"""
Agent: the tool-calling loop.
Flow:
1. Receive user message, load session + profile
2. Build tool schemas from profile's enabled_tools
3. Loop (up to max_iterations):
a. Call LLM with session.context (may be compressed) + tool schemas
b. If finish_reason == "stop" -> stream final response
c. If finish_reason == "tool_calls" -> execute tools, append to both
session.messages (display history) and session.context (LLM context)
4. After StreamEnd: run workers sequentially (e.g. context compression)
session.messages — full display history, never compressed
session.context — what the LLM sees; workers may compress this
"""
import asyncio
import json
from datetime import datetime, timezone
from pathlib import Path
from typing import TYPE_CHECKING, AsyncGenerator
import structlog
from navi.config import settings
from navi.exceptions import MaxIterationsReached, SessionNotFound
from navi.llm.base import LLMBackend, Message, ToolCallRequest
from navi.tools.base import Tool, current_event_sink, current_stop_event
from .compressor import compress_context, should_compress
from .events import (
AgentEvent,
ContextCompressed,
PlanReady,
StreamEnd,
StreamStopped,
TextDelta,
ThinkingDelta,
ThinkingEnd,
ToolEvent,
ToolStarted,
TurnThinking,
)
from .registry import BackendRegistry, ProfileRegistry, ToolRegistry
from .session import SessionStore
if TYPE_CHECKING:
from navi.memory.store import MemoryStore
from navi.workers.base import Worker, WorkerContext
_USER_ENABLED_FILE = Path(settings.tools_dir) / "enabled.json"
def _load_user_enabled_tools() -> list[str]:
try:
return json.loads(_USER_ENABLED_FILE.read_text())
except Exception:
return []
log = structlog.get_logger()
# Sentinel: placed in the event sink by the tool wrapper to signal completion.
_TOOL_DONE = object()
class Agent:
def __init__(
self,
session_store: "SessionStore | None",
profile_registry: ProfileRegistry,
tool_registry: ToolRegistry,
backend_registry: BackendRegistry,
workers: list["Worker"] | None = None,
memory_store: "MemoryStore | None" = None,
) -> None:
self._sessions = session_store
self._profiles = profile_registry
self._tools = tool_registry
self._backends = backend_registry
self._workers: list["Worker"] = workers or []
self._memory = memory_store
# ------------------------------------------------------------------
# Public interface
# ------------------------------------------------------------------
async def run(self, session_id: str, user_message: str, images: list[str] | None = None) -> str:
"""Non-streaming: run the full tool-calling loop and return the final text."""
session = await self._sessions.get(session_id)
if session is None:
raise SessionNotFound(session_id)
profile = self._profiles.get(session.profile_id)
tools = self._tool_list(profile.enabled_tools)
tool_schemas = [t.schema() for t in tools]
llm = self._get_backend(profile.llm_backend)
mem = await self._memory_msg()
# Expose session_id to tools (e.g. SSH connection pool) via ContextVar
from navi.tools.base import current_session_id as _sid_var
_sid_var.set(session_id)
user_msg = Message(role="user", content=user_message, images=images or None,
created_at=datetime.now(timezone.utc))
session.messages.append(user_msg)
session.context.append(user_msg)
await self._sessions.save(session)
for iteration in range(profile.max_iterations):
log.debug("agent.iteration", session_id=session_id, iteration=iteration)
response = await llm.complete(
self._build_context(session.context, profile, mem),
tools=tool_schemas if tools else None,
temperature=profile.temperature,
model=profile.model,
)
if response.finish_reason == "stop" or not response.tool_calls:
content = response.content or ""
assistant_msg = Message(role="assistant", content=content,
created_at=datetime.now(timezone.utc))
session.messages.append(assistant_msg)
session.context.append(assistant_msg)
await self._sessions.save(session)
return content
# Tool calls turn — append to both messages and context
assistant_msg = Message(
role="assistant",
content=response.content,
tool_calls=response.tool_calls,
)
session.messages.append(assistant_msg)
session.context.append(assistant_msg)
tool_results, image_injections = await self._execute_tool_calls(response.tool_calls, tools)
session.messages.extend(tool_results)
session.context.extend(tool_results)
# Image injections are synthetic LLM helpers — context only
session.context.extend(image_injections)
await self._sessions.save(session)
raise MaxIterationsReached(profile.max_iterations)
async def run_ephemeral(
self,
user_message: str,
profile_id: str,
max_iterations: int = 20,
exclude_tools: list[str] | None = None,
) -> str:
"""
Run a sub-agent loop without a persistent session.
Intended for spawning from tools (e.g. SpawnAgentTool).
No DB reads/writes — uses a temporary in-memory context.
Tools listed in exclude_tools are stripped from the tool list
(use this to prevent recursion: exclude 'spawn_agent').
"""
import uuid as _uuid
# Give each sub-agent its own scratchpad namespace so parallel or
# sequential sub-agents don't clobber each other's working notes.
from navi.tools.base import current_session_id as _sid_var
_sid_var.set(f"subagent_{_uuid.uuid4().hex[:12]}")
profile = self._profiles.get(profile_id)
exclude = set(exclude_tools or [])
tools = [t for t in self._tool_list(profile.enabled_tools) if t.name not in exclude]
tool_schemas = [t.schema() for t in tools]
llm = self._get_backend(profile.llm_backend)
mem = await self._memory_msg()
# Sub-agent context: only user/assistant/tool messages — system is injected dynamically.
context: list[Message] = [
Message(role="user", content=user_message, created_at=datetime.now(timezone.utc))
]
# Read the event sink set by the parent run_stream() for this tool call.
# If None (e.g. called from run(), not run_stream()), events are silently dropped.
sink = current_event_sink.get()
log.info("agent.subagent.start", profile_id=profile_id, max_iterations=max_iterations)
stop_event = current_stop_event.get()
tool_map = {t.name: t for t in tools}
for iteration in range(max_iterations):
if stop_event and stop_event.is_set():
return accumulated_text if iteration > 0 else ""
log.debug("agent.subagent.iteration", iteration=iteration)
accumulated_text = ""
accumulated_thinking = ""
turn_tool_calls: list[ToolCallRequest] | None = None
async for chunk in llm.stream_complete(
self._build_context(context, profile, mem),
tools=tool_schemas if tools else None,
temperature=profile.temperature,
model=profile.model,
):
if stop_event and stop_event.is_set():
break
if chunk.thinking:
accumulated_thinking += chunk.thinking
if chunk.delta:
accumulated_text += chunk.delta
if chunk.tool_calls:
turn_tool_calls = chunk.tool_calls
if stop_event and stop_event.is_set():
return accumulated_text
if not turn_tool_calls:
log.info("agent.subagent.complete", iterations=iteration + 1,
result_len=len(accumulated_text))
return accumulated_text
# Emit accumulated thinking before tool calls
if accumulated_thinking and sink is not None:
log.debug("agent.subagent.turn_thinking", length=len(accumulated_thinking))
await sink.put(TurnThinking(thinking=accumulated_thinking, is_subagent=True))
context.append(Message(
role="assistant",
content=accumulated_text or None,
tool_calls=turn_tool_calls,
))
# Execute each tool call sequentially, emitting events to parent sink
for tc in turn_tool_calls:
if sink is not None:
await sink.put(ToolStarted(
tool_name=tc.name, arguments=tc.arguments, is_subagent=True
))
tool = tool_map.get(tc.name)
image_msg = None
if tool is None:
content = f"Error: tool '{tc.name}' not found."
success = False
else:
log.info("tool.execute.subagent", tool=tc.name, args=tc.arguments)
result = await tool.execute(tc.arguments)
content = result.to_message_content()
success = result.success
if result.success and result.metadata and result.metadata.get("is_image"):
b64 = result.metadata.get("base64")
if b64:
image_msg = Message(
role="user",
content=f"[Image loaded via {tc.name} — analyse it]",
images=[b64],
)
if sink is not None:
await sink.put(ToolEvent(
tool_name=tc.name, arguments=tc.arguments,
result=content, success=success, is_subagent=True,
))
context.append(Message(role="tool", content=content,
tool_call_id=tc.id, name=tc.name))
if image_msg:
context.append(image_msg)
log.warning("agent.subagent.max_iterations", max_iterations=max_iterations)
return "[Sub-agent reached iteration limit without a final answer]"
async def run_stream(
self, session_id: str, user_message: str, images: list[str] | None = None
) -> AsyncGenerator[AgentEvent, None]:
"""
Streaming variant. Yields AgentEvent objects:
- ThinkingDelta / ThinkingEnd: reasoning chunks
- ToolEvent: tool call + result
- TextDelta / StreamEnd: final streamed response
- ContextCompressed: emitted by workers after compression
"""
session = await self._sessions.get(session_id)
if session is None:
raise SessionNotFound(session_id)
profile = self._profiles.get(session.profile_id)
tools = self._tool_list(profile.enabled_tools)
tool_schemas = [t.schema() for t in tools]
llm = self._get_backend(profile.llm_backend)
mem = await self._memory_msg()
# Expose session_id to tools (e.g. SSH connection pool) via ContextVar
from navi.tools.base import current_session_id as _sid_var
_sid_token = _sid_var.set(session_id)
# Pre-turn compression: if the last turn filled the context past the
# threshold, compress NOW before calling the LLM. This prevents the
# model from seeing an over-full context and generating gibberish
# (e.g. a "summary of the conversation" instead of a real answer).
if (
settings.context_compression_enabled
and session.context_token_count > 0
and should_compress(
session.context_token_count,
settings.ollama_num_ctx,
settings.context_compression_threshold,
)
):
try:
new_context = await compress_context(
context=session.context,
llm=llm,
model=profile.model,
temperature=settings.context_summary_temperature,
keep_recent=settings.context_keep_recent,
)
if new_context is not None:
count_before = len(session.context)
session.context = new_context
session.context_token_count = 0
log.info(
"agent.preturn_compress",
session_id=session_id,
before=count_before,
after=len(new_context),
)
except Exception:
log.warning("agent.preturn_compress_failed", session_id=session_id, exc_info=True)
user_msg = Message(role="user", content=user_message, images=images or None,
created_at=datetime.now(timezone.utc))
session.messages.append(user_msg)
session.context.append(user_msg)
# Persist user message immediately so it survives a client disconnect
# before the assistant reply is ready.
await self._sessions.save(session)
stop_event = current_stop_event.get()
# Planning phase — runs a fast, non-streaming LLM call to produce a
# step-by-step plan BEFORE the tool-calling loop starts. The plan is
# injected into session.context as an assistant message so the model
# naturally continues from it, and emitted as PlanReady for the UI.
if profile.planning_enabled:
for _ev in await self._run_planning(session, profile, llm, mem):
yield _ev
# Tool-calling loop — uses stream_complete() for every turn so thinking
# is captured in real-time via ThinkingDelta/ThinkingEnd events.
for iteration in range(profile.max_iterations):
# Cooperative stop: check before each LLM call
if stop_event and stop_event.is_set():
await self._sessions.save(session)
yield StreamStopped()
return
accumulated_text = ""
turn_tool_calls: list[ToolCallRequest] | None = None
thinking_active = False
context_tokens: int | None = None
async for chunk in llm.stream_complete(
self._build_context(session.context, profile, mem),
tools=tool_schemas if tools else None,
temperature=profile.temperature,
model=profile.model,
):
# Cooperative stop: break cleanly so aclose() is called on the
# generator → Ollama stream closes gracefully, model stays in VRAM.
if stop_event and stop_event.is_set():
if thinking_active:
yield ThinkingEnd()
break
if chunk.prompt_tokens is not None or chunk.completion_tokens is not None:
context_tokens = (chunk.prompt_tokens or 0) + (chunk.completion_tokens or 0)
if chunk.thinking:
if not thinking_active:
thinking_active = True
yield ThinkingDelta(delta=chunk.thinking)
elif chunk.delta:
if thinking_active:
thinking_active = False
yield ThinkingEnd()
accumulated_text += chunk.delta
yield TextDelta(delta=chunk.delta)
if chunk.tool_calls:
turn_tool_calls = chunk.tool_calls
if chunk.finish_reason and thinking_active:
thinking_active = False
yield ThinkingEnd()
# Stopped mid-stream — save partial response and exit
if stop_event and stop_event.is_set():
if accumulated_text:
session.messages.append(Message(
role="assistant", content=accumulated_text,
created_at=datetime.now(timezone.utc),
))
await self._sessions.save(session)
yield StreamStopped()
return
if not turn_tool_calls:
# Final response — text already streamed above
assistant_msg = Message(role="assistant", content=accumulated_text,
created_at=datetime.now(timezone.utc))
session.messages.append(assistant_msg)
session.context.append(assistant_msg)
session.context_token_count = context_tokens or 0
await self._sessions.save(session)
yield StreamEnd(
full_content=accumulated_text,
context_tokens=context_tokens,
max_context_tokens=settings.ollama_num_ctx,
)
for event in await self._run_workers(session, llm, profile.model, context_tokens):
yield event
return
# Tool calls turn — record to session and execute
assistant_msg = Message(
role="assistant",
content=accumulated_text or None,
tool_calls=turn_tool_calls,
)
session.messages.append(assistant_msg)
session.context.append(assistant_msg)
tool_map = {t.name: t for t in tools}
for tc in turn_tool_calls:
# 1. Announce immediately so the UI shows a pending card
yield ToolStarted(tool_name=tc.name, arguments=tc.arguments)
# 2. Create a sink queue for sub-agent events from this tool call.
# create_task() snapshots the current ContextVar values, so the
# task will inherit current_event_sink = sink.
sink: asyncio.Queue = asyncio.Queue()
sink_token = current_event_sink.set(sink)
result_holder: list = []
async def _run_with_sentinel(_tc=tc, _holder=result_holder, _sink=sink):
try:
_holder.append(await self._run_single_tool(_tc, tool_map))
except BaseException as exc:
_holder.append(exc)
finally:
await _sink.put(_TOOL_DONE)
tool_task = asyncio.create_task(_run_with_sentinel())
current_event_sink.reset(sink_token) # outer ctx restored; task has its own copy
# 3. Block on the sink until the sentinel arrives.
# Sub-agent ToolStarted/ToolEvent objects come through here in real time.
while True:
item = await sink.get()
if item is _TOOL_DONE:
break
yield item
# 4. Re-raise tool exception or unpack result
r = result_holder[0] if result_holder else RuntimeError("tool task produced no result")
if isinstance(r, BaseException):
raise r
tool_event, msg, image_msg = r
# 5. Yield the completed ToolEvent and record in session
yield tool_event
session.messages.append(msg)
session.context.append(msg)
if image_msg:
session.context.append(image_msg)
# 6. Cooperative stop: check after tool execution before next LLM call
if stop_event and stop_event.is_set():
await self._sessions.save(session)
yield StreamStopped()
return
# 7. If switch_profile was called this iteration, reload profile + tools.
# switch_profile updates the DB but run_stream() holds a local session
# object — without this check the final save would overwrite the change
# and the next LLM call would still use the old tool schemas.
fresh = await self._sessions.get(session_id)
if fresh and fresh.profile_id != session.profile_id:
session.profile_id = fresh.profile_id
profile = self._profiles.get(session.profile_id)
tools = self._tool_list(profile.enabled_tools)
tool_schemas = [t.schema() for t in tools]
llm = self._get_backend(profile.llm_backend)
log.info(
"agent.profile_reloaded",
session_id=session_id,
new_profile=session.profile_id,
)
await self._sessions.save(session)
raise MaxIterationsReached(profile.max_iterations)
# ------------------------------------------------------------------
# Internal helpers
# ------------------------------------------------------------------
async def _run_planning(
self,
session,
profile,
llm: LLMBackend,
mem: "Message | None",
) -> list[AgentEvent]:
"""
Pre-loop planning phase: ask the LLM to outline a step-by-step plan
for the current request (no tools, think=False for speed).
The plan is injected into session.context as an assistant message so
the model sees it as its own prior statement and naturally continues.
Returns a list of events to yield (either [PlanReady] or [] on failure).
"""
planning_system = Message(
role="system",
content=(
self._build_system_prompt(profile.system_prompt)
+ "\n\n---\n\n"
"[PLANNING] Decide whether this request requires a multi-step plan.\n\n"
"If the request is a simple question, a conversation, or can be answered "
"in a single step without tools — respond with exactly: DIRECT\n\n"
"Otherwise outline a concise numbered plan (max 6 steps). "
"Be specific: name which tools you will use and what you expect to find. "
"Do not execute anything yet."
),
)
planning_ctx: list[Message] = [planning_system]
if mem:
planning_ctx.append(mem)
# Include all prior context (history) but exclude any existing system messages
planning_ctx.extend(m for m in session.context if m.role != "system")
try:
response = await llm.complete(
planning_ctx,
tools=None,
temperature=0.3,
model=profile.model,
think=False,
)
plan_text = (response.content or "").strip()
if not plan_text:
return []
# Model signalled that no plan is needed — proceed directly
if plan_text.upper().startswith("DIRECT"):
log.debug("agent.planning_skipped", reason="direct")
return []
# Sanity check: a real plan should have at least one numbered step
import re as _re
if not _re.search(r"^\s*\d+[\.\)]", plan_text, _re.MULTILINE):
log.debug("agent.planning_skipped", reason="no_numbered_steps")
return []
# Inject plan as assistant message so the main loop starts with it in context
session.context.append(Message(role="assistant", content=plan_text))
log.debug("agent.plan_ready", length=len(plan_text))
return [PlanReady(plan=plan_text)]
except Exception:
log.warning("agent.planning_failed", exc_info=True)
return []
async def _run_workers(
self,
session,
llm: LLMBackend,
model: str,
context_tokens: int | None,
) -> list[AgentEvent]:
"""Run all workers sequentially; collect their events."""
from navi.workers.base import WorkerContext
ctx = WorkerContext(
session_id=session.id,
context_tokens=context_tokens,
max_context_tokens=settings.ollama_num_ctx,
llm=llm,
model=model,
temperature=settings.context_summary_temperature,
session_store=self._sessions,
)
events: list[AgentEvent] = []
for worker in self._workers:
try:
result = await worker.run(session, ctx)
events.extend(result.events)
except Exception:
log.warning("agent.worker_failed",
worker=type(worker).__name__, exc_info=True)
return events
async def _memory_msg(self) -> "Message | None":
"""Return an ephemeral system message with the user memory summary, or None."""
if not self._memory:
return None
summary = await self._memory.get_summary()
if not summary:
return None
return Message(role="system", content=f"## What I remember about the user\n\n{summary}")
def _build_context(
self,
session_context: list[Message],
profile: "AgentProfile",
mem: "Message | None",
) -> list[Message]:
"""Build the full LLM context for one call.
System prompt is injected fresh from the current profile every time —
it is NOT stored in session.context so that profile switches take
effect immediately without touching stored history.
Memory (if any) is placed right after the system message.
Any system messages already in session.context are stripped (migration safety).
"""
system_msg = Message(
role="system",
content=self._build_system_prompt(profile.system_prompt),
)
conv = [m for m in session_context if m.role != "system"]
result: list[Message] = [system_msg]
if mem:
result.append(mem)
result.extend(conv)
return result
def _build_system_prompt(self, profile_prompt: str) -> str:
persona = settings.navi_persona.strip()
if persona:
return f"{persona}\n\n---\n\n{profile_prompt}"
return profile_prompt
def _tool_list(self, enabled: list[str]) -> list[Tool]:
names = list(enabled)
extra = _load_user_enabled_tools()
for name in extra:
if name not in names:
names.append(name)
result = []
for name in names:
try:
result.append(self._tools.get(name))
except Exception:
pass
return result
def _get_backend(self, backend_key: str) -> LLMBackend:
return self._backends.get(backend_key)
async def _run_single_tool(
self,
tc: ToolCallRequest,
tool_map: dict[str, Tool],
) -> tuple[ToolEvent, Message, "Message | None"]:
"""Execute one tool call and return (ToolEvent, tool_msg, optional_image_msg).
Called via asyncio.create_task() from run_stream() so that the parent
generator can drain the event sink queue concurrently.
"""
tool = tool_map.get(tc.name)
image_msg = None
if tool is None:
content = f"Error: tool '{tc.name}' not found."
event = ToolEvent(tool_name=tc.name, arguments=tc.arguments,
result=content, success=False)
else:
log.info("tool.execute", tool=tc.name, args=tc.arguments)
result = await tool.execute(tc.arguments)
content = result.to_message_content()
event = ToolEvent(tool_name=tc.name, arguments=tc.arguments,
result=content, success=result.success)
if result.success and result.metadata and result.metadata.get("is_image"):
b64 = result.metadata.get("base64")
if b64:
image_msg = Message(
role="user",
content=f"[Image loaded via {tc.name} — analyse it]",
images=[b64],
)
msg = Message(role="tool", content=content, tool_call_id=tc.id, name=tc.name)
return event, msg, image_msg
async def _execute_tool_calls(
self, tool_calls: list[ToolCallRequest], tools: list[Tool]
) -> tuple[list[Message], list[Message]]:
tool_map = {t.name: t for t in tools}
async def _run_one(tc: ToolCallRequest) -> tuple[Message, Message | None]:
tool = tool_map.get(tc.name)
image_msg = None
if tool is None:
content = f"Error: tool '{tc.name}' not found."
else:
log.info("tool.execute", tool=tc.name, args=tc.arguments)
result = await tool.execute(tc.arguments)
content = result.to_message_content()
if result.success and result.metadata and result.metadata.get("is_image"):
b64 = result.metadata.get("base64")
if b64:
image_msg = Message(
role="user",
content=f"[Image loaded via {tc.name} — analyse it]",
images=[b64],
)
tool_msg = Message(role="tool", content=content, tool_call_id=tc.id, name=tc.name)
return tool_msg, image_msg
pairs = await asyncio.gather(*[_run_one(tc) for tc in tool_calls])
tool_msgs = [p[0] for p in pairs]
image_msgs = [p[1] for p in pairs if p[1] is not None]
return tool_msgs, image_msgs
async def _execute_tool_calls_streaming(
self, tool_calls: list[ToolCallRequest], tools: list[Tool]
) -> tuple[list[tuple[ToolEvent, Message]], list[Message]]:
tool_map = {t.name: t for t in tools}
async def _run_one(tc: ToolCallRequest) -> tuple[ToolEvent, Message, Message | None]:
tool = tool_map.get(tc.name)
image_msg = None
if tool is None:
content = f"Error: tool '{tc.name}' not found."
event = ToolEvent(
tool_name=tc.name, arguments=tc.arguments, result=content, success=False
)
else:
log.info("tool.execute", tool=tc.name, args=tc.arguments)
result = await tool.execute(tc.arguments)
content = result.to_message_content()
event = ToolEvent(
tool_name=tc.name,
arguments=tc.arguments,
result=content,
success=result.success,
)
if result.success and result.metadata and result.metadata.get("is_image"):
b64 = result.metadata.get("base64")
if b64:
image_msg = Message(
role="user",
content=f"[Image loaded via {tc.name} — analyse it]",
images=[b64],
)
msg = Message(role="tool", content=content, tool_call_id=tc.id, name=tc.name)
return event, msg, image_msg
triples = await asyncio.gather(*[_run_one(tc) for tc in tool_calls])
pairs = [(t[0], t[1]) for t in triples]
image_msgs = [t[2] for t in triples if t[2] is not None]
return pairs, image_msgs
