Dynamic subagents use the interpreter runtime, which is in beta. APIs and lifecycle behavior may change between releases.
Interpreters require
langchain-quickjs>=0.2.0 and Python >=3.11.Quickstart
Dynamic subagents require interpreter middleware. Install and wire up the interpreter first. The built-in general-purpose subagent handles basic fan-out without extra configuration.from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="google_genai:gemini-3.5-flash",
subagents=[{
"name": "reviewer",
"description": "Reviews code for security issues, citing lines and severity",
"system_prompt": "You are a security-focused code reviewer. Report issues with line numbers and severity.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="openai:gpt-5.5",
subagents=[{
"name": "reviewer",
"description": "Reviews code for security issues, citing lines and severity",
"system_prompt": "You are a security-focused code reviewer. Report issues with line numbers and severity.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="anthropic:claude-sonnet-4-6",
subagents=[{
"name": "reviewer",
"description": "Reviews code for security issues, citing lines and severity",
"system_prompt": "You are a security-focused code reviewer. Report issues with line numbers and severity.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="openrouter:z-ai/glm-5.2",
subagents=[{
"name": "reviewer",
"description": "Reviews code for security issues, citing lines and severity",
"system_prompt": "You are a security-focused code reviewer. Report issues with line numbers and severity.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="fireworks:accounts/fireworks/models/glm-5p2",
subagents=[{
"name": "reviewer",
"description": "Reviews code for security issues, citing lines and severity",
"system_prompt": "You are a security-focused code reviewer. Report issues with line numbers and severity.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="baseten:zai-org/GLM-5.2",
subagents=[{
"name": "reviewer",
"description": "Reviews code for security issues, citing lines and severity",
"system_prompt": "You are a security-focused code reviewer. Report issues with line numbers and severity.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="ollama:north-mini-code-1.0",
subagents=[{
"name": "reviewer",
"description": "Reviews code for security issues, citing lines and severity",
"system_prompt": "You are a security-focused code reviewer. Report issues with line numbers and severity.",
}],
middleware=[CodeInterpreterMiddleware()],
)
result = agent.invoke({
"messages": [{"role": "user", "content": "Run a workflow that reviews every file in src/routes/ and summarizes the top risks."}]
})
The word “workflow” is a useful trigger. The interpreter system prompt treats “workflow” as a signal to organize work through the interpreter, dispatching subagents with
task() from code rather than grinding through items one model-chosen tool call at a time. Phrasing a request as a “workflow” is a deliberate lever you can pull to opt into dynamic orchestration. For a single, direct delegation, phrase the request plainly instead.Using dynamic subagents with
dcode, the LangChain terminal coding agent? dcode ships with the code interpreter enabled, so dynamic subagents work out of the box. See the dcode subagents page for setup and usage details.How it works
When an agent has subagents and interpreter middleware, the interpreter exposes a built-intask() global that dispatches subagents from code. A task spanning many independent units (reviewing every file in a directory, triaging a batch of tickets) becomes a loop that fans the work out, so it runs deterministically instead of one model-chosen tool call at a time.
Subagent orchestration also supports recursive language model (RLM) workflows, the approach described in the Recursive Language Models paper: keep the working set in interpreter variables, select slices, call subagents with task(), and synthesize the results.
Many orchestration workflows combine dynamic subagents with programmatic tool calling (PTC): use tools.* from interpreter code to discover or filter inputs, then dispatch subagents with task(). PTC is off by default; enable it with an explicit allowlist on interpreter middleware.
task() is a capability bridge into subagent execution, similar to PTC for tools. For isolation defaults, approval boundaries, and middleware options, see Security and Configuration.
Multi-turn orchestration can persist interpreter variables across agent turns when using
mode="thread" (the default). See Persistence on the interpreters page.task() takes the following inputs:
description: The prompt for the subagentsubagentType: Which configured subagent to runresponseSchema(optional): Structured output
task() runs a full agentic loop and resolves to the subagent’s result:
const review = await task({
description: "Review src/auth/login.ts for auth issues. Cite line numbers.",
subagentType: "reviewer",
responseSchema: {
type: "object",
properties: {
issues: { type: "array", items: { type: "object", properties: {
file: { type: "string" }, line: { type: "number" },
severity: { type: "string" }, description: { type: "string" },
}}},
},
},
});
// With responseSchema, the result is already a typed value, so no JSON.parse is needed.
const critical = review.issues.filter((issue) => issue.severity === "high");
responseSchema, the resolved value is already a typed JavaScript object; only call JSON.parse if a subagent intentionally returned a JSON string.
Patterns
The agent picks a strategy from the shape of the task; these emerge from how it writes interpreter code, not from configuration, and the subagents you make available determine what it can do. Every pattern shares the same orchestration approach: hold work in JS variables, dispatch subagents withtask(), and combine results in code. The diagrams below show the common shapes, each with a runnable example.
Classify and act
Items are classified first, then each item is handled by a specialized subagent based on its classification. This lets you process mixed inputs where different items need different expertise. Use cases: Triaging support tickets, error logs, user feedback, or any batch of items that need different handling depending on their type.Example: classify and act
Example: classify and act
What you configureWhat the agent writes
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="google_genai:gemini-3.5-flash",
subagents=[
{
"name": "bug-fixer",
"description": "Investigates bug reports and provides reproduction steps",
"system_prompt": "You are a bug triage specialist. Investigate each bug report and provide clear reproduction steps.",
},
{
"name": "feature-analyst",
"description": "Evaluates feature requests for feasibility and effort",
"system_prompt": "You are a product analyst. Evaluate each feature request for technical feasibility, estimated effort, and potential impact.",
},
{
"name": "support-agent",
"description": "Answers user questions based on documentation",
"system_prompt": "You are a support specialist. Answer user questions clearly based on the available documentation.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="openai:gpt-5.5",
subagents=[
{
"name": "bug-fixer",
"description": "Investigates bug reports and provides reproduction steps",
"system_prompt": "You are a bug triage specialist. Investigate each bug report and provide clear reproduction steps.",
},
{
"name": "feature-analyst",
"description": "Evaluates feature requests for feasibility and effort",
"system_prompt": "You are a product analyst. Evaluate each feature request for technical feasibility, estimated effort, and potential impact.",
},
{
"name": "support-agent",
"description": "Answers user questions based on documentation",
"system_prompt": "You are a support specialist. Answer user questions clearly based on the available documentation.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="anthropic:claude-sonnet-4-6",
subagents=[
{
"name": "bug-fixer",
"description": "Investigates bug reports and provides reproduction steps",
"system_prompt": "You are a bug triage specialist. Investigate each bug report and provide clear reproduction steps.",
},
{
"name": "feature-analyst",
"description": "Evaluates feature requests for feasibility and effort",
"system_prompt": "You are a product analyst. Evaluate each feature request for technical feasibility, estimated effort, and potential impact.",
},
{
"name": "support-agent",
"description": "Answers user questions based on documentation",
"system_prompt": "You are a support specialist. Answer user questions clearly based on the available documentation.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="openrouter:z-ai/glm-5.2",
subagents=[
{
"name": "bug-fixer",
"description": "Investigates bug reports and provides reproduction steps",
"system_prompt": "You are a bug triage specialist. Investigate each bug report and provide clear reproduction steps.",
},
{
"name": "feature-analyst",
"description": "Evaluates feature requests for feasibility and effort",
"system_prompt": "You are a product analyst. Evaluate each feature request for technical feasibility, estimated effort, and potential impact.",
},
{
"name": "support-agent",
"description": "Answers user questions based on documentation",
"system_prompt": "You are a support specialist. Answer user questions clearly based on the available documentation.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="fireworks:accounts/fireworks/models/glm-5p2",
subagents=[
{
"name": "bug-fixer",
"description": "Investigates bug reports and provides reproduction steps",
"system_prompt": "You are a bug triage specialist. Investigate each bug report and provide clear reproduction steps.",
},
{
"name": "feature-analyst",
"description": "Evaluates feature requests for feasibility and effort",
"system_prompt": "You are a product analyst. Evaluate each feature request for technical feasibility, estimated effort, and potential impact.",
},
{
"name": "support-agent",
"description": "Answers user questions based on documentation",
"system_prompt": "You are a support specialist. Answer user questions clearly based on the available documentation.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="baseten:zai-org/GLM-5.2",
subagents=[
{
"name": "bug-fixer",
"description": "Investigates bug reports and provides reproduction steps",
"system_prompt": "You are a bug triage specialist. Investigate each bug report and provide clear reproduction steps.",
},
{
"name": "feature-analyst",
"description": "Evaluates feature requests for feasibility and effort",
"system_prompt": "You are a product analyst. Evaluate each feature request for technical feasibility, estimated effort, and potential impact.",
},
{
"name": "support-agent",
"description": "Answers user questions based on documentation",
"system_prompt": "You are a support specialist. Answer user questions clearly based on the available documentation.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="ollama:north-mini-code-1.0",
subagents=[
{
"name": "bug-fixer",
"description": "Investigates bug reports and provides reproduction steps",
"system_prompt": "You are a bug triage specialist. Investigate each bug report and provide clear reproduction steps.",
},
{
"name": "feature-analyst",
"description": "Evaluates feature requests for feasibility and effort",
"system_prompt": "You are a product analyst. Evaluate each feature request for technical feasibility, estimated effort, and potential impact.",
},
{
"name": "support-agent",
"description": "Answers user questions based on documentation",
"system_prompt": "You are a support specialist. Answer user questions clearly based on the available documentation.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
// The agent has already classified each ticket; this routes every item to
// the right specialist and collects the handled results.
const SPECIALIST = { bug: "bug-fixer", feature: "feature-analyst", question: "support-agent" };
const handled = await Promise.all(
tickets.map((ticket) =>
task({
description: `Handle this ${ticket.category}:\n${ticket.text}`,
subagentType: SPECIALIST[ticket.category],
}),
),
);
// ... group handled results by category into a single triage report
handled;
Fan-out and synthesize
The agent dispatches the same kind of work across many items in parallel, then combines the results. Use cases: Code review across a directory, analyzing a batch of documents, processing log files, running the same check across many services. Discovering files from interpreter code requires programmatic tool calling (PTC). Enableglob in the PTC allowlist on interpreter middleware.
Example: fan-out and synthesize
Example: fan-out and synthesize
What you configureWhat the agent writes
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="google_genai:gemini-3.5-flash",
subagents=[{
"name": "reviewer",
"description": "Reviews code for security issues, citing lines and severity",
"system_prompt": "You are a security-focused code reviewer. Read the file carefully and report any authentication or authorization issues with line numbers and severity.",
}],
middleware=[CodeInterpreterMiddleware(ptc=["glob"])],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="openai:gpt-5.5",
subagents=[{
"name": "reviewer",
"description": "Reviews code for security issues, citing lines and severity",
"system_prompt": "You are a security-focused code reviewer. Read the file carefully and report any authentication or authorization issues with line numbers and severity.",
}],
middleware=[CodeInterpreterMiddleware(ptc=["glob"])],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="anthropic:claude-sonnet-4-6",
subagents=[{
"name": "reviewer",
"description": "Reviews code for security issues, citing lines and severity",
"system_prompt": "You are a security-focused code reviewer. Read the file carefully and report any authentication or authorization issues with line numbers and severity.",
}],
middleware=[CodeInterpreterMiddleware(ptc=["glob"])],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="openrouter:z-ai/glm-5.2",
subagents=[{
"name": "reviewer",
"description": "Reviews code for security issues, citing lines and severity",
"system_prompt": "You are a security-focused code reviewer. Read the file carefully and report any authentication or authorization issues with line numbers and severity.",
}],
middleware=[CodeInterpreterMiddleware(ptc=["glob"])],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="fireworks:accounts/fireworks/models/glm-5p2",
subagents=[{
"name": "reviewer",
"description": "Reviews code for security issues, citing lines and severity",
"system_prompt": "You are a security-focused code reviewer. Read the file carefully and report any authentication or authorization issues with line numbers and severity.",
}],
middleware=[CodeInterpreterMiddleware(ptc=["glob"])],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="baseten:zai-org/GLM-5.2",
subagents=[{
"name": "reviewer",
"description": "Reviews code for security issues, citing lines and severity",
"system_prompt": "You are a security-focused code reviewer. Read the file carefully and report any authentication or authorization issues with line numbers and severity.",
}],
middleware=[CodeInterpreterMiddleware(ptc=["glob"])],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="ollama:north-mini-code-1.0",
subagents=[{
"name": "reviewer",
"description": "Reviews code for security issues, citing lines and severity",
"system_prompt": "You are a security-focused code reviewer. Read the file carefully and report any authentication or authorization issues with line numbers and severity.",
}],
middleware=[CodeInterpreterMiddleware(ptc=["glob"])],
)
// One reviewer per file, dispatched in parallel, then findings merged.
const files = (await tools.glob({ pattern: "src/routes/**/*.ts" }))
.split("\n")
.filter(Boolean);
const reviews = await Promise.all(
files.map((file) =>
task({
description: `Review ${file} for authentication issues. Cite line numbers.`,
subagentType: "reviewer",
responseSchema: issuesSchema, // -> { issues: [{ file, line, severity }] }
}),
),
);
const issues = reviews.flatMap((r) => r.issues);
// ... sort by severity, drop duplicates, summarize the top risks
issues;
Adversarial verification
A two-pass pattern. The first pass produces findings. The second pass sends each finding to independent verifiers, and only findings that survive agreement are kept. This reduces false positives when confidence matters more than speed. Use cases: Security audits where false positives are costly, compliance checks, any review where you need high confidence in findings.Example: adversarial verification
Example: adversarial verification
What you configureWhat the agent writes
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="google_genai:gemini-3.5-flash",
subagents=[
{
"name": "reviewer",
"description": "Finds potential security vulnerabilities in code",
"system_prompt": "You are a security auditor. Find potential vulnerabilities and report each with file, line, and description.",
},
{
"name": "verifier",
"description": "Independently verifies whether a reported vulnerability is real",
"system_prompt": "You are a security verification specialist. Given a reported vulnerability, independently verify whether it is exploitable. Be skeptical. Only confirm real issues.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="openai:gpt-5.5",
subagents=[
{
"name": "reviewer",
"description": "Finds potential security vulnerabilities in code",
"system_prompt": "You are a security auditor. Find potential vulnerabilities and report each with file, line, and description.",
},
{
"name": "verifier",
"description": "Independently verifies whether a reported vulnerability is real",
"system_prompt": "You are a security verification specialist. Given a reported vulnerability, independently verify whether it is exploitable. Be skeptical. Only confirm real issues.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="anthropic:claude-sonnet-4-6",
subagents=[
{
"name": "reviewer",
"description": "Finds potential security vulnerabilities in code",
"system_prompt": "You are a security auditor. Find potential vulnerabilities and report each with file, line, and description.",
},
{
"name": "verifier",
"description": "Independently verifies whether a reported vulnerability is real",
"system_prompt": "You are a security verification specialist. Given a reported vulnerability, independently verify whether it is exploitable. Be skeptical. Only confirm real issues.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="openrouter:z-ai/glm-5.2",
subagents=[
{
"name": "reviewer",
"description": "Finds potential security vulnerabilities in code",
"system_prompt": "You are a security auditor. Find potential vulnerabilities and report each with file, line, and description.",
},
{
"name": "verifier",
"description": "Independently verifies whether a reported vulnerability is real",
"system_prompt": "You are a security verification specialist. Given a reported vulnerability, independently verify whether it is exploitable. Be skeptical. Only confirm real issues.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="fireworks:accounts/fireworks/models/glm-5p2",
subagents=[
{
"name": "reviewer",
"description": "Finds potential security vulnerabilities in code",
"system_prompt": "You are a security auditor. Find potential vulnerabilities and report each with file, line, and description.",
},
{
"name": "verifier",
"description": "Independently verifies whether a reported vulnerability is real",
"system_prompt": "You are a security verification specialist. Given a reported vulnerability, independently verify whether it is exploitable. Be skeptical. Only confirm real issues.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="baseten:zai-org/GLM-5.2",
subagents=[
{
"name": "reviewer",
"description": "Finds potential security vulnerabilities in code",
"system_prompt": "You are a security auditor. Find potential vulnerabilities and report each with file, line, and description.",
},
{
"name": "verifier",
"description": "Independently verifies whether a reported vulnerability is real",
"system_prompt": "You are a security verification specialist. Given a reported vulnerability, independently verify whether it is exploitable. Be skeptical. Only confirm real issues.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="ollama:north-mini-code-1.0",
subagents=[
{
"name": "reviewer",
"description": "Finds potential security vulnerabilities in code",
"system_prompt": "You are a security auditor. Find potential vulnerabilities and report each with file, line, and description.",
},
{
"name": "verifier",
"description": "Independently verifies whether a reported vulnerability is real",
"system_prompt": "You are a security verification specialist. Given a reported vulnerability, independently verify whether it is exploitable. Be skeptical. Only confirm real issues.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
// Pass 1: audit. Pass 2: verify each finding independently; keep only confirmed.
const { findings } = await task({
description: "Audit the payments module for vulnerabilities.",
subagentType: "reviewer",
responseSchema: findingsSchema, // -> { findings: [{ id, file, line, description }] }
});
const verdicts = await Promise.all(
findings.map((f) =>
task({
description: `Verify ${f.file}:${f.line} (${f.description}). Confirm or refute.`,
subagentType: "verifier",
responseSchema: verdictSchema, // -> { confirmed: boolean }
}),
),
);
const confirmed = findings.filter((_, i) => verdicts[i]?.confirmed);
// ... report only the confirmed vulnerabilities
confirmed;
Generate and filter
Multiple subagents generate independent solutions to the same problem. The agent compares, scores, and filters the results in code, keeping only the best. Use cases: Architecture proposals, refactoring strategies, content variations, any task where exploring multiple options before committing produces a better outcome.Example: generate and filter
Example: generate and filter
What you configureWhat the agent writes
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="google_genai:gemini-3.5-flash",
subagents=[{
"name": "architect",
"description": "Proposes a database schema design with tradeoff analysis",
"system_prompt": "You are a database architect. Propose a schema design for the given requirements. Include tradeoffs, migration considerations, and a clear rationale.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="openai:gpt-5.5",
subagents=[{
"name": "architect",
"description": "Proposes a database schema design with tradeoff analysis",
"system_prompt": "You are a database architect. Propose a schema design for the given requirements. Include tradeoffs, migration considerations, and a clear rationale.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="anthropic:claude-sonnet-4-6",
subagents=[{
"name": "architect",
"description": "Proposes a database schema design with tradeoff analysis",
"system_prompt": "You are a database architect. Propose a schema design for the given requirements. Include tradeoffs, migration considerations, and a clear rationale.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="openrouter:z-ai/glm-5.2",
subagents=[{
"name": "architect",
"description": "Proposes a database schema design with tradeoff analysis",
"system_prompt": "You are a database architect. Propose a schema design for the given requirements. Include tradeoffs, migration considerations, and a clear rationale.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="fireworks:accounts/fireworks/models/glm-5p2",
subagents=[{
"name": "architect",
"description": "Proposes a database schema design with tradeoff analysis",
"system_prompt": "You are a database architect. Propose a schema design for the given requirements. Include tradeoffs, migration considerations, and a clear rationale.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="baseten:zai-org/GLM-5.2",
subagents=[{
"name": "architect",
"description": "Proposes a database schema design with tradeoff analysis",
"system_prompt": "You are a database architect. Propose a schema design for the given requirements. Include tradeoffs, migration considerations, and a clear rationale.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="ollama:north-mini-code-1.0",
subagents=[{
"name": "architect",
"description": "Proposes a database schema design with tradeoff analysis",
"system_prompt": "You are a database architect. Propose a schema design for the given requirements. Include tradeoffs, migration considerations, and a clear rationale.",
}],
middleware=[CodeInterpreterMiddleware()],
)
// Generate independent proposals in parallel, then score and keep the best.
const proposals = await Promise.all(
[1, 2, 3].map((n) =>
task({
description: `Approach ${n}: redesign the orders schema, with tradeoffs.`,
subagentType: "architect",
responseSchema: designSchema, // -> { design, tradeoffs }
}),
),
);
// ... score each proposal against the requirements
const best = proposals.sort((a, b) => score(b) - score(a))[0];
best;
Tournament
Variations are compared head-to-head by a judge subagent, with winners advancing through elimination rounds. Use cases: Optimization under subjective criteria, style selection, choosing between competing implementations.Example: tournament
Example: tournament
What you configureWhat the agent writes
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="google_genai:gemini-3.5-flash",
subagents=[
{
"name": "writer",
"description": "Rewrites a function with a focus on readability and clarity",
"system_prompt": "You are an expert programmer focused on clean code. Rewrite the given function to maximize readability. Explain your choices.",
},
{
"name": "judge",
"description": "Compares two code implementations and picks the more readable one",
"system_prompt": "You are a code quality judge. Compare two implementations and pick the more readable one. Justify your choice with specific criteria.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="openai:gpt-5.5",
subagents=[
{
"name": "writer",
"description": "Rewrites a function with a focus on readability and clarity",
"system_prompt": "You are an expert programmer focused on clean code. Rewrite the given function to maximize readability. Explain your choices.",
},
{
"name": "judge",
"description": "Compares two code implementations and picks the more readable one",
"system_prompt": "You are a code quality judge. Compare two implementations and pick the more readable one. Justify your choice with specific criteria.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="anthropic:claude-sonnet-4-6",
subagents=[
{
"name": "writer",
"description": "Rewrites a function with a focus on readability and clarity",
"system_prompt": "You are an expert programmer focused on clean code. Rewrite the given function to maximize readability. Explain your choices.",
},
{
"name": "judge",
"description": "Compares two code implementations and picks the more readable one",
"system_prompt": "You are a code quality judge. Compare two implementations and pick the more readable one. Justify your choice with specific criteria.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="openrouter:z-ai/glm-5.2",
subagents=[
{
"name": "writer",
"description": "Rewrites a function with a focus on readability and clarity",
"system_prompt": "You are an expert programmer focused on clean code. Rewrite the given function to maximize readability. Explain your choices.",
},
{
"name": "judge",
"description": "Compares two code implementations and picks the more readable one",
"system_prompt": "You are a code quality judge. Compare two implementations and pick the more readable one. Justify your choice with specific criteria.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="fireworks:accounts/fireworks/models/glm-5p2",
subagents=[
{
"name": "writer",
"description": "Rewrites a function with a focus on readability and clarity",
"system_prompt": "You are an expert programmer focused on clean code. Rewrite the given function to maximize readability. Explain your choices.",
},
{
"name": "judge",
"description": "Compares two code implementations and picks the more readable one",
"system_prompt": "You are a code quality judge. Compare two implementations and pick the more readable one. Justify your choice with specific criteria.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="baseten:zai-org/GLM-5.2",
subagents=[
{
"name": "writer",
"description": "Rewrites a function with a focus on readability and clarity",
"system_prompt": "You are an expert programmer focused on clean code. Rewrite the given function to maximize readability. Explain your choices.",
},
{
"name": "judge",
"description": "Compares two code implementations and picks the more readable one",
"system_prompt": "You are a code quality judge. Compare two implementations and pick the more readable one. Justify your choice with specific criteria.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="ollama:north-mini-code-1.0",
subagents=[
{
"name": "writer",
"description": "Rewrites a function with a focus on readability and clarity",
"system_prompt": "You are an expert programmer focused on clean code. Rewrite the given function to maximize readability. Explain your choices.",
},
{
"name": "judge",
"description": "Compares two code implementations and picks the more readable one",
"system_prompt": "You are a code quality judge. Compare two implementations and pick the more readable one. Justify your choice with specific criteria.",
},
],
middleware=[CodeInterpreterMiddleware()],
)
// Generate variants, then judge pairwise until a single winner remains.
let bracket = await Promise.all(
[1, 2, 3, 4, 5].map((n) =>
task({ description: `Rewrite processOrder for readability (variant ${n}).`, subagentType: "writer" }),
),
);
while (bracket.length > 1) {
const winners = [];
for (let i = 0; i < bracket.length; i += 2) {
if (bracket[i + 1] === undefined) { winners.push(bracket[i]); break; }
const { winner } = await task({
description: `Pick the more readable:\n\nA:\n${bracket[i]}\n\nB:\n${bracket[i + 1]}`,
subagentType: "judge",
responseSchema: pickSchema, // -> { winner: "A" | "B" }
});
winners.push(winner === "A" ? bracket[i] : bracket[i + 1]);
}
bracket = winners;
}
bracket[0]; // the winning rewrite
Loop until done
The agent runs a discovery loop, deduplicating against what it has already found, until no new results appear. Useful when the scope of the work is not known upfront. Use cases: Exhaustive search, dead code detection, dependency audits, any sweep where you want completeness rather than a fixed number of results.Example: loop until done
Example: loop until done
What you configureWhat the agent writes
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="google_genai:gemini-3.5-flash",
subagents=[{
"name": "analyzer",
"description": "Analyzes code for unused exports, functions, and dead code paths",
"system_prompt": "You are a code analyst specializing in dead code detection. Find unused exports, unreachable functions, and orphaned modules. Report each with file path and evidence.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="openai:gpt-5.5",
subagents=[{
"name": "analyzer",
"description": "Analyzes code for unused exports, functions, and dead code paths",
"system_prompt": "You are a code analyst specializing in dead code detection. Find unused exports, unreachable functions, and orphaned modules. Report each with file path and evidence.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="anthropic:claude-sonnet-4-6",
subagents=[{
"name": "analyzer",
"description": "Analyzes code for unused exports, functions, and dead code paths",
"system_prompt": "You are a code analyst specializing in dead code detection. Find unused exports, unreachable functions, and orphaned modules. Report each with file path and evidence.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="openrouter:z-ai/glm-5.2",
subagents=[{
"name": "analyzer",
"description": "Analyzes code for unused exports, functions, and dead code paths",
"system_prompt": "You are a code analyst specializing in dead code detection. Find unused exports, unreachable functions, and orphaned modules. Report each with file path and evidence.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="fireworks:accounts/fireworks/models/glm-5p2",
subagents=[{
"name": "analyzer",
"description": "Analyzes code for unused exports, functions, and dead code paths",
"system_prompt": "You are a code analyst specializing in dead code detection. Find unused exports, unreachable functions, and orphaned modules. Report each with file path and evidence.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="baseten:zai-org/GLM-5.2",
subagents=[{
"name": "analyzer",
"description": "Analyzes code for unused exports, functions, and dead code paths",
"system_prompt": "You are a code analyst specializing in dead code detection. Find unused exports, unreachable functions, and orphaned modules. Report each with file path and evidence.",
}],
middleware=[CodeInterpreterMiddleware()],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="ollama:north-mini-code-1.0",
subagents=[{
"name": "analyzer",
"description": "Analyzes code for unused exports, functions, and dead code paths",
"system_prompt": "You are a code analyst specializing in dead code detection. Find unused exports, unreachable functions, and orphaned modules. Report each with file path and evidence.",
}],
middleware=[CodeInterpreterMiddleware()],
)
// Keep dispatching rounds, deduping against what's found, until a round adds nothing.
const seen = new Set();
const found = [];
while (true) {
const { items } = await task({
description: `Find dead code. Already found: ${[...seen].join(", ") || "(none)"}.`,
subagentType: "analyzer",
responseSchema: itemsSchema, // -> { items: [{ id, file }] }
});
const fresh = items.filter((i) => !seen.has(i.id));
if (fresh.length === 0) break; // converged: nothing new
for (const i of fresh) { seen.add(i.id); found.push(i); }
}
found;
task() dispatches from inside an already-running eval call. It does not go through the normal tool calling path, so interrupt_on approval workflows on the parent agent are not enforced per dispatch. Gate the eval tool itself if you need approval before subagent orchestration runs.Disable dynamic subagents
Subagent dispatch is on by default whenever the agent has subagents. Disable it if you want subagents to be available only through the normaltask tool path. For other middleware options, see Configuration on the interpreters page.
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="google_genai:gemini-3.5-flash",
subagents=[{"name": "reviewer", "description": "Reviews code", "system_prompt": "Review code."}],
middleware=[CodeInterpreterMiddleware(subagents=False)],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="openai:gpt-5.5",
subagents=[{"name": "reviewer", "description": "Reviews code", "system_prompt": "Review code."}],
middleware=[CodeInterpreterMiddleware(subagents=False)],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="anthropic:claude-sonnet-4-6",
subagents=[{"name": "reviewer", "description": "Reviews code", "system_prompt": "Review code."}],
middleware=[CodeInterpreterMiddleware(subagents=False)],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="openrouter:z-ai/glm-5.2",
subagents=[{"name": "reviewer", "description": "Reviews code", "system_prompt": "Review code."}],
middleware=[CodeInterpreterMiddleware(subagents=False)],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="fireworks:accounts/fireworks/models/glm-5p2",
subagents=[{"name": "reviewer", "description": "Reviews code", "system_prompt": "Review code."}],
middleware=[CodeInterpreterMiddleware(subagents=False)],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="baseten:zai-org/GLM-5.2",
subagents=[{"name": "reviewer", "description": "Reviews code", "system_prompt": "Review code."}],
middleware=[CodeInterpreterMiddleware(subagents=False)],
)
from deepagents import create_deep_agent
from langchain_quickjs import CodeInterpreterMiddleware
agent = create_deep_agent(
model="ollama:north-mini-code-1.0",
subagents=[{"name": "reviewer", "description": "Reviews code", "system_prompt": "Review code."}],
middleware=[CodeInterpreterMiddleware(subagents=False)],
)
See also
- Interpreters: QuickJS setup, programmatic tool calling, persistence, security, and middleware configuration
- Subagents: Configure subagent names, descriptions, and system prompts
- Event streaming: Stream updates from the coordinator and delegated subagents
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