YourAiWorkforce — a local-8B multi-agent pipeline, clamped for trust and squeezed for cost

In one line: a multi-agent planning pipeline that turns a stochastic, slow 8B local model into something reliable and cheap enough to actually use — running entirely on a 16GB MacBook at zero API cost. A founder's rough idea → structured artifacts (PRD + architecture doc).

One orchestrator routes work to specialist subagents. A human approves each step. Every model call runs on a local Ollama model — Phase 0 runs entirely on qwen3:8b (orchestrator, planner, and a temp=0 critic instance) — no API, no cost, nothing leaves the machine.

It's built on LangGraph StateGraph, and the whole design follows one rule I kept relearning the hard way: prompt is suggestion, graph is law.

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Why this project

With a commercial API (GPT-4, Claude), multi-agent systems "just work" — the model does the heavy lifting and there's no engineering story in it. This project starts from the opposite constraint: a small local model that is both unreliable and expensive to run, on a 16GB machine. Two problems, two pillars — and the whole repo is those two shells:

• Clamp — for reliability. An 8B model breaks instructions probabilistically: tell the persona "don't call yourself the PM" and it does; tell it "don't save on turn 1" and it does; it leaks thinking tokens into replies. So instead of trusting the model, I clamp it — a separate critic, a save-validation gate, response post-processing, state isolation: each probabilistic component wrapped in a deterministic shell.
• Squeeze — for efficiency. On-device, every discarded token is wall-clock and every context window is RAM. So I don't guess, I measure and cut: instrument every model call, size num_ctx per role, kill reasoning where it buys nothing. One measured result — the done-check dropped from ~25s to ~0.3s at matched accuracy, because prompt design beat inference-time compute. Each component wrapped in a cheap shell. → docs/metrics/

📌 Current scope: Phase 0 (idea → PRD → architecture) works end-to-end. agents/ contains persona designs through Phase 1–6 (build / QA / deploy), but only Phase 0 is wired in code — the rest is roadmap (see below).

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Architecture

Two services. The Go gateway owns the client-facing protocol; the Python service owns the graph. The split is not cosmetic — see why the gateway exists.

flowchart LR
    B["browser<br/>(SSE client)"]
    G["Go gateway :8080<br/>protocol · streaming · UI"]
    P["Python FastAPI :8000<br/>LangGraph orchestrator"]
    O["Ollama<br/>qwen3:8b (local)"]

    B -->|"POST /chat"| G
    G -->|"event: token / interrupt / done"| B
    G -->|"POST / (raw token stream)"| P
    G -->|"GET /state/:thread_id"| P
    P --> O

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Inside the Python service, the graph itself:

flowchart TD
    START([START]) --> ORC[orchestrator<br/>qwen3:8b]
    ORC -->|tool_call| ROUTE{route}
    ROUTE -->|call subagent| BRIDGE[bridge<br/>tool_call → brief]
    ROUTE -->|reset_project| GATE[approval_gate<br/>HITL approval]
    ROUTE -->|none| END([END])

    BRIDGE -->|goto| PD[product_discovery<br/>subgraph]
    BRIDGE -->|goto| SA[system_architect<br/>subgraph]

    subgraph phase0 [Phase 0 subgraphs · internal conversation loop]
        PD
        SA
    end

    PD --> REVIEW[review]
    SA --> REVIEW
    GATE --> BRIDGE
    REVIEW --> ORC

    PD -.->|save| PRD[(prd.md)]
    SA -.->|save| ARCH[(architecture.md)]

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• orchestrator: takes the conversation and decides which subagent to delegate to, via a tool-call. Runs qwen3:8b.
• bridge: converts the orchestrator's tool-call into a brief (HumanMessage) for the subagent and routes with Command(goto=...). It uses LangGraph's subgraph-as-node mechanism directly, so a subagent's interrupt propagates to the parent automatically and the resume value flows back in automatically.
• phase-0 subgraphs: product_discovery (→ PRD) and system_architect (→ architecture doc). Each is a conversational subgraph cycling through an internal model → save → check_done → wait_for_user loop.
• review / approval_gate: returns to the orchestrator after reviewing an artifact; risky actions require human approval.

Key source: src/agent.py (graph assembly), src/libs/subgraph.py (conversational subgraph builder), src/subagents/planners/ (phase-0 agents).

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Engineering highlights

The harder problems this project turned on — clamping the model for reliability, squeezing it for cost, and the system built around both. Full write-ups, code, and traces → docs/engineering-notes.md.

• Squeeze · a deterministic done-check, then measured — a temp=0 critic with reasoning off and a safety-biased prompt matches thinking-mode accuracy at ~48× lower latency; prompt design beat inference-time compute, proven with a per-call instrumentation harness (numbers).
• Clamp · state isolation across a subgraph boundary — strip a subagent's internal turns from the parent thread with RemoveMessage, while keeping LangGraph's native interrupt propagation.
• Clamp · making an illegal action impossible — dynamic tool binding so the model physically can't call save before it's allowed, instead of asking it not to.
• System · reconstructing why the stream ended — the Python token stream is byte-identical whether a turn finished or paused for approval, so the Go gateway makes a second state call and emits explicit interrupt/done events. The load-bearing reason the gateway exists.

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Tech stack

Layer	Tech
Orchestration	LangGraph (StateGraph, subgraph-as-node, interrupt/Command)
LLM runtime	Ollama (local) — qwen3:8b for all Phase 0 roles (deepseek-r1:8b evaluated as critic, rejected — see model log)
Serving	FastAPI (ASGI) + langgraph dev
Gateway / BFF	Go (stdlib only — net/http, goroutines, context, embed)
Client protocol	SSE — token / interrupt / done / error (designed at the gateway)
State	SqliteSaver checkpointer (async/sync file sharing)
Observability	LangSmith tracing
Efficiency	per-call instrumentation harness + labeled eval → docs/metrics/
Packaging	uv, Docker / docker-compose

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Running it

# 1. Pull the local model (Ollama required) — Phase 0 runs entirely on qwen3:8b
ollama pull qwen3:8b

# 2. Environment variables
cp .env.example .env    # fill in LANGSMITH_API_KEY, MODEL_BASE_URL, etc.

# 3. Python orchestrator (port 8000)
uv sync
uv run uvicorn src.main:app --port 8000
# or LangGraph Studio: uv run langgraph dev

# 4. Go gateway + web UI (port 8080)
cd gateway && go run .
# then open http://localhost:8080
# override the upstream with UPSTREAM_BASE (default: http://localhost:8000)

# 5. (optional) containers
docker-compose up --build

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Implementation status vs roadmap

This repo deliberately narrows scope to Phase 0 as a "finished product" to gain depth. Given the code-generation ceiling of an 8B local model, stretching all the way to Phase 1–6 (actual code generation) would produce an "ambitious but non-working demo".

Scope	Status
Phase 0 — Product Discovery (idea → PRD)	✅ wired in code
Phase 0 — System Architect (PRD → architecture)	✅ wired in code
Orchestrator routing · HITL approval gate · state isolation	✅ wired in code
Phase 1–6 (build/QA/security/deploy agents)	📐 persona designs only (agents/) · roadmap
Go gateway — SSE relay of the interrupt/resume protocol	✅ wired in code (gateway/)
Go gateway — thin streaming web UI	✅ wired in code (gateway/static/)
Go gateway — session management · artifact-serving API	🚧 planned

See it actually run → docs/samples/ holds a real, unedited PRD generated end-to-end by the product_discovery agent (with the model's rough edges left in, documented honestly).

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How this was built

Pair-programmed with Claude Code. The architecture, the decisions, and the trade-offs documented here are mine; much of the implementation was AI-assisted.

License

All rights reserved. This repository is public for portfolio/demonstration purposes only — you may read the source to evaluate the work, but no license is granted to reuse, copy, modify, or redistribute it. See LICENSE.