local-fusion

Run an OpenRouter Fusion-style multi-model council locally — no OpenRouter, no hosted router, no billing middleman.

local-fusion sends one prompt to a panel of models, has a judge model compare their answers into structured analysis, then has a synthesizer model write the final answer. It runs against any OpenAI-compatible server (Ollama, LM Studio, llama.cpp, vLLM, MLX, LocalAI, …) and/or against the subscription models you have configured in Pi (ChatGPT, Claude, GLM/ZAI, Kimi) — without you holding any API keys.

It ships four runtimes from one small, dependency-free codebase:

There is a Node.js implementation (src/, zero dependencies) and a parallel Python implementation (local_fusion/) with matching core behavior.

Table of contents

• Why

• How it works

• Requirements

• Install

• Quick start

• Configuration reference

• Backends: local OpenAI vs Pi subscriptions

• Commands

  • ask — one-shot council

  • serve — OpenAI-compatible endpoint

  • looped — Looped PI Fusion

  • mcp — MCP server for Claude Code

• Using inside a Pi agent

• Python implementation

• Output schema

• Project layout

• Testing

• Troubleshooting

• Limitations

• License

Related MCP server: consult-mcp

Why

OpenRouter's Fusion router has five useful ideas. local-fusion recreates all of them with no hosted dependency:

1. A panel of configurable models answers the same prompt.

2. Panel calls run in parallel when the runtimes can handle it.

3. A judge compares the answers instead of naively merging them.

4. The judge returns structured analysis: consensus, contradictions, partial coverage, unique insights, blind spots, notes.

5. A synthesizer writes the final answer from the raw responses plus the judge analysis.

The payoff is diversity plus a strict judge. Best results come from genuinely different model families; fusing one model under different "perspective" system prompts still helps, but that is mostly extra test-time compute rather than true diversity.

What it deliberately does not do: OpenRouter, web search, web fetch, hosted routing, or your-own-API-key billing.

How it works

                    ┌──────────────────────────── panel (parallel or sequential) ───────────────────────────┐
   prompt  ───────► │  model A (perspective 1)     model B (perspective 2)     model C (perspective 3)  ...   │
                    └───────────────────────────────────────────────┬──────────────────────────────────────┘
                                                                     │ raw independent answers
                                                                     ▼
                                                           ┌──────── judge ────────┐
                                                           │ strict JSON analysis:  │
                                                           │  consensus / contradic │
                                                           │  partial / unique /    │
                                                           │  blind spots / notes   │
                                                           └───────────┬───────────┘
                                                                       │ analysis + raw answers
                                                                       ▼
                                                        ┌──────── synthesizer ────────┐
                                                        │  one final answer, resolving │
                                                        │  contradictions explicitly   │
                                                        └───────────┬─────────────────┘
                                                                    ▼
                                              final_answer  (+ full trace via --json / structuredContent)

The harness is degradation-tolerant:

• If some panel models fail, it proceeds with the survivors and records degradation_reasons.

• If all panel models fail, it returns status: "error" with an empty answer.

• If the judge returns non-JSON, it falls back to a heuristic analysis and notes the degradation.

• If the synthesizer fails, it returns the first panel answer rather than nothing.

Requirements

• Node.js ≥ 20 (the Node runtime is dependency-free — nothing to npm install).

• Python 3.10+ — only if you use the Python implementation.

• At least one model source:

  • One or more local OpenAI-compatible servers exposing /v1/chat/completions, and/or

  • Pi installed and authenticated, to use subscription/OAuth models.

Known-compatible local servers:

Install

git clone https://github.com/kelvincushman/local-fusion.git
cd local-fusion

# Node runtime needs nothing installed. Verify it runs:
node src/cli.mjs --help

# Optional: link the `local-fusion` bin onto your PATH
npm link            # then: local-fusion ask "..."

Quick start

A) Local models (Ollama example)

ollama pull qwen2.5-coder:14b
ollama serve

cp config.example.json local-fusion.config.json   # then edit baseUrl/model to match your servers
node src/cli.mjs ask --config local-fusion.config.json "Compare ridge, lasso, and elastic-net regression. Where does each shine?"

⚠️ The committed local-fusion.config.json is configured for the Pi backend (subscription models). For pure local servers, base your config on config.example.json instead.

B) Subscription models via Pi (no API keys of your own)

# Pi owns auth; log in once to the providers you want
pi /login

# The shipped config runs GLM-5.2 + GPT-5.4 + Kimi K2 this way
node src/cli.mjs ask "Design a local-first AI writing assistant architecture"

Print the full trace as JSON:

node src/cli.mjs ask --json "Design a local-first AI writing assistant architecture"

Pipe a prompt from stdin:

pbpaste | node src/cli.mjs ask --json

Configuration reference

Config is a single JSON file (default local-fusion.config.json, override with --config). Paths are resolved relative to your current working directory.

Top-level options

Per-model config

Example: pure local servers

See config.example.json — three local panel members (coder / generalist / skeptic), a strict judge, and a synthesizer, all pointed at Ollama / LM Studio / llama.cpp.

Example: Pi subscription council

See local-fusion.config.json — GLM-5.2 (generalist) + GPT-5.4 (builder) + Kimi K2 (critic) panel, Kimi K2 judge, GPT-5.4 synthesizer.

Backends: local OpenAI vs Pi subscriptions

local-fusion mixes two backends freely — even within a single panel.

backend: "openai" (default). A plain HTTP POST to baseUrl/chat/completions. Use for Ollama, LM Studio, llama.cpp, vLLM, MLX, LocalAI, or any OpenAI-compatible endpoint.

backend: "pi". Spawns a headless pi subprocess and uses Pi's stored auth (~/.pi/agent/auth.json), including OAuth subscriptions (ChatGPT Plus/Pro, Claude Pro/Max) and ZAI/Kimi. local-fusion holds no API keys of its own. Use provider + modelId instead of baseUrl/apiKey. All pi calls funnel through one subprocess, so they are serialized even when parallel: true.

💳 Subscription billing note. With backend: "pi", Anthropic subscription access (Claude Pro/Max) through a third-party harness is billed per-token from your extra usage budget, not your plan limits. If Opus errors with 400 ... Add more at claude.ai/settings/usage, add budget there. GLM-5.2 (free) and GPT-5.4 (ChatGPT subscription) are not affected.

🔒 Kimi Code / Kimi K2. The kimi-coding provider is gated to approved coding-agent clients — a direct backend: "openai" call returns access_terminated_error. It works only through the pi backend (which presents the approved client identity). Auth it with pi /login → Kimi For Coding.

Commands

All commands share these options where relevant:

--config <path>   Config file. Default: local-fusion.config.json
--json            (ask) print the full Fusion JSON result
--host <host>     (serve) default 127.0.0.1
--port <port>     (serve) default 8787
--rootDir <path>  (mcp) file-backed run state. Default: ./runs/mcp
--runDir <path>   (looped) artifact trail directory
--step <n>        (looped) current loop step, default 1
--runId <id>      (looped) override the generated run id

ask — one-shot council

node src/cli.mjs ask "Your question or instruction"
node src/cli.mjs ask --json "Your question"           # full trace
node src/cli.mjs ask --config my.config.json "..."    # alternate config
echo "prompt from stdin" | node src/cli.mjs ask --json

Without --json it prints just final_answer (and any degradation notes to stderr). With --json it prints the full result object.

serve — OpenAI-compatible endpoint

node src/cli.mjs serve --port 8787

Then call it like any OpenAI chat endpoint:

curl http://127.0.0.1:8787/v1/chat/completions \
  -H 'content-type: application/json' \
  -d '{
    "model": "local/fusion",
    "messages": [
      { "role": "user", "content": "What are the strongest arguments for and against carbon taxes?" }
    ]
  }'

• The assistant answer is at choices[0].message.content.

• The full Fusion trace is attached at the top-level local_fusion field of the response.

• Health check: GET /health → { "ok": true }.

The serve endpoint is prose-only — it returns a synthesized natural-language answer. It is not intended to back a tool-calling agent; for that, use the MCP server.

looped — Looped PI Fusion

A prompt driver layer over an agent loop (implements docs/prd-pi-agent-fusion-loop.md). It does not execute shell, edit files, or run tests — per run it:

1. Resolves model assignments for four view-roles + a conductor from a roster.

2. Dispatches Explorer / Builder / Critic / Performance Sentinel views (each a role-specialized council call).

3. The Loop Conductor reads the views + a run-state snapshot and emits a machine-parseable decision JSON.

4. Writes an artifact trail under <runDir>/artifacts/pi-fusion/.

5. Returns { decision, prompt, summary } for the caller to inject as the next agent prompt.

node src/cli.mjs looped "Implement and test feature X" \
  --config looped-fusion.config.json \
  --runDir ./runs/last \
  --step 1 \
  --heartbeat fresh

This command requires model_roster and role_model_policy in the config — see looped-fusion.config.json and docs/using-looped-fusion.md.

mcp — MCP server for Claude Code

Lets Claude Code / Opus stay the executor while local-fusion becomes the checking + council layer it calls as tools. The flow is pull-based (MCP tools are client-initiated): Claude Code calls a tool, gets the next instruction, executes, reports evidence, and asks again.

Start the server:

node src/cli.mjs mcp --config local-fusion.config.json --rootDir ./runs/mcp

The server speaks the MCP stdio transport (newline-delimited JSON) that Claude Code uses; legacy LSP-style Content-Length framing is also accepted (auto-detected from the client's first bytes).

Register with Claude Code (user scope = available in every project):

claude mcp add local-fusion --scope user -- \
  node /absolute/path/to/local-fusion/src/cli.mjs mcp \
  --config /absolute/path/to/local-fusion/local-fusion.config.json \
  --rootDir /absolute/path/to/local-fusion/runs/mcp

claude mcp get local-fusion          # expect: ✔ Connected

Use --scope project instead to write a shared .mcp.json at the repo root (Claude Code requires explicit approval for project-scoped servers before first use).

Tools exposed:

The default routing keeps the expensive path optional:

Opus executes → looped_report → checker gate
   ├─ done + high confidence  → looped_next returns complete
   ├─ incomplete + concrete fix → direct retry prompt
   ├─ uncertain / high-risk    → fusion council → conductor prompt
   └─ blocked                  → pause for human

Run artifacts are written under runs/mcp/<run_id>/ (state.json, report-N.json, check-N.json, fusion-review-N.json, next-N.json). runs/ is gitignored. Full details and the recommended Claude Code priming prompt are in docs/using-mcp-connector.md.

Using inside a Pi agent

local-fusion can run as a tool a Pi agent consults for a multi-model second opinion. The Pi skill and project AGENTS.md are already set up — launch pi from this repo and ask it to "get a local-fusion council opinion on X". See docs/using-with-pi.md.

Python implementation

A parallel implementation lives in local_fusion/ with matching core fusion + looped behavior (it does not serve MCP — that is Node-only).

python3 -m local_fusion ask "Compare ridge, lasso, and elastic-net regression."
python3 -m local_fusion ask --json "Design a local-first AI writing assistant architecture"
pbpaste | python3 -m local_fusion ask --json
python3 -m local_fusion serve --port 8787

Output schema

ask --json and the MCP fusion_ask tool return:

{
  "status": "ok",                       // or "error" when all panel models fail
  "final_answer": "…synthesized answer…",
  "analysis": {
    "consensus": ["points most models agreed on"],
    "contradictions": [
      { "topic": "…", "stances": [{ "model": "…", "stance": "…" }] }
    ],
    "partial_coverage": [{ "models": ["…"], "point": "…" }],
    "unique_insights": [{ "model": "…", "insight": "…" }],
    "blind_spots": ["important missing topics"],
    "judge_notes": ["guidance for the synthesizer"]
  },
  "responses": [{ "model": "…", "content": "raw panel answer" }],
  "failed_models": [{ "model": "…", "error": "…" }],
  "degradation_reasons": ["Some panel models failed.", "…"],
  "raw_judge_output": "…optional raw judge text…"
}

When the judge returns non-JSON, analysis is a heuristic fallback and degradation_reasons explains why.

Project layout

local-fusion/
├─ src/                        # Node implementation (zero dependencies)
│  ├─ cli.mjs                  # entrypoint: ask | looped | serve | mcp
│  ├─ fusion.mjs               # panel → judge → synthesizer core
│  ├─ looped.mjs               # Looped PI Fusion prompt driver
│  ├─ mcp.mjs                  # MCP stdio server (ndjson + Content-Length)
│  ├─ mcp-connector.mjs        # build/check loop logic behind the MCP tools
│  ├─ openai-compatible.mjs    # backend: "openai" transport
│  ├─ pi-transport.mjs         # backend: "pi" transport (headless subprocess)
│  └─ config.mjs               # JSON config loader
├─ local_fusion/              # Python implementation (core parity, no MCP)
├─ test/                       # Node tests (node --test)
├─ tests/                      # Python tests (unittest)
├─ docs/                       # PRD + usage guides + blog posts
├─ .pi/                        # Pi skill + extension for in-agent use
├─ config.example.json         # local-server starter config
├─ local-fusion.config.json    # Pi-subscription council config (default)
└─ looped-fusion.config.json   # roster + role policy for `looped`

Testing

# Node (23 tests)
node --test

# Python (15 tests)
python3 -m unittest discover -s tests

The Node suite covers the fusion core, the looped driver, the MCP connector routing, and the MCP wire transport — including framing detection and a full newline-delimited initialize/tools/list handshake, so the Claude Code transport can't silently regress.

Troubleshooting

Limitations

• MCP serving is Node-only. Python has core fusion/looped parity but does not serve MCP stdio.

• MCP is pull-based. Claude Code must call the tools; local-fusion cannot push into an already-running conversation.

• pi-backend calls are serialized through one subprocess even with parallel: true.

• GLM quota can degrade the council — check degradation_reasons in fusion_ask / looped_fuse_review output.

License

MIT © Kelvin Cushman