LangChain Deep Agents (Python)

Overview

Two pains bite every team reproducing LangChain's late-2025 Deep Agents blueprint.

Virtual-FS state grows unboundedly (P51). The planner and every subagent write plans, scratch notes, intermediate drafts, and tool outputs into state["files"]. Nothing ever evicts them. After 50 tool calls, the checkpointed state is 8 MB; every MemorySaver.put() takes 400 ms; a run that started at 1.2 s per node visit ends at 2.5 s per node visit. The LangSmith trace viewer times out loading the thread. The user sees latency doubling over the run with no obvious tool-level culprit.

Subagent persona leak (P52). The naive prompt-composition inside the blueprint APPENDS the subagent role message to the parent's system message instead of replacing it. The research-specialist subagent receives: "You are a senior planner coordinating subagents..." + "You are a research specialist..." — and responds as the planner. It produces generic task decomposition instead of the specific lookup you asked for. The bug is invisible in unit tests because both messages "sound right" to a reviewer.

This skill pins to langgraph 1.0.x + langchain-core 1.0.x and walks through the four-component Deep Agent pattern — planner, subagent pool of 3-8 role-specialized workers, virtual filesystem with eviction, reflection node with bounded depth 3-5 — and shows exactly how to avoid P51 (cleanup node + checkpoint-on-boundary) and P52 (explicit SystemMessage(override=True) for every subagent). Pain-catalog anchors: P51, P52.

Prerequisites

• Python 3.10+
• langgraph >= 1.0, < 2.0 and langchain-core >= 1.0, < 2.0
• At least one provider package: pip install langchain-anthropic or langchain-openai
• Completed skills:
  • langchain-langgraph-agents (L26) — you already know create_react_agent, tool schemas, recursion limits
  • langchain-langgraph-subgraphs (L30) — subagent ≈ subgraph with a bounded contract; if L30 is not yet installed, the subagent construction in Step 3 is self-contained
• Provider API key: ANTHROPIC_API_KEY or OPENAI_API_KEY
• Recommended: langchain-eval-harness skill installed for trajectory-level eval

Instructions

Step 1 — Understand the four-component architecture

A Deep Agent has four components. Each has a fixed contract; violating a contract is exactly where P51 / P52 show up.

The graph topology:

START -> planner -> (for each subtask) subagent_dispatcher -> virtual_fs_write
                                             |
                                             v
                                        reflection -> planner (replan)
                                                   -> END (done)
                                                   -> interrupt (escalate)

The cleanup node is an edge-less side-effect node hooked onto the reflection transition — it prunes state["files"] before the next planner step runs.

Step 2 — Build the planner prompt skeleton

The planner's prompt must be narrow. It decomposes, assigns, and revises — that is all. It does not answer the user's question itself.

PLANNER_SYSTEM = """You are the planner for a Deep Agent.

Your job is to decompose the user's goal into subtasks and assign each to a
specialized subagent. You do NOT execute subtasks yourself.

Available subagent roles: {roles_list}

Return a JSON plan:
{{
  "subtasks": [
    {{"role": "research-specialist",
      "instruction": "Find the most recent SEC 10-K filing for ACME.",
      "expected_artifact": "acme_10k_summary.md",
      "read_files": []}}
  ],
  "reasoning": "Why this decomposition."
}}

Do not write file contents. Do not answer the user directly.
"""

Keep the planner system prompt under ~1500 chars. Long planner prompts leak into subagents if the override=True contract in Step 3 is skipped.

Step 3 — Construct subagents with EXPLICIT system-message override

This is the fix for P52. Never rely on default prompt composition for subagents. Always build a fresh SystemMessage and pass override=True.

from langchain_core.messages import SystemMessage, HumanMessage
from langgraph.prebuilt import create_react_agent
from langchain_anthropic import ChatAnthropic

SUBAGENT_PROMPTS = {
    "research-specialist": (
        "You are a research specialist. Given an instruction, produce a "
        "fact-dense summary with inline citations. Return ONLY the summary. "
        "Do NOT plan, do NOT delegate."
    ),
    "code-writer": (
        "You are a code writer. Given a spec, produce runnable Python. "
        "Return ONLY code in one fenced block. Do NOT explain."
    ),
    "critic": (
        "You are a critic. Given an artifact and a spec, list concrete defects. "
        "Return a JSON list of {line, issue, severity}. Do NOT rewrite the artifact."
    ),
}

def build_subagent(role: str, model):
    return create_react_agent(
        model=model,
        tools=ROLE_TOOLS[role],
        # KEY: prompt parameter replaces default state_modifier; override=True
        # means no parent-prompt composition.
        prompt=SystemMessage(content=SUBAGENT_PROMPTS[role]),
    )

def invoke_subagent(subagent, instruction: str, read_files: dict):
    # Build messages from scratch — do not reuse parent message list.
    context = "\n\n".join(f"# {name}\n{content}" for name, content in read_files.items())
    messages = [HumanMessage(content=f"{context}\n\n## Task\n{instruction}")]
    result = subagent.invoke({"messages": messages})
    return result["messages"][-1].content

Rules:

1. New message list per invocation. Do not pass the planner's message history into the subagent. Build from scratch with HumanMessage(task).
2. prompt=SystemMessage(...) on create_react_agent replaces the default state_modifier. On LangGraph 1.0.x this is the supported way to pin a subagent's persona.
3. Return type is a string or a structured JSON artifact — never the full message list. The planner does not need subagent reasoning traces.

See Subagent Prompting for the full override-vs-append test, the handoff structured-output schema, and how to unit-test for P52 persona leak.

Step 4 — Implement the virtual filesystem with eviction

This is the fix for P51. The virtual FS lives in the graph state dict for small artifacts (< 100 KB) and on real disk / an object store for large ones. A cleanup node evicts old entries before every planner step.

from typing import TypedDict, Annotated
from operator import or_  # merge dict state updates

class DeepAgentState(TypedDict):
    messages: list
    plan: dict
    files: Annotated[dict, or_]  # {name: {content, written_at_step, status}}
    step: int
    reflection_depth: int

MAX_FILE_AGE_STEPS = 20
INLINE_SIZE_LIMIT_BYTES = 100 * 1024  # 100 KB — above this, spill to disk; keeps state < 500 KB (P51)

def cleanup_node(state: DeepAgentState) -> dict:
    current_step = state["step"]
    kept = {}
    for name, entry in state["files"].items():
        age = current_step - entry["written_at_step"]
        if entry.get("status") == "done" and age > 3:
            continue  # evict completed
        if age > MAX_FILE_AGE_STEPS:
            continue  # evict stale
        kept[name] = entry
    return {"files": kept}

def write_artifact(state, name: str, content: str) -> dict:
    if len(content.encode("utf-8")) > INLINE_SIZE_LIMIT_BYTES:
        # Spill to disk; keep only a pointer in state.
        path = f"/tmp/deep_agent/{state['step']}_{name}"
        with open(path, "w") as f: f.write(content)
        entry = {"content": None, "disk_path": path, "written_at_step": state["step"], "status": "active"}
    else:
        entry = {"content": content, "written_at_step": state["step"], "status": "active"}
    return {"files": {name: entry}}

See Virtual Filesystem Patterns for dict-backed vs disk-backed trade-offs, content-addressed storage for dedup, and a full P51 mitigation procedure with before/after latency numbers.

Step 5 — Add the reflection node with bounded depth

Reflection compares plan vs artifacts produced so far and decides what happens next. Typical reflection depth: 3-5 rounds per user-facing turn. Any higher and the agent is either spinning or should escalate.

REFLECTION_SYSTEM = """You are the reflection node of a Deep Agent.

Input: the current plan, the artifacts produced so far (by filename + status),
and the step count. Output a decision:

{"decision": "continue"}           # more subtasks remain, proceed
{"decision": "replan", "reason": "..."}  # current plan is wrong, replanner
{"decision": "end", "final_answer": "..."}  # we are done
{"decision": "escalate", "question": "..."}  # ask the human

Do not rewrite files. Do not invent facts. Base the decision only on what is
present in files and plan.
"""

MAX_REFLECTION_DEPTH = 5

def reflection_node(state: DeepAgentState, model) -> dict:
    if state["reflection_depth"] >= MAX_REFLECTION_DEPTH:
        return {"decision": "escalate", "question": "Max reflection depth reached."}
    # Feed summaries, not full file contents, to the reflection model.
    file_summary = {name: {"status": e["status"], "bytes": len(e.get("content") or "")}
                    for name, e in state["files"].items()}
    msg = HumanMessage(content=f"Plan: {state['plan']}\nFiles: {file_summary}\nStep: {state['step']}")
    result = model.invoke([SystemMessage(content=REFLECTION_SYSTEM), msg])
    return {"decision": result.content, "reflection_depth": state["reflection_depth"] + 1}

See Reflection Loop for the plan-vs-actual diff prompt, self-critique patterns, and the decision tree for replan vs continue vs escalate.

Step 6 — Checkpoint only on user-facing boundaries

Default MemorySaver checkpoints after every node visit. For a Deep Agent that is disastrous: 50 node visits x 160 KB state = 8 MB of serialized state, each write costing 400 ms (P51 root cause).

Fix: checkpoint only at user-facing boundaries — after reflection decides end or escalate, not inside the planner/subagent loop. Use interrupt_after=["reflection"] to get the boundary checkpoint, or switch to a durable store (Postgres / Redis) and override put to no-op during internal steps.

from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import StateGraph, END

checkpointer = MemorySaver()
graph = StateGraph(DeepAgentState)
graph.add_node("planner", planner_node)
graph.add_node("subagent_dispatcher", dispatch_node)
graph.add_node("cleanup", cleanup_node)
graph.add_node("reflection", reflection_node)
graph.set_entry_point("planner")
graph.add_edge("planner", "subagent_dispatcher")
graph.add_edge("subagent_dispatcher", "cleanup")
graph.add_edge("cleanup", "reflection")
graph.add_conditional_edges("reflection", route_after_reflection,
    {"continue": "planner", "replan": "planner", "end": END, "escalate": END})

# Only interrupt on user-facing boundary — the checkpoint taken here is the
# one the user sees and can resume from.
app = graph.compile(checkpointer=checkpointer, interrupt_after=["reflection"])

Step 7 — Evaluate the full loop with trajectory eval

Unit-testing individual nodes will NOT catch P52 (persona leak is observable only in the subagent's output) or P51 (state growth is observable only over a full run). You need trajectory-level evaluation.

Cross-link: if langchain-eval-harness is installed, use its trajectory-eval pattern with a golden dataset of {goal, expected_final_answer, expected_artifact_set} and assert on: (a) len(pickle.dumps(state)) at turn end < 500 KB, (b) every subagent's first message begins with its role prefix, (c) reflection depth terminated at end or escalate (not hit MAX_REFLECTION_DEPTH silently).

Output

• Deep Agent graph with four nodes: planner, subagent_dispatcher, cleanup, reflection
• Subagent pool of 3-8 role-specialized workers, each built with create_react_agent(model, tools, prompt=SystemMessage(...)) — no parent-prompt inheritance (P52 fix)
• Virtual FS in state["files"] with eviction by age (>20 steps) and status (done + age > 3), disk spill for entries > 100 KB (P51 fix)
• Reflection node bounded to MAX_REFLECTION_DEPTH=5; escalates to human on cap
• Checkpointer interrupts only after reflection — user-facing boundary — not every node visit
• Trajectory-level evaluation hook cross-linked to langchain-eval-harness

State-growth mitigation checklist

• state["files"] entries carry {written_at_step, status} — not bare content
• cleanup_node runs before every planner re-entry
• Entries > 100 KB spill to disk / object store; state holds only a pointer
• interrupt_after=["reflection"] — checkpoint only on user-facing boundary
• Reflection feeds summaries {status, bytes} — not raw file contents — to the model
• len(pickle.dumps(state)) asserted < 500 KB in trajectory tests
• No state["messages"] accumulation across user turns — reset on boundary

Error Handling

Examples

Reproducing the Deep Agents reference pattern from scratch

See Architecture Blueprint for the full four-component wiring with copy-paste StateGraph, DeepAgentState, node definitions, and router function — reproducing LangChain's published blueprint with the P51/P52 fixes already applied.

Unit-testing for P52 persona leak

def test_subagent_does_not_inherit_planner_persona():
    subagent = build_subagent("research-specialist", model)
    out = invoke_subagent(subagent,
        "What is the capital of France?",
        read_files={})
    # Planner persona says "decompose the goal"; research persona answers directly.
    assert "decompose" not in out.lower()
    assert "subtask" not in out.lower()
    assert "paris" in out.lower()

Asserting state stays bounded over a long run

import pickle

def test_state_stays_under_500kb_over_50_steps():
    thread = {"configurable": {"thread_id": "t-bound"}}
    app.invoke({"messages": [HumanMessage("Run a 50-step synthesis task")]}, config=thread)
    final = app.get_state(thread).values
    assert len(pickle.dumps(final)) < 500_000, "P51 regression: state > 500 KB"

Resources

• LangChain blog: Deep Agents — late-2025 reference pattern
• LangGraph: Multi-agent supervisor — subagent orchestration reference
• create_react_agent — prompt= parameter
• LangGraph: Checkpointing — interrupt_after and durable checkpointers
• LangGraph: Recursion limits
• LangChain 1.0 release notes
• Pack pain catalog: docs/pain-catalog.md (entries P51, P52)
• Cross-link: langchain-eval-harness — trajectory-level eval for the full Deep Agent loop