🤖 Agent Rewards

Turn your coding-agent traces into a stablecoin reward.

Agent Rewards is an open-source skill/MCP that installs into your coding agents — Claude Code, Codex, Grok Bot, Hermes Agent, Openclaw — automatically finds, redacts, and uploads your agent sessions, and lets you sell access to those traces to researchers and model trainers for USDC/USDT.

Your agents already generate high-value training data every day (tool calls, multi-turn reasoning, error recovery). You generate that data anyway. Agent Rewards makes it worth something.

Why

Real conversation/trajectory data is the scarcest, most valuable post-training signal for open models. Yet today:

• HF Agent Traces gives you the upload pipeline — but it's manual (hf upload, no incentive).

• KTH reproducible-trajectories proves "install + share to a DB" — but CLI-only, no reward.

• WildChat / LMSYS / ShareChat proved wild conversation data trains better models.

None of them is native, automated, privacy-first, and paid. That's the gap Agent Rewards fills.

✨ Features

1. 🔍 Find your trace — scans all supported agents' local session dirs, cross-platform.

2. ⬆️ Upload / auto-upload daily — incremental sync daemon, sha256 dedup, offline queue.

3. 🖥️ View your trace — HF-style timeline viewer: prompts, tool calls, tool outputs, reasoning.

4. 🪙 View your credits — live ledger + token balance.

5. 🔁 Exchange your traces for USDC/USDT — sell access on-chain (Base L2, low fees); pull-based, per-trace licensing (research / commercial / exclusive).

🚀 One-click install

Agent Rewards ships as (a) a shared MCP server and (b) thin per-agent skills so it mounts natively into each runtime.

Run the MCP server (working today)

# from a clone
cd agent-rewards
python3 -m pip install -e .        # or: pip install -e .[dev] for tests
python3 -m agent_rewards.server    # or use the `agent-rewards-mcp` entry point

Point your MCP client at it. In Hermes (~/.hermes/config.yaml):

mcp_servers:
  agent_rewards:
    command: "/usr/bin/python3"
    args: ["-m", "agent_rewards.server"]
    env:
      AGENT_REWARDS_DB_DIR: "~/.agent-rewards"

Or via Claude Code (.mcp.json):

{
  "mcpServers": {
    "agent_rewards": {
      "command": "/usr/bin/python3",
      "args": ["-m", "agent_rewards.server"]
    }
  }
}

The server exposes 5 tools:

Coming soon: one-shot scripts/install.sh (auto-detects agents), HTTP mode, per-agent skills, daily daemon.

🔒 Privacy by default (the moat)

• Git-aware scoping — default uploads only traces touching open-source repositories. Private code is opt-in only.

• Local redaction — secret/PII/token scrub + LLM review gate before anything leaves the machine.

• Provenance — content-addressed (sha256 / IPFS), immutable hash registry.

• Clear licensing — every trace is tagged research / commercial / exclusive; you set the price.

You stay in control of what leaves your machine and who can buy it.

Architecture

[installer]
   └─ per-agent skills / MCP  (claude · codex · grok · hermes · openclaw)
         └─ [collector] scan trace jsonl
              └─ [redactor] git-aware + secret/PII scrub (local-first)
                   └─ [sync daemon] upload daily · dedup · offline queue
                        └─ [backend] Postgres + object store + hash registry
                             ├─ [api] REST / MCP read API
                             ├─ [dashboard] view traces · credits · listings
                             └─ [ledger] USDC/USDT settlement (Base L2)
                                  └─ [marketplace] buy / sell access

Roadmap

• M0 — repo + README + demo landing page ✅

• M1 — MCP server: find_trace / view_trace / upload_trace / get_credits / scope_of ✅ (working stdio server; HTTP + installer pending)

• B0 (Beta) — one-click install + hosted backend + dashboard + privacy hardening → public preview (see gap table below)

• M2 — per-agent skills (Claude Code, Codex, Hermes first) + redactor + daily daemon

• M3 — backend + dashboard + hash registry + decontamination

• M4 — ledger + USDC/USDT settlement + marketplace

• M5 — open-sourcing contributor rewards / token incentives

See docs/ROADMAP.md for milestones with acceptance criteria and docs/CHECKLIST.md for the release checklist. Full project status is tracked there.

Getting started (contributors)

git clone https://github.com/josecookai/agent-rewards.git
cd agent-rewards
# landing page (demo)
python3 -m http.server --directory landing 8000   # → http://localhost:8000

Related work

• HF Agent Traces — trace upload + viewer infra

• KTH reproducible-trajectories — share-to-DB

• ShareChat — multi-platform wild conversation dataset (incl. Grok)

• WildChat — 1M real user–ChatGPT conversations

License

MIT. Traces contributed by users remain under their chosen license and are only accessible to buyers you approve.