ThingsO

Repository intelligence

zai-org/Open-AutoGLM

Editorial

An Open Phone Agent Model & Framework. Unlocking the AI Phone for Everyone. In ThingsO it is evaluated as a ai agent framework or agent application.

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61Health
Editorial

What it is

94% confidence

An Open Phone Agent Model & Framework. Unlocking the AI Phone for Everyone. In ThingsO it is evaluated as a ai agent framework or agent application.

Product typeAI agent framework or agent application
Primary roleCoordinate model reasoning, tools, memory, and multi-step task execution.
Categoryai-agent
Interactionprogrammatic API
Editorial

Problem → solution

86% confidence

Problem

Building useful AI agents requires more than a model call: applications need tool execution, state, control flow, retries, context, and boundaries around autonomous actions.

Pain points

  • Building useful AI agents requires more than a model call: applications need tool execution, state, control flow, retries, context, and boundaries around autonomous actions.

Solution approach

Provide reusable agent abstractions and runtime patterns that connect models with tools, state, orchestration, and application-specific execution logic.

Why it matters

The project is useful when teams need the ai-agent capability without building every supporting primitive from scratch.

Editorial

Why it is different

Differentiators

  • Repository-stated scope: An Open Phone Agent Model & Framework. Unlocking the AI Phone for Everyone.
  • Its curated role in the ThingsO catalog is ai-agent; exact implementation differentiation is verified from repository evidence rather than assumed from popularity.

Design philosophy

  • Prefer the project’s documented public interfaces and extension points over undocumented internals.

Unique capabilities

  • Repository-stated scope: An Open Phone Agent Model & Framework. Unlocking the AI Phone for Everyone.
  • Its curated role in the ThingsO catalog is ai-agent; exact implementation differentiation is verified from repository evidence rather than assumed from popularity.

Design trade-offs

  • More autonomy increases operational and safety complexity.
  • Framework abstractions can simplify orchestration while constraining low-level control.
Editorial

Who should use it

76% confidence

Target users

  • AI application developers
  • agent platform teams
  • automation engineers

Jobs to be done

  • build tool-using agents
  • orchestrate multi-step model workflows
  • integrate models with application actions

Best for

  • teams building agentic product features
  • developers needing reusable orchestration primitives

Not ideal for

  • simple single-prompt features
  • workloads that do not need model-driven control flow
Editorial

Architecture

72% confidence

The baseline architecture for this ai-agent project is interpreted from its product category, while concrete runtime, technology, code paths, commands, and deployment evidence are compiled from the current repository snapshot.

Architecture style

Agent runtime organized around model calls, tools/actions, state, and orchestration components.

inferred · 80% confidence

Execution model

A request or task enters an agent loop/workflow where model decisions select actions until a result or stopping condition is reached.

inferred · 82% confidence

State model

State behavior depends on the selected runtime/deployment; inspect the project’s execution modules and persistence configuration for durable-state requirements.

inferred · 55% confidence

Persistence

Persistence requirements are workload/deployment specific unless explicitly established by a captured manifest/container document.

inferred · 52% confidence

Concurrency

Concurrency is implementation/runtime specific; verify worker, async or parallel execution settings before capacity planning.

inferred · 52% confidence

Scaling

Scale according to the runtime’s supported process/service model and validate shared state, model hardware and external rate limits before horizontal replication.

inferred · 52% confidence

Core components

Agent runtime

Coordinates the task lifecycle and model/tool loop.

Model adapter

Connects the runtime to one or more language-model providers.

Tool layer

Exposes controlled application or external actions to the agent.

Data / control flow

  1. Task/context enters the agent runtime and is prepared for model reasoning.
  2. Model output drives tool calls or intermediate steps, and results are folded back into the task context.
Editorial

Technology

88% confidence
primary language

Python

Primary language reported by the current GitHub repository snapshot.

known
AI provider client

OpenAI client/API

Declared project dependency associated with AI provider client.

known
HTTP client

Requests

Declared project dependency associated with HTTP client.

known
build/package

Python requirements manifest

Defines dependency, packaging or build metadata.

known
build/package

Python setuptools packaging

Defines dependency, packaging or build metadata.

known
Editorial

Codebase map

92% confidence

The semantic codebase map is derived from the captured repository tree. Key visible areas include docs, examples, scripts.

docs

Project documentation.

examples

Usage examples/reference implementations.

scripts

Development/automation scripts.

Start reading

  • docs
  • examples
  • scripts

Entry points

  • main.py

Extension points

Not established from available evidence.

Editorial

Developer workflow

82% confidence

Local setup

The README provides executable setup/run commands; a representative captured command is `pip install -r requirements.txt`.

known · 80% confidence
install dependencies/runtime · pip install -r requirements.txt
install dependencies/runtime · pip install -e .
setup or run project · python main.py --base-url https://open.bigmodel.cn/api/paas/v4 --model "autoglm-phone" --apikey "your-bigmodel-api-key" "打开美团搜索附近的火锅店"
setup or run project · python main.py --base-url https://api-inference.modelscope.cn/v1 --model "ZhipuAI/AutoGLM-Phone-9B" --apikey "your-modelscope-api-key" "打开美团搜索附近的火锅店"
run development mode · python3 -m vllm.entrypoints.openai.api_server \
run development mode · python3 -m sglang.launch_server --model-path zai-org/AutoGLM-Phone-9B \
setup or run project · python scripts/check_deployment_cn.py --base-url http://你的IP:你的端口/v1 --model 模型名称
setup or run project · python main.py --base-url http://localhost:8000/v1 --model "autoglm-phone-9b"

Build

Not established from available evidence.

unknown · 0% confidence

Tests

Not established from available evidence.

unknown · 0% confidence

Lint

Not established from available evidence.

unknown · 0% confidence

Typecheck

Not established from available evidence.

unknown · 0% confidence

CI/CD

Not established from available evidence.

unknown · 0% confidence

Contribution

Not established from available evidence.

unknown · 0% confidence

Release process

Not established from available evidence.

unknown · 0% confidence
Editorial

Integration & extension

Extension model

Extend through tools, model/provider adapters, agent definitions, memory/state components, or workflow hooks exposed by the project.

inferred · 72% confidence

Plugin system

Not established from available evidence.

unknown · 0% confidence

Adding an extension

Start with documented public APIs and the codebase extension/provider/integration paths identified by the semantic tree map.

inferred · 58% confidence

APIs

Not established from available evidence.

Protocols

Not established from available evidence.

Ecosystem integrations

  • Validate concrete integrations against the current repository docs and codebase map before adoption.
Editorial

Deployment & operations

64% confidence

Minimum deployment

Install/invoke the project inside a compatible host runtime or application; a universal standalone service is not required by the product type.

inferred · 68% confidence

Production topology

Production topology is deployment-specific; validate stateful services, worker/runtime boundaries and external dependencies before high-availability scale-out.

inferred · 54% confidence

Persistence

Persistence requirements are workload/deployment specific unless explicitly established by a captured manifest/container document.

inferred · 52% confidence

Configuration

Configuration is supplied through the project’s documented runtime/application settings; inspect README and captured configuration files for exact keys.

inferred · 62% confidence

Scaling

Scale according to the runtime’s supported process/service model and validate shared state, model hardware and external rate limits before horizontal replication.

inferred · 52% confidence

Observability

Not established from available evidence.

unknown · 0% confidence

Backup / upgrade

Not established from available evidence.

unknown · 0% confidence

Failure recovery

Recovery planning should cover persistent state, generated artifacts and external integration credentials; exact procedures are deployment-specific.

inferred · 50% confidence

Resource profile

Resource requirements depend on workload and selected runtime/model; benchmark the intended production workload before sizing infrastructure.

inferred · 50% confidence

Operational risks

  • External APIs, models or runtime dependencies can change independently of this repository.
  • Upgrades should be tested against the adopting application’s integrations and persisted state.
Editorial

Security & privacy

Authentication

Not established from available evidence.

unknown · 0% confidence

Authorization

Not established from available evidence.

unknown · 0% confidence

Secrets

Use the project’s supported secret/configuration mechanism and keep service credentials outside source control.

inferred · 52% confidence

Network exposure

Not established from available evidence.

unknown · 0% confidence

Sandboxing

Not established from available evidence.

unknown · 0% confidence

Data persisted

Not established from available evidence.

unknown · 0% confidence

Data leaving system

Data can leave the deployment when configured external APIs, model providers or remote sources are used; exact flows depend on user configuration.

inferred · 50% confidence

Telemetry

Not established from available evidence.

unknown · 0% confidence

Security considerations

  • Tool-enabled agents should receive least-privilege credentials and explicit boundaries for external actions.
Editorial

Decision guide

Choose when

  • teams building agentic product features
  • developers needing reusable orchestration primitives

Avoid when

  • simple single-prompt features
  • workloads that do not need model-driven control flow

Evaluate first

  • Confirm the current license and project activity meet your requirements.
  • Prototype the project against one representative production workflow.
  • Review the generated Technology, Codebase, Developer Workflow, Deployment, and Security evidence sections before committing to adoption.

Trade-offs

  • More autonomy increases operational and safety complexity.
  • Framework abstractions can simplify orchestration while constraining low-level control.
Learning curvemedium
Operational complexitymedium
Migration costmedium
Lock-inmedium
Editorial

Project signals & learning

Maturity

established with strong public adoption signals

inferred · 84% confidence

Governance

Maintained under GitHub owner `zai-org`; detailed governance/decision rights are not fully established by the bounded evidence pack.

inferred · 62% confidence

Licensing

GitHub reports SPDX license `Apache-2.0`; verify repository license text and dependency obligations for the intended use.

known · 90% confidence

Adoption signals

  • GitHub snapshot: 26,079 stars
  • GitHub snapshot: 4,020 forks

Ecosystem

Not established from available evidence.

What you can learn

  • Study zai-org/Open-AutoGLM to understand practical implementation choices in the ai-agent problem space.
  • Compare its public extension model with its internal module boundaries before reusing patterns elsewhere.

Suggested reading order

  • docs
  • examples
  • scripts

editorial / chatgpt-gpt-5.6-sol-manual · 78% overall confidence

Classification

Ai Agent capability Ai Agent capability EditorialLocal deployment Self Hosted deployment Cli interface

Related repositories

Editorial

Build Ideas

Deterministic · health-v1

Project Health

Maintenance60
Adoption87
Community39
Documentation100
Operations0
License clarity100
Maturity24
Metadata100
Source fact

GitHub source facts

Stars26.1K
Forks4K
Open issues262
Watchers26.1K
LanguagePython
LicenseApache-2.0
Default branchmain
Snapshot2026-08-24
Source fact

Evidence & provenance

manifest · 4381c204c2e3ed27f4ededdcb419cae0abe9c5d5 · 2d7f0a20e1…manifest · b35c2b193497a6ba46e430ea1c961b293ad0bdc6 · ee4c874f55…readme · 2af1de1ecb44f51c31256cd0bd2aa12842866423 · de9c289c41…repository_tree · main · db5f3afc5c…