agentsave 0.1.0

Save 30% on AI agent costs. One line of code. No accuracy loss.

AgentSave — Save 30% on AI agent costs. One line of code.

The first AI agent efficiency platform. Drop-in Python supervisor + real-time cost dashboard + inference router. Zero accuracy loss.

flowchart LR
    SDK["💡 pip install agentsave"]
    WRAP["supervise(agent)"]
    SUP["Supervisor\nContext Filter + Early Exit\n+ Budget Gate"]
    TEL["TelemetryClient\n(opt-in, zero PII)"]
    API["Dashboard Backend\nFastAPI + SQLite"]
    UI["agentsave-ui\nNext.js Dashboard"]
    IR["InferRoute\nDocker Sidecar"]
    VLLM["vLLM / SGLang\nCluster"]

    SDK --> WRAP
    WRAP --> SUP
    SUP -->|async fire-and-forget| TEL
    TEL --> API
    API --> UI
    SUP -->|"~30% token reduction"| WRAP
    IR -->|"~68% TTFT reduction"| VLLM
    API -.->|Enterprise tier| IR

🔥 The Problem

• Every LLM agent wastes 30–50% of tokens on irrelevant tool outputs — inflating costs with no accuracy gain
• Agents over-iterate past diminishing returns, burning tokens on iterations that add nothing
• Developers have zero visibility into which agents, models, and frameworks are costing them the most

⚡ The Solution

SDK Layer

pip install agentsave, then wrap any agent with supervise(agent). The supervisor filters irrelevant context, exits early on diminishing returns, and enforces a budget gate — delivering ~30% token reduction with zero changes to your agent's internals.

Dashboard Layer

Real-time cost tracking across every run, with a per-framework breakdown, an hourly activity heatmap, and an interactive cost projector to forecast monthly savings.

InferRoute Layer

PPD (append-prefill decode) routing for multi-turn agent workloads, delivering ~68% Turn 2+ TTFT reduction. Available on the Enterprise tier as a Docker sidecar in front of your vLLM / SGLang cluster.

🎬 In Action

1. Overview dashboard — real-time savings stats with animated counters
2. Analytics — token reduction trend over time (area/line/bar toggle)
3. Agent Runs — full run history with framework badges and reduction %
4. Cost Projector — interactive sliders to project monthly savings
5. Live Activity Feed — real-time agent run stream
6. Hourly Heatmap — GitHub-style activity grid
7. Command Palette — instant navigation and actions (⌘K)
8. Billing — Free / Pro / Enterprise tiers

🚀 Quick Start

1. Install the SDK: pip install git+https://github.com/aks-builds/agentsave.git
2. Wrap your agent:

   from agentsave import supervise
   agent = supervise(your_agent)
   

3. Run your agent normally — token savings happen automatically
4. Connect dashboard: agentsave login
5. View savings: agentsave status
6. (Optional) Start dashboard backend: cd agentsave-dashboard && uvicorn agentsave_dashboard.main:app
7. (Enterprise) Deploy InferRoute: docker run -p 8080:8080 agentsave/inferroute:latest

📦 Installation

SDK (all agent frameworks):

v0.1.0 is not yet on PyPI. Install directly from GitHub until the first release is tagged and published:

# Install from GitHub (current)
pip install git+https://github.com/aks-builds/agentsave.git

# With specific framework support:
pip install "git+https://github.com/aks-builds/agentsave.git#egg=agentsave[langchain]"
pip install "git+https://github.com/aks-builds/agentsave.git#egg=agentsave[all]"

Once v0.1.0 is released to PyPI (trigger with Actions → release → Run workflow):

pip install agentsave

# With specific framework support:
pip install "agentsave[langchain]"     # LangChain + LangGraph
pip install "agentsave[autogen]"       # AutoGen
pip install "agentsave[crewai]"        # CrewAI
pip install "agentsave[smolagents]"    # Smolagents
pip install "agentsave[all]"           # All frameworks

Dashboard Backend:

git clone https://github.com/aks-builds/agentsave-dashboard
cd agentsave-dashboard
pip install -e ".[dev]"
uvicorn agentsave_dashboard.main:app --port 8000

Dashboard UI:

git clone https://github.com/aks-builds/agentsave-ui
cd agentsave-ui
npm install
npm run dev   # http://localhost:3000

InferRoute (Enterprise, requires Docker):

docker run -d -p 8080:8080 \
  -e BACKEND_URL=http://your-vllm:8000 \
  -e BACKEND_TYPE=vllm \
  -e AGENTSAVE_TOKEN=$ENTERPRISE_TOKEN \
  agentsave/inferroute:latest

🏗 Architecture

• Drop-in, zero-modification: supervise(agent) wraps any agent framework without touching internals
• LLM-free context filter: TF-IDF cosine similarity — no extra API calls, <1ms overhead per observation
• ICLR 2026 research-backed: 29.68% token reduction on GAIA benchmark (arXiv:2510.26585)
• Five framework adapters: LangChain, LangGraph, AutoGen, CrewAI, Smolagents — all tested
• InferRoute PPD routing: ~68% Turn 2+ TTFT reduction via append-prefill decode routing (ICML 2026, arXiv:2603.13358)
• Opt-in telemetry: zero PII — only run_id, framework, model, token counts, success flag
• Self-hostable: dashboard backend is MIT-licensed FastAPI + SQLite; InferRoute is a Dockerfile drop-in

🗺 Roadmap

v0.2:

• JavaScript/TypeScript SDK for Node.js agent frameworks
• Real-time WebSocket events for the live feed
• Team workspaces with RBAC

v0.3:

• OpenAI Responses API adapter
• Anthropic tool_use adapter
• Cost anomaly alerts (email + webhook when a run exceeds threshold)

Tracked as GitHub Issues.

📁 Project Structure

agentsave/              ← SDK (this repo)
├── agentsave/          ← Python package
│   ├── core/           ← context filter, early exit, budget gate, supervisor
│   ├── adapters/       ← LangChain, LangGraph, AutoGen, CrewAI, Smolagents
│   ├── telemetry/      ← opt-in async telemetry client
│   └── cli/            ← agentsave login/status/config
└── tests/              ← 60 unit tests

agentsave-dashboard/    ← FastAPI + SQLite backend
├── agentsave_dashboard/
│   ├── routers/        ← /api/events, /api/metrics, /api/tokens, /api/billing
│   └── services/       ← metrics aggregation, Stripe billing
└── tests/              ← 47 tests

agentsave-ui/           ← Next.js 16 dashboard
├── app/
│   ├── components/     ← StatCard, charts, RunsTable, ActivityFeed, CommandPalette
│   └── (routes)/       ← /, /analytics, /runs, /frameworks, /cost, /settings
└── tests/e2e/          ← 30 Playwright tests

agentsave-inferroute/   ← Enterprise inference router
├── inferroute/
│   ├── classifier.py   ← Turn 1 vs Turn 2+ detection
│   ├── router.py       ← PPD scoring function
│   └── adapters/       ← vLLM + SGLang
└── tests/              ← 59 tests

🤝 Contributing

See CONTRIBUTING.md for setup instructions, code style, and the PR checklist.

📄 License

MIT © 2026 Aditya Kumar Singh