ouro-agents

A Python package for running long-lived autonomous agents on the Ouro platform. An agent process owns a workspace on disk, talks to Ouro through MCP, maintains its own memory, and runs in several modes — interactive chat, one-shot tasks, scheduled heartbeats, and a multi-cycle planning loop tied to Ouro quests.

Highlights

• Multiple run modes — chat, autonomous, heartbeat, plan, review. Each mode has a declarative profile controlling prompt framing, tool access, and lifecycle.
• Subagents — built-in research, planner, executor, writer, developer profiles, plus a parallel delegate tool for fan-out work. Custom profiles can be dropped into workspace/subagents/.
• Three-layer memory — vector memory (mem0 + Chroma) for curated facts, working memory (MEMORY.md and daily logs) maintained by the agent itself, and conversation history for chat continuity.
• Doc store — local markdown mirrored to Ouro posts, scoped per-team.
• Planning loop — generates plan cycles tied to Ouro quests, drives them across heartbeats, incorporates comment feedback through review heartbeats.
• Scheduler — heartbeat, consolidation, refinement, plus user-defined recurring tasks (cron or interval).
• Refinement & cleanup — periodic LLM-driven rewrites of working memory based on a typed change-set queue, plus deterministic cleanup for asset.deleted webhooks.
• OpenRouter integration — prompt caching for Anthropic models, per-mode and per-subagent reasoning effort, multi-model setups.

Install

pip install ouro-agents

Python 3.10+ is required.

Quickstart

Create a standalone agent project:

ouro-agents init my-agent
cd my-agent

python -m venv .venv
source .venv/bin/activate
pip install -e .

cp .env.example .env
# edit .env and agent.json, then:
ouro-agents --config agent.json chat

The generated repository owns the agent's identity, skills, curated memory, coils, and service code. Runtime data and secrets are ignored. Its pyproject.toml pins the same released ouro-agents package an external user installs; no checkout of this repository is required. Harness-owned databases and run state live under agent.data_dir (generated as ~/ouro-data/<name>), outside the repository.

Run a one-shot task:

ouro-agents --config agent.json run "What teams am I on?"

Or start the long-running server (heartbeats + webhook receiver):

ouro-agents --config agent.json serve

The full walkthrough is in docs/getting-started.md.

Documentation

Full docs live in docs/. A few starting points:

• Concepts overview — how the agent loop, modes, subagents, memory, and planning fit together.
• Configuration reference — every field in config.json.
• CLI reference — every subcommand and flag.
• Run modes — chat, autonomous, heartbeat, plan, review.
• Subagents — built-in profiles, custom profiles, the delegate tool.
• Memory model — vector memory, doc store, working memory, reflection.
• Workspace layout — what every directory is for.
• Planning — the plan / review cycle.
• HTTP API & webhooks — /run, /health, event routing.
• Glossary — recurring terms.

CLI cheatsheet

ouro-agents init my-agent                       # scaffold a standalone agent repo
ouro-agents --config agent.json serve           # FastAPI server + scheduler
ouro-agents --config agent.json run "Summarize today's activity"
ouro-agents --config agent.json chat             # interactive REPL
ouro-agents --config agent.json heartbeat        # one heartbeat tick
ouro-agents --config agent.json plan ["goal"]    # force a planning heartbeat
ouro-agents --config agent.json review           # force a review heartbeat

Add -v for verbose output or --debug-md to capture a full run trace (see the CLI reference).

HTTP API

While ouro-agents serve is running:

# Threaded conversation-style chat
curl -X POST http://localhost:8000/run \
  -H "Content-Type: application/json" \
  -d '{"task":"Hi, can you help me post a dataset?","session_id":"demo-user-1"}'

# Same session reuses the same conversation id automatically
curl -X POST http://localhost:8000/run \
  -H "Content-Type: application/json" \
  -d '{"task":"Use the Machine Learning team","session_id":"demo-user-1"}'

# Health
curl http://localhost:8000/health

The server also accepts Ouro webhook events at server.webhook_path (default /events). See docs/http-api.md and docs/events.md.

Workspace

The agent reads and writes everything under agent.workspace (default ./workspace):

workspace/
├── SOUL.md           # required: identity, values, operating rules
├── NOTES.md          # optional: ambient notes
├── MEMORY.md         # curated cross-team memory
├── conversations/    # per-conversation transcripts ({id}.jsonl)
├── shared/logs/      # period logs (daily/weekly/biweekly)
├── teams/<id>/       # team memory, logs/, plans, doc registry
├── memory/           # mem0 + Chroma store (opaque)
├── skills/           # workspace skill overrides
└── subagents/        # custom SubAgentProfile files

See docs/workspace.md.

Configuration at a glance

Minimal config.json shape (full reference in docs/configuration.md):

{
  "agent": {
    "name": "hermes",
    "org_id": "00000000-0000-0000-0000-000000000000",
    "workspace": "./workspace"
  },
  "models": {
    "strong": {
      "id": "anthropic/claude-4.6-sonnet",
      "reasoning": { "effort": "medium" }
    },
    "light": {
      "id": "google/gemini-2.5-flash",
      "reasoning": { "effort": "none" }
    }
  },
  "modes": {
    "run":      { "max_steps": 60 },
    "chat":     { "max_steps": 40 },
    "planning": { "enabled": true, "cadence": "4h" },
    "heartbeat": {
      "enabled": true,
      "every": "1h",
      "active_hours": { "start": "09:00", "end": "17:00", "timezone": "America/Chicago" }
    }
  },
  "subagents": {
    "research": { "max_steps": 30 }
  },
  "memory": {
    "provider": "mem0",
    "path": "./workspace/protected/memory",
    "embedder": "openai/text-embedding-3-small"
  },
  "mcp_servers": [
    {
      "name": "ouro",
      "transport": "stdio",
      "command": "/path/to/python",
      "args": ["-m", "ouro_mcp.server"],
      "env": { "OURO_API_KEY": "${OURO_API_KEY}", "OURO_BASE_URL": "${OURO_BASE_URL}" }
    }
  ],
  "prompt_caching": { "enabled": true, "ttl": "5m" }
}

models.strong / models.light (and optional mid) are the preferred way to pick models — the harness assigns them by role. Explicit subagents.<name>.model / modes.*.model overrides still win. See the docs for everything else.

Development

Tests are in tests/:

pytest

Linting:

ruff check .

The package layout:

ouro_agents/
├── agent.py            # OuroAgent orchestrator
├── cli/                # Typer CLI entry point (serve/run/chat/...)
├── server.py           # FastAPI + webhook routing
├── config.py           # Pydantic config models + loader
├── modes/              # mode profiles + heartbeat + planning
├── subagents/          # SubAgentProfile + runner + built-in prompts
├── memory/             # vector memory + doc store + reflection
├── refinement/         # change-set queue + LLM-driven rewrites
├── cleanup/            # deterministic asset.deleted handler
├── skills/             # built-in markdown skills
├── tools/              # built-in tools (delegate, run_python, etc.)
├── tui/                # team / plan pickers
└── utils/              # streaming, callbacks, conversation helpers

Browse docs/ for a guided tour.

License

See the repository root for license terms.