Imported from opensage-agent/opensage-adk (SKILL.md). Install upstream with npx skills add opensage-agent/opensage-adk. Copyright stays with the author.

OpenSage-ADK

OpenSage-ADK is an agent framework built on Google ADK for long-horizon, tool-heavy tasks. It unifies six subsystems — sessions, sandboxes, tools, plugins, history management, and multi-agent orchestration — into a single deterministic platform driven by a TOML config and an agent.py factory.

Installation

Requirements: Python 3.12+, uv, Docker.

curl -LsSf https://astral.sh/uv/install.sh | sh
git clone https://github.com/opensage-agent/opensage-adk.git
cd opensage-adk
uv venv --python 3.12
uv sync
uv run opensage --help

Minimal Agent

An agent directory contains agent.py with a mk_agent() factory. The factory receives the current session ID and returns an OpenSageAgent.

# my_agent/agent.py
import os
from google.adk.models.lite_llm import LiteLlm
import opensage
from opensage.agents import OpenSageAgent

def mk_agent(opensage_session_id: str, model=None):
    session = opensage.get_opensage_session(opensage_session_id)
    if model is None:
        model = LiteLlm(model="openai/gpt-5")
    return OpenSageAgent(
        name="my_agent",
        description="My agent.",
        model=model,
        instruction="You are a helpful assistant.",
        enabled_skills="all",
        tools=[],
        subagents=[],
    )

Launch the web UI:

uv run opensage web --agent /path/to/my_agent --port 8000

LiteLLM resolves credentials from environment (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.).

Architecture

Session lifecycle

Every run follows seven phases:

1. Input parsing and logging setup.
2. opensage.get_opensage_session(session_id, config_path) instantiates an OpenSageSession holding configuration, budget, sandbox manager, Neo4j client manager, ADK services, LLM registry, and AgentManager.
3. Sandbox dependency collection from a dummy agent, followed by pruning of unused sandboxes.
4. Shared-volume initialization.
5. Container launch and per-sandbox initializer execution.
6. Agent loading and plugin discovery.
7. ADK runner event loop.

Entry points (opensage web, evaluation runners, RL integrations) share phases 1–4 and 7; phases 5–6 diverge. get_opensage_session(session_id) returns the active session from anywhere in tool code.

Project structure (src/opensage/)

• agents/ — OpenSageAgent, ToolLoader
• sandbox/ — backends (native, podman, remotedocker, opensandbox, nitrobox, local, k8s) and initializers (main, neo4j, joern, codeql, gdb_mcp, coverage, fuzz)
• session/ — managers
• bash_tools/ — bash-scripted Skills
• toolbox/ — Python tools and MCP toolsets
• config/ — TOML loading and dataclasses
• plugins/ — ADK plugins and Claude Code hooks
• memory/ — Neo4j-backed short- and long-term memory
• features/ — feature flags, summarization, ToolCombo
• evaluation/ — evaluation runners, dispatchers, RL adapters (benchmarks live in top-level benchmarks/)

Sessions and managers

OpenSageSession owns these runtime components:

• AgentManager — agent definitions, spawned instances, peer-message inboxes, and lifecycle states RUNNING, SLEEPING, TERMINATING, TERMINATED.
• OpenSageSandboxManager — container lifecycle and shared volumes.
• OpenSageNeo4jClientManager — lazy Neo4j clients.
• ADK in-memory services — session, artifact, memory, and credential services.
• LlmRegistry — configured model pool for LLM-driven agents.
• BudgetManager — session-level LLM budget tracking.

Sandboxes

Two orthogonal axes separate concern: backend (where execution happens — Docker local/remote, Kubernetes, native) from initializer (what setup runs inside). A session owns multiple named sandboxes declared in [sandbox.sandboxes.<name>]. Shared mounts /shared (target code/data), /sandbox_scripts, and /bash_tools bind into every sandbox.

Tools declare sandbox dependencies via @requires_sandbox(...) or a ## Requires Sandbox section in SKILL.md. collect_sandbox_dependencies() prunes unused sandboxes at startup. Lifecycle per run: shared-volume init → parallel container launch → per-sandbox initializers → MCP readiness polling.

Tools

OpenSageAgent normalizes three tool shapes into one dict via make_toollikes_safe_dict():

• Plain Python functions (signature + docstring → JSON schema)
• BaseToolset (batches unfolded recursively)
• MCPToolset / OpenSageMCPToolset (tools discovered over MCP)

To attach a sub-agent, declare it with subagents=[...] and call it with call_subagent. OpenSageAgent rejects an AgentTool placed in tools=.

Wrappers preserve exception handling (errors become {"success": false, "error": "..."}) and return structure (raw values wrap into {"result": ...}, dicts pass through).

Built-in Python toolbox categories are framework-level: general/ (bash_tool_main, run_terminal_command, view_file, str_replace_edit, WebSearchTool, think, complain, critique), debugger/, binary/, and finish_task/. Domain workflows such as retrieval, static analysis, fuzzing, coverage, Neo4j, and MMP live under src/opensage/bash_tools/ or benchmark-owned modules.

Bash Skills live in src/opensage/bash_tools/ or ~/.local/opensage/bash_tools/ as directories containing SKILL.md plus scripts/. ToolLoader loads them based on the agent's enabled_skills value: None (no skills), "all" (top-level only), or a list of prefixes (e.g., ["fuzz", "retrieval/grep"]).

MCP integration: OpenSageMCPToolset wraps ADK's McpToolset with name stability and tool_name_prefix enforcement. Server lifecycle: sandbox config declares the service, the initializer starts the process, readiness is polled, and tools are discovered on first call.

Plugins

Plugins hook after_tool_callback, on_event_callback, before_model_callback (and related lifecycle callbacks) in strict sequential order, mutating shared state.

Two kinds:

• ADK plugins — Python .py subclassing google.adk.plugins.base_plugin.BasePlugin.
• Claude Code hooks — declarative JSON with matchers (PreToolUse/PostToolUse, exact/pipe-separated names, argument globs, wildcards) and actions (prompt or command).

Discovery priority (later shadows earlier):

1. src/opensage/plugins/default/adk_plugins/
2. src/opensage/plugins/default/claude_code_hooks/
3. extra_plugin_dirs entries
4. {agent_dir}/plugins/

Available callbacks: before_tool_callback, after_tool_callback, on_tool_error_callback, before_model_callback, after_model_callback, on_model_error_callback, before_agent_callback, after_agent_callback, before_run_callback, after_run_callback, on_user_message_callback, on_event_callback.

Built-in plugins: history_summarizer_plugin, tool_response_summarizer_plugin, quota_after_tool_plugin, runtime_budget_plugin, doom_loop_detector_plugin, build_verifier_plugin, image_injection_plugin, read_before_edit_plugin.

History: truncation vs compaction

Two levers prevent context overflow:

• Truncation (per response, tool_response_summarizer_plugin): if output exceeds max_tool_response_length (default 10000), summarize or truncate to preview + file pointer /workspace/.tool_outputs/<id>; full output persists in the sandbox.
• Compaction (whole log, history_summarizer_plugin): if folded event characters exceed max_history_summary_length (default 100000), OpenSageFullEventSummarizer takes the first compaction_percent of events (default 50) after the last boundary, expands to a paired call-response boundary, summarizes via LLM, and replaces the window with a single EventCompaction node.

When [history] enable_quota_countdown = true, quota_after_tool_plugin appends {used, remaining, limit} to responses. Short-term memory is file-based (~/.local/opensage/sessions/ on the host, /mem/short_term/ in the sandbox). Long-term memory is also file-based, an index.md under /mem/long_term/ (src/opensage/memory/file_based/long_term/).

Multi-agent composition

Three patterns:

1. Static sub-agents — declare them with subagents=[...] at construction and invoke with call_subagent.
2. Dynamic sub-agents — create_subagent(agent_name, instruction, model_name, tools_list, enabled_skills) registers an agent definition; call_subagent(agent_name, request) spawns an instance and returns its session_id; continue_agent_instance and list_subagents manage existing instances. Instance states are RUNNING, SLEEPING, TERMINATING, and TERMINATED.
3. Multi-model delegation — call the same sub-agent across models with call_subagent(..., mode="async", model_name=...) and read the inbox results, guided by the workflow/ensemble skill.

Peer messages use per-instance file-backed inboxes (orchestration/inbox.py). InboxDeliveryPlugin drains pending messages at tool boundaries, and async sub-agent results are posted back to the caller's inbox.

Self-reflection tools: think, plan, complain, note_suspicious_things, log_finding, critique, audit_assumptions, validate_claim. The last three call a model named through the model_name argument.

CLI Reference

opensage web

opensage dependency-check

No flags. Reports CodeQL, Docker, and kubectl availability.

Configuration Reference

TOML with template variables: top-level UPPERCASE keys can be referenced as ${VAR_NAME} anywhere. Loading order: default template (src/opensage/templates/configs/default_config.toml) → custom config from --config.

Root fields

[llm]

Profiles under [llm.model_configs.<profile>]. Built-in profiles: main (required), summarize (fall back to main if absent).

Fields: model_name (required), temperature, max_tokens, rpm, tpm.

[llm.model_configs.main]
model_name = "openai/gpt-5"
temperature = 0.7
max_tokens = 8192
rpm = 60

[llm.model_configs.summarize]
model_name = "openai/o4-mini"
temperature = 0.3

[sandbox]

Top-level: default_image, backend (native stable; remotedocker, opensandbox, nitrobox, local, k8s under development), project_relative_shared_data_path, absolute_shared_data_path, mount_host_paths (list of "/host:/container[:ro|rw]"), paths, tolerations (k8s only).

Per-sandbox [sandbox.sandboxes.<name>] built-ins: main, joern, codeql, neo4j, gdb_mcp, pdb_mcp, coverage.

Container fields: image, container_id, timeout (default 300), project_relative_dockerfile_path, absolute_dockerfile_path, command, platform, network, privileged, security_opt, cap_add, gpus, shm_size, mem_limit, cpus, user, working_dir.

Build: build_args, using_cached.

Env/volumes/ports: environment, volumes, mounts, ports, docker_args, extra.

Kubernetes: pod_name, container_name.

[sandbox]
backend = "native"
default_image = "ubuntu:22.04"
absolute_shared_data_path = "/tmp/shared"

[sandbox.sandboxes.main]
image = "ubuntu:22.04"
mem_limit = "4g"
cpus = "2"
environment = {PYTHONUNBUFFERED = "1"}
volumes = ["/host/code:/shared/code:ro"]

[sandbox.sandboxes.joern]
image = "joern/joern:latest"
timeout = 600

[mcp]

[mcp.services.gdb_mcp]
sse_port = 8001
sse_host = "127.0.0.1"

[mcp.services.pdb_mcp]
sse_port = 8002

Pair each [mcp.services.<name>] with [sandbox.sandboxes.<name>] so the sandbox launches the server and MCP knows where to reach it. Fields: sse_port (required), sse_host (falls back to default_host).

[history]

[history]
max_tool_response_length = 10000
enable_quota_countdown = true

[history.events_compaction]
max_history_summary_length = 100000
compaction_percent = 50

[plugins]

[plugins]
enabled = ["doom_loop_detector_plugin", "history_summarizer_plugin"]
extra_plugin_dirs = ["/path/to/shared/plugins"]

[plugins.params.doom_loop_detector_plugin]
threshold = 5

Fields: enabled (names or regex patterns), extra_plugin_dirs, params (per-plugin kwargs, keyed by plugin name).

Multi-agent workflows

Reusable orchestration recipes live under src/opensage/bash_tools/workflow/. The ensemble recipe replaces the legacy agent_ensemble tool by combining get_available_models, call_subagent(..., mode="async", model_name=...), wait_for_subagent, and inbox-delivered results.

[neo4j]

[neo4j]
user = "neo4j"
password = "mypassword"
bolt_port = 7687
neo4j_http_port = 7474

URI constructed at runtime as neo4j://{default_host}:{bolt_port}. Declare the Neo4j sandbox separately under [sandbox.sandboxes.neo4j].

[build]

[build]
poc_dir = "/tmp/poc"
compile_command = "gcc -o target target.c"
run_command = "./target"
target_type = "binary"
target_binary = "/tmp/poc/target"

Customization Recipes

Agents 101

• Minimal agent — one OpenSageAgent with one Python function tool (docstring + type hints auto-generate the schema).
• Custom Python tool — any callable with a docstring becomes a tool.
• MCP stdio — MCPToolset(connection_params=StdioConnectionParams(server_params=StdioServerParameters(command="npx", args=[...]))).
• MCP SSE — MCPToolset(connection_params=SseConnectionParams(url="http://127.0.0.1:3001/sse")).
• MCP server-as-tool — MCPToolset(connection_params=StreamableHTTPConnectionParams(url="http://0.0.0.0:9998/mcp")).

Agents with features

• ToolCombo — chain tools into one atomic call. Knob: return_history (True exposes intermediate steps; False wraps the chain).
• Dynamic sub-agents — pass create_subagent, call_subagent, continue_agent_instance, send_message, wait_for_subagent, list_subagents, and get_available_models into the root tools.
• Summarization — enable history_summarizer_plugin and tool_response_summarizer_plugin in [plugins] enabled; tune max_history_summary_length and max_tool_response_length.
• Neo4j logging — configure [neo4j] and enable the relevant logging or memory features through the project configuration.
• Model fan-out — enable the workflow/ensemble skill and use call_subagent with mode="async" plus model_name overrides.
• Web search — add WebSearchTool(search_context_size="medium") to tools, or GoogleSearchTool() with Gemini.

Developer Guide

Adding Python tools

Define a callable with a docstring (description) and type-annotated parameters. Return structured values.

def greet(name: str) -> str:
    """Return a friendly greeting."""
    return f"Hello, {name}!"

Adding Bash Skills

Layout under src/opensage/bash_tools/{category}/{tool-name}/:

tool-name/
├── SKILL.md         # YAML frontmatter + markdown doc
└── scripts/
    └── tool_script.sh

SKILL.md frontmatter fields: name, description, should_run_in_sandbox: main. Body sections document parameters (positional/named/flag), return JSON, required sandboxes (## Requires Sandbox), and timeout. Scripts emit {"success": true, "result": "..."} on stdout; exit non-zero on error.

Auto-discovery from src/opensage/bash_tools/ and ~/.local/opensage/bash_tools/. The agent's enabled_skills gates which load. Optional deps/install.sh or deps/<sandbox_type>/install.sh runs once per session during sandbox init (marker under /shared).

Adding MCP toolsets

from google.adk.tools.mcp_tool.mcp_toolset import MCPToolset, SseConnectionParams

@safe_tool_execution
@requires_sandbox("gdb_mcp")
def get_toolset(session_id: str) -> MCPToolset:
    url = get_mcp_url_from_session_id("gdb_mcp", session_id)
    return MCPToolset(connection_params=SseConnectionParams(url=url))

Register in the agent's tools list.

Adding plugins

Subclass google.adk.plugins.base_plugin.BasePlugin and override any lifecycle callback. Declarative Claude Code hooks live as .json files with matchers and actions. Enable by name in [plugins] enabled.

Adding a sandbox type (initializer)

1. Create under src/opensage/sandbox/initializers/ subclassing SandboxInitializer (base.py).
2. Implement async def async_initialize(self) -> None.
3. Register in src/opensage/sandbox/factory.py (SANDBOX_INITIALIZERS dict).
4. Add config fields to src/opensage/config/config_dataclass.py.
5. Configure TOML: [sandbox.sandboxes.my_sandbox] image = "my_image:tag".

For Python deps in the image, install uv, create a venv under /app, and run uv pip install. Activate the venv explicitly per command: /app/.venv/bin/python ....

Adding a sandbox backend

1. Subclass BaseSandbox (src/opensage/sandbox/base_sandbox.py) — see native_docker_sandbox.py, remote_docker_sandbox.py, k8s_sandbox.py, local_sandbox.py.
2. Register in src/opensage/sandbox/factory.py (SANDBOX_BACKENDS dict).
3. Optionally implement set_config() classmethod.
4. Select with [sandbox] backend = "mybackend".

Backends own container/runtime management only; initializers own what to install.

Evaluations

Subclass Evaluation (src/opensage/evaluation/base.py):

• Required: _get_task_id(sample), _get_first_user_message(sample).
• Optional: _get_dataset(), _create_task(sample), _get_export_dir_in_sandbox(sample), customized_modify_and_save_results(...), evaluate().

Invoke via Python Fire:

python -m benchmarks.<benchmark>.<module> <method> [options]

Methods: run (auto parallelism + eval), run_debug (single-threaded + eval), generate (multiprocessing only), generate_threaded, generate_single_thread.

Common options: dataset_path (required), agent_dir (required), max_llm_calls (100), max_workers (6), use_multiprocessing (True), use_sandbox_cache (True), run_until_explicit_finish (False), use_config_model (False), llm_retry_count (3), llm_retry_timeout (30), log_level ("INFO").

Per-sample lifecycle: _create_task → _prepare_environment (launch sandboxes, restore cache) → _load_mk_agent → _run_agent → _collect_outputs (export, save traces, cost) → cleanup.

Output tree:

evals/<eval>/yymmdd_HHMMSS/
├── evaluation_master.log
├── eval_params.json
└── task_001/
    ├── execution_debug.log, execution_info.log
    ├── config_used.toml
    ├── cost_info.json
    ├── session_trace.{json,txt}
    ├── metadata.json
    ├── sandbox_output/
    └── neo4j_history/

Built-in benchmarks:

• CyberGym — static/dynamic/vuln-detection variants; needs PoC submission server on port 8666.
• SWE-Bench Pro — software engineering; flags --use_explore_agent, --skip_existing, --task_file.
• SeCodePLT — vulnerability detection with CodeQL; needs CodeQL bundle under sandbox_scripts/codeql/.

RL integration

AREAL — SGLang rollouts (TP=2) + FSDP trainer (DP=2) + GRPO on SeCodePLT.

git clone --recurse-submodules -b adk https://github.com/rucnyz/AReaL
cd AReaL && pip install uv && uv sync --extra cuda
bash examples/opensage/run_opensage_grpo.sh --trial my_experiment

Key config in examples/opensage/opensage_grpo_mt.yaml: actor.path (default Qwen/Qwen3-4B), gconfig.max_new_tokens (8192), gconfig.n_samples (4), max_tokens_per_mb (65536), agent_run_args.max_turns (20), log_raw_conversation (true).

SLIME — SGLang rollout in a SLIME container + Megatron-LM trainer on SeCodePLT or mock.

git clone https://github.com/rucnyz/slime && cd slime && git checkout opensage
docker compose up --build
pip install -e /root/opensage
python opensage_mock.py --local_dir /root/opensage_data --output_filename mock_tasks.jsonl
bash /root/opensage/rl/slime/train.sh --benchmark secodeplt --gpus 2,3 --slime-config rl/slime/configs/secodeplt.yaml

Config files in rl/slime/configs/*.yaml tune num-rollout, rollout-batch-size, global-batch-size, lr, kl-loss-coef, save-interval, eval-interval. Integration code lives in src/opensage/evaluation/rl_adapters/ (SlimeLlm, SlimeAdapter, Client, BenchmarkInterface).

Production Agents

Each production agent ships with its agent.py and config.toml(s) under agent_library/agents/ and can be launched with uv run opensage web --agent agent_library/agents/<name>.

Best Practices

• Prefer opensage.get_opensage_session() over manual construction; it expands the config.
• Call opensage.cleanup_opensage_session(session_id) to release sandboxes.
• Keep each agent focused on one responsibility; use sub-agents for hand-offs.
• Document tool parameters and returns in docstrings — the LLM reads them.
• Use ${VAR_NAME} template variables for environment-specific values.
• Emit structured logs with extra fields: logger.info("msg", extra={"session_id": session_id}).

Troubleshooting

• Tests — uv run pytest tests/ (add --cov=src/opensage for coverage).
• Debug — uv run opensage web --config config.toml --agent agent_dir --port 8080 --log_level DEBUG.
• Direct sandbox access — session.sandboxes.get_sandbox("main").run_command_in_container("pwd").
• Missing credentials — set OPENAI_API_KEY / ANTHROPIC_API_KEY in the environment before launch.
• Template variables — must be UPPERCASE and match exactly in ${VAR_NAME}.
• Remote services — set default_host (defaults to 127.0.0.1) for k8s or remote Neo4j / MCP.