adversarial proposer). No key value or env-var name ever reaches the key or the stored payload.

Enable it per run with --cache (or disable a config default with --no-cache):

conclave ask "..." --council fast --cache

or set a default in ~/.conclave/config.yml:

cache: true

A cache hit returns the prior CouncilResult with cached: true set and does not touch the network. Entries live under $XDG_CACHE_HOME/conclave (else ~/.cache/conclave); a corrupt or unreadable entry is treated as a miss and never crashes a run.

Test

pytest

The suite mocks the httpx transport, so it needs no network and no API keys.

Extending: custom OpenAI-compatible providers

conclave's provider layer is an adapter registry over a single httpx transport (resolve_adapter in src/conclave/adapters/). The first-class providers are adapters; adding a new provider family is one registration. Adding any OpenAI-compatible endpoint (a local server, a gateway, another vendor's /chat/completions, or an aggregator/router you choose to use) needs no code — just an endpoints: entry in your config that names the base URL and the env-var that holds its key:

endpoints:
  myllm:
    base_url: https://my-gateway.internal/v1
    api_key_env: MYLLM_API_KEY
models:
  mymodel: myllm/some-model-id

The endpoint is referenced by name only; the key value is read from MYLLM_API_KEY at call time and never stored in config or results.

Name		Name	Last commit message	Last commit date
Latest commit History 68 Commits
.github		.github
docs		docs
src/conclave		src/conclave
tests		tests
.gitignore		.gitignore
.gitleaks.toml		.gitleaks.toml
CHANGELOG.md		CHANGELOG.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
DOCUMENTATION_INDEX.md		DOCUMENTATION_INDEX.md
LICENSE		LICENSE
README.md		README.md
RELEASING.md		RELEASING.md
SECURITY.md		SECURITY.md
SYSTEM_CONTEXT_DIAGRAM.md		SYSTEM_CONTEXT_DIAGRAM.md
config.example.yml		config.example.yml
pyproject.toml		pyproject.toml
requirements-dev.lock		requirements-dev.lock

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conclave

Install

Bring your own keys

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Auditable verdict

Streaming (synthesize/raw)

Debate and adversarial modes

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Result cache (optional, off by default)

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Extending: custom OpenAI-compatible providers

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conclave

Install

Bring your own keys

Quickstart (CLI)

Quickstart (library)

Auditable verdict

Streaming (synthesize/raw)

Debate and adversarial modes

Synthesizer behavior

Config (optional)

Result cache (optional, off by default)

Test

Extending: custom OpenAI-compatible providers

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