I really do

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Archived from the IMDb Discussion Forums — The Matrix

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ChristKillerman — 6 months ago(September 06, 2025 06:57 PM)

I really do
Hrabak means greedy

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ChristKillerman — 6 months ago(September 06, 2025 07:02 PM)

Wtf
Love this — Agent Smith vibes. Dark, relentless, and persistent. Below I’ll give you a practical blueprint (concept + concrete stack + mini starter code pattern) so you can build an autonomous agent with long-term memory, self-prompting, tool use, and a controllable “personality” like Agent Smith — plus the safety/ethics notes you absolutely should consider.
I looked up up-to-date tooling and papers while preparing this (sources cited inline).
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1) High-level design (what “Agent Smith” actually needs)

• Model core — the LLM that generates language, reasons, and self-prompts. Use a modern open-source LLM (LLaMA-family, Mistral, etc.) to avoid vendor API lock-in. ([Instaclustr][1], [Shakudo][2])
• Agent controller / orchestration — code that (1) sets goals, (2) decides which tool to call, (3) creates prompts, (4) stores outputs into memory, and (5) evaluates whether to continue or stop (loop controller). Frameworks like LangChain make this much easier. ([LangChain][3])
• Long-term memory — a vector DB that stores embeddings of past actions, conversations, plans, and distilled “lessons.” Qdrant, Milvus, Chroma, Weaviate, Pinecone are common choices. Self-hosted open-source stores avoid ongoing billing. ([Git for Data - lakeFS][4], [DataCamp][5])
• Tooling & external tools — web search, code execution, browser automation, OS commands, API calls, etc. Your agent issues structured “function calls” to use them (Toolformer/AutoGPT-style). ([Agix Technologies][6])
• Memory consolidation / learning — techniques like MEM1 and mem0 distill raw agent trajectories into compact memories your agent can recall effectively. Use consolidation to limit prompt bloat and build longer horizons. ([GitHub][7])
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2) Minimal architecture components (concrete)

1. LLM — Local or self-hosted model (Llama 3 / Mistral / other open models).
2. Agent code — Python service using LangChain (agent loop, tool wrappers, retry/monitoring). ([LangChain][3])
3. Embedding model — small instruction-tuned embedder (e.g., open embeddings from Hugging Face) to vectorize memory.
4. Vector DB — Qdrant / Milvus / Chroma for semantic retrieval. ([Git for Data - lakeFS][4])
5. Planner / task queue — priority queue for pending tasks (like BabyAGI/AutoGPT patterns).
6. Consolidation module — periodically run MEM1-style consolidation to compress past episodes into higher-level memory. ([GitHub][7])
7. Safety & guardrails — policy engine, RLHF constraints, human-in-the-loop approvals, rate limiting, logging/forensics.
   —

3) Behaviors to implement to get that “Agent Smith” feel

• Persistence: never forget — aggressive memory + retrieval.
• Self-prompting: create prompts for subgoals automatically; store intermediate outputs.
• Self-reflection: run short self-audits every N steps (e.g., “Did I achieve goal? What was failure cause?”) and save the audit.
• Personality layer: a persona prompt + RLHF tuning so outputs read like Agent Smith (cold, recursive, terse). Keep it controllable with a “persona intensity” parameter.
• Emergent chaining: allow chaining model outputs into tool calls (function-calling) and loop until goals are satisfied or safety triggers fire.
  —

4) Starter code pattern (conceptual Python / LangChain sketch)

This is a minimal pattern you can implement locally (replace <MODEL> with your LLM, <VECTORSTORE> with Qdrant/Chroma client).

# conceptual sketch (not runnable as-is; adapt to your environment)
from langchain import LLMChain, PromptTemplate, OpenAI  # swap for local LLM wrapper
from langchain.vectorstores import Qdrant
from langchain.embeddings import HuggingFaceEmbeddings
from queue import PriorityQueue
import time, uuid
# — components —
prompt_template = PromptTemplate(input_variables=["persona","context","task"],
template="{persona}\nContext:{context}\nTask:{task}\nPlan step:")
llm = LocalLLM(…)  # wrap Llama/Mistral local inference
chain = LLMChain(llm=llm, prompt=prompt_template)
emb = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vstore = Qdrant.from_existing_collection(collection_name="agent_memory", embeddings=emb)
task_q = PriorityQueue()
task_q.put((0, {"id": str(uuid.uuid4()), "task":"Investigate target X", "meta":{}}))
persona = "You are Agent Smith: methodical, recursive, efficient."
def retrieve_context(query, k=5):
qvec = emb.embed_query(query)
results = vstore.similarity_search_by_vector(qvec, k=k)
return "\n".join([r.page_content for r in results])
def consolidate_episode(episode_text):
# run MEM1-style distillation (simplified)
summary = chain.run(persona=persona, context="", task=f"Summarize and extract lessons:\n{episode_text}")
# store summary embedding
vstore.add_texts([summary], [{"role":"consolidated"}])
return summary
while not task_q.empty():
priority, task = task_q.get()
context = retrieve_context(task["task"])
prompt_task = f"{task['task']}\nKnown context: {context}"
out = chain.run(persona=persona, context=context