chimera-deliberation 0.2.0

Dynamic multi-model deliberation gateway

Chimera — Dynamic Multi-Model Deliberation Gateway

One API call. A team of models. One answer.

Chimera takes your prompt, dispatches it to a hand-picked team of LLMs (each with a custom subtask scoped to their strengths), and an aggregator merges their outputs using dispatcher-written instructions. One dispatcher model call designs the entire deliberation at once.

Quickstart

# Install
pip install chimera-deliberation[full]
# Or for just the API server:
pip install chimera-deliberation[server]

# Configure
cp chimera.yaml.example chimera.yaml
# Add your API keys (at minimum: DEEPSEEK_API_KEY)

# Run
chimera "What is the capital of France?"          # CLI
chimera serve                                       # REST API + web UI
chimera-mcp                                        # MCP tools for agents

Open http://localhost:8765/web/ for the web UI with live DAG visualization.

Python:

from chimera import Engine, ChimeraConfig, load_config

config = load_config()
engine = Engine(config, LiteLLMGateway(config))
result = await engine.deliberate("Explain quantum computing.")
print(result.answer)  # merged output from multiple models

OpenAI-compatible:

curl -X POST http://localhost:8765/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "auto", "messages": [{"role": "user", "content": "Hello"}]}'

Architecture

flowchart TB
    subgraph Client
        A[User Prompt]
    end

    subgraph Dispatcher["Dispatcher (1 call)"]
        B[Designs DAG<br/>Picks models by category<br/>Writes custom prompts<br/>Writes merge instructions]
    end

    subgraph Workers["Workers (parallel)"]
        C1[Worker A<br/>domain-scoped task]
        C2[Worker B<br/>domain-scoped task]
        C3[Worker C<br/>domain-scoped task]
    end

    subgraph Aggregation
        D[Aggregator<br/>merges with dispatcher instructions]
    end

    A --> B
    B --> C1
    B --> C2
    B --> C3
    C1 --> D
    C2 --> D
    C3 --> D
    D --> E[Final Answer]

Formation Types

flowchart LR
    subgraph Simple["Simple (2 workers)"]
        S1[W1] --> SA[Aggregator]
        S2[W2] --> SA
    end

    subgraph Debate["Debate (3 workers + merge)"]
        D1[W1] --> DA1[Agg 1]
        D2[W2] --> DA1
        D2 --> DA2[Agg 2]
        D3[W3] --> DA2
        DA1 --> DM[Merge]
        DA2 --> DM
    end

    subgraph Custom["Custom DAG (client-defined)"]
        C1[Researcher] --> C2[Critic]
        C2 --> C3[Polisher]
        C3 --> C4[Final]
    end

Flow: Request to Answer

sequenceDiagram
    participant Client
    participant Engine
    participant Dispatcher
    participant Workers
    participant Aggregator

    Client->>Engine: POST /v1/chat/completions
    Engine->>Dispatcher: Design formation
    Dispatcher->>Dispatcher: Pick models by category weights
    Dispatcher->>Dispatcher: Write per-worker prompts + merge instructions
    Dispatcher-->>Engine: DAG + prompts + instructions

    par Workers (parallel)
        Engine->>Workers: Worker A (custom prompt)
        Engine->>Workers: Worker B (custom prompt)
        Engine->>Workers: Worker C (custom prompt)
        Workers-->>Engine: responses
    end

    Engine->>Aggregator: Merge with dispatcher instructions
    Aggregator-->>Engine: Final answer

    Engine-->>Client: JSON response + trace

Quick Start

# Install
pipx install chimera[full]

# Configure
cp chimera.yaml.example chimera.yaml
# Edit chimera.yaml with your API keys

# Run
chimera deliberate "Compare React, Vue, and Svelte for a real-time dashboard"

# Or as an API server
chimera serve
# → http://localhost:8000/v1/chat/completions (OpenAI-compatible)
# → http://localhost:8000/docs (OpenAPI docs)

# Or as MCP server for Hermes/AI agents
chimera-mcp

Model Selection

The dispatcher picks models using category-weighted scoring:

Category	What it measures
code	Programming, debugging, software engineering
analysis	Data analysis, research, evaluation
reasoning	Logic, math, complex problem-solving
design	Creative work, UX, content generation
audit	Fact-checking, safety, correctness review

Each model in the catalog has a score (0.0–1.0) per category. The dispatcher matches task domains to model strengths. You can override any model choice per request:

{
  "model": "auto",
  "messages": [{"role": "user", "content": "..."}],
  "dispatcher_model": "deepseek/deepseek-v4-flash",
  "allowed_models": ["deepseek/deepseek-v4-pro", "z-ai/glm-5.2"],
  "stage_models": {"worker_1": "openrouter/anthropic/claude-sonnet-4"}
}

Custom DAGs

Send your own formation structure at request time:

{
  "model": "custom",
  "allow_custom_dag": true,
  "dag": {
    "stages": [
      {"id": "researcher", "kind": "worker", "model": "openrouter/anthropic/claude-sonnet-4"},
      {"id": "critic", "kind": "aggregator", "model": "z-ai/glm-5.2", "depends_on": ["researcher"]},
      {"id": "polisher", "kind": "worker", "model": "deepseek/deepseek-v4-pro", "depends_on": ["critic"]},
      {"id": "final", "kind": "aggregator", "model": "openrouter/anthropic/claude-sonnet-4", "depends_on": ["polisher"]}
    ],
    "edges": [["researcher","critic"], ["critic","polisher"], ["polisher","final"]]
  },
  "messages": [{"role": "user", "content": "..."}]
}

The dispatcher writes custom prompts for each stage but uses YOUR structure exactly.

Interfaces

Interface	Endpoint	Use
REST API	POST /v1/chat/completions	OpenAI-compatible drop-in
REST API	POST /v1/deliberate	Full control (DAG, overrides, trace)
REST API	GET /v1/models	Model catalog with weights
REST API	GET /v1/formations	Available formation presets
CLI	chimera deliberate	Command-line usage
MCP	chimera_deliberate	Hermes / AI agent integration

Response Trace

Every deliberation returns a full trace:

request_id: a71b3f2c...
formation: auto
source: auto        ← not "fallback" — dispatcher designed it
total_tokens: 12345
total_cost: $0.012
total_duration_ms: 15234

dispatch: V4 Flash (1,234ms, 800+420 tok)
workers:
  worker_rust: Claude Sonnet 4 (4,500ms, 300+1,200 tok)
  worker_go: Kimi K2.7 (8,200ms, 250+800 tok)
aggregator: V4 Flash (2,100ms, 3,000+500 tok)

Providers

Chimera uses LiteLLM under the hood. Supported providers:

Provider	Direct API	Via OpenRouter	Models
DeepSeek	✅	✅	v4-flash, v4-pro, r1
Anthropic	—	✅	Sonnet 4, Opus 4.7/4.8, Haiku 4.5
OpenAI	—	✅	GPT-5.5, GPT-5.1
xAI	—	✅	Grok 4.20
Google	—	✅	Gemini 3.5 Flash, 3.1 Pro, 2.5 Flash
Z.AI	✅ (direct)	✅	GLM-5.2
MoonshotAI	—	✅	Kimi K2.7 Code, K2.6
MiniMax	—	✅	M3
Meta	—	✅	Llama 4 Maverick