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Each visit is one message to a Letta agent (split if longer than the chunk size); the agent manages its core, recall
and archival memory with its own tools. Questions go to the same agent with the clinician answering prompt. After each
answer the agent is restored to its state before the question: the question's messages are deleted, archival and
core-memory edits are undone, and the system message gets its earlier text back. The system prompt is
data/letta_memgpt_chat_0.7.0.txt. With `max_steps`, a chain at the cap is asked for its final reply.
"""
from __future__ import annotations
import importlib
import importlib.util
import json
import logging
import os
import shutil
import sys
import time
import types
from pathlib import Path
from typing import Any, Dict, List
from .base import BaseAgent, MemoryBuildResult, MemoryContext
from ..llm_client import ENDPOINTS, MAX_ATTEMPTS, USAGE, api_key_for, http_timeout
from ..prompts import ANSWER_NOCTX
from .hipporag_agent import _unwrap_answer, _VISIT_HEADER_RE
logger = logging.getLogger(__name__)
_ACTIVE_LETTA_AGENT: "LettaAgent | None" = None
LETTA_ROOT = (Path(__file__).resolve().parents[2]
/ "vendor" / "letta")
_OPTIONAL_DEPS = {
"composio": ("composio", "composio.constants", "composio.client", "composio.client.collections",
"composio.exceptions", "composio_langchain"),
"mcp": ("mcp", "mcp.client", "mcp.client.sse", "mcp.client.stdio", "mcp.types"),
}
def _stub_missing_optional_deps() -> None:
"""Register import stubs for composio and mcp when they are not installed; the agent never uses them."""
for root, names in _OPTIONAL_DEPS.items():
try:
__import__(root)
continue
except ImportError:
pass
for name in names:
if name in sys.modules:
continue
m = types.ModuleType(name)
m.__path__ = []
m.__getattr__ = lambda attr, _n=name: type(attr, (), {})
m.DEFAULT_ENTITY_ID = "default"
sys.modules[name] = m
_NO_TEXT = object()
def _message_kind(msg) -> str:
"""The message_type of a Letta message ('assistant_message', 'tool_call_message', ...), else its class name."""
kind = getattr(msg, "message_type", None)
if kind is None:
return type(msg).__name__
return str(kind.value) if hasattr(kind, "value") else str(kind)
def _content_text(content):
"""Text of an assistant message's content: a string, a list of parts, or an object with `text` or
`message`; _NO_TEXT when the content has none of these."""
if isinstance(content, str):
return content
if isinstance(content, list):
pieces = []
for part in content:
if hasattr(part, "text"):
pieces.append(part.text)
elif isinstance(part, str):
pieces.append(part)
elif hasattr(part, "content"):
pieces.append(str(part.content))
return " ".join(pieces)
for attr in ("text", "message"):
if hasattr(content, attr):
return getattr(content, attr)
return _NO_TEXT
def _tool_call(msg) -> tuple:
"""(name, arguments, tool_call_id) of a tool-call message; JSON-string arguments are decoded when possible."""
call = getattr(msg, "tool_call", None)
if call:
name, args = getattr(call, "name", ""), getattr(call, "arguments", "{}")
else:
name = getattr(msg, "function_name", None) or getattr(msg, "name", "")
args = getattr(msg, "function_args", None) or getattr(msg, "arguments", {})
if isinstance(args, str):
try:
args = json.loads(args)
except json.JSONDecodeError:
pass
return name, args, (getattr(call, "tool_call_id", None) if call else None)
def _read_response(response) -> Dict[str, Any]:
"""The reply of one Letta step: the assistant message (or the message of a send_message call), the tool calls
with their returns (truncated to 1,000 characters), the reasoning messages and the token usage."""
out = {"assistant_message": "", "function_calls": [], "internal_monologue": [], "usage": {}}
usage = getattr(response, "usage", None)
if usage:
out["usage"] = {k: getattr(usage, k, 0) for k in ("completion_tokens", "prompt_tokens", "total_tokens")}
for msg in getattr(response, "messages", None) or []:
kind, cls = _message_kind(msg), str(type(msg))
if kind == "assistant_message" or "AssistantMessage" in cls:
content = getattr(msg, "content", None)
text = _content_text(content) if content else _NO_TEXT
if text is not _NO_TEXT:
out["assistant_message"] = text
elif kind == "tool_call_message" or "ToolCall" in cls:
name, args, call_id = _tool_call(msg)
if not name:
continue
out["function_calls"].append({"name": name, "tool_call_id": call_id,
"arguments": args if isinstance(args, dict) else str(args)})
if name == "send_message" and isinstance(args, dict):
said = args.get("message", "")
if said and not out["assistant_message"]:
out["assistant_message"] = said
elif kind in ("reasoning_message", "internal_monologue") or "Reasoning" in cls or "InternalMonologue" in cls:
thought = (getattr(msg, "reasoning", None) or getattr(msg, "internal_monologue", None)
or getattr(msg, "content", ""))
if thought:
out["internal_monologue"].append(thought)
elif kind in ("tool_return_message", "function_return") or "ToolReturn" in cls or "FunctionReturn" in cls:
returned = getattr(msg, "tool_return", None) or getattr(msg, "function_return", None)
if not (returned and out["function_calls"]):
continue
call_id = getattr(msg, "tool_call_id", None)
owner = next((c for c in reversed(out["function_calls"])
if call_id and c.get("tool_call_id") == call_id), out["function_calls"][-1])
owner["return"] = str(returned)[:1000]
return out
class LettaAgent(BaseAgent):
ANSWERS_INTERNALLY = True
MEMORIZE_WITH_LLM = True
def __init__(self, *, model: str | None = None, temperature: float,
max_tokens: int, retrieve_num: int,
context_window: int,
embedding_model: str | None = None,
embedding_dim: int | None = None, embedding_chunk_size: int,
memorize_chunk_tokens: int,
memorize_chunk_overlap_tokens: int,
memory_persona: str = ("I am an assistant helping with medical QA "
"while preserving long-term memory."),
memory_human: str = ("The user is a patient in a longitudinal "
"medical dialogue setting."),
internal_base_url: str | None = None,
embedding_base_url: str | None = None,
message_buffer_autoclear: bool,
max_steps: int | None,
working_dir: str | None = None, **params):
super().__init__(**params)
if not working_dir:
raise ValueError("LettaAgent requires working_dir (the runner sets it per run)")
self.model = model
self.temperature = temperature
self.max_tokens = max_tokens
self.retrieve_num = retrieve_num
self.context_window = context_window
self.embedding_model = embedding_model
self.embedding_dim = embedding_dim
self.embedding_chunk_size = embedding_chunk_size
self.memorize_chunk_tokens = memorize_chunk_tokens
self.memorize_chunk_overlap_tokens = memorize_chunk_overlap_tokens
self.max_question_tokens = context_window
self.memory_persona = memory_persona
self.memory_human = memory_human
self.message_buffer_autoclear = bool(message_buffer_autoclear)
self.max_steps = max_steps
embedding_base_url = embedding_base_url or ENDPOINTS["embedding"]
self.api_key = api_key_for(internal_base_url)
if api_key_for(embedding_base_url) != self.api_key:
raise ValueError("Letta sends one API key to its LLM and its embedding endpoint; give the memory (or "
"answer) and embedding endpoints the same api_key_env in backbone.yaml")
_default = os.environ.get("OPENAI_BASE_URL") or "https://api.openai.com/v1"
self.llm_base_url = internal_base_url or _default
self.embedding_base_url = embedding_base_url or _default
self.persistence_root = (Path(working_dir).resolve() / "letta_db")
if self.persistence_root.exists():
shutil.rmtree(self.persistence_root)
self._agent_ids: Dict[str, str] = {}
self._visits_memorized = 0
self._id2visit: Dict[str, int] = {}
self._cur_visit = 0
self._last_hits: List[str] = []
self._answering = False
self._answer_passages: List[str] = []
self._set_letta_dir()
_stub_missing_optional_deps()
self._import_letta()
self._instrument_clients()
self._connect()
def _set_letta_dir(self) -> None:
self.persistence_root.mkdir(parents=True, exist_ok=True)
os.environ["LETTA_DIR"] = str(self.persistence_root)
os.environ["LETTA_LETTA_DIR"] = str(self.persistence_root)
def _import_letta(self) -> None:
init_file = LETTA_ROOT / "__init__.py"
if not init_file.exists():
raise ImportError(f"Letta source not found at {LETTA_ROOT}")
for key in [k for k in sys.modules if k == "letta" or k.startswith("letta.")]:
del sys.modules[key]
spec = importlib.util.spec_from_file_location(
"letta", str(init_file), submodule_search_locations=[str(LETTA_ROOT)])
module = importlib.util.module_from_spec(spec)
sys.modules["letta"] = module
spec.loader.exec_module(module)
def _instrument_clients(self) -> None:
"""Add SDK retries and embedding-usage logging to the engine's clients, and hook passage inserts and
searches to record the visit of each passage, the passages written while answering and the passages found."""
import openai as _openai
usage_log = USAGE.log
def _retrying_openai(**kw):
kw["max_retries"] = MAX_ATTEMPTS - 1
kw.setdefault("timeout", http_timeout())
return _openai.OpenAI(**kw)
llm_api = importlib.import_module("letta.llm_api.openai")
llm_api.OpenAI = _retrying_openai
oc = importlib.import_module("letta.llm_api.openai_client")
oc.OpenAI = _retrying_openai
class _TrackedEmbeddings:
def __init__(self, real):
self._real = real
def create(self, **kw):
resp = self._real.embeddings.create(**kw)
u = getattr(resp, "usage", None)
if u is not None:
usage_log(kw.get("model", "?"), "embed",
int(getattr(u, "prompt_tokens", 0) or 0), 0)
return resp
class _TrackedClient:
def __init__(self, real):
self.embeddings = _TrackedEmbeddings(real)
def _tracked_openai(**kw):
kw["max_retries"] = MAX_ATTEMPTS - 1
kw.setdefault("timeout", http_timeout())
return _TrackedClient(_openai.OpenAI(**kw))
pm = importlib.import_module("letta.services.passage_manager")
pm.OpenAI = _tracked_openai
emb = importlib.import_module("letta.embeddings")
emb.OpenAI = _retrying_openai
def _tracked_get_text_embedding(self, text: str):
resp = self.client.embeddings.create(input=text, model=self.model)
u = getattr(resp, "usage", None)
if u is not None:
usage_log(self.model, "embed",
int(getattr(u, "prompt_tokens", 0) or 0), 0)
return resp.data[0].embedding
emb.OpenAIEmbeddings.get_text_embedding = _tracked_get_text_embedding
global _ACTIVE_LETTA_AGENT
_ACTIVE_LETTA_AGENT = self
def _agent():
return _ACTIVE_LETTA_AGENT
if not getattr(pm.PassageManager.insert_passage, "_memebench_hooked", False):
_orig_insert = pm.PassageManager.insert_passage
def _hooked_insert(pm_self, *a, **kw):
out = _orig_insert(pm_self, *a, **kw)
ids = [str(p.id) for p in (out or [])]
for pid in ids:
_agent()._id2visit[pid] = _agent()._cur_visit
if _agent()._answering:
_agent()._answer_passages.extend(ids)
try:
_agent()._trace("write_op", op="archival_insert",
visit=_agent()._cur_visit, passage_ids=ids,
text=kw.get("text"))
except Exception:
pass
return out
_hooked_insert._memebench_hooked = True
pm.PassageManager.insert_passage = _hooked_insert
am = importlib.import_module("letta.services.agent_manager")
if not getattr(am.AgentManager.list_passages, "_memebench_hooked", False):
_orig_list = am.AgentManager.list_passages
def _hooked_list(am_self, *a, **kw):
out = _orig_list(am_self, *a, **kw)
if kw.get("query_text"):
_agent()._last_hits = [str(p.id) for p in (out or [])]
return out
_hooked_list._memebench_hooked = True
am.AgentManager.list_passages = _hooked_list
def _connect(self) -> None:
model_settings = importlib.import_module("letta.settings").model_settings
if self.api_key:
model_settings.openai_api_key = self.api_key
model_settings.openai_api_base = self.embedding_base_url
client_module = importlib.import_module("letta.client.client")
schema = {name: importlib.import_module(f"letta.schemas.{name}")
for name in ("llm_config", "embedding_config", "memory")}
self._LLMConfig = schema["llm_config"].LLMConfig
self._EmbeddingConfig = schema["embedding_config"].EmbeddingConfig
self._ChatMemory = schema["memory"].ChatMemory
self._client = client_module.create_client(base_url=None, token=None)
if self.max_steps is not None:
self._client.server.max_chaining_steps = int(self.max_steps)
os.environ["MEMEBENCH_LETTA_FINAL_REPLY_AT_CAP"] = "1"
logger.info("[Letta] max_steps=%d; the agent gives its final reply at the cap", int(self.max_steps))
def _llm_config(self):
return self._LLMConfig(model=self.model, model_endpoint_type="openai",
model_endpoint=self.llm_base_url, model_wrapper=None,
context_window=self.context_window,
temperature=self.temperature, max_tokens=self.max_tokens)
def _embedding_config(self):
return self._EmbeddingConfig(embedding_endpoint_type="openai",
embedding_endpoint=self.embedding_base_url,
embedding_model=self.embedding_model,
embedding_dim=self.embedding_dim,
embedding_chunk_size=self.embedding_chunk_size)
def _system_prompt(self) -> str:
return (Path(__file__).resolve().parent / "data"
/ "letta_memgpt_chat_0.7.0.txt").read_text(encoding="utf-8")
def _agent_for(self, context_id: str) -> str:
existing = self._agent_ids.get(context_id)
if existing:
return existing
name = f"memebench_letta_ctx_{context_id}_{int(time.time())}"
state = self._client.create_agent(
name=name,
system=self._system_prompt(),
llm_config=self._llm_config(),
embedding_config=self._embedding_config(),
memory=self._ChatMemory(persona=self.memory_persona, human=self.memory_human),
metadata={"retrieve_num": self.retrieve_num,
"embedding_chunk_size": self.embedding_chunk_size},
message_buffer_autoclear=self.message_buffer_autoclear,
)
self._agent_ids[context_id] = str(state.id)
logger.info(f"[Letta] created agent {state.id} for context {context_id}")
return str(state.id)
def _context(self) -> str:
return self._context_id if self._context_id is not None else "0"
def _log_usage(self, parsed: Dict[str, Any]) -> None:
u = parsed.get("usage") or {}
p, c = int(u.get("prompt_tokens", 0) or 0), int(u.get("completion_tokens", 0) or 0)
if p or c:
USAGE.log(self.model, "chat", p, c)
def memorize(self, text: str, visit_number: int, date: str) -> MemoryBuildResult:
self._cur_visit = int(visit_number)
t0 = time.time()
agent_id = self._agent_for(self._context())
chunks = self.split_tokens(text, self.memorize_chunk_tokens,
self.memorize_chunk_overlap_tokens)
writes = 0
calls: List[str] = []
for i, chunk in enumerate(chunks):
try:
response = self._client.user_message(agent_id=agent_id, message=chunk)
except Exception as e:
logger.error(f"[Letta] user_message failed on v{visit_number} chunk {i}: {e}")
return MemoryBuildResult(success=False, method="letta", action="user_message",
visit_number=visit_number,
chunk_count=self._visits_memorized,
time_cost=time.time() - t0,
extra={"error": str(e)[:200], "failed_chunk": i})
parsed = _read_response(response)
self._log_usage(parsed)
try:
self._trace("tool_calls", stage="write", chunk=i,
calls=[{"name": fc.get("name"), "arguments": fc.get("arguments")}
for fc in parsed["function_calls"]],
assistant_message=parsed.get("assistant_message"))
except Exception:
pass
for fc in parsed["function_calls"]:
calls.append(fc["name"])
if fc["name"] in ("archival_memory_insert", "core_memory_append",
"core_memory_replace"):
writes += 1
if fc["name"] in ("core_memory_append", "core_memory_replace"):
a = fc.get("arguments") if isinstance(fc.get("arguments"), dict) else {}
try:
self._trace("write_op",
op=("core_append" if fc["name"].endswith("append")
else "core_replace"),
visit=int(visit_number),
block=a.get("label"),
text=a.get("content") or a.get("new_content"),
text_before=a.get("old_content"),
passage_ids=[], chunk=i)
except Exception:
pass
self._visits_memorized += 1
return MemoryBuildResult(success=True, method="letta", action="user_message",
visit_number=visit_number,
chunk_count=self._visits_memorized,
added_chunks=writes,
time_cost=time.time() - t0,
extra={"memory_writes": writes, "tool_calls": calls})
def _state_before_question(self, agent_id: str) -> Dict[str, Any]:
"""The newest stored message, the in-context message ids, the core memory blocks and the system message
(id and text) of the agent."""
srv, actor = self._client.server, self._client.user
last = srv.message_manager.list_messages_for_agent(agent_id=agent_id, actor=actor, limit=1,
ascending=False)
agent = srv.agent_manager.get_agent_by_id(agent_id=agent_id, actor=actor)
in_context = [str(m) for m in (agent.message_ids or [])]
system = srv.message_manager.get_message_by_id(message_id=in_context[0], actor=actor)
return {"last": str(last[0].id) if last else None,
"in_context": in_context,
"blocks": {b.label: b.value for b in agent.memory.blocks},
"system": (str(system.id), system.content[0].text)}
def _forget_question(self, agent_id: str, before: Dict[str, Any]) -> Dict[str, int]:
"""Return the agent to `before`: delete the messages stored since, the archival passages written while
answering, and restore the core memory blocks, the in-context message ids and the text of the system
message. Letta rewrites the system message in place (same id) whenever core memory or archival memory
changes, so restoring the ids alone would keep the text compiled while answering."""
srv, actor = self._client.server, self._client.user
agent = srv.agent_manager.get_agent_by_id(agent_id=agent_id, actor=actor)
if [str(m) for m in (agent.message_ids or [])] != before["in_context"]:
srv.agent_manager.set_in_context_messages(agent_id=agent_id, message_ids=before["in_context"],
actor=actor)
new = srv.message_manager.list_messages_for_agent(agent_id=agent_id, actor=actor, after=before["last"],
limit=None)
for m in new:
srv.message_manager.delete_message_by_id(message_id=m.id, actor=actor)
for pid in self._answer_passages:
srv.passage_manager.delete_passage_by_id(passage_id=pid, actor=actor)
self._id2visit.pop(pid, None)
n_passages = len(self._answer_passages)
self._answer_passages = []
block_update = importlib.import_module("letta.schemas.block").BlockUpdate
n_blocks = 0
for label, value in before["blocks"].items():
cur = srv.agent_manager.get_block_with_label(agent_id=agent_id, block_label=label, actor=actor)
if cur.value != value:
srv.block_manager.update_block(block_id=cur.id, block_update=block_update(value=value), actor=actor)
n_blocks += 1
system_id, system_text = before["system"]
n_system = 0
if srv.message_manager.get_message_by_id(message_id=system_id, actor=actor).content[0].text != system_text:
message_update = importlib.import_module("letta.schemas.message").MessageUpdate
srv.message_manager.update_message_by_id(message_id=system_id,
message_update=message_update(content=system_text), actor=actor)
n_system = 1
return {"messages": len(new), "passages": n_passages, "blocks": n_blocks, "system": n_system}
def build_memory_context(self, question: str, as_of: str = "",
display_question: str | None = None) -> MemoryContext:
agent_id = self._agent_for(self._context())
before = self._state_before_question(agent_id)
self._answering, self._answer_passages = True, []
try:
ctx = self._answer(agent_id, question, as_of, display_question)
finally:
self._answering = False
forgotten = self._forget_question(agent_id, before)
ctx.extra = dict(ctx.extra or {}, forgotten=forgotten)
return ctx
def _answer(self, agent_id: str, question: str, as_of: str,
display_question: str | None) -> MemoryContext:
wrapped = ANSWER_NOCTX.format(as_of=as_of, qtext=display_question or question)
wrapped = self.cut_tokens(wrapped, self.max_question_tokens)
t0 = time.time()
self._last_hits = []
try:
response = self._client.user_message(agent_id=agent_id, message=wrapped)
except Exception as e:
kind = ("context_window_exceeded" if "ContextWindowExceeded" in type(e).__name__
else "agent_error")
logger.error(f"[Letta] query failed ({kind}): {e}")
return MemoryContext(mem="", n_tok=0, retrieved_count=0, error=True,
answer=f"[err {kind}]",
extra={"method": "letta", "error": str(e)[:200],
"error_kind": kind})
parsed = _read_response(response)
self._log_usage(parsed)
searches = [fc for fc in parsed["function_calls"]
if fc["name"] in ("archival_memory_search", "conversation_search")
and fc.get("return")]
blocks = [self.cut_tokens(fc["return"], self.context_window // 4)
for fc in searches]
mem = "\n\n".join(f"[Memory {i + 1}]\n{b}" for i, b in enumerate(blocks))
visits = sorted({int(v) for b in blocks for v in _VISIT_HEADER_RE.findall(b)}
| {self._id2visit[pid] for pid in self._last_hits
if pid in self._id2visit})
try:
self._trace("tool_calls", stage="query", question=question,
calls=[{"name": fc.get("name"), "arguments": fc.get("arguments")}
for fc in parsed["function_calls"]],
n_blocks=len(blocks))
except Exception:
pass
try:
self._trace("query_retrieve", question=question, as_of=as_of,
hits=[{"tool": fc.get("name"),
"query": (fc.get("arguments") or {}).get("query")
if isinstance(fc.get("arguments"), dict) else None,
"tool_call_id": fc.get("tool_call_id"),
"return": (fc.get("return") or "")[:2000]}
for fc in searches],
n_hits=len(searches), n_blocks=len(blocks),
visits=visits,
visit_source={"header_regex": sorted({int(v) for b in blocks
for v in _VISIT_HEADER_RE.findall(b)}),
"id_map": sorted({self._id2visit[pid]
for pid in self._last_hits
if pid in self._id2visit})},
anchored_ids=len(self._id2visit),
retrieved_passage_ids=list(self._last_hits))
except Exception:
pass
return MemoryContext(
mem=mem, n_tok=self.count_tokens(mem), retrieved_count=len(blocks),
retrieved_visits=visits,
retrieved_memories=[{"visit": None, "memory": b[:300]} for b in blocks],
answer=_unwrap_answer(parsed["assistant_message"] or ""),
extra={"method": "letta",
"tool_calls": [fc["name"] for fc in parsed["function_calls"]],
"query_time_s": round(time.time() - t0, 2)},
)
def dump_memory(self, out_dir):
from .base import snapshot_tree
db = Path(self.persistence_root)
if not db.is_dir():
return None
files = snapshot_tree(db, out_dir / "letta_db")
(out_dir / "id2visit.json").write_text(
json.dumps(self._id2visit, ensure_ascii=False), encoding="utf-8")
return {"files": files, "anchored_ids": len(self._id2visit)}
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