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Alexandre Teixeira
837fbfd0ea
The compact (clean v3) runtime had no effective context window: it learned a limit only reactively from a provider 400/413 and its terminal metrics carried no context_length. PR #41 addressed the reporting gap by probing provider metadata between the last model byte and [DONE], unauthenticated, and folded known-table and endpoint evidence into one "known" flag. Resolve the window once, before the first model request, instead: - src/agent_runtime/context_resolution.py adds a typed ContextResolution (effective value, evidence class, source, all observations, conflicts, provider_io, cached, secret-free probe errors). Evidence classes stay distinct: runtime_confirmed (llama.cpp /slots, /props, or a limit the provider stated this turn), provider_advertised (models catalog), operator_declared (client_runtime_context.model_context_window), known_table, unknown (0, never a default). - Selection is deterministic: runtime beats provider beats table; an operator declaration caps measured evidence and replaces weaker evidence. Disagreements are recorded as conflicts; a declaration below a measured value is a cap, above it a contradiction. - The provider probe forwards the turn's credentials only to the provider's own origin, runs URL resolution off the event loop, is bounded by one deadline, never raises, and caches remote results per credential fingerprint (shorter TTL for failures; local servers are re-probed). - stream_preview resolves at preparation (or accepts a supplied resolution), seeds the proactive trim budget from it when evidence is not unknown, and terminal metrics report only the stored resolution plus any limit the provider stated during the turn. Metrics perform no discovery. src/agent_loop.py and the regular runtime's legacy model_context probe are unchanged. A conftest guard keeps tests that drive the compact runtime with placeholder endpoints from performing real DNS/HTTP lookups.
A self-hosted AI workspace for chat, agents, research, documents, email, notes, calendar, and local model workflows.
Quick Start · Setup Guide · Contributing · Roadmap
Quick Start
devis the default branch and gets the newest changes first. Usemainif you want the more curated branch.
git clone https://github.com/odysseus-dev/odysseus.git
cd odysseus
cp .env.example .env
docker compose up -d --build
Open http://localhost:7011 when the containers are healthy. The first admin password is printed in docker compose logs odysseus.
Native installs, GPU notes, Windows/macOS instructions, HTTPS, and configuration live in the setup guide.
Features
- Chat + Agents — local/API models, tools, MCP, files, shell, skills, and memory.
- Cookbook — hardware-aware model recommendations, downloads, and serving.
- Deep Research — multi-step web research with source reading and report generation.
- Compare — blind side-by-side model testing and synthesis.
- Documents — writing-first editor with AI edits, suggestions, Markdown, HTML, CSV, and syntax highlighting.
- Email — IMAP/SMTP inbox with triage, tags, summaries, reminders, and reply drafts.
- Notes, Tasks + Calendar — reminders, todos, scheduled agent tasks, and CalDAV sync.
- Extras — gallery/image editor, themes, uploads, web search, presets, sessions, and 2FA.
Demo
A full hover-to-play tour lives on the Odysseus landing page. Its source lives under website/.
Contributing
Help is welcome. The best entry points are fresh-install testing, provider setup bugs, mobile/editor polish, docs, and small focused refactors. See CONTRIBUTING.md and ROADMAP.md.
Security
Odysseus is a self-hosted workspace with powerful local tools. Keep auth enabled, keep private data out of Git, and do not expose raw model/service ports publicly.
- Keep
AUTH_ENABLED=truefor any network-accessible deployment. - Keep
LOCALHOST_BYPASS=falseoutside local development.
Deployment details are in the setup guide.
Star History
License
AGPL-3.0-or-later -- see LICENSE and ACKNOWLEDGMENTS.md.