Skip to main content

Changelog

0.18.0 (2026-09-20)

  • fix(ci): the four checks #1165 turned red, each at its own layer
  • feat(scaffold): ask the daemon about a topic this venv does not have

jaato-server

The runtime and daemon behind jaato — a multi-provider agentic tool orchestrator. This package holds the session runtime, the plugin system, ~24 model-provider adapters, and the daemon that clients connect to over a Unix socket or WebSocket.

You install this on the machine where agents should run. To talk to it from Python, install jaato-sdk; for a terminal client, jaato-tui.

Installation

pip install jaato-server
# or:  uv pip install jaato-server

The base install carries the daemon, the session runtime and the plugins that need no third-party SDK. Provider SDKs and the heavier tool plugins are extras, so you install only the ones you use:

Extra Brings
google Google GenAI / Vertex AI
openai OpenAI (Chat Completions + the Responses API)
azure-openai Azure OpenAI, including Microsoft Entra ID auth
bedrock AWS Bedrock (boto3)
openrouter, nim, ovhcloud OpenAI-compatible gateways
github-models GitHub Models (azure-ai-inference)
web web_fetch / web_search — extraction, PDF and search backends
interactive interactive_shell — PTY sessions (pexpect / wexpect)
ast ast_search — structural code search
notebook, diagrams, templates, kaggle the matching tool plugins
telemetry OpenTelemetry tracing
kerberos SPNEGO/Negotiate proxy auth
all everything above, plus the dev tooling
pip install 'jaato-server[all]'

Anthropic's SDK, mcp, websockets and jaato-sdk are base dependencies — Claude, MCP servers and the WebSocket transport work out of the box. Requires Python 3.12+.

Quick start

Start the daemon:

# Local clients over a Unix domain socket
jaato-server --ipc-socket /tmp/jaato.sock --daemon

# Add a WebSocket port for remote or browser clients.  A bearer token is
# generated at ~/.jaato/ws.token on first WS start if you supply none.
jaato-server --ipc-socket /tmp/jaato.sock --web-socket :8080 --daemon

jaato-server --status
jaato-server --stop

Then drive it from Python:

import asyncio
import jaato          # from jaato-sdk

async def main():
    async with jaato.session(mode="ipc", profile="researcher") as s:
        print(await s.ask("What changed in this repo last week?"))

asyncio.run(main())

The same code runs against an embedded runtime (mode="in_process", no daemon), a local daemon ("ipc"), or a remote one ("ws").

Model providers

One session interface over every backend below; a profile picks the provider and model, and enter_tier can switch bindings mid-session.

Hosted APIs Anthropic, OpenAI, Azure OpenAI, Google GenAI / Vertex AI, AWS Bedrock, GitHub Models, Zhipu AI, MiniMax, Moonshot Kimi, Xiaomi MiMo
Gateways OpenRouter (300+ models), NVIDIA NIM, Nebius Token Factory, OVHcloud AI Endpoints, Doubleword
Self-hosted Ollama, LM Studio, vLLM, NVIDIA TensorRT-LLM, Triton
On-device Chrome built-in AI (Gemini Nano, over the DevTools Protocol)
Subscription-backed Claude Code CLI, Google Antigravity

Several ship an interactive auth command (anthropic-auth login, github-auth login, openrouter-auth key …) so a credential need never be pasted into a shell profile.

Plugins

Tools reach the model through plugins, discovered from this package and from any installed distribution declaring a jaato.plugins entry point. Built-ins include:

  • Execution — cli (shell), interactive_shell (PTY sessions for REPLs, debuggers, wizards), notebook, mcp (any stdio MCP server)
  • Code and files — file_edit, filesystem_query, ast_search, lsp, references
  • Web — web_fetch, web_search, webmcp, service_connector (call an OpenAPI service by alias), webhook (inbound events)
  • Orchestration — subagent, todo, clarification, waypoint, reliability, template
  • Context management — four GC strategies (gc_truncate, gc_summarize, gc_hybrid, gc_budget) plus provider prompt-caching plugins
  • Control — permission, sandbox_manager, telemetry

Agent profiles

A profile is a YAML file in .jaato/profiles/ binding a model, a provider, a plugin set, a GC strategy and resource ceilings:

name: researcher
description: Deep research profile
provider: anthropic
model: claude-sonnet-4-20250514
plugins: [cli, web_search, memory, todo(preload)]
default_agent: researcher        # the persona in .jaato/agents/researcher.md
runtime_limits:
  max_parallel_tools: 2
  max_orphan_seconds: 300
budget_control:
  limits: {usd: 5.0, tool_calls: 200}
  degrade:
    - {at: 95,  action: finalize}
    - {at: 100, action: abort}

jaato-scaffold interrogates the installed framework rather than a copy of the docs, so it cannot go stale:

jaato-scaffold explain profile         # every profile key, with its vocabulary
jaato-scaffold explain providers       # providers, their knobs and caveats
jaato-scaffold explain plugins         # what is installed, and where it came from
jaato-scaffold validate                # check a workspace before you run it
jaato-scaffold new client --profile researcher

explain covers 20-odd scopes — env, tiers, completion, runtime, paths, services among them.

Isolation and secrets

Sessions run in a subprocess runner, and the daemon confines it where the host allows:

  • AppArmor — a per-boundary profile confines the runner to its workspace when the LSM is available. Opt in per client, or require it with JAATO_REQUIRE_APPARMOR=1 so an unconfined host refuses to start rather than degrading silently.
  • cgroup v2 — runtime_limits caps memory, pids and CPU; tool timeouts, output size, tool-pool width and two wall-clock bounds are enforced by the framework itself.
  • Secret scrubbing — provider keys and tokens are stripped from the environment handed to every model-driven subprocess by default; scrub_secret_env narrows or waives it, and waiving is announced.
  • Permissions — every tool call passes a policy that can allow, deny or ask, per session, with the decision recorded.

Configuration

Workspace configuration lives under .jaato/ (profiles, agents, GC settings, permissions) with ~/.jaato/ as the fallback tier. MCP servers are declared in .mcp.json. Credentials resolve from a profile knob, then the environment, then a stored auth file — pass:// and vault:// URIs are resolved daemon-side so a secret never sits on disk in the clear.

jaato-scaffold explain env prints every environment variable the installed tree reads, each tagged with its scope and the typed profile key that supersedes it.

Documentation

License

BUSL-1.1. See LICENSE.

Metadata

Release files for jaato-server 0.18.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for jaato-server 0.18.0
File Size Uploaded
jaato_server-0.18.0.tar.gz 6.3 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for jaato-server 0.18.0
File Interpreter ABI Platform
jaato_server-0.18.0-py3-none-any.whl Python 3 none any Details

Total release size: 13.6 MB

Release files / jaato_server-0.18.0.tar.gz

Download URL jaato_server-0.18.0.tar.gz
Size 6.3 MB
Tags Source
SHA-256 checksum
How to use checksums
c3ad71ec134932b4a215febf2dadb3e9e40f85228c5d96fdb65f71ae7a81ffe6
BLAKE2b-256 checksum
How to use checksums
fcfcf7f87c3c2cb53b0194da61c7e1323d6a473ec867d1c1c5645f1cd2c2c124
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 20, 2026.

Transparency log

Release files / jaato_server-0.18.0-py3-none-any.whl

Download URL jaato_server-0.18.0-py3-none-any.whl
Size 7.2 MB
Tags Python 3
SHA-256 checksum
How to use checksums
8310c6922a2bbdf94396ce37eb718d90ae9edc70244c159829ff5497daae3a52
BLAKE2b-256 checksum
How to use checksums
bac242beb63bcd37e00d959f40cdc5688a9c9fe3291b1c102ac1312e2521e9b2
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 20, 2026.

Transparency log

Release history Release notifications | RSS feed

1.2.0

2 release files

1.1.0

2 release files

1.0.0

2 release files

0.19.0

2 release files

This release

0.18.0 This release

2 release files

0.17.0

2 release files

0.16.0

2 release files

0.15.0

2 release files

0.14.0

2 release files

0.13.0

2 release files

0.12.0

2 release files

0.11.0

2 release files

0.10.0

2 release files

0.9.0

2 release files

0.8.0

2 release files

0.7.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page