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merge main into plano-session_pinning

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This commit is contained in:
Adil Hafeez 2026-03-26 10:32:22 -07:00
commit 71437d2b2c
113 changed files with 5445 additions and 4353 deletions
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2
.claude/skills/release/SKILL.md
View file

@ -25,4 +25,6 @@ Update the version string in ALL of these files:
Do NOT change version strings in `*.lock` files or `Cargo.lock`.
After updating all version strings, run `cd cli && uv lock` to update the lock file with the new version.
After making changes, show a summary of all files modified and the old → new version.

4
.github/workflows/ci.yml vendored
View file

@ -133,13 +133,13 @@ jobs:
load: true
tags: |
${{ env.PLANO_DOCKER_IMAGE }}
${{ env.DOCKER_IMAGE }}:0.4.11
${{ env.DOCKER_IMAGE }}:0.4.14
${{ env.DOCKER_IMAGE }}:latest
cache-from: type=gha
cache-to: type=gha,mode=max
- name: Save image as artifact
run: docker save ${{ env.PLANO_DOCKER_IMAGE }} ${{ env.DOCKER_IMAGE }}:0.4.11 ${{ env.DOCKER_IMAGE }}:latest -o /tmp/plano-image.tar
run: docker save ${{ env.PLANO_DOCKER_IMAGE }} ${{ env.DOCKER_IMAGE }}:0.4.14 ${{ env.DOCKER_IMAGE }}:latest -o /tmp/plano-image.tar
- name: Upload image artifact
uses: actions/upload-artifact@v6

1
.gitignore vendored
View file

@ -152,3 +152,4 @@ apps/*/dist/
.cursor/
.agents
docs/do/

1
.pre-commit-config.yaml
View file

@ -4,6 +4,7 @@ repos:
hooks:
- id: check-yaml
exclude: config/envoy.template*
args: [--allow-multiple-documents]
- id: end-of-file-fixer
- id: trailing-whitespace
- repo: local

3
Dockerfile
View file

@ -49,7 +49,8 @@ FROM python:3.14-slim AS arch
RUN set -eux; \
apt-get update; \
apt-get install -y --no-install-recommends gettext-base curl; \
apt-get upgrade -y; \
apt-get install -y --no-install-recommends gettext-base curl procps; \
apt-get clean; rm -rf /var/lib/apt/lists/*
RUN pip install --no-cache-dir supervisor

2
apps/www/src/components/Hero.tsx
View file

@ -24,7 +24,7 @@ export function Hero() {
>
<div className="inline-flex flex-wrap items-center gap-1.5 sm:gap-2 px-3 sm:px-4 py-1 rounded-full bg-[rgba(185,191,255,0.4)] border border-[var(--secondary)] shadow backdrop-blur hover:bg-[rgba(185,191,255,0.6)] transition-colors cursor-pointer">
<span className="text-xs sm:text-sm font-medium text-black/65">
v0.4.11
v0.4.14
</span>
<span className="text-xs sm:text-sm font-medium text-black ">
—

2
build_filter_image.sh
View file

@ -1 +1 @@
docker build -f Dockerfile . -t katanemo/plano -t katanemo/plano:0.4.11
docker build -f Dockerfile . -t katanemo/plano -t katanemo/plano:0.4.14

2
cli/planoai/__init__.py
View file

@ -1,3 +1,3 @@
"""Plano CLI - Intelligent Prompt Gateway."""
__version__ = "0.4.11"
__version__ = "0.4.14"

86
cli/planoai/config_generator.py
View file

@ -3,18 +3,17 @@ import os
from planoai.utils import convert_legacy_listeners
from jinja2 import Environment, FileSystemLoader
import yaml
from jsonschema import validate
from jsonschema import validate, ValidationError
from urllib.parse import urlparse
from copy import deepcopy
from planoai.consts import DEFAULT_OTEL_TRACING_GRPC_ENDPOINT
SUPPORTED_PROVIDERS_WITH_BASE_URL = [
"azure_openai",
"ollama",
"qwen",
"amazon_bedrock",
"arch",
"plano",
]
SUPPORTED_PROVIDERS_WITHOUT_BASE_URL = [
@ -368,47 +367,52 @@ def validate_and_render_schema():
llms_with_endpoint.append(model_provider)
llms_with_endpoint_cluster_names.add(cluster_name)
if len(model_usage_name_keys) > 0:
routing_model_provider = config_yaml.get("routing", {}).get(
"model_provider", None
overrides_config = config_yaml.get("overrides", {})
# Build lookup of model names (already prefix-stripped by config processing)
model_name_set = {mp.get("model") for mp in updated_model_providers}
# Auto-add arch-router provider if routing preferences exist and no provider matches the router model
router_model = overrides_config.get("llm_routing_model", "Arch-Router")
# Strip provider prefix for comparison since config processing strips prefixes from model names
router_model_id = (
router_model.split("/", 1)[1] if "/" in router_model else router_model
)
if len(model_usage_name_keys) > 0 and router_model_id not in model_name_set:
updated_model_providers.append(
{
"name": "arch-router",
"provider_interface": "plano",
"model": router_model_id,
"internal": True,
}
)
if (
routing_model_provider
and routing_model_provider not in model_provider_name_set
):
raise Exception(
f"Routing model_provider {routing_model_provider} is not defined in model_providers"
)
if (
routing_model_provider is None
and "arch-router" not in model_provider_name_set
):
updated_model_providers.append(
{
"name": "arch-router",
"provider_interface": "arch",
"model": config_yaml.get("routing", {}).get("model", "Arch-Router"),
"internal": True,
}
)
# Always add arch-function model provider if not already defined
if "arch-function" not in model_provider_name_set:
updated_model_providers.append(
{
"name": "arch-function",
"provider_interface": "arch",
"provider_interface": "plano",
"model": "Arch-Function",
"internal": True,
}
)
if "plano-orchestrator" not in model_provider_name_set:
# Auto-add plano-orchestrator provider if no provider matches the orchestrator model
orchestrator_model = overrides_config.get(
"agent_orchestration_model", "Plano-Orchestrator"
)
orchestrator_model_id = (
orchestrator_model.split("/", 1)[1]
if "/" in orchestrator_model
else orchestrator_model
)
if orchestrator_model_id not in model_name_set:
updated_model_providers.append(
{
"name": "plano-orchestrator",
"provider_interface": "arch",
"model": "Plano-Orchestrator",
"name": "plano/orchestrator",
"provider_interface": "plano",
"model": orchestrator_model_id,
"internal": True,
}
)
@ -426,6 +430,16 @@ def validate_and_render_schema():
"Please provide model_providers either under listeners or at root level, not both. Currently we don't support multiple listeners with model_providers"
)
# Validate input_filters IDs on listeners reference valid agent/filter IDs
for listener in listeners:
listener_input_filters = listener.get("input_filters", [])
for fc_id in listener_input_filters:
if fc_id not in agent_id_keys:
raise Exception(
f"Listener '{listener.get('name', 'unknown')}' references input_filters id '{fc_id}' "
f"which is not defined in agents or filters. Available ids: {', '.join(sorted(agent_id_keys))}"
)
# Validate model aliases if present
if "model_aliases" in config_yaml:
model_aliases = config_yaml["model_aliases"]
@ -503,11 +517,15 @@ def validate_prompt_config(plano_config_file, plano_config_schema_file):
try:
validate(config_yaml, config_schema_yaml)
except Exception as e:
print(
f"Error validating plano_config file: {plano_config_file}, schema file: {plano_config_schema_file}, error: {e}"
except ValidationError as e:
path = (
" → ".join(str(p) for p in e.absolute_path) if e.absolute_path else "root"
)
raise e
raise ValidationError(
f"{e.message}\n Location: {path}\n Value: {e.instance}"
) from None
except Exception as e:
raise
if __name__ == "__main__":

2
cli/planoai/consts.py
View file

@ -5,7 +5,7 @@ PLANO_COLOR = "#969FF4"
SERVICE_NAME_ARCHGW = "plano"
PLANO_DOCKER_NAME = "plano"
PLANO_DOCKER_IMAGE = os.getenv("PLANO_DOCKER_IMAGE", "katanemo/plano:0.4.11")
PLANO_DOCKER_IMAGE = os.getenv("PLANO_DOCKER_IMAGE", "katanemo/plano:0.4.14")
DEFAULT_OTEL_TRACING_GRPC_ENDPOINT = "http://localhost:4317"
# Native mode constants

2
cli/planoai/core.py
View file

@ -165,7 +165,7 @@ def _resolve_cli_agent_endpoint(plano_config_yaml: dict) -> tuple[str, int]:
if isinstance(listeners, list):
for listener in listeners:
if listener.get("type") in ["model", "model_listener"]:
if listener.get("type") == "model":
host = listener.get("host") or listener.get("address") or "0.0.0.0"
port = listener.get("port", 12000)
return host, port

13
cli/planoai/native_runner.py
View file

@ -420,9 +420,16 @@ def native_validate_config(plano_config_file):
with _temporary_env(overrides):
from planoai.config_generator import validate_and_render_schema
# Suppress verbose print output from config_generator
with contextlib.redirect_stdout(io.StringIO()):
validate_and_render_schema()
# Suppress verbose print output from config_generator but capture errors
captured = io.StringIO()
try:
with contextlib.redirect_stdout(captured):
validate_and_render_schema()
except SystemExit:
# validate_and_render_schema calls exit(1) on failure after
# printing to stdout; re-raise so the caller gets a useful message.
output = captured.getvalue().strip()
raise Exception(output) if output else Exception("Config validation failed")
def native_logs(debug=False, follow=False):

4
cli/planoai/utils.py
View file

@ -89,7 +89,7 @@ def convert_legacy_listeners(
) -> tuple[list, dict | None, dict | None]:
llm_gateway_listener = {
"name": "egress_traffic",
"type": "model_listener",
"type": "model",
"port": 12000,
"address": "0.0.0.0",
"timeout": "30s",
@ -98,7 +98,7 @@ def convert_legacy_listeners(
prompt_gateway_listener = {
"name": "ingress_traffic",
"type": "prompt_listener",
"type": "prompt",
"port": 10000,
"address": "0.0.0.0",
"timeout": "30s",

2
cli/pyproject.toml
View file

@ -1,6 +1,6 @@
[project]
name = "planoai"
version = "0.4.11"
version = "0.4.14"
description = "Python-based CLI tool to manage Plano."
authors = [{name = "Katanemo Labs, Inc."}]
readme = "README.md"

16
cli/test/test_config_generator.py
View file

@ -104,13 +104,13 @@ listeners:
agents:
- id: simple_tmobile_rag_agent
description: t-mobile virtual assistant for device contracts.
filter_chain:
input_filters:
- query_rewriter
- context_builder
- response_generator
- id: research_agent
description: agent to research and gather information from various sources.
filter_chain:
input_filters:
- research_agent
- response_generator
port: 8000
@ -376,7 +376,7 @@ def test_convert_legacy_llm_providers():
assert updated_providers == [
{
"name": "egress_traffic",
"type": "model_listener",
"type": "model",
"port": 12000,
"address": "0.0.0.0",
"timeout": "30s",
@ -384,7 +384,7 @@ def test_convert_legacy_llm_providers():
},
{
"name": "ingress_traffic",
"type": "prompt_listener",
"type": "prompt",
"port": 10000,
"address": "0.0.0.0",
"timeout": "30s",
@ -400,7 +400,7 @@ def test_convert_legacy_llm_providers():
},
],
"name": "egress_traffic",
"type": "model_listener",
"type": "model",
"port": 12000,
"timeout": "30s",
}
@ -410,7 +410,7 @@ def test_convert_legacy_llm_providers():
"name": "ingress_traffic",
"port": 10000,
"timeout": "30s",
"type": "prompt_listener",
"type": "prompt",
}
@ -449,7 +449,7 @@ def test_convert_legacy_llm_providers_no_prompt_gateway():
"name": "egress_traffic",
"port": 12000,
"timeout": "30s",
"type": "model_listener",
"type": "model",
}
]
assert llm_gateway == {
@ -461,7 +461,7 @@ def test_convert_legacy_llm_providers_no_prompt_gateway():
},
],
"name": "egress_traffic",
"type": "model_listener",
"type": "model",
"port": 12000,
"timeout": "30s",
}

2
cli/uv.lock generated
View file

@ -337,7 +337,7 @@ wheels = [
[[package]]
name = "planoai"
version = "0.4.9"
version = "0.4.14"
source = { editable = "." }
dependencies = [
{ name = "click" },

4
config/envoy.template.yaml
View file

@ -594,13 +594,13 @@ static_resources:
clusters:
- name: arch
- name: plano
connect_timeout: {{ upstream_connect_timeout | default('5s') }}
type: LOGICAL_DNS
dns_lookup_family: V4_ONLY
lb_policy: ROUND_ROBIN
load_assignment:
cluster_name: arch
cluster_name: plano
endpoints:
- lb_endpoints:
- endpoint:

20
config/plano_config_schema.yaml
View file

@ -85,7 +85,7 @@ properties:
type: string
default:
type: boolean
filter_chain:
input_filters:
type: array
items:
type: string
@ -93,6 +93,14 @@ properties:
required:
- id
- description
input_filters:
type: array
items:
type: string
output_filters:
type: array
items:
type: string
additionalProperties: false
required:
- type
@ -173,7 +181,7 @@ properties:
provider_interface:
type: string
enum:
- arch
- plano
- claude
- deepseek
- groq
@ -220,7 +228,7 @@ properties:
provider_interface:
type: string
enum:
- arch
- plano
- claude
- deepseek
- groq
@ -271,6 +279,12 @@ properties:
upstream_tls_ca_path:
type: string
description: "Path to the trusted CA bundle for upstream TLS verification. Default is '/etc/ssl/certs/ca-certificates.crt'."
llm_routing_model:
type: string
description: "Model name for the LLM router (e.g., 'Arch-Router'). Must match a model in model_providers."
agent_orchestration_model:
type: string
description: "Model name for the agent orchestrator (e.g., 'Plano-Orchestrator'). Must match a model in model_providers."
system_prompt:
type: string
prompt_targets:

37
config/supervisord.conf
View file

@ -1,14 +1,33 @@
[supervisord]
nodaemon=true
pidfile=/var/run/supervisord.pid
[program:config_generator]
command=/bin/sh -c "\
uv run python -m planoai.config_generator && \
envsubst < /app/plano_config_rendered.yaml > /app/plano_config_rendered.env_sub.yaml && \
envsubst < /etc/envoy/envoy.yaml > /etc/envoy.env_sub.yaml && \
touch /tmp/config_ready || \
(echo 'Config generation failed, shutting down'; kill -15 $(cat /var/run/supervisord.pid))"
priority=10
autorestart=false
startsecs=0
stdout_logfile=/dev/stdout
redirect_stderr=true
stdout_logfile_maxbytes=0
stderr_logfile_maxbytes=0
[program:brightstaff]
command=sh -c "\
envsubst < /app/plano_config_rendered.yaml > /app/plano_config_rendered.env_sub.yaml && \
while [ ! -f /tmp/config_ready ]; do echo '[brightstaff] Waiting for config generation...'; sleep 0.5; done && \
RUST_LOG=${LOG_LEVEL:-info} \
PLANO_CONFIG_PATH_RENDERED=/app/plano_config_rendered.env_sub.yaml \
/app/brightstaff 2>&1 | \
tee /var/log/brightstaff.log | \
while IFS= read -r line; do echo '[brightstaff]' \"$line\"; done"
while IFS= read -r line; do echo '[brightstaff]' \"$line\"; done; \
echo '[brightstaff] Process exited, shutting down'; kill -15 $(cat /var/run/supervisord.pid)"
priority=20
autorestart=false
stdout_logfile=/dev/stdout
redirect_stderr=true
stdout_logfile_maxbytes=0
@ -16,13 +35,15 @@ stderr_logfile_maxbytes=0
[program:envoy]
command=/bin/sh -c "\
uv run python -m planoai.config_generator && \
envsubst < /etc/envoy/envoy.yaml > /etc/envoy.env_sub.yaml && \
envoy -c /etc/envoy.env_sub.yaml \
--component-log-level wasm:${LOG_LEVEL:-info} \
--log-format '[%%Y-%%m-%%d %%T.%%e][%%l] %%v' 2>&1 | \
while [ ! -f /tmp/config_ready ]; do echo '[plano_logs] Waiting for config generation...'; sleep 0.5; done && \
envoy -c /etc/envoy.env_sub.yaml \
--component-log-level wasm:${LOG_LEVEL:-info} \
--log-format '[%%Y-%%m-%%d %%T.%%e][%%l] %%v' 2>&1 | \
tee /var/log/envoy.log | \
while IFS= read -r line; do echo '[plano_logs]' \"$line\"; done"
while IFS= read -r line; do echo '[plano_logs]' \"$line\"; done; \
echo '[plano_logs] Process exited, shutting down'; kill -15 $(cat /var/run/supervisord.pid)"
priority=20
autorestart=false
stdout_logfile=/dev/stdout
redirect_stderr=true
stdout_logfile_maxbytes=0

29
crates/brightstaff/src/app_state.rs Normal file
View file

@ -0,0 +1,29 @@
use std::collections::HashMap;
use std::sync::Arc;
use common::configuration::{Agent, FilterPipeline, Listener, ModelAlias, SpanAttributes};
use common::llm_providers::LlmProviders;
use tokio::sync::RwLock;
use crate::router::llm::RouterService;
use crate::router::orchestrator::OrchestratorService;
use crate::state::StateStorage;
/// Shared application state bundled into a single Arc-wrapped struct.
///
/// Instead of cloning 8+ individual `Arc`s per connection, a single
/// `Arc<AppState>` is cloned once and passed to the request handler.
pub struct AppState {
pub router_service: Arc<RouterService>,
pub orchestrator_service: Arc<OrchestratorService>,
pub model_aliases: Option<HashMap<String, ModelAlias>>,
pub llm_providers: Arc<RwLock<LlmProviders>>,
pub agents_list: Option<Vec<Agent>>,
pub listeners: Vec<Listener>,
pub state_storage: Option<Arc<dyn StateStorage>>,
pub llm_provider_url: String,
pub span_attributes: Option<SpanAttributes>,
/// Shared HTTP client for upstream LLM requests (connection pooling / keep-alive).
pub http_client: reqwest::Client,
pub filter_pipeline: Arc<FilterPipeline>,
}

41
crates/brightstaff/src/handlers/agents/errors.rs Normal file
View file

@ -0,0 +1,41 @@
use bytes::Bytes;
use http_body_util::combinators::BoxBody;
use hyper::Response;
use serde_json::json;
use tracing::{info, warn};
use crate::handlers::response::ResponseHandler;
/// Build a JSON error response from an `AgentFilterChainError`, logging the
/// full error chain along the way.
///
/// Returns `Ok(Response)` so it can be used directly as a handler return value.
pub fn build_error_chain_response<E: std::error::Error>(
err: &E,
) -> Result<Response<BoxBody<Bytes, hyper::Error>>, hyper::Error> {
let mut error_chain = Vec::new();
let mut current: &dyn std::error::Error = err;
loop {
error_chain.push(current.to_string());
match current.source() {
Some(source) => current = source,
None => break,
}
}
warn!(error_chain = ?error_chain, "agent chat error chain");
warn!(root_error = ?err, "root error");
let error_json = json!({
"error": {
"type": "AgentFilterChainError",
"message": err.to_string(),
"error_chain": error_chain,
"debug_info": format!("{:?}", err)
}
});
info!(error = %error_json, "structured error info");
Ok(ResponseHandler::create_json_error_response(&error_json))
}

0
crates/brightstaff/src/handlers/jsonrpc.rs → crates/brightstaff/src/handlers/agents/jsonrpc.rs
View file

5
crates/brightstaff/src/handlers/agents/mod.rs Normal file
View file

@ -0,0 +1,5 @@
pub mod errors;
pub mod jsonrpc;
pub mod orchestrator;
pub mod pipeline;
pub mod selector;

367
crates/brightstaff/src/handlers/agent_chat_completions.rs → crates/brightstaff/src/handlers/agents/orchestrator.rs
View file

@ -2,63 +2,56 @@ use std::sync::Arc;
use std::time::Instant;
use bytes::Bytes;
use common::configuration::SpanAttributes;
use common::errors::BrightStaffError;
use common::llm_providers::LlmProviders;
use hermesllm::apis::OpenAIMessage;
use hermesllm::clients::SupportedAPIsFromClient;
use hermesllm::providers::request::ProviderRequest;
use hermesllm::ProviderRequestType;
use http_body_util::combinators::BoxBody;
use http_body_util::BodyExt;
use hyper::{Request, Response, StatusCode};
use hyper::{Request, Response};
use opentelemetry::trace::get_active_span;
use serde::ser::Error as SerError;
use tokio::sync::RwLock;
use tracing::{debug, info, info_span, warn, Instrument};
use super::agent_selector::{AgentSelectionError, AgentSelector};
use super::pipeline_processor::{PipelineError, PipelineProcessor};
use super::response_handler::ResponseHandler;
use crate::router::plano_orchestrator::OrchestratorService;
use super::errors::build_error_chain_response;
use super::pipeline::{PipelineError, PipelineProcessor};
use super::selector::{AgentSelectionError, AgentSelector};
use crate::app_state::AppState;
use crate::handlers::extract_request_id;
use crate::handlers::response::ResponseHandler;
use crate::tracing::{collect_custom_trace_attributes, operation_component, set_service_name};
/// Main errors for agent chat completions
#[derive(Debug, thiserror::Error)]
pub enum AgentFilterChainError {
#[error("Forwarded error: {0}")]
Brightstaff(#[from] BrightStaffError),
#[error("Agent selection error: {0}")]
Selection(#[from] AgentSelectionError),
#[error("Pipeline processing error: {0}")]
Pipeline(#[from] PipelineError),
#[error("Response handling error: {0}")]
Response(#[from] common::errors::BrightStaffError),
#[error("Request parsing error: {0}")]
RequestParsing(#[from] serde_json::Error),
RequestParsing(String),
#[error("HTTP error: {0}")]
Http(#[from] hyper::Error),
#[error("Unsupported endpoint: {0}")]
UnsupportedEndpoint(String),
#[error("No agents configured")]
NoAgentsConfigured,
#[error("Agent '{0}' not found in configuration")]
AgentNotFound(String),
#[error("No messages in conversation history")]
EmptyHistory,
#[error("Agent chain completed without producing a response")]
IncompleteChain,
}
pub async fn agent_chat(
request: Request<hyper::body::Incoming>,
orchestrator_service: Arc<OrchestratorService>,
_: String,
agents_list: Arc<tokio::sync::RwLock<Option<Vec<common::configuration::Agent>>>>,
listeners: Arc<tokio::sync::RwLock<Vec<common::configuration::Listener>>>,
span_attributes: Arc<Option<SpanAttributes>>,
llm_providers: Arc<RwLock<LlmProviders>>,
state: Arc<AppState>,
) -> Result<Response<BoxBody<Bytes, hyper::Error>>, hyper::Error> {
let request_id = extract_request_id(&request);
let custom_attrs =
collect_custom_trace_attributes(request.headers(), span_attributes.as_ref().as_ref());
// Extract request_id from headers or generate a new one
let request_id: String = match request
.headers()
.get(common::consts::REQUEST_ID_HEADER)
.and_then(|h| h.to_str().ok())
.map(|s| s.to_string())
{
Some(id) => id,
None => uuid::Uuid::new_v4().to_string(),
};
collect_custom_trace_attributes(request.headers(), state.span_attributes.as_ref());
// Create a span with request_id that will be included in all log lines
let request_span = info_span!(
@ -74,17 +67,7 @@ pub async fn agent_chat(
// Set service name for orchestrator operations
set_service_name(operation_component::ORCHESTRATOR);
match handle_agent_chat_inner(
request,
orchestrator_service,
agents_list,
listeners,
llm_providers,
request_id,
custom_attrs,
)
.await
{
match handle_agent_chat_inner(request, state, request_id, custom_attrs).await {
Ok(response) => Ok(response),
Err(err) => {
// Check if this is a client error from the pipeline that should be cascaded
@ -101,7 +84,6 @@ pub async fn agent_chat(
"client error from agent"
);
// Create error response with the original status code and body
let error_json = serde_json::json!({
"error": "ClientError",
"agent": agent,
@ -109,52 +91,19 @@ pub async fn agent_chat(
"agent_response": body
});
let status_code = hyper::StatusCode::from_u16(*status)
.unwrap_or(hyper::StatusCode::INTERNAL_SERVER_ERROR);
let json_string = error_json.to_string();
return Ok(BrightStaffError::ForwardedError {
status_code,
message: json_string,
}
.into_response());
let mut response =
Response::new(ResponseHandler::create_full_body(json_string));
*response.status_mut() = hyper::StatusCode::from_u16(*status)
.unwrap_or(hyper::StatusCode::INTERNAL_SERVER_ERROR);
response.headers_mut().insert(
hyper::header::CONTENT_TYPE,
hyper::header::HeaderValue::from_static("application/json"),
);
return Ok(response);
}
// Print detailed error information with full error chain for other errors
let mut error_chain = Vec::new();
let mut current_error: &dyn std::error::Error = &err;
// Collect the full error chain
loop {
error_chain.push(current_error.to_string());
match current_error.source() {
Some(source) => current_error = source,
None => break,
}
}
// Log the complete error chain
warn!(error_chain = ?error_chain, "agent chat error chain");
warn!(root_error = ?err, "root error");
// Create structured error response as JSON
let error_json = serde_json::json!({
"error": {
"type": "AgentFilterChainError",
"message": err.to_string(),
"error_chain": error_chain,
"debug_info": format!("{:?}", err)
}
});
// Log the error for debugging
info!(error = %error_json, "structured error info");
Ok(BrightStaffError::ForwardedError {
status_code: StatusCode::BAD_REQUEST,
message: error_json.to_string(),
}
.into_response())
build_error_chain_response(&err)
}
}
}
@ -162,19 +111,22 @@ pub async fn agent_chat(
.await
}
async fn handle_agent_chat_inner(
/// Parsed and validated agent request data.
struct AgentRequest {
client_request: ProviderRequestType,
messages: Vec<OpenAIMessage>,
request_headers: hyper::HeaderMap,
request_id: Option<String>,
}
/// Parse the incoming HTTP request, resolve the listener, and extract messages.
async fn parse_agent_request(
request: Request<hyper::body::Incoming>,
orchestrator_service: Arc<OrchestratorService>,
agents_list: Arc<tokio::sync::RwLock<Option<Vec<common::configuration::Agent>>>>,
listeners: Arc<tokio::sync::RwLock<Vec<common::configuration::Listener>>>,
llm_providers: Arc<RwLock<LlmProviders>>,
request_id: String,
custom_attrs: std::collections::HashMap<String, String>,
) -> Result<Response<BoxBody<Bytes, hyper::Error>>, AgentFilterChainError> {
// Initialize services
let agent_selector = AgentSelector::new(orchestrator_service);
let mut pipeline_processor = PipelineProcessor::default();
let response_handler = ResponseHandler::new();
state: &AppState,
request_id: &str,
custom_attrs: &std::collections::HashMap<String, String>,
) -> Result<(AgentRequest, common::configuration::Listener, AgentSelector), AgentFilterChainError> {
let agent_selector = AgentSelector::new(Arc::clone(&state.orchestrator_service));
// Extract listener name from headers
let listener_name = request
@ -183,16 +135,11 @@ async fn handle_agent_chat_inner(
.and_then(|name| name.to_str().ok());
// Find the appropriate listener
let listener: common::configuration::Listener = {
let listeners = listeners.read().await;
agent_selector
.find_listener(listener_name, &listeners)
.await?
};
let listener = agent_selector.find_listener(listener_name, &state.listeners)?;
get_active_span(|span| {
span.update_name(listener.name.to_string());
for (key, value) in &custom_attrs {
for (key, value) in custom_attrs {
span.set_attribute(opentelemetry::KeyValue::new(key.clone(), value.clone()));
}
});
@ -200,24 +147,20 @@ async fn handle_agent_chat_inner(
info!(listener = %listener.name, "handling request");
// Parse request body
let request_path = request
.uri()
.path()
.to_string()
let full_path = request.uri().path().to_string();
let request_path = full_path
.strip_prefix("/agents")
.unwrap()
.unwrap_or(&full_path)
.to_string();
let request_headers = {
let mut headers = request.headers().clone();
headers.remove(common::consts::ENVOY_ORIGINAL_PATH_HEADER);
// Set the request_id in headers if not already present
if !headers.contains_key(common::consts::REQUEST_ID_HEADER) {
headers.insert(
common::consts::REQUEST_ID_HEADER,
hyper::header::HeaderValue::from_str(&request_id).unwrap(),
);
if let Ok(val) = hyper::header::HeaderValue::from_str(request_id) {
headers.insert(common::consts::REQUEST_ID_HEADER, val);
}
}
headers
@ -230,63 +173,62 @@ async fn handle_agent_chat_inner(
"received request body"
);
// Determine the API type from the endpoint
let api_type =
SupportedAPIsFromClient::from_endpoint(request_path.as_str()).ok_or_else(|| {
let err_msg = format!("Unsupported endpoint: {}", request_path);
warn!("{}", err_msg);
AgentFilterChainError::RequestParsing(serde_json::Error::custom(err_msg))
warn!(path = %request_path, "unsupported endpoint");
AgentFilterChainError::UnsupportedEndpoint(request_path.clone())
})?;
let mut client_request =
match ProviderRequestType::try_from((&chat_request_bytes[..], &api_type)) {
Ok(request) => request,
Err(err) => {
warn!("failed to parse request as ProviderRequestType: {}", err);
let err_msg = format!("Failed to parse request: {}", err);
return Err(AgentFilterChainError::RequestParsing(
serde_json::Error::custom(err_msg),
));
}
};
let client_request = ProviderRequestType::try_from((&chat_request_bytes[..], &api_type))
.map_err(|err| {
warn!(error = %err, "failed to parse request as ProviderRequestType");
AgentFilterChainError::RequestParsing(format!("Failed to parse request: {}", err))
})?;
// If model is not specified in the request, resolve from default provider
if client_request.model().is_empty() {
match llm_providers.read().await.default() {
Some(default_provider) => {
let default_model = default_provider.name.clone();
info!(default_model = %default_model, "no model specified in request, using default provider");
client_request.set_model(default_model);
}
None => {
let err_msg = "No model specified in request and no default provider configured";
warn!("{}", err_msg);
return Ok(BrightStaffError::NoModelSpecified.into_response());
}
}
}
let message: Vec<OpenAIMessage> = client_request.get_messages();
let messages: Vec<OpenAIMessage> = client_request.get_messages();
let request_id = request_headers
.get(common::consts::REQUEST_ID_HEADER)
.and_then(|val| val.to_str().ok())
.map(|s| s.to_string());
// Create agent map for pipeline processing and agent selection
let agent_map = {
let agents = agents_list.read().await;
let agents = agents.as_ref().unwrap();
agent_selector.create_agent_map(agents)
};
Ok((
AgentRequest {
client_request,
messages,
request_headers,
request_id,
},
listener,
agent_selector,
))
}
/// Select agents via the orchestrator model and record selection metrics.
async fn select_and_build_agent_map(
agent_selector: &AgentSelector,
state: &AppState,
messages: &[OpenAIMessage],
listener: &common::configuration::Listener,
request_id: Option<String>,
) -> Result<
(
Vec<common::configuration::AgentFilterChain>,
std::collections::HashMap<String, common::configuration::Agent>,
),
AgentFilterChainError,
> {
let agents = state
.agents_list
.as_ref()
.ok_or(AgentFilterChainError::NoAgentsConfigured)?;
let agent_map = agent_selector.create_agent_map(agents);
// Select appropriate agents using arch orchestrator llm model
let selection_start = Instant::now();
let selected_agents = agent_selector
.select_agents(&message, &listener, request_id.clone())
.select_agents(messages, listener, request_id)
.await?;
// Record selection attributes on the current orchestrator span
let selection_elapsed_ms = selection_start.elapsed().as_secs_f64() * 1000.0;
get_active_span(|span| {
span.set_attribute(opentelemetry::KeyValue::new(
@ -316,12 +258,25 @@ async fn handle_agent_chat_inner(
"selected agents for execution"
);
// Execute agents sequentially, passing output from one to the next
let mut current_messages = message.clone();
Ok((selected_agents, agent_map))
}
/// Execute the agent chain: run each selected agent sequentially, streaming
/// the final agent's response back to the client.
async fn execute_agent_chain(
selected_agents: &[common::configuration::AgentFilterChain],
agent_map: &std::collections::HashMap<String, common::configuration::Agent>,
client_request: ProviderRequestType,
messages: Vec<OpenAIMessage>,
request_headers: &hyper::HeaderMap,
custom_attrs: &std::collections::HashMap<String, String>,
) -> Result<Response<BoxBody<Bytes, hyper::Error>>, AgentFilterChainError> {
let mut pipeline_processor = PipelineProcessor::default();
let response_handler = ResponseHandler::new();
let mut current_messages = messages;
let agent_count = selected_agents.len();
for (agent_index, selected_agent) in selected_agents.iter().enumerate() {
// Get agent name
let agent_name = selected_agent.id.clone();
let is_last_agent = agent_index == agent_count - 1;
@ -332,18 +287,40 @@ async fn handle_agent_chat_inner(
"processing agent"
);
// Process the filter chain
let chat_history = pipeline_processor
.process_filter_chain(
&current_messages,
selected_agent,
&agent_map,
&request_headers,
)
.await?;
let chat_history = if selected_agent
.input_filters
.as_ref()
.map(|f| !f.is_empty())
.unwrap_or(false)
{
let filter_body = serde_json::json!({
"model": client_request.model(),
"messages": current_messages,
});
let filter_bytes =
serde_json::to_vec(&filter_body).map_err(PipelineError::ParseError)?;
// Get agent details and invoke
let agent = agent_map.get(&agent_name).unwrap();
let filtered_bytes = pipeline_processor
.process_raw_filter_chain(
&filter_bytes,
selected_agent,
agent_map,
request_headers,
"/v1/chat/completions",
)
.await?;
let filtered_body: serde_json::Value =
serde_json::from_slice(&filtered_bytes).map_err(PipelineError::ParseError)?;
serde_json::from_value(filtered_body["messages"].clone())
.map_err(PipelineError::ParseError)?
} else {
current_messages.clone()
};
let agent = agent_map
.get(&agent_name)
.ok_or_else(|| AgentFilterChainError::AgentNotFound(agent_name.clone()))?;
debug!(agent = %agent_name, "invoking agent");
@ -357,7 +334,7 @@ async fn handle_agent_chat_inner(
set_service_name(operation_component::AGENT);
get_active_span(|span| {
span.update_name(format!("{} /v1/chat/completions", agent_name));
for (key, value) in &custom_attrs {
for (key, value) in custom_attrs {
span.set_attribute(opentelemetry::KeyValue::new(key.clone(), value.clone()));
}
});
@ -367,28 +344,25 @@ async fn handle_agent_chat_inner(
&chat_history,
client_request.clone(),
agent,
&request_headers,
request_headers,
)
.await
}
.instrument(agent_span.clone())
.await?;
// If this is the last agent, return the streaming response
if is_last_agent {
info!(
agent = %agent_name,
"completed agent chain, returning response"
);
// Capture the orchestrator span (parent of the agent span) so it
// stays open for the full streaming duration alongside the agent span.
let orchestrator_span = tracing::Span::current();
return async {
response_handler
.create_streaming_response(
llm_response,
tracing::Span::current(), // agent span (inner)
orchestrator_span, // orchestrator span (outer)
tracing::Span::current(),
orchestrator_span,
)
.await
.map_err(AgentFilterChainError::from)
@ -397,7 +371,6 @@ async fn handle_agent_chat_inner(
.await;
}
// For intermediate agents, collect the full response and pass to next agent
debug!(agent = %agent_name, "collecting response from intermediate agent");
let response_text = async { response_handler.collect_full_response(llm_response).await }
.instrument(agent_span)
@ -409,11 +382,11 @@ async fn handle_agent_chat_inner(
"agent completed, passing response to next agent"
);
// remove last message and add new one at the end
let last_message = current_messages.pop().unwrap();
let Some(last_message) = current_messages.pop() else {
warn!(agent = %agent_name, "no messages in conversation history");
return Err(AgentFilterChainError::EmptyHistory);
};
// Create a new message with the agent's response as assistant message
// and add it to the conversation history
current_messages.push(OpenAIMessage {
role: hermesllm::apis::openai::Role::Assistant,
content: Some(hermesllm::apis::openai::MessageContent::Text(response_text)),
@ -425,6 +398,34 @@ async fn handle_agent_chat_inner(
current_messages.push(last_message);
}
// This should never be reached since we return in the last agent iteration
unreachable!("Agent execution loop should have returned a response")
Err(AgentFilterChainError::IncompleteChain)
}
async fn handle_agent_chat_inner(
request: Request<hyper::body::Incoming>,
state: Arc<AppState>,
request_id: String,
custom_attrs: std::collections::HashMap<String, String>,
) -> Result<Response<BoxBody<Bytes, hyper::Error>>, AgentFilterChainError> {
let (agent_req, listener, agent_selector) =
parse_agent_request(request, &state, &request_id, &custom_attrs).await?;
let (selected_agents, agent_map) = select_and_build_agent_map(
&agent_selector,
&state,
&agent_req.messages,
&listener,
agent_req.request_id,
)
.await?;
execute_agent_chain(
&selected_agents,
&agent_map,
agent_req.client_request,
agent_req.messages,
&agent_req.request_headers,
&custom_attrs,
)
.await
}

393
crates/brightstaff/src/handlers/pipeline_processor.rs → crates/brightstaff/src/handlers/agents/pipeline.rs
View file

@ -1,5 +1,6 @@
use std::collections::HashMap;
use bytes::Bytes;
use common::configuration::{Agent, AgentFilterChain};
use common::consts::{
ARCH_UPSTREAM_HOST_HEADER, BRIGHT_STAFF_SERVICE_NAME, ENVOY_RETRY_HEADER, TRACE_PARENT_HEADER,
@ -11,7 +12,7 @@ use opentelemetry::global;
use opentelemetry_http::HeaderInjector;
use tracing::{debug, info, instrument, warn};
use crate::handlers::jsonrpc::{
use super::jsonrpc::{
JsonRpcId, JsonRpcNotification, JsonRpcRequest, JsonRpcResponse, JSON_RPC_VERSION,
MCP_INITIALIZE, MCP_INITIALIZE_NOTIFICATION, TOOL_CALL_METHOD,
};
@ -35,8 +36,6 @@ pub enum PipelineError {
NoResultInResponse(String),
#[error("No structured content in response from agent '{0}'")]
NoStructuredContentInResponse(String),
#[error("No messages in response from agent '{0}'")]
NoMessagesInResponse(String),
#[error("Client error from agent '{agent}' (HTTP {status}): {body}")]
ClientError {
agent: String,
@ -79,74 +78,11 @@ impl PipelineProcessor {
}
}
// /// Process the filter chain of agents (all except the terminal agent)
// #[instrument(
// skip(self, chat_history, agent_filter_chain, agent_map, request_headers),
// fields(
// filter_count = agent_filter_chain.filter_chain.as_ref().map(|fc| fc.len()).unwrap_or(0),
// message_count = chat_history.len()
// )
// )]
#[allow(clippy::too_many_arguments)]
pub async fn process_filter_chain(
&mut self,
chat_history: &[Message],
agent_filter_chain: &AgentFilterChain,
agent_map: &HashMap<String, Agent>,
request_headers: &HeaderMap,
) -> Result<Vec<Message>, PipelineError> {
let mut chat_history_updated = chat_history.to_vec();
// If filter_chain is None or empty, proceed without filtering
let filter_chain = match agent_filter_chain.filter_chain.as_ref() {
Some(fc) if !fc.is_empty() => fc,
_ => return Ok(chat_history_updated),
};
for agent_name in filter_chain {
debug!(agent = %agent_name, "processing filter agent");
let agent = agent_map
.get(agent_name)
.ok_or_else(|| PipelineError::AgentNotFound(agent_name.clone()))?;
let tool_name = agent.tool.as_deref().unwrap_or(&agent.id);
info!(
agent = %agent_name,
tool = %tool_name,
url = %agent.url,
agent_type = %agent.agent_type.as_deref().unwrap_or("mcp"),
conversation_len = chat_history.len(),
"executing filter"
);
if agent.agent_type.as_deref().unwrap_or("mcp") == "mcp" {
chat_history_updated = self
.execute_mcp_filter(&chat_history_updated, agent, request_headers)
.await?;
} else {
chat_history_updated = self
.execute_http_filter(&chat_history_updated, agent, request_headers)
.await?;
}
info!(
agent = %agent_name,
updated_len = chat_history_updated.len(),
"filter completed"
);
}
Ok(chat_history_updated)
}
/// Build common MCP headers for requests
fn build_mcp_headers(
&self,
/// Prepare headers shared by all agent/filter requests: removes
/// content-length, injects trace context, sets upstream host and retry.
fn build_agent_headers(
request_headers: &HeaderMap,
agent_id: &str,
session_id: Option<&str>,
) -> Result<HeaderMap, PipelineError> {
let mut headers = request_headers.clone();
headers.remove(hyper::header::CONTENT_LENGTH);
@ -167,24 +103,34 @@ impl PipelineProcessor {
headers.insert(
ENVOY_RETRY_HEADER,
hyper::header::HeaderValue::from_str("3").unwrap(),
hyper::header::HeaderValue::from_static("3"),
);
Ok(headers)
}
/// Build headers for MCP requests (adds Accept, Content-Type, optional session id).
fn build_mcp_headers(
&self,
request_headers: &HeaderMap,
agent_id: &str,
session_id: Option<&str>,
) -> Result<HeaderMap, PipelineError> {
let mut headers = Self::build_agent_headers(request_headers, agent_id)?;
headers.insert(
"Accept",
hyper::header::HeaderValue::from_static("application/json, text/event-stream"),
);
headers.insert(
"Content-Type",
hyper::header::HeaderValue::from_static("application/json"),
);
if let Some(sid) = session_id {
headers.insert(
"mcp-session-id",
hyper::header::HeaderValue::from_str(sid).unwrap(),
);
if let Ok(val) = hyper::header::HeaderValue::from_str(sid) {
headers.insert("mcp-session-id", val);
}
}
Ok(headers)
@ -261,14 +207,17 @@ impl PipelineProcessor {
Ok(response)
}
/// Build a tools/call JSON-RPC request
fn build_tool_call_request(
/// Build a tools/call JSON-RPC request with a full body dict and path hint.
/// Used by execute_mcp_filter_raw so MCP tools receive the same contract as HTTP filters.
fn build_tool_call_request_with_body(
&self,
tool_name: &str,
messages: &[Message],
body: &serde_json::Value,
path: &str,
) -> Result<JsonRpcRequest, PipelineError> {
let mut arguments = HashMap::new();
arguments.insert("messages".to_string(), serde_json::to_value(messages)?);
arguments.insert("body".to_string(), serde_json::to_value(body)?);
arguments.insert("path".to_string(), serde_json::to_value(path)?);
let mut params = HashMap::new();
params.insert("name".to_string(), serde_json::to_value(tool_name)?);
@ -282,35 +231,28 @@ impl PipelineProcessor {
})
}
/// Send request to a specific agent and return the response content
#[instrument(
skip(self, messages, agent, request_headers),
fields(
agent_id = %agent.id,
filter_name = %agent.id,
message_count = messages.len()
)
)]
async fn execute_mcp_filter(
/// Like execute_mcp_filter_raw but passes the full raw body dict + path hint as MCP tool arguments.
/// The MCP tool receives (body: dict, path: str) and returns the modified body dict.
async fn execute_mcp_filter_raw(
&mut self,
messages: &[Message],
raw_bytes: &[u8],
agent: &Agent,
request_headers: &HeaderMap,
) -> Result<Vec<Message>, PipelineError> {
// Set service name for this filter span
request_path: &str,
) -> Result<Bytes, PipelineError> {
set_service_name(operation_component::AGENT_FILTER);
// Update current span name to include filter name
use opentelemetry::trace::get_active_span;
get_active_span(|span| {
span.update_name(format!("execute_mcp_filter ({})", agent.id));
span.update_name(format!("execute_mcp_filter_raw ({})", agent.id));
});
// Get or create MCP session
let body: serde_json::Value =
serde_json::from_slice(raw_bytes).map_err(PipelineError::ParseError)?;
let mcp_session_id = if let Some(session_id) = self.agent_id_session_map.get(&agent.id) {
session_id.clone()
} else {
let session_id = self.get_new_session_id(&agent.id, request_headers).await;
let session_id = self.get_new_session_id(&agent.id, request_headers).await?;
self.agent_id_session_map
.insert(agent.id.clone(), session_id.clone());
session_id
@ -321,11 +263,10 @@ impl PipelineProcessor {
mcp_session_id, agent.id
);
// Build JSON-RPC request
let tool_name = agent.tool.as_deref().unwrap_or(&agent.id);
let json_rpc_request = self.build_tool_call_request(tool_name, messages)?;
let json_rpc_request =
self.build_tool_call_request_with_body(tool_name, &body, request_path)?;
// Build headers
let agent_headers =
self.build_mcp_headers(request_headers, &agent.id, Some(&mcp_session_id))?;
@ -335,7 +276,6 @@ impl PipelineProcessor {
let http_status = response.status();
let response_bytes = response.bytes().await?;
// Handle HTTP errors
if !http_status.is_success() {
let error_body = String::from_utf8_lossy(&response_bytes).to_string();
return Err(if http_status.is_client_error() {
@ -353,20 +293,12 @@ impl PipelineProcessor {
});
}
info!(
"Response from agent {}: {}",
agent.id,
String::from_utf8_lossy(&response_bytes)
);
// Parse SSE response
let data_chunk = self.parse_sse_response(&response_bytes, &agent.id)?;
let response: JsonRpcResponse = serde_json::from_str(&data_chunk)?;
let response_result = response
.result
.ok_or_else(|| PipelineError::NoResultInResponse(agent.id.clone()))?;
// Check if error field is set in response result
if response_result
.get("isError")
.and_then(|v| v.as_bool())
@ -388,21 +320,28 @@ impl PipelineProcessor {
});
}
// Extract structured content and parse messages
let response_json = response_result
// FastMCP puts structured Pydantic return values in structuredContent.result,
// but plain dicts land in content[0].text as a JSON string. Try both.
let result = if let Some(structured) = response_result
.get("structuredContent")
.ok_or_else(|| PipelineError::NoStructuredContentInResponse(agent.id.clone()))?;
.and_then(|v| v.get("result"))
.cloned()
{
structured
} else {
let text = response_result
.get("content")
.and_then(|v| v.as_array())
.and_then(|arr| arr.first())
.and_then(|v| v.get("text"))
.and_then(|v| v.as_str())
.ok_or_else(|| PipelineError::NoStructuredContentInResponse(agent.id.clone()))?;
serde_json::from_str(text).map_err(PipelineError::ParseError)?
};
let messages: Vec<Message> = response_json
.get("result")
.and_then(|v| v.as_array())
.ok_or_else(|| PipelineError::NoMessagesInResponse(agent.id.clone()))?
.iter()
.map(|msg_value| serde_json::from_value(msg_value.clone()))
.collect::<Result<Vec<Message>, _>>()
.map_err(PipelineError::ParseError)?;
Ok(messages)
Ok(Bytes::from(
serde_json::to_vec(&result).map_err(PipelineError::ParseError)?,
))
}
/// Build an initialize JSON-RPC request
@ -464,18 +403,19 @@ impl PipelineProcessor {
Ok(())
}
async fn get_new_session_id(&self, agent_id: &str, request_headers: &HeaderMap) -> String {
async fn get_new_session_id(
&self,
agent_id: &str,
request_headers: &HeaderMap,
) -> Result<String, PipelineError> {
info!("initializing MCP session for agent {}", agent_id);
let initialize_request = self.build_initialize_request();
let headers = self
.build_mcp_headers(request_headers, agent_id, None)
.expect("Failed to build headers for initialization");
let headers = self.build_mcp_headers(request_headers, agent_id, None)?;
let response = self
.send_mcp_request(&initialize_request, &headers, agent_id)
.await
.expect("Failed to initialize MCP session");
.await?;
info!("initialize response status: {}", response.status());
@ -483,8 +423,13 @@ impl PipelineProcessor {
.headers()
.get("mcp-session-id")
.and_then(|v| v.to_str().ok())
.expect("No mcp-session-id in response")
.to_string();
.map(|s| s.to_string())
.ok_or_else(|| {
PipelineError::NoContentInResponse(format!(
"No mcp-session-id header in initialize response from agent {}",
agent_id
))
})?;
info!(
"created new MCP session for agent {}: {}",
@ -493,88 +438,63 @@ impl PipelineProcessor {
// Send initialized notification
self.send_initialized_notification(agent_id, &session_id, &headers)
.await
.expect("Failed to send initialized notification");
.await?;
session_id
Ok(session_id)
}
/// Execute a HTTP-based filter agent
/// Execute a raw bytes filter — POST bytes to agent.url, receive bytes back.
/// Used for input and output filters where the full raw request/response is passed through.
/// No MCP protocol wrapping; agent_type is ignored.
#[instrument(
skip(self, messages, agent, request_headers),
skip(self, raw_bytes, agent, request_headers),
fields(
agent_id = %agent.id,
agent_url = %agent.url,
filter_name = %agent.id,
message_count = messages.len()
bytes_len = raw_bytes.len()
)
)]
async fn execute_http_filter(
async fn execute_raw_filter(
&mut self,
messages: &[Message],
raw_bytes: &[u8],
agent: &Agent,
request_headers: &HeaderMap,
) -> Result<Vec<Message>, PipelineError> {
// Set service name for this filter span
request_path: &str,
) -> Result<Bytes, PipelineError> {
set_service_name(operation_component::AGENT_FILTER);
// Update current span name to include filter name
use opentelemetry::trace::get_active_span;
get_active_span(|span| {
span.update_name(format!("execute_http_filter ({})", agent.id));
span.update_name(format!("execute_raw_filter ({})", agent.id));
});
// Build headers
let mut agent_headers = request_headers.clone();
agent_headers.remove(hyper::header::CONTENT_LENGTH);
// Inject OpenTelemetry trace context automatically
agent_headers.remove(TRACE_PARENT_HEADER);
global::get_text_map_propagator(|propagator| {
let cx =
tracing_opentelemetry::OpenTelemetrySpanExt::context(&tracing::Span::current());
propagator.inject_context(&cx, &mut HeaderInjector(&mut agent_headers));
});
agent_headers.insert(
ARCH_UPSTREAM_HOST_HEADER,
hyper::header::HeaderValue::from_str(&agent.id)
.map_err(|_| PipelineError::AgentNotFound(agent.id.clone()))?,
);
agent_headers.insert(
ENVOY_RETRY_HEADER,
hyper::header::HeaderValue::from_str("3").unwrap(),
);
let mut agent_headers = Self::build_agent_headers(request_headers, &agent.id)?;
agent_headers.insert(
"Accept",
hyper::header::HeaderValue::from_static("application/json"),
);
agent_headers.insert(
"Content-Type",
hyper::header::HeaderValue::from_static("application/json"),
);
debug!(
"Sending HTTP request to agent {} at URL: {}",
agent.id, agent.url
);
// Append the original request path so the filter endpoint encodes the API format.
// e.g. agent.url="http://host/anonymize" + request_path="/v1/chat/completions"
// -> POST http://host/anonymize/v1/chat/completions
let url = format!("{}{}", agent.url, request_path);
debug!(agent = %agent.id, url = %url, "sending raw filter request");
// Send messages array directly as request body
let response = self
.client
.post(&agent.url)
.post(&url)
.headers(agent_headers)
.json(&messages)
.body(raw_bytes.to_vec())
.send()
.await?;
let http_status = response.status();
let response_bytes = response.bytes().await?;
// Handle HTTP errors
if !http_status.is_success() {
let error_body = String::from_utf8_lossy(&response_bytes).to_string();
return Err(if http_status.is_client_error() {
@ -592,17 +512,56 @@ impl PipelineProcessor {
});
}
debug!(
"Response from HTTP agent {}: {}",
agent.id,
String::from_utf8_lossy(&response_bytes)
);
debug!(agent = %agent.id, bytes_len = response_bytes.len(), "raw filter response received");
Ok(response_bytes)
}
// Parse response - expecting array of messages directly
let messages: Vec<Message> =
serde_json::from_slice(&response_bytes).map_err(PipelineError::ParseError)?;
/// Process a chain of raw-bytes filters sequentially.
/// Input: raw request or response bytes. Output: filtered bytes.
/// Each agent receives the output of the previous one.
pub async fn process_raw_filter_chain(
&mut self,
raw_bytes: &[u8],
agent_filter_chain: &AgentFilterChain,
agent_map: &HashMap<String, Agent>,
request_headers: &HeaderMap,
request_path: &str,
) -> Result<Bytes, PipelineError> {
let filter_chain = match agent_filter_chain.input_filters.as_ref() {
Some(fc) if !fc.is_empty() => fc,
_ => return Ok(Bytes::copy_from_slice(raw_bytes)),
};
Ok(messages)
let mut current_bytes = Bytes::copy_from_slice(raw_bytes);
for agent_name in filter_chain {
debug!(agent = %agent_name, "processing raw filter agent");
let agent = agent_map
.get(agent_name)
.ok_or_else(|| PipelineError::AgentNotFound(agent_name.clone()))?;
let agent_type = agent.agent_type.as_deref().unwrap_or("mcp");
info!(
agent = %agent_name,
url = %agent.url,
agent_type = %agent_type,
bytes_len = current_bytes.len(),
"executing raw filter"
);
current_bytes = if agent_type == "mcp" {
self.execute_mcp_filter_raw(&current_bytes, agent, request_headers, request_path)
.await?
} else {
self.execute_raw_filter(&current_bytes, agent, request_headers, request_path)
.await?
};
info!(agent = %agent_name, bytes_len = current_bytes.len(), "raw filter completed");
}
Ok(current_bytes)
}
/// Send request to terminal agent and return the raw response for streaming
@ -615,36 +574,15 @@ impl PipelineProcessor {
terminal_agent: &Agent,
request_headers: &HeaderMap,
) -> Result<reqwest::Response, PipelineError> {
// let mut request = original_request.clone();
original_request.set_messages(messages);
let request_url = "/v1/chat/completions";
let request_body = ProviderRequestType::to_bytes(&original_request).unwrap();
// let request_body = serde_json::to_string(&request)?;
let request_body = ProviderRequestType::to_bytes(&original_request)
.map_err(|e| PipelineError::NoContentInResponse(e.to_string()))?;
debug!("sending request to terminal agent {}", terminal_agent.id);
let mut agent_headers = request_headers.clone();
agent_headers.remove(hyper::header::CONTENT_LENGTH);
// Inject OpenTelemetry trace context automatically
agent_headers.remove(TRACE_PARENT_HEADER);
global::get_text_map_propagator(|propagator| {
let cx =
tracing_opentelemetry::OpenTelemetrySpanExt::context(&tracing::Span::current());
propagator.inject_context(&cx, &mut HeaderInjector(&mut agent_headers));
});
agent_headers.insert(
ARCH_UPSTREAM_HOST_HEADER,
hyper::header::HeaderValue::from_str(&terminal_agent.id)
.map_err(|_| PipelineError::AgentNotFound(terminal_agent.id.clone()))?,
);
agent_headers.insert(
ENVOY_RETRY_HEADER,
hyper::header::HeaderValue::from_str("3").unwrap(),
);
let agent_headers = Self::build_agent_headers(request_headers, &terminal_agent.id)?;
let response = self
.client
@ -661,24 +599,13 @@ impl PipelineProcessor {
#[cfg(test)]
mod tests {
use super::*;
use hermesllm::apis::openai::{Message, MessageContent, Role};
use mockito::Server;
use std::collections::HashMap;
fn create_test_message(role: Role, content: &str) -> Message {
Message {
role,
content: Some(MessageContent::Text(content.to_string())),
name: None,
tool_calls: None,
tool_call_id: None,
}
}
fn create_test_pipeline(agents: Vec<&str>) -> AgentFilterChain {
AgentFilterChain {
id: "test-agent".to_string(),
filter_chain: Some(agents.iter().map(|s| s.to_string()).collect()),
input_filters: Some(agents.iter().map(|s| s.to_string()).collect()),
description: None,
default: None,
}
@ -690,12 +617,19 @@ mod tests {
let agent_map = HashMap::new();
let request_headers = HeaderMap::new();
let messages = vec![create_test_message(Role::User, "Hello")];
let body = serde_json::json!({"messages": [{"role": "user", "content": "Hello"}]});
let raw_bytes = serde_json::to_vec(&body).unwrap();
let pipeline = create_test_pipeline(vec!["nonexistent-agent", "terminal-agent"]);
let result = processor
.process_filter_chain(&messages, &pipeline, &agent_map, &request_headers)
.process_raw_filter_chain(
&raw_bytes,
&pipeline,
&agent_map,
&request_headers,
"/v1/chat/completions",
)
.await;
assert!(result.is_err());
@ -725,11 +659,12 @@ mod tests {
agent_type: None,
};
let messages = vec![create_test_message(Role::User, "Hello")];
let body = serde_json::json!({"messages": [{"role": "user", "content": "Hello"}]});
let raw_bytes = serde_json::to_vec(&body).unwrap();
let request_headers = HeaderMap::new();
let result = processor
.execute_mcp_filter(&messages, &agent, &request_headers)
.execute_mcp_filter_raw(&raw_bytes, &agent, &request_headers, "/v1/chat/completions")
.await;
match result {
@ -764,11 +699,12 @@ mod tests {
agent_type: None,
};
let messages = vec![create_test_message(Role::User, "Ping")];
let body = serde_json::json!({"messages": [{"role": "user", "content": "Ping"}]});
let raw_bytes = serde_json::to_vec(&body).unwrap();
let request_headers = HeaderMap::new();
let result = processor
.execute_mcp_filter(&messages, &agent, &request_headers)
.execute_mcp_filter_raw(&raw_bytes, &agent, &request_headers, "/v1/chat/completions")
.await;
match result {
@ -816,11 +752,12 @@ mod tests {
agent_type: None,
};
let messages = vec![create_test_message(Role::User, "Hi")];
let body = serde_json::json!({"messages": [{"role": "user", "content": "Hi"}]});
let raw_bytes = serde_json::to_vec(&body).unwrap();
let request_headers = HeaderMap::new();
let result = processor
.execute_mcp_filter(&messages, &agent, &request_headers)
.execute_mcp_filter_raw(&raw_bytes, &agent, &request_headers, "/v1/chat/completions")
.await;
match result {

26
crates/brightstaff/src/handlers/agent_selector.rs → crates/brightstaff/src/handlers/agents/selector.rs
View file

@ -7,7 +7,7 @@ use common::configuration::{
use hermesllm::apis::openai::Message;
use tracing::{debug, warn};
use crate::router::plano_orchestrator::OrchestratorService;
use crate::router::orchestrator::OrchestratorService;
/// Errors that can occur during agent selection
#[derive(Debug, thiserror::Error)]
@ -37,7 +37,7 @@ impl AgentSelector {
}
/// Find listener by name from the request headers
pub async fn find_listener(
pub fn find_listener(
&self,
listener_name: Option<&str>,
listeners: &[common::configuration::Listener],
@ -84,7 +84,7 @@ impl AgentSelector {
}
/// Convert agent descriptions to orchestration preferences
async fn convert_agent_description_to_orchestration_preferences(
fn convert_agent_description_to_orchestration_preferences(
&self,
agents: &[AgentFilterChain],
) -> Vec<AgentUsagePreference> {
@ -121,9 +121,7 @@ impl AgentSelector {
return Ok(vec![agents[0].clone()]);
}
let usage_preferences = self
.convert_agent_description_to_orchestration_preferences(agents)
.await;
let usage_preferences = self.convert_agent_description_to_orchestration_preferences(agents);
debug!(
"Agents usage preferences for orchestration: {}",
serde_json::to_string(&usage_preferences).unwrap_or_default()
@ -172,12 +170,13 @@ impl AgentSelector {
#[cfg(test)]
mod tests {
use super::*;
use common::configuration::{AgentFilterChain, Listener};
use common::configuration::{AgentFilterChain, Listener, ListenerType};
fn create_test_orchestrator_service() -> Arc<OrchestratorService> {
Arc::new(OrchestratorService::new(
"http://localhost:8080".to_string(),
"test-model".to_string(),
"plano-orchestrator".to_string(),
))
}
@ -186,14 +185,17 @@ mod tests {
id: name.to_string(),
description: Some(description.to_string()),
default: Some(is_default),
filter_chain: Some(vec![name.to_string()]),
input_filters: Some(vec![name.to_string()]),
}
}
fn create_test_listener(name: &str, agents: Vec<AgentFilterChain>) -> Listener {
Listener {
listener_type: ListenerType::Agent,
name: name.to_string(),
agents: Some(agents),
input_filters: None,
output_filters: None,
port: 8080,
router: None,
}
@ -218,9 +220,7 @@ mod tests {
let listener2 = create_test_listener("other-listener", vec![]);
let listeners = vec![listener1.clone(), listener2];
let result = selector
.find_listener(Some("test-listener"), &listeners)
.await;
let result = selector.find_listener(Some("test-listener"), &listeners);
assert!(result.is_ok());
assert_eq!(result.unwrap().name, "test-listener");
@ -233,9 +233,7 @@ mod tests {
let listeners = vec![create_test_listener("other-listener", vec![])];
let result = selector
.find_listener(Some("nonexistent"), &listeners)
.await;
let result = selector.find_listener(Some("nonexistent"), &listeners);
assert!(result.is_err());
matches!(

87
crates/brightstaff/src/handlers/integration_tests.rs
View file

@ -3,12 +3,11 @@ use std::sync::Arc;
use hermesllm::apis::openai::{ChatCompletionsRequest, Message, MessageContent, Role};
use hyper::header::HeaderMap;
use crate::handlers::agent_selector::{AgentSelectionError, AgentSelector};
use crate::handlers::pipeline_processor::PipelineProcessor;
use crate::router::plano_orchestrator::OrchestratorService;
use common::errors::BrightStaffError;
use http_body_util::BodyExt;
use hyper::StatusCode;
use crate::handlers::agents::pipeline::PipelineProcessor;
use crate::handlers::agents::selector::{AgentSelectionError, AgentSelector};
use crate::handlers::response::ResponseHandler;
use crate::router::orchestrator::OrchestratorService;
/// Integration test that demonstrates the modular agent chat flow
/// This test shows how the three main components work together:
/// 1. AgentSelector - selects the appropriate agents based on orchestration
@ -17,12 +16,13 @@ use hyper::StatusCode;
#[cfg(test)]
mod tests {
use super::*;
use common::configuration::{Agent, AgentFilterChain, Listener};
use common::configuration::{Agent, AgentFilterChain, Listener, ListenerType};
fn create_test_orchestrator_service() -> Arc<OrchestratorService> {
Arc::new(OrchestratorService::new(
"http://localhost:8080".to_string(),
"test-model".to_string(),
"plano-orchestrator".to_string(),
))
}
@ -63,7 +63,7 @@ mod tests {
let agent_pipeline = AgentFilterChain {
id: "terminal-agent".to_string(),
filter_chain: Some(vec![
input_filters: Some(vec![
"filter-agent".to_string(),
"terminal-agent".to_string(),
]),
@ -72,8 +72,11 @@ mod tests {
};
let listener = Listener {
listener_type: ListenerType::Agent,
name: "test-listener".to_string(),
agents: Some(vec![agent_pipeline.clone()]),
input_filters: None,
output_filters: None,
port: 8080,
router: None,
};
@ -82,9 +85,7 @@ mod tests {
let messages = vec![create_test_message(Role::User, "Hello world!")];
// Test 1: Agent Selection
let selected_listener = agent_selector
.find_listener(Some("test-listener"), &listeners)
.await;
let selected_listener = agent_selector.find_listener(Some("test-listener"), &listeners);
assert!(selected_listener.is_ok());
let listener = selected_listener.unwrap();
@ -106,47 +107,40 @@ mod tests {
// Create a pipeline with empty filter chain to avoid network calls
let test_pipeline = AgentFilterChain {
id: "terminal-agent".to_string(),
filter_chain: Some(vec![]), // Empty filter chain - no network calls needed
input_filters: Some(vec![]), // Empty filter chain - no network calls needed
description: None,
default: None,
};
let headers = HeaderMap::new();
let request_bytes = serde_json::to_vec(&request).expect("failed to serialize request");
let result = pipeline_processor
.process_filter_chain(&request.messages, &test_pipeline, &agent_map, &headers)
.process_raw_filter_chain(
&request_bytes,
&test_pipeline,
&agent_map,
&headers,
"/v1/chat/completions",
)
.await;
println!("Pipeline processing result: {:?}", result);
assert!(result.is_ok());
let processed_messages = result.unwrap();
// With empty filter chain, should return the original messages unchanged
assert_eq!(processed_messages.len(), 1);
if let Some(MessageContent::Text(content)) = &processed_messages[0].content {
let processed_bytes = result.unwrap();
// With empty filter chain, should return the original bytes unchanged
let processed_request: ChatCompletionsRequest =
serde_json::from_slice(&processed_bytes).expect("failed to deserialize response");
assert_eq!(processed_request.messages.len(), 1);
if let Some(MessageContent::Text(content)) = &processed_request.messages[0].content {
assert_eq!(content, "Hello world!");
} else {
panic!("Expected text content");
}
// Test 4: Error Response Creation
let err = BrightStaffError::ModelNotFound("gpt-5-secret".to_string());
let response = err.into_response();
assert_eq!(response.status(), StatusCode::NOT_FOUND);
// Helper to extract body as JSON
let body_bytes = response.into_body().collect().await.unwrap().to_bytes();
let body: serde_json::Value = serde_json::from_slice(&body_bytes).unwrap();
assert_eq!(body["error"]["code"], "ModelNotFound");
assert_eq!(
body["error"]["details"]["rejected_model_id"],
"gpt-5-secret"
);
assert!(body["error"]["message"]
.as_str()
.unwrap()
.contains("gpt-5-secret"));
let error_response = ResponseHandler::create_bad_request("Test error");
assert_eq!(error_response.status(), hyper::StatusCode::BAD_REQUEST);
println!("✅ All modular components working correctly!");
}
@ -157,7 +151,7 @@ mod tests {
let agent_selector = AgentSelector::new(router_service);
// Test listener not found
let result = agent_selector.find_listener(Some("nonexistent"), &[]).await;
let result = agent_selector.find_listener(Some("nonexistent"), &[]);
assert!(result.is_err());
assert!(matches!(
@ -165,21 +159,12 @@ mod tests {
AgentSelectionError::ListenerNotFound(_)
));
let technical_reason = "Database connection timed out";
let err = BrightStaffError::InternalServerError(technical_reason.to_string());
let response = err.into_response();
// --- 1. EXTRACT BYTES ---
let body_bytes = response.into_body().collect().await.unwrap().to_bytes();
// --- 2. DECLARE body_json HERE ---
let body_json: serde_json::Value =
serde_json::from_slice(&body_bytes).expect("Failed to parse JSON body");
// --- 3. USE body_json ---
assert_eq!(body_json["error"]["code"], "InternalServerError");
assert_eq!(body_json["error"]["details"]["reason"], technical_reason);
// Test error response creation
let error_response = ResponseHandler::create_internal_error("Pipeline failed");
assert_eq!(
error_response.status(),
hyper::StatusCode::INTERNAL_SERVER_ERROR
);
println!("✅ Error handling working correctly!");
}

572
crates/brightstaff/src/handlers/llm.rs
View file

@ -1,572 +0,0 @@
use bytes::Bytes;
use common::configuration::{ModelAlias, SpanAttributes};
use common::consts::{
ARCH_IS_STREAMING_HEADER, ARCH_PROVIDER_HINT_HEADER, REQUEST_ID_HEADER, TRACE_PARENT_HEADER,
};
use common::llm_providers::LlmProviders;
use hermesllm::apis::openai_responses::InputParam;
use hermesllm::clients::{SupportedAPIsFromClient, SupportedUpstreamAPIs};
use hermesllm::{ProviderRequest, ProviderRequestType};
use http_body_util::combinators::BoxBody;
use http_body_util::BodyExt;
use hyper::header::{self};
use hyper::{Request, Response};
use opentelemetry::global;
use opentelemetry::trace::get_active_span;
use opentelemetry_http::HeaderInjector;
use std::collections::HashMap;
use std::sync::Arc;
use tokio::sync::RwLock;
use tracing::{debug, info, info_span, warn, Instrument};
use crate::handlers::router_chat::router_chat_get_upstream_model;
use crate::handlers::utils::{
create_streaming_response, truncate_message, ObservableStreamProcessor,
};
use crate::router::llm_router::RouterService;
use crate::state::response_state_processor::ResponsesStateProcessor;
use crate::state::{
extract_input_items, retrieve_and_combine_input, StateStorage, StateStorageError,
};
use crate::tracing::{
collect_custom_trace_attributes, llm as tracing_llm, operation_component, set_service_name,
};
use common::errors::BrightStaffError;
pub async fn llm_chat(
request: Request<hyper::body::Incoming>,
router_service: Arc<RouterService>,
full_qualified_llm_provider_url: String,
model_aliases: Arc<Option<HashMap<String, ModelAlias>>>,
llm_providers: Arc<RwLock<LlmProviders>>,
span_attributes: Arc<Option<SpanAttributes>>,
state_storage: Option<Arc<dyn StateStorage>>,
) -> Result<Response<BoxBody<Bytes, hyper::Error>>, hyper::Error> {
let request_path = request.uri().path().to_string();
let request_headers = request.headers().clone();
let request_id: String = match request_headers
.get(REQUEST_ID_HEADER)
.and_then(|h| h.to_str().ok())
.map(|s| s.to_string())
{
Some(id) => id,
None => uuid::Uuid::new_v4().to_string(),
};
let custom_attrs =
collect_custom_trace_attributes(&request_headers, span_attributes.as_ref().as_ref());
// Create a span with request_id that will be included in all log lines
let request_span = info_span!(
"llm",
component = "llm",
request_id = %request_id,
http.method = %request.method(),
http.path = %request_path,
llm.model = tracing::field::Empty,
llm.tools = tracing::field::Empty,
llm.user_message_preview = tracing::field::Empty,
llm.temperature = tracing::field::Empty,
);
// Execute the rest of the handler inside the span
llm_chat_inner(
request,
router_service,
full_qualified_llm_provider_url,
model_aliases,
llm_providers,
custom_attrs,
state_storage,
request_id,
request_path,
request_headers,
)
.instrument(request_span)
.await
}
#[allow(clippy::too_many_arguments)]
async fn llm_chat_inner(
request: Request<hyper::body::Incoming>,
router_service: Arc<RouterService>,
full_qualified_llm_provider_url: String,
model_aliases: Arc<Option<HashMap<String, ModelAlias>>>,
llm_providers: Arc<RwLock<LlmProviders>>,
custom_attrs: HashMap<String, String>,
state_storage: Option<Arc<dyn StateStorage>>,
request_id: String,
request_path: String,
mut request_headers: hyper::HeaderMap,
) -> Result<Response<BoxBody<Bytes, hyper::Error>>, hyper::Error> {
// Set service name for LLM operations
set_service_name(operation_component::LLM);
get_active_span(|span| {
for (key, value) in &custom_attrs {
span.set_attribute(opentelemetry::KeyValue::new(key.clone(), value.clone()));
}
});
// Extract or generate traceparent - this establishes the trace context for all spans
let traceparent: String = match request_headers
.get(TRACE_PARENT_HEADER)
.and_then(|h| h.to_str().ok())
.map(|s| s.to_string())
{
Some(tp) => tp,
None => {
use uuid::Uuid;
let trace_id = Uuid::new_v4().to_string().replace("-", "");
let generated_tp = format!("00-{}-0000000000000000-01", trace_id);
warn!(
generated_traceparent = %generated_tp,
"TRACE_PARENT header missing, generated new traceparent"
);
generated_tp
}
};
let raw_bytes = request.collect().await?.to_bytes();
debug!(
body = %String::from_utf8_lossy(&raw_bytes),
"request body received"
);
// Extract routing_policy from request body if present
let (chat_request_bytes, inline_routing_policy) =
match crate::handlers::routing_service::extract_routing_policy(&raw_bytes, false) {
Ok(result) => result,
Err(err) => {
warn!(error = %err, "failed to parse request JSON");
return Ok(BrightStaffError::InvalidRequest(format!(
"Failed to parse request: {}",
err
))
.into_response());
}
};
let mut client_request = match ProviderRequestType::try_from((
&chat_request_bytes[..],
&SupportedAPIsFromClient::from_endpoint(request_path.as_str()).unwrap(),
)) {
Ok(request) => request,
Err(err) => {
warn!(
error = %err,
"failed to parse request as ProviderRequestType"
);
return Ok(BrightStaffError::InvalidRequest(format!(
"Failed to parse request: {}",
err
))
.into_response());
}
};
// === v1/responses state management: Extract input items early ===
let mut original_input_items = Vec::new();
let client_api = SupportedAPIsFromClient::from_endpoint(request_path.as_str());
let is_responses_api_client = matches!(
client_api,
Some(SupportedAPIsFromClient::OpenAIResponsesAPI(_))
);
// If model is not specified in the request, resolve from default provider
let model_from_request = client_request.model().to_string();
let model_from_request = if model_from_request.is_empty() {
match llm_providers.read().await.default() {
Some(default_provider) => {
let default_model = default_provider.name.clone();
info!(default_model = %default_model, "no model specified in request, using default provider");
client_request.set_model(default_model.clone());
default_model
}
None => {
let err_msg = "No model specified in request and no default provider configured";
warn!("{}", err_msg);
return Ok(BrightStaffError::NoModelSpecified.into_response());
}
}
} else {
model_from_request
};
// Model alias resolution: update model field in client_request immediately
// This ensures all downstream objects use the resolved model
let temperature = client_request.get_temperature();
let is_streaming_request = client_request.is_streaming();
let alias_resolved_model = resolve_model_alias(&model_from_request, &model_aliases);
let (provider_id, _) = get_provider_info(&llm_providers, &alias_resolved_model).await;
// Validate that the requested model exists in configuration
// This matches the validation in llm_gateway routing.rs
if llm_providers
.read()
.await
.get(&alias_resolved_model)
.is_none()
{
warn!(model = %alias_resolved_model, "model not found in configured providers");
return Ok(BrightStaffError::ModelNotFound(alias_resolved_model).into_response());
}
// Handle provider/model slug format (e.g., "openai/gpt-4")
// Extract just the model name for upstream (providers don't understand the slug)
let model_name_only = if let Some((_, model)) = alias_resolved_model.split_once('/') {
model.to_string()
} else {
alias_resolved_model.clone()
};
// Extract tool names and user message preview for span attributes
let tool_names = client_request.get_tool_names();
let user_message_preview = client_request
.get_recent_user_message()
.map(|msg| truncate_message(&msg, 50));
let span = tracing::Span::current();
if let Some(temp) = temperature {
span.record(tracing_llm::TEMPERATURE, tracing::field::display(temp));
}
if let Some(tools) = &tool_names {
let formatted_tools = tools
.iter()
.map(|name| format!("{}(...)", name))
.collect::<Vec<_>>()
.join("\n");
span.record(tracing_llm::TOOLS, formatted_tools.as_str());
}
if let Some(preview) = &user_message_preview {
span.record(tracing_llm::USER_MESSAGE_PREVIEW, preview.as_str());
}
// Extract messages for signal analysis (clone before moving client_request)
let messages_for_signals = Some(client_request.get_messages());
// Set the model to just the model name (without provider prefix)
// This ensures upstream receives "gpt-4" not "openai/gpt-4"
client_request.set_model(model_name_only.clone());
if client_request.remove_metadata_key("plano_preference_config") {
debug!("removed plano_preference_config from metadata");
}
if let Some(ref client_api_kind) = client_api {
let upstream_api =
provider_id.compatible_api_for_client(client_api_kind, is_streaming_request);
client_request.normalize_for_upstream(provider_id, &upstream_api);
}
// === v1/responses state management: Determine upstream API and combine input if needed ===
// Do this BEFORE routing since routing consumes the request
// Only process state if state_storage is configured
let mut should_manage_state = false;
if is_responses_api_client {
if let (
ProviderRequestType::ResponsesAPIRequest(ref mut responses_req),
Some(ref state_store),
) = (&mut client_request, &state_storage)
{
// Extract original input once
original_input_items = extract_input_items(&responses_req.input);
// Get the upstream path and check if it's ResponsesAPI
let upstream_path = get_upstream_path(
&llm_providers,
&alias_resolved_model,
&request_path,
&alias_resolved_model,
is_streaming_request,
)
.await;
let upstream_api = SupportedUpstreamAPIs::from_endpoint(&upstream_path);
// Only manage state if upstream is NOT OpenAIResponsesAPI (needs translation)
should_manage_state = !matches!(
upstream_api,
Some(SupportedUpstreamAPIs::OpenAIResponsesAPI(_))
);
if should_manage_state {
// Retrieve and combine conversation history if previous_response_id exists
if let Some(ref prev_resp_id) = responses_req.previous_response_id {
match retrieve_and_combine_input(
state_store.clone(),
prev_resp_id,
original_input_items, // Pass ownership instead of cloning
)
.await
{
Ok(combined_input) => {
// Update both the request and original_input_items
responses_req.input = InputParam::Items(combined_input.clone());
original_input_items = combined_input;
info!(
items = original_input_items.len(),
"updated request with conversation history"
);
}
Err(StateStorageError::NotFound(_)) => {
// Return 409 Conflict when previous_response_id not found
warn!(previous_response_id = %prev_resp_id, "previous response_id not found");
return Ok(BrightStaffError::ConversationStateNotFound(
prev_resp_id.to_string(),
)
.into_response());
}
Err(e) => {
// Log warning but continue on other storage errors
warn!(
previous_response_id = %prev_resp_id,
error = %e,
"failed to retrieve conversation state"
);
// Restore original_input_items since we passed ownership
original_input_items = extract_input_items(&responses_req.input);
}
}
}
} else {
debug!("upstream supports ResponsesAPI natively");
}
}
}
// Serialize request for upstream BEFORE router consumes it
let client_request_bytes_for_upstream = ProviderRequestType::to_bytes(&client_request).unwrap();
// Determine routing using the dedicated router_chat module
// This gets its own span for latency and error tracking
let routing_span = info_span!(
"routing",
component = "routing",
http.method = "POST",
http.target = %request_path,
model.requested = %model_from_request,
model.alias_resolved = %alias_resolved_model,
route.selected_model = tracing::field::Empty,
routing.determination_ms = tracing::field::Empty,
);
let routing_result = match async {
set_service_name(operation_component::ROUTING);
router_chat_get_upstream_model(
router_service,
client_request, // Pass the original request - router_chat will convert it
&traceparent,
&request_path,
&request_id,
inline_routing_policy,
)
.await
}
.instrument(routing_span)
.await
{
Ok(result) => result,
Err(err) => {
return Ok(BrightStaffError::ForwardedError {
status_code: err.status_code,
message: err.message,
}
.into_response());
}
};
// Determine final model to use
// Router returns "none" as a sentinel value when it doesn't select a specific model
let router_selected_model = routing_result.model_name;
let resolved_model = if router_selected_model != "none" {
// Router selected a specific model via routing preferences
router_selected_model
} else {
// Router returned "none" sentinel, use validated resolved_model from request
alias_resolved_model.clone()
};
tracing::Span::current().record(tracing_llm::MODEL_NAME, resolved_model.as_str());
let span_name = if model_from_request == resolved_model {
format!("POST {} {}", request_path, resolved_model)
} else {
format!(
"POST {} {} -> {}",
request_path, model_from_request, resolved_model
)
};
get_active_span(|span| {
span.update_name(span_name.clone());
});
debug!(
url = %full_qualified_llm_provider_url,
provider_hint = %resolved_model,
upstream_model = %model_name_only,
"Routing to upstream"
);
request_headers.insert(
ARCH_PROVIDER_HINT_HEADER,
header::HeaderValue::from_str(&resolved_model).unwrap(),
);
request_headers.insert(
header::HeaderName::from_static(ARCH_IS_STREAMING_HEADER),
header::HeaderValue::from_str(&is_streaming_request.to_string()).unwrap(),
);
// remove content-length header if it exists
request_headers.remove(header::CONTENT_LENGTH);
// Inject current LLM span's trace context so upstream spans are children of plano(llm)
global::get_text_map_propagator(|propagator| {
let cx = tracing_opentelemetry::OpenTelemetrySpanExt::context(&tracing::Span::current());
propagator.inject_context(&cx, &mut HeaderInjector(&mut request_headers));
});
// Capture start time right before sending request to upstream
let request_start_time = std::time::Instant::now();
let _request_start_system_time = std::time::SystemTime::now();
let llm_response = match reqwest::Client::new()
.post(&full_qualified_llm_provider_url)
.headers(request_headers)
.body(client_request_bytes_for_upstream)
.send()
.await
{
Ok(res) => res,
Err(err) => {
return Ok(BrightStaffError::InternalServerError(format!(
"Failed to send request: {}",
err
))
.into_response());
}
};
// copy over the headers and status code from the original response
let response_headers = llm_response.headers().clone();
let upstream_status = llm_response.status();
let mut response = Response::builder().status(upstream_status);
let headers = response.headers_mut().unwrap();
for (header_name, header_value) in response_headers.iter() {
headers.insert(header_name, header_value.clone());
}
// Build LLM span with actual status code using constants
let byte_stream = llm_response.bytes_stream();
// Create base processor for metrics and tracing
let base_processor = ObservableStreamProcessor::new(
operation_component::LLM,
span_name,
request_start_time,
messages_for_signals,
);
// === v1/responses state management: Wrap with ResponsesStateProcessor ===
// Only wrap if we need to manage state (client is ResponsesAPI AND upstream is NOT ResponsesAPI AND state_storage is configured)
let streaming_response = if let (true, false, Some(state_store)) = (
should_manage_state,
original_input_items.is_empty(),
state_storage,
) {
// Extract Content-Encoding header to handle decompression for state parsing
let content_encoding = response_headers
.get("content-encoding")
.and_then(|v| v.to_str().ok())
.map(|s| s.to_string());
// Wrap with state management processor to store state after response completes
let state_processor = ResponsesStateProcessor::new(
base_processor,
state_store,
original_input_items,
alias_resolved_model.clone(),
resolved_model.clone(),
is_streaming_request,
false, // Not OpenAI upstream since should_manage_state is true
content_encoding,
request_id,
);
create_streaming_response(byte_stream, state_processor, 16)
} else {
// Use base processor without state management
create_streaming_response(byte_stream, base_processor, 16)
};
match response.body(streaming_response.body) {
Ok(response) => Ok(response),
Err(err) => Ok(BrightStaffError::InternalServerError(format!(
"Failed to create response: {}",
err
))
.into_response()),
}
}
/// Resolves model aliases by looking up the requested model in the model_aliases map.
/// Returns the target model if an alias is found, otherwise returns the original model.
fn resolve_model_alias(
model_from_request: &str,
model_aliases: &Arc<Option<HashMap<String, ModelAlias>>>,
) -> String {
if let Some(aliases) = model_aliases.as_ref() {
if let Some(model_alias) = aliases.get(model_from_request) {
debug!(
"Model Alias: 'From {}' -> 'To {}'",
model_from_request, model_alias.target
);
return model_alias.target.clone();
}
}
model_from_request.to_string()
}
/// Calculates the upstream path for the provider based on the model name.
/// Looks up provider configuration, gets the ProviderId and base_url_path_prefix,
/// then uses target_endpoint_for_provider to calculate the correct upstream path.
async fn get_upstream_path(
llm_providers: &Arc<RwLock<LlmProviders>>,
model_name: &str,
request_path: &str,
resolved_model: &str,
is_streaming: bool,
) -> String {
let (provider_id, base_url_path_prefix) = get_provider_info(llm_providers, model_name).await;
// Calculate the upstream path using the proper API
let client_api = SupportedAPIsFromClient::from_endpoint(request_path)
.expect("Should have valid API endpoint");
client_api.target_endpoint_for_provider(
&provider_id,
request_path,
resolved_model,
is_streaming,
base_url_path_prefix.as_deref(),
)
}
/// Helper function to get provider info (ProviderId and base_url_path_prefix)
async fn get_provider_info(
llm_providers: &Arc<RwLock<LlmProviders>>,
model_name: &str,
) -> (hermesllm::ProviderId, Option<String>) {
let providers_lock = llm_providers.read().await;
// Try to find by model name or provider name using LlmProviders::get
// This handles both "gpt-4" and "openai/gpt-4" formats
if let Some(provider) = providers_lock.get(model_name) {
let provider_id = provider.provider_interface.to_provider_id();
let prefix = provider.base_url_path_prefix.clone();
return (provider_id, prefix);
}
// Fall back to default provider
if let Some(provider) = providers_lock.default() {
let provider_id = provider.provider_interface.to_provider_id();
let prefix = provider.base_url_path_prefix.clone();
(provider_id, prefix)
} else {
// Last resort: use OpenAI as hardcoded fallback
warn!("No default provider found, falling back to OpenAI");
(hermesllm::ProviderId::OpenAI, None)
}
}

780
crates/brightstaff/src/handlers/llm/mod.rs Normal file
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@ -0,0 +1,780 @@
use bytes::Bytes;
use common::configuration::{FilterPipeline, ModelAlias};
use common::consts::{ARCH_IS_STREAMING_HEADER, ARCH_PROVIDER_HINT_HEADER};
use common::llm_providers::LlmProviders;
use hermesllm::apis::openai::Message;
use hermesllm::apis::openai_responses::InputParam;
use hermesllm::clients::{SupportedAPIsFromClient, SupportedUpstreamAPIs};
use hermesllm::{ProviderRequest, ProviderRequestType};
use http_body_util::combinators::BoxBody;
use http_body_util::BodyExt;
use hyper::header::{self};
use hyper::{Request, Response, StatusCode};
use opentelemetry::global;
use opentelemetry::trace::get_active_span;
use opentelemetry_http::HeaderInjector;
use std::collections::HashMap;
use std::sync::Arc;
use tokio::sync::RwLock;
use tracing::{debug, info, info_span, warn, Instrument};
pub(crate) mod model_selection;
use crate::app_state::AppState;
use crate::handlers::agents::pipeline::PipelineProcessor;
use crate::handlers::extract_or_generate_traceparent;
use crate::handlers::extract_request_id;
use crate::handlers::full;
use crate::state::response_state_processor::ResponsesStateProcessor;
use crate::state::{
extract_input_items, retrieve_and_combine_input, StateStorage, StateStorageError,
};
use crate::streaming::{
create_streaming_response, create_streaming_response_with_output_filter, truncate_message,
ObservableStreamProcessor, StreamProcessor,
};
use crate::tracing::{
collect_custom_trace_attributes, llm as tracing_llm, operation_component, set_service_name,
};
use model_selection::router_chat_get_upstream_model;
pub async fn llm_chat(
request: Request<hyper::body::Incoming>,
state: Arc<AppState>,
) -> Result<Response<BoxBody<Bytes, hyper::Error>>, hyper::Error> {
let request_path = request.uri().path().to_string();
let request_headers = request.headers().clone();
let request_id = extract_request_id(&request);
let custom_attrs =
collect_custom_trace_attributes(&request_headers, state.span_attributes.as_ref());
// Create a span with request_id that will be included in all log lines
let request_span = info_span!(
"llm",
component = "llm",
request_id = %request_id,
http.method = %request.method(),
http.path = %request_path,
llm.model = tracing::field::Empty,
llm.tools = tracing::field::Empty,
llm.user_message_preview = tracing::field::Empty,
llm.temperature = tracing::field::Empty,
);
// Execute the rest of the handler inside the span
llm_chat_inner(
request,
state,
custom_attrs,
request_id,
request_path,
request_headers,
)
.instrument(request_span)
.await
}
async fn llm_chat_inner(
request: Request<hyper::body::Incoming>,
state: Arc<AppState>,
custom_attrs: HashMap<String, String>,
request_id: String,
request_path: String,
mut request_headers: hyper::HeaderMap,
) -> Result<Response<BoxBody<Bytes, hyper::Error>>, hyper::Error> {
// Set service name for LLM operations
set_service_name(operation_component::LLM);
get_active_span(|span| {
for (key, value) in &custom_attrs {
span.set_attribute(opentelemetry::KeyValue::new(key.clone(), value.clone()));
}
});
let traceparent = extract_or_generate_traceparent(&request_headers);
let full_qualified_llm_provider_url = format!("{}{}", state.llm_provider_url, request_path);
// --- Phase 1: Parse and validate the incoming request ---
let parsed = match parse_and_validate_request(
request,
&request_path,
&state.model_aliases,
&state.llm_providers,
)
.await
{
Ok(p) => p,
Err(response) => return Ok(response),
};
let PreparedRequest {
mut client_request,
chat_request_bytes,
model_from_request,
alias_resolved_model,
model_name_only,
is_streaming_request,
is_responses_api_client,
messages_for_signals,
temperature,
tool_names,
user_message_preview,
inline_routing_policy,
client_api,
provider_id,
} = parsed;
// Record LLM-specific span attributes
let span = tracing::Span::current();
if let Some(temp) = temperature {
span.record(tracing_llm::TEMPERATURE, tracing::field::display(temp));
}
if let Some(tools) = &tool_names {
let formatted_tools = tools
.iter()
.map(|name| format!("{}(...)", name))
.collect::<Vec<_>>()
.join("\n");
span.record(tracing_llm::TOOLS, formatted_tools.as_str());
}
if let Some(preview) = &user_message_preview {
span.record(tracing_llm::USER_MESSAGE_PREVIEW, preview.as_str());
}
// --- Phase 1b: Input filter processing for model listener ---
if let Some(ref input_chain) = state.filter_pipeline.input {
if !input_chain.is_empty() {
debug!(input_filters = ?input_chain.filter_ids, "processing model listener input filters");
let chain = input_chain.to_agent_filter_chain("model_listener");
let mut pipeline_processor = PipelineProcessor::default();
match pipeline_processor
.process_raw_filter_chain(
&chat_request_bytes,
&chain,
&input_chain.agents,
&request_headers,
&request_path,
)
.await
{
Ok(filtered_bytes) => {
let api_type = SupportedAPIsFromClient::from_endpoint(request_path.as_str())
.expect("endpoint validated in parse_and_validate_request");
match ProviderRequestType::try_from((&filtered_bytes[..], &api_type)) {
Ok(updated_request) => {
client_request = updated_request;
info!("input filter chain processed successfully");
}
Err(parse_err) => {
warn!(error = %parse_err, "input filter returned invalid request JSON");
return Ok(common::errors::BrightStaffError::InvalidRequest(format!(
"Input filter returned invalid request: {}",
parse_err
))
.into_response());
}
}
}
Err(super::agents::pipeline::PipelineError::ClientError {
agent,
status,
body,
}) => {
warn!(agent = %agent, status = %status, body = %body, "client error from filter chain");
let error_json = serde_json::json!({
"error": "FilterChainError",
"agent": agent,
"status": status,
"agent_response": body
});
let mut error_response = Response::new(full(error_json.to_string()));
*error_response.status_mut() =
StatusCode::from_u16(status).unwrap_or(StatusCode::BAD_REQUEST);
error_response.headers_mut().insert(
hyper::header::CONTENT_TYPE,
hyper::header::HeaderValue::from_static("application/json"),
);
return Ok(error_response);
}
Err(err) => {
warn!(error = %err, "filter chain processing failed");
let mut internal_error =
Response::new(full(format!("Filter chain processing failed: {}", err)));
*internal_error.status_mut() = StatusCode::INTERNAL_SERVER_ERROR;
return Ok(internal_error);
}
}
}
}
// Normalize for upstream after input filters
if let Some(ref client_api_kind) = client_api {
let upstream_api =
provider_id.compatible_api_for_client(client_api_kind, is_streaming_request);
client_request.normalize_for_upstream(provider_id, &upstream_api);
}
// --- Phase 2: Resolve conversation state (v1/responses API) ---
let state_ctx = match resolve_conversation_state(
&mut client_request,
is_responses_api_client,
&state.state_storage,
&state.llm_providers,
&alias_resolved_model,
&request_path,
is_streaming_request,
)
.await
{
Ok(ctx) => ctx,
Err(response) => return Ok(response),
};
// Serialize request for upstream BEFORE router consumes it
let client_request_bytes_for_upstream: Bytes =
match ProviderRequestType::to_bytes(&client_request) {
Ok(bytes) => bytes.into(),
Err(err) => {
warn!(error = %err, "failed to serialize request for upstream");
let mut r = Response::new(full(format!("Failed to serialize request: {}", err)));
*r.status_mut() = StatusCode::INTERNAL_SERVER_ERROR;
return Ok(r);
}
};
// --- Phase 3: Route the request ---
let routing_span = info_span!(
"routing",
component = "routing",
http.method = "POST",
http.target = %request_path,
model.requested = %model_from_request,
model.alias_resolved = %alias_resolved_model,
route.selected_model = tracing::field::Empty,
routing.determination_ms = tracing::field::Empty,
);
let routing_result = match async {
set_service_name(operation_component::ROUTING);
router_chat_get_upstream_model(
Arc::clone(&state.router_service),
client_request,
&traceparent,
&request_path,
&request_id,
inline_routing_policy,
)
.await
}
.instrument(routing_span)
.await
{
Ok(result) => result,
Err(err) => {
let mut internal_error = Response::new(full(err.message));
*internal_error.status_mut() = err.status_code;
return Ok(internal_error);
}
};
// Determine final model (router returns "none" when it doesn't select a specific model)
let router_selected_model = routing_result.model_name;
let resolved_model = if router_selected_model != "none" {
router_selected_model
} else {
alias_resolved_model.clone()
};
tracing::Span::current().record(tracing_llm::MODEL_NAME, resolved_model.as_str());
// --- Phase 4: Forward to upstream and stream back ---
send_upstream(
&state.http_client,
&full_qualified_llm_provider_url,
&mut request_headers,
client_request_bytes_for_upstream,
&model_from_request,
&alias_resolved_model,
&resolved_model,
&model_name_only,
&request_path,
is_streaming_request,
messages_for_signals,
state_ctx,
state.state_storage.clone(),
request_id,
&state.filter_pipeline,
)
.await
}
// ---------------------------------------------------------------------------
// Phase 1 — Parse & validate the incoming request
// ---------------------------------------------------------------------------
/// All pre-validated request data extracted from the raw HTTP request.
struct PreparedRequest {
client_request: ProviderRequestType,
chat_request_bytes: Bytes,
model_from_request: String,
alias_resolved_model: String,
model_name_only: String,
is_streaming_request: bool,
is_responses_api_client: bool,
messages_for_signals: Option<Vec<Message>>,
temperature: Option<f32>,
tool_names: Option<Vec<String>>,
user_message_preview: Option<String>,
inline_routing_policy: Option<Vec<common::configuration::ModelUsagePreference>>,
client_api: Option<SupportedAPIsFromClient>,
provider_id: hermesllm::ProviderId,
}
/// Parse the body, resolve the model alias, and validate the model exists.
///
/// Returns `Err(Response)` for early-exit error responses (400 etc.).
async fn parse_and_validate_request(
request: Request<hyper::body::Incoming>,
request_path: &str,
model_aliases: &Option<HashMap<String, ModelAlias>>,
llm_providers: &Arc<RwLock<LlmProviders>>,
) -> Result<PreparedRequest, Response<BoxBody<Bytes, hyper::Error>>> {
let raw_bytes = request
.collect()
.await
.map_err(|_| {
let mut r = Response::new(full("Failed to read request body"));
*r.status_mut() = StatusCode::BAD_REQUEST;
r
})?
.to_bytes();
debug!(
body = %String::from_utf8_lossy(&raw_bytes),
"request body received"
);
// Extract routing_policy from request body if present
let (chat_request_bytes, inline_routing_policy) =
crate::handlers::routing_service::extract_routing_policy(&raw_bytes, false).map_err(
|err| {
warn!(error = %err, "failed to parse request JSON");
let mut r = Response::new(full(format!("Failed to parse request: {}", err)));
*r.status_mut() = StatusCode::BAD_REQUEST;
r
},
)?;
let api_type = SupportedAPIsFromClient::from_endpoint(request_path).ok_or_else(|| {
warn!(path = %request_path, "unsupported endpoint");
let mut r = Response::new(full(format!("Unsupported endpoint: {}", request_path)));
*r.status_mut() = StatusCode::BAD_REQUEST;
r
})?;
let mut client_request = ProviderRequestType::try_from((&chat_request_bytes[..], &api_type))
.map_err(|err| {
warn!(error = %err, "failed to parse request as ProviderRequestType");
let mut r = Response::new(full(format!("Failed to parse request: {}", err)));
*r.status_mut() = StatusCode::BAD_REQUEST;
r
})?;
let is_responses_api_client =
matches!(api_type, SupportedAPIsFromClient::OpenAIResponsesAPI(_));
let client_api = Some(api_type);
let model_from_request = client_request.model().to_string();
let temperature = client_request.get_temperature();
let is_streaming_request = client_request.is_streaming();
let alias_resolved_model = resolve_model_alias(&model_from_request, model_aliases);
let (provider_id, _) = get_provider_info(llm_providers, &alias_resolved_model).await;
// Validate model exists in configuration
if llm_providers
.read()
.await
.get(&alias_resolved_model)
.is_none()
{
let err_msg = format!(
"Model '{}' not found in configured providers",
alias_resolved_model
);
warn!(model = %alias_resolved_model, "model not found in configured providers");
let mut r = Response::new(full(err_msg));
*r.status_mut() = StatusCode::BAD_REQUEST;
return Err(r);
}
// Strip provider prefix for upstream (e.g. "openai/gpt-4" → "gpt-4")
let model_name_only = alias_resolved_model
.split_once('/')
.map(|(_, model)| model.to_string())
.unwrap_or_else(|| alias_resolved_model.clone());
// Extract span attributes and messages before mutating client_request
let tool_names = client_request.get_tool_names();
let user_message_preview = client_request
.get_recent_user_message()
.map(|msg| truncate_message(&msg, 50));
let messages_for_signals = Some(client_request.get_messages());
// Set the upstream model name and strip routing metadata
client_request.set_model(model_name_only.clone());
if client_request.remove_metadata_key("archgw_preference_config") {
debug!("removed archgw_preference_config from metadata");
}
if client_request.remove_metadata_key("plano_preference_config") {
debug!("removed plano_preference_config from metadata");
}
Ok(PreparedRequest {
client_request,
chat_request_bytes,
model_from_request,
alias_resolved_model,
model_name_only,
is_streaming_request,
is_responses_api_client,
messages_for_signals,
temperature,
tool_names,
user_message_preview,
inline_routing_policy,
client_api,
provider_id,
})
}
// ---------------------------------------------------------------------------
// Phase 2 — Resolve conversation state (v1/responses API)
// ---------------------------------------------------------------------------
/// Holds the state management context resolved from a v1/responses request.
struct ConversationStateContext {
should_manage_state: bool,
original_input_items: Vec<hermesllm::apis::openai_responses::InputItem>,
}
/// If the client uses the v1/responses API and the upstream provider doesn't
/// support it natively, we manage conversation state ourselves.
///
/// This resolves `previous_response_id`, merges conversation history, and
/// updates the request in place.
///
/// Returns `Err(Response)` for early-exit (e.g. 409 Conflict).
async fn resolve_conversation_state(
client_request: &mut ProviderRequestType,
is_responses_api_client: bool,
state_storage: &Option<Arc<dyn StateStorage>>,
llm_providers: &Arc<RwLock<LlmProviders>>,
alias_resolved_model: &str,
request_path: &str,
is_streaming_request: bool,
) -> Result<ConversationStateContext, Response<BoxBody<Bytes, hyper::Error>>> {
if !is_responses_api_client {
return Ok(ConversationStateContext {
should_manage_state: false,
original_input_items: Vec::new(),
});
}
let (responses_req, state_store) = match (client_request, state_storage) {
(ProviderRequestType::ResponsesAPIRequest(ref mut req), Some(store)) => (req, store),
_ => {
return Ok(ConversationStateContext {
should_manage_state: false,
original_input_items: Vec::new(),
});
}
};
let mut original_input_items = extract_input_items(&responses_req.input);
// Check whether the upstream supports v1/responses natively
let upstream_path = get_upstream_path(
llm_providers,
alias_resolved_model,
request_path,
alias_resolved_model,
is_streaming_request,
)
.await;
let upstream_api = SupportedUpstreamAPIs::from_endpoint(&upstream_path);
let should_manage_state = !matches!(
upstream_api,
Some(SupportedUpstreamAPIs::OpenAIResponsesAPI(_))
);
if !should_manage_state {
debug!("upstream supports ResponsesAPI natively");
return Ok(ConversationStateContext {
should_manage_state: false,
original_input_items,
});
}
// Retrieve and combine conversation history if previous_response_id exists
if let Some(ref prev_resp_id) = responses_req.previous_response_id {
match retrieve_and_combine_input(state_store.clone(), prev_resp_id, original_input_items)
.await
{
Ok(combined_input) => {
responses_req.input = InputParam::Items(combined_input.clone());
original_input_items = combined_input;
info!(
items = original_input_items.len(),
"updated request with conversation history"
);
}
Err(StateStorageError::NotFound(_)) => {
warn!(previous_response_id = %prev_resp_id, "previous response_id not found");
let err_msg = format!(
"Conversation state not found for previous_response_id: {}",
prev_resp_id
);
let mut r = Response::new(full(err_msg));
*r.status_mut() = StatusCode::CONFLICT;
return Err(r);
}
Err(e) => {
warn!(
previous_response_id = %prev_resp_id,
error = %e,
"failed to retrieve conversation state"
);
// Restore original_input_items since we passed ownership
original_input_items = extract_input_items(&responses_req.input);
}
}
}
Ok(ConversationStateContext {
should_manage_state,
original_input_items,
})
}
// ---------------------------------------------------------------------------
// Phase 4 — Forward to upstream and stream the response back
// ---------------------------------------------------------------------------
#[allow(clippy::too_many_arguments)]
async fn send_upstream(
http_client: &reqwest::Client,
upstream_url: &str,
request_headers: &mut hyper::HeaderMap,
body: bytes::Bytes,
model_from_request: &str,
alias_resolved_model: &str,
resolved_model: &str,
model_name_only: &str,
request_path: &str,
is_streaming_request: bool,
messages_for_signals: Option<Vec<Message>>,
state_ctx: ConversationStateContext,
state_storage: Option<Arc<dyn StateStorage>>,
request_id: String,
filter_pipeline: &Arc<FilterPipeline>,
) -> Result<Response<BoxBody<Bytes, hyper::Error>>, hyper::Error> {
let span_name = if model_from_request == resolved_model {
format!("POST {} {}", request_path, resolved_model)
} else {
format!(
"POST {} {} -> {}",
request_path, model_from_request, resolved_model
)
};
get_active_span(|span| {
span.update_name(span_name.clone());
});
debug!(
url = %upstream_url,
provider_hint = %resolved_model,
upstream_model = %model_name_only,
"Routing to upstream"
);
if let Ok(val) = header::HeaderValue::from_str(resolved_model) {
request_headers.insert(ARCH_PROVIDER_HINT_HEADER, val);
}
request_headers.insert(
header::HeaderName::from_static(ARCH_IS_STREAMING_HEADER),
header::HeaderValue::from_static(if is_streaming_request {
"true"
} else {
"false"
}),
);
request_headers.remove(header::CONTENT_LENGTH);
// Inject current span's trace context so upstream spans are children of plano(llm)
global::get_text_map_propagator(|propagator| {
let cx = tracing_opentelemetry::OpenTelemetrySpanExt::context(&tracing::Span::current());
propagator.inject_context(&cx, &mut HeaderInjector(request_headers));
});
let request_start_time = std::time::Instant::now();
let llm_response = match http_client
.post(upstream_url)
.headers(request_headers.clone())
.body(body)
.send()
.await
{
Ok(res) => res,
Err(err) => {
let err_msg = format!("Failed to send request: {}", err);
let mut internal_error = Response::new(full(err_msg));
*internal_error.status_mut() = StatusCode::INTERNAL_SERVER_ERROR;
return Ok(internal_error);
}
};
// Propagate upstream headers and status
let response_headers = llm_response.headers().clone();
let upstream_status = llm_response.status();
let mut response = Response::builder().status(upstream_status);
if let Some(headers) = response.headers_mut() {
for (name, value) in response_headers.iter() {
headers.insert(name, value.clone());
}
}
let byte_stream = llm_response.bytes_stream();
// Create base processor for metrics and tracing
let base_processor = ObservableStreamProcessor::new(
operation_component::LLM,
span_name,
request_start_time,
messages_for_signals,
);
let output_filter_request_headers = if filter_pipeline.has_output_filters() {
Some(request_headers.clone())
} else {
None
};
// Pick the right processor: state-aware if needed, otherwise base metrics-only.
let processor: Box<dyn StreamProcessor> = if let (true, false, Some(state_store)) = (
state_ctx.should_manage_state,
state_ctx.original_input_items.is_empty(),
state_storage,
) {
let content_encoding = response_headers
.get("content-encoding")
.and_then(|v| v.to_str().ok())
.map(|s| s.to_string());
Box::new(ResponsesStateProcessor::new(
base_processor,
state_store,
state_ctx.original_input_items,
alias_resolved_model.to_string(),
resolved_model.to_string(),
is_streaming_request,
false,
content_encoding,
request_id,
))
} else {
Box::new(base_processor)
};
let streaming_response = if let (Some(output_chain), Some(filter_headers)) = (
filter_pipeline.output.as_ref().filter(|c| !c.is_empty()),
output_filter_request_headers,
) {
create_streaming_response_with_output_filter(
byte_stream,
processor,
output_chain.clone(),
filter_headers,
request_path.to_string(),
)
} else {
create_streaming_response(byte_stream, processor)
};
match response.body(streaming_response.body) {
Ok(response) => Ok(response),
Err(err) => {
let err_msg = format!("Failed to create response: {}", err);
let mut internal_error = Response::new(full(err_msg));
*internal_error.status_mut() = StatusCode::INTERNAL_SERVER_ERROR;
Ok(internal_error)
}
}
}
// ---------------------------------------------------------------------------
// Helpers
// ---------------------------------------------------------------------------
/// Resolves model aliases by looking up the requested model in the model_aliases map.
/// Returns the target model if an alias is found, otherwise returns the original model.
fn resolve_model_alias(
model_from_request: &str,
model_aliases: &Option<HashMap<String, ModelAlias>>,
) -> String {
if let Some(aliases) = model_aliases.as_ref() {
if let Some(model_alias) = aliases.get(model_from_request) {
debug!(
"Model Alias: 'From {}' -> 'To {}'",
model_from_request, model_alias.target
);
return model_alias.target.clone();
}
}
model_from_request.to_string()
}
/// Calculates the upstream path for the provider based on the model name.
async fn get_upstream_path(
llm_providers: &Arc<RwLock<LlmProviders>>,
model_name: &str,
request_path: &str,
resolved_model: &str,
is_streaming: bool,
) -> String {
let (provider_id, base_url_path_prefix) = get_provider_info(llm_providers, model_name).await;
let Some(client_api) = SupportedAPIsFromClient::from_endpoint(request_path) else {
return request_path.to_string();
};
client_api.target_endpoint_for_provider(
&provider_id,
request_path,
resolved_model,
is_streaming,
base_url_path_prefix.as_deref(),
)
}
/// Helper to get provider info (ProviderId and base_url_path_prefix).
async fn get_provider_info(
llm_providers: &Arc<RwLock<LlmProviders>>,
model_name: &str,
) -> (hermesllm::ProviderId, Option<String>) {
let providers_lock = llm_providers.read().await;
if let Some(provider) = providers_lock.get(model_name) {
let provider_id = provider.provider_interface.to_provider_id();
let prefix = provider.base_url_path_prefix.clone();
return (provider_id, prefix);
}
if let Some(provider) = providers_lock.default() {
let provider_id = provider.provider_interface.to_provider_id();
let prefix = provider.base_url_path_prefix.clone();
(provider_id, prefix)
} else {
warn!("No default provider found, falling back to OpenAI");
(hermesllm::ProviderId::OpenAI, None)
}
}

14
crates/brightstaff/src/handlers/router_chat.rs → crates/brightstaff/src/handlers/llm/model_selection.rs
View file

@ -5,7 +5,8 @@ use hyper::StatusCode;
use std::sync::Arc;
use tracing::{debug, info, warn};
use crate::router::llm_router::RouterService;
use crate::router::llm::RouterService;
use crate::streaming::truncate_message;
use crate::tracing::routing;
pub struct RoutingResult {
@ -103,16 +104,7 @@ pub async fn router_chat_get_upstream_model(
.map_or("None".to_string(), |c| c.to_string().replace('\n', "\\n"))
});
const MAX_MESSAGE_LENGTH: usize = 50;
let latest_message_for_log = if latest_message_for_log.chars().count() > MAX_MESSAGE_LENGTH {
let truncated: String = latest_message_for_log
.chars()
.take(MAX_MESSAGE_LENGTH)
.collect();
format!("{}...", truncated)
} else {
latest_message_for_log
};
let latest_message_for_log = truncate_message(&latest_message_for_log, 50);
info!(
has_usage_preferences = usage_preferences.is_some(),

57
crates/brightstaff/src/handlers/mod.rs
View file

@ -1,14 +1,57 @@
pub mod agent_chat_completions;
pub mod agent_selector;
pub mod agents;
pub mod function_calling;
pub mod jsonrpc;
pub mod llm;
pub mod models;
pub mod pipeline_processor;
pub mod response_handler;
pub mod router_chat;
pub mod response;
pub mod routing_service;
pub mod utils;
#[cfg(test)]
mod integration_tests;
use bytes::Bytes;
use common::consts::TRACE_PARENT_HEADER;
use http_body_util::combinators::BoxBody;
use http_body_util::{BodyExt, Empty, Full};
use hyper::Request;
use tracing::warn;
/// Wrap a chunk into a `BoxBody` for hyper responses.
pub fn full<T: Into<Bytes>>(chunk: T) -> BoxBody<Bytes, hyper::Error> {
Full::new(chunk.into())
.map_err(|never| match never {})
.boxed()
}
/// An empty HTTP body (used for 404 / OPTIONS responses).
pub fn empty() -> BoxBody<Bytes, hyper::Error> {
Empty::<Bytes>::new()
.map_err(|never| match never {})
.boxed()
}
/// Extract request ID from incoming request headers, or generate a new UUID v4.
pub fn extract_request_id<T>(request: &Request<T>) -> String {
request
.headers()
.get(common::consts::REQUEST_ID_HEADER)
.and_then(|h| h.to_str().ok())
.map(|s| s.to_string())
.unwrap_or_else(|| uuid::Uuid::new_v4().to_string())
}
/// Extract or generate a W3C `traceparent` header value.
pub fn extract_or_generate_traceparent(headers: &hyper::HeaderMap) -> String {
headers
.get(TRACE_PARENT_HEADER)
.and_then(|h| h.to_str().ok())
.map(|s| s.to_string())
.unwrap_or_else(|| {
let trace_id = uuid::Uuid::new_v4().to_string().replace("-", "");
let tp = format!("00-{}-0000000000000000-01", trace_id);
warn!(
generated_traceparent = %tp,
"TRACE_PARENT header missing, generated new traceparent"
);
tp
})
}

37
crates/brightstaff/src/handlers/models.rs
View file

@ -1,10 +1,11 @@
use bytes::Bytes;
use common::llm_providers::LlmProviders;
use http_body_util::{combinators::BoxBody, BodyExt, Full};
use http_body_util::combinators::BoxBody;
use hyper::{Response, StatusCode};
use serde_json;
use std::sync::Arc;
use super::full;
pub async fn list_models(
llm_providers: Arc<tokio::sync::RwLock<LlmProviders>>,
) -> Response<BoxBody<Bytes, hyper::Error>> {
@ -12,27 +13,15 @@ pub async fn list_models(
let models = prov.to_models();
match serde_json::to_string(&models) {
Ok(json) => {
let body = Full::new(Bytes::from(json))
.map_err(|never| match never {})
.boxed();
Response::builder()
.status(StatusCode::OK)
.header("Content-Type", "application/json")
.body(body)
.unwrap()
}
Err(_) => {
let body = Full::new(Bytes::from_static(
b"{\"error\":\"Failed to serialize models\"}",
))
.map_err(|never| match never {})
.boxed();
Response::builder()
.status(StatusCode::INTERNAL_SERVER_ERROR)
.header("Content-Type", "application/json")
.body(body)
.unwrap()
}
Ok(json) => Response::builder()
.status(StatusCode::OK)
.header("Content-Type", "application/json")
.body(full(json))
.unwrap(),
Err(_) => Response::builder()
.status(StatusCode::INTERNAL_SERVER_ERROR)
.header("Content-Type", "application/json")
.body(full("{\"error\":\"Failed to serialize models\"}"))
.unwrap(),
}
}

53
crates/brightstaff/src/handlers/response_handler.rs → crates/brightstaff/src/handlers/response.rs
View file

@ -4,7 +4,7 @@ use hermesllm::apis::OpenAIApi;
use hermesllm::clients::{SupportedAPIsFromClient, SupportedUpstreamAPIs};
use hermesllm::SseEvent;
use http_body_util::combinators::BoxBody;
use http_body_util::{BodyExt, Full, StreamBody};
use http_body_util::StreamBody;
use hyper::body::Frame;
use hyper::Response;
use tokio::sync::mpsc;
@ -12,6 +12,8 @@ use tokio_stream::wrappers::ReceiverStream;
use tokio_stream::StreamExt;
use tracing::{info, warn, Instrument};
use super::full;
/// Service for handling HTTP responses and streaming
pub struct ResponseHandler;
@ -22,9 +24,40 @@ impl ResponseHandler {
/// Create a full response body from bytes
pub fn create_full_body<T: Into<Bytes>>(chunk: T) -> BoxBody<Bytes, hyper::Error> {
Full::new(chunk.into())
.map_err(|never| match never {})
.boxed()
full(chunk)
}
/// Create a JSON error response with BAD_REQUEST status
pub fn create_json_error_response(
json: &serde_json::Value,
) -> Response<BoxBody<Bytes, hyper::Error>> {
let body = Self::create_full_body(json.to_string());
let mut response = Response::new(body);
*response.status_mut() = hyper::StatusCode::BAD_REQUEST;
response.headers_mut().insert(
hyper::header::CONTENT_TYPE,
hyper::header::HeaderValue::from_static("application/json"),
);
response
}
/// Create a BAD_REQUEST error response with a message
pub fn create_bad_request(message: &str) -> Response<BoxBody<Bytes, hyper::Error>> {
let json = serde_json::json!({"error": message});
Self::create_json_error_response(&json)
}
/// Create an INTERNAL_SERVER_ERROR response with a message
pub fn create_internal_error(message: &str) -> Response<BoxBody<Bytes, hyper::Error>> {
let json = serde_json::json!({"error": message});
let body = Self::create_full_body(json.to_string());
let mut response = Response::new(body);
*response.status_mut() = hyper::StatusCode::INTERNAL_SERVER_ERROR;
response.headers_mut().insert(
hyper::header::CONTENT_TYPE,
hyper::header::HeaderValue::from_static("application/json"),
);
response
}
/// Create a streaming response from a reqwest response.
@ -112,7 +145,9 @@ impl ResponseHandler {
let upstream_api =
SupportedUpstreamAPIs::OpenAIChatCompletions(OpenAIApi::ChatCompletions);
let sse_iter = SseStreamIter::try_from(response_bytes.as_ref()).unwrap();
let sse_iter = SseStreamIter::try_from(response_bytes.as_ref()).map_err(|e| {
BrightStaffError::StreamError(format!("Failed to parse SSE stream: {}", e))
})?;
let mut accumulated_text = String::new();
for sse_event in sse_iter {
@ -122,7 +157,13 @@ impl ResponseHandler {
}
let transformed_event =
SseEvent::try_from((sse_event, &client_api, &upstream_api)).unwrap();
match SseEvent::try_from((sse_event, &client_api, &upstream_api)) {
Ok(event) => event,
Err(e) => {
warn!(error = ?e, "failed to transform SSE event, skipping");
continue;
}
};
// Try to get provider response and extract content delta
match transformed_event.provider_response() {

30
crates/brightstaff/src/handlers/routing_service.rs
View file

@ -1,6 +1,6 @@
use bytes::Bytes;
use common::configuration::{ModelUsagePreference, SpanAttributes};
use common::consts::{REQUEST_ID_HEADER, SESSION_ID_HEADER, TRACE_PARENT_HEADER};
use common::consts::{REQUEST_ID_HEADER, SESSION_ID_HEADER};
use common::errors::BrightStaffError;
use hermesllm::clients::SupportedAPIsFromClient;
use hermesllm::ProviderRequestType;
@ -10,8 +10,9 @@ use hyper::{Request, Response, StatusCode};
use std::sync::Arc;
use tracing::{debug, info, info_span, warn, Instrument};
use crate::handlers::router_chat::router_chat_get_upstream_model;
use crate::router::llm_router::RouterService;
use super::extract_or_generate_traceparent;
use crate::handlers::llm::model_selection::router_chat_get_upstream_model;
use crate::router::llm::RouterService;
use crate::tracing::{collect_custom_trace_attributes, operation_component, set_service_name};
const ROUTING_POLICY_SIZE_WARNING_BYTES: usize = 5120;
@ -75,7 +76,7 @@ pub async fn routing_decision(
request: Request<hyper::body::Incoming>,
router_service: Arc<RouterService>,
request_path: String,
span_attributes: Arc<Option<SpanAttributes>>,
span_attributes: &Option<SpanAttributes>,
) -> Result<Response<BoxBody<Bytes, hyper::Error>>, hyper::Error> {
let request_headers = request.headers().clone();
let request_id: String = request_headers
@ -89,8 +90,7 @@ pub async fn routing_decision(
.and_then(|h| h.to_str().ok())
.map(|s| s.to_string());
let custom_attrs =
collect_custom_trace_attributes(&request_headers, span_attributes.as_ref().as_ref());
let custom_attrs = collect_custom_trace_attributes(&request_headers, span_attributes.as_ref());
let request_span = info_span!(
"routing_decision",
@ -129,23 +129,7 @@ async fn routing_decision_inner(
}
});
// Extract or generate traceparent
let traceparent: String = match request_headers
.get(TRACE_PARENT_HEADER)
.and_then(|h| h.to_str().ok())
.map(|s| s.to_string())
{
Some(tp) => tp,
None => {
let trace_id = uuid::Uuid::new_v4().to_string().replace("-", "");
let generated_tp = format!("00-{}-0000000000000000-01", trace_id);
warn!(
generated_traceparent = %generated_tp,
"TRACE_PARENT header missing, generated new traceparent"
);
generated_tp
}
};
let traceparent = extract_or_generate_traceparent(&request_headers);
// Extract trace_id from traceparent (format: 00-{trace_id}-{span_id}-{flags})
let trace_id = traceparent

3
crates/brightstaff/src/lib.rs
View file

@ -1,6 +1,7 @@
pub mod app_state;
pub mod handlers;
pub mod router;
pub mod signals;
pub mod state;
pub mod streaming;
pub mod tracing;
pub mod utils;

558
crates/brightstaff/src/main.rs
View file

@ -1,119 +1,179 @@
use brightstaff::handlers::agent_chat_completions::agent_chat;
use brightstaff::app_state::AppState;
use brightstaff::handlers::agents::orchestrator::agent_chat;
use brightstaff::handlers::empty;
use brightstaff::handlers::function_calling::function_calling_chat_handler;
use brightstaff::handlers::llm::llm_chat;
use brightstaff::handlers::models::list_models;
use brightstaff::handlers::routing_service::routing_decision;
use brightstaff::router::llm_router::RouterService;
use brightstaff::router::plano_orchestrator::OrchestratorService;
use brightstaff::router::llm::RouterService;
use brightstaff::router::orchestrator::OrchestratorService;
use brightstaff::state::memory::MemoryConversationalStorage;
use brightstaff::state::postgresql::PostgreSQLConversationStorage;
use brightstaff::state::StateStorage;
use brightstaff::utils::tracing::init_tracer;
use brightstaff::tracing::init_tracer;
use bytes::Bytes;
use common::configuration::{Agent, Configuration};
use common::consts::{
CHAT_COMPLETIONS_PATH, MESSAGES_PATH, OPENAI_RESPONSES_API_PATH, PLANO_ORCHESTRATOR_MODEL_NAME,
use common::configuration::{
Agent, Configuration, FilterPipeline, ListenerType, ResolvedFilterChain,
};
use common::consts::{CHAT_COMPLETIONS_PATH, MESSAGES_PATH, OPENAI_RESPONSES_API_PATH};
use common::llm_providers::LlmProviders;
use http_body_util::{combinators::BoxBody, BodyExt, Empty};
use http_body_util::combinators::BoxBody;
use hyper::body::Incoming;
use hyper::header::HeaderValue;
use hyper::server::conn::http1;
use hyper::service::service_fn;
use hyper::{Method, Request, Response, StatusCode};
use hyper_util::rt::TokioIo;
use opentelemetry::global;
use opentelemetry::trace::FutureExt;
use opentelemetry::{global, Context};
use opentelemetry_http::HeaderExtractor;
use std::collections::HashMap;
use std::sync::Arc;
use std::{env, fs};
use tokio::net::TcpListener;
use tokio::sync::RwLock;
use tracing::{debug, info, warn};
pub mod router;
const BIND_ADDRESS: &str = "0.0.0.0:9091";
const DEFAULT_ROUTING_LLM_PROVIDER: &str = "arch-router";
const DEFAULT_ROUTING_MODEL_NAME: &str = "Arch-Router";
const DEFAULT_ORCHESTRATOR_LLM_PROVIDER: &str = "plano-orchestrator";
const DEFAULT_ORCHESTRATOR_MODEL_NAME: &str = "Plano-Orchestrator";
// Utility function to extract the context from the incoming request headers
fn extract_context_from_request(req: &Request<Incoming>) -> Context {
global::get_text_map_propagator(|propagator| {
propagator.extract(&HeaderExtractor(req.headers()))
})
/// CORS pre-flight response for the models endpoint.
fn cors_preflight() -> Result<Response<BoxBody<Bytes, hyper::Error>>, hyper::Error> {
let mut response = Response::new(empty());
*response.status_mut() = StatusCode::NO_CONTENT;
let h = response.headers_mut();
h.insert("Allow", HeaderValue::from_static("GET, OPTIONS"));
h.insert("Access-Control-Allow-Origin", HeaderValue::from_static("*"));
h.insert(
"Access-Control-Allow-Headers",
HeaderValue::from_static("Authorization, Content-Type"),
);
h.insert(
"Access-Control-Allow-Methods",
HeaderValue::from_static("GET, POST, OPTIONS"),
);
h.insert("Content-Type", HeaderValue::from_static("application/json"));
Ok(response)
}
fn empty() -> BoxBody<Bytes, hyper::Error> {
Empty::<Bytes>::new()
.map_err(|never| match never {})
.boxed()
}
// ---------------------------------------------------------------------------
// Configuration loading
// ---------------------------------------------------------------------------
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
let bind_address = env::var("BIND_ADDRESS").unwrap_or_else(|_| BIND_ADDRESS.to_string());
// loading plano_config.yaml file (before tracing init so we can read tracing config)
let plano_config_path = env::var("PLANO_CONFIG_PATH_RENDERED")
/// Load and parse the YAML configuration file.
///
/// The path is read from `PLANO_CONFIG_PATH_RENDERED` (env) or falls back to
/// `./plano_config_rendered.yaml`.
fn load_config() -> Result<Configuration, Box<dyn std::error::Error + Send + Sync>> {
let path = env::var("PLANO_CONFIG_PATH_RENDERED")
.unwrap_or_else(|_| "./plano_config_rendered.yaml".to_string());
eprintln!("loading plano_config.yaml from {}", plano_config_path);
eprintln!("loading plano_config.yaml from {}", path);
let config_contents =
fs::read_to_string(&plano_config_path).expect("Failed to read plano_config.yaml");
let contents = fs::read_to_string(&path).map_err(|e| format!("failed to read {path}: {e}"))?;
let config: Configuration =
serde_yaml::from_str(&config_contents).expect("Failed to parse plano_config.yaml");
serde_yaml::from_str(&contents).map_err(|e| format!("failed to parse {path}: {e}"))?;
// Initialize tracing using config.yaml tracing section
let _tracer_provider = init_tracer(config.tracing.as_ref());
info!(path = %plano_config_path, "loaded plano_config.yaml");
Ok(config)
}
let plano_config = Arc::new(config);
// ---------------------------------------------------------------------------
// Application state initialization
// ---------------------------------------------------------------------------
// combine agents and filters into a single list of agents
let all_agents: Vec<Agent> = plano_config
/// Build the shared [`AppState`] from a parsed [`Configuration`].
async fn init_app_state(
config: &Configuration,
) -> Result<AppState, Box<dyn std::error::Error + Send + Sync>> {
let llm_provider_url =
env::var("LLM_PROVIDER_ENDPOINT").unwrap_or_else(|_| "http://localhost:12001".to_string());
// Combine agents and filters into a single list
let all_agents: Vec<Agent> = config
.agents
.as_deref()
.unwrap_or_default()
.iter()
.chain(plano_config.filters.as_deref().unwrap_or_default())
.chain(config.filters.as_deref().unwrap_or_default())
.cloned()
.collect();
// Create expanded provider list for /v1/models endpoint
let llm_providers = LlmProviders::try_from(plano_config.model_providers.clone())
.expect("Failed to create LlmProviders");
let llm_providers = Arc::new(RwLock::new(llm_providers));
let combined_agents_filters_list = Arc::new(RwLock::new(Some(all_agents)));
let listeners = Arc::new(RwLock::new(plano_config.listeners.clone()));
let llm_provider_url =
env::var("LLM_PROVIDER_ENDPOINT").unwrap_or_else(|_| "http://localhost:12001".to_string());
let global_agent_map: HashMap<String, Agent> = all_agents
.iter()
.map(|a| (a.id.clone(), a.clone()))
.collect();
let listener = TcpListener::bind(bind_address).await?;
let routing_model_name: String = plano_config
.routing
.as_ref()
.and_then(|r| r.model.clone())
.unwrap_or_else(|| DEFAULT_ROUTING_MODEL_NAME.to_string());
let llm_providers = LlmProviders::try_from(config.model_providers.clone())
.map_err(|e| format!("failed to create LlmProviders: {e}"))?;
let routing_llm_provider = plano_config
.routing
.as_ref()
.and_then(|r| r.model_provider.clone())
let model_listener_count = config
.listeners
.iter()
.filter(|l| l.listener_type == ListenerType::Model)
.count();
if model_listener_count > 1 {
return Err(format!(
"only one model listener is allowed, found {}",
model_listener_count
)
.into());
}
let model_listener = config
.listeners
.iter()
.find(|l| l.listener_type == ListenerType::Model);
let resolve_chain = |filter_ids: Option<Vec<String>>| -> Option<ResolvedFilterChain> {
filter_ids.map(|ids| {
let agents = ids
.iter()
.filter_map(|id| {
global_agent_map
.get(id)
.map(|a: &Agent| (id.clone(), a.clone()))
})
.collect();
ResolvedFilterChain {
filter_ids: ids,
agents,
}
})
};
let filter_pipeline = Arc::new(FilterPipeline {
input: resolve_chain(model_listener.and_then(|l| l.input_filters.clone())),
output: resolve_chain(model_listener.and_then(|l| l.output_filters.clone())),
});
let overrides = config.overrides.clone().unwrap_or_default();
let routing_model_name: String = overrides
.llm_routing_model
.as_deref()
.map(|m| m.split_once('/').map(|(_, id)| id).unwrap_or(m))
.unwrap_or(DEFAULT_ROUTING_MODEL_NAME)
.to_string();
let routing_llm_provider = config
.model_providers
.iter()
.find(|p| p.model.as_deref() == Some(routing_model_name.as_str()))
.map(|p| p.name.clone())
.unwrap_or_else(|| DEFAULT_ROUTING_LLM_PROVIDER.to_string());
let session_ttl_seconds = plano_config
let session_ttl_seconds = config
.routing
.as_ref()
.and_then(|r| r.session_ttl_seconds);
let session_max_entries = plano_config
let session_max_entries = config
.routing
.as_ref()
.and_then(|r| r.session_max_entries);
let router_service: Arc<RouterService> = Arc::new(RouterService::new(
plano_config.model_providers.clone(),
let router_service = Arc::new(RouterService::new(
config.model_providers.clone(),
format!("{llm_provider_url}{CHAT_COMPLETIONS_PATH}"),
routing_model_name,
routing_llm_provider,
@ -133,202 +193,212 @@ async fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
});
}
let orchestrator_service: Arc<OrchestratorService> = Arc::new(OrchestratorService::new(
let orchestrator_model_name: String = overrides
.agent_orchestration_model
.as_deref()
.map(|m| m.split_once('/').map(|(_, id)| id).unwrap_or(m))
.unwrap_or(DEFAULT_ORCHESTRATOR_MODEL_NAME)
.to_string();
let orchestrator_llm_provider: String = config
.model_providers
.iter()
.find(|p| p.model.as_deref() == Some(orchestrator_model_name.as_str()))
.map(|p| p.name.clone())
.unwrap_or_else(|| DEFAULT_ORCHESTRATOR_LLM_PROVIDER.to_string());
let orchestrator_service = Arc::new(OrchestratorService::new(
format!("{llm_provider_url}{CHAT_COMPLETIONS_PATH}"),
PLANO_ORCHESTRATOR_MODEL_NAME.to_string(),
orchestrator_model_name,
orchestrator_llm_provider,
));
let model_aliases = Arc::new(plano_config.model_aliases.clone());
let span_attributes = Arc::new(
plano_config
.tracing
.as_ref()
.and_then(|tracing| tracing.span_attributes.clone()),
);
let state_storage = init_state_storage(config).await?;
// Initialize trace collector and start background flusher
// Tracing is enabled if the tracing config is present in plano_config.yaml
// Pass Some(true/false) to override, or None to use env var OTEL_TRACING_ENABLED
// OpenTelemetry automatic instrumentation is configured in utils/tracing.rs
let span_attributes = config
.tracing
.as_ref()
.and_then(|tracing| tracing.span_attributes.clone());
// Initialize conversation state storage for v1/responses
// Configurable via plano_config.yaml state_storage section
// If not configured, state management is disabled
// Environment variables are substituted by envsubst before config is read
let state_storage: Option<Arc<dyn StateStorage>> =
if let Some(storage_config) = &plano_config.state_storage {
let storage: Arc<dyn StateStorage> = match storage_config.storage_type {
common::configuration::StateStorageType::Memory => {
info!(
storage_type = "memory",
"initialized conversation state storage"
);
Arc::new(MemoryConversationalStorage::new())
}
common::configuration::StateStorageType::Postgres => {
let connection_string = storage_config
.connection_string
.as_ref()
.expect("connection_string is required for postgres state_storage");
Ok(AppState {
router_service,
orchestrator_service,
model_aliases: config.model_aliases.clone(),
llm_providers: Arc::new(RwLock::new(llm_providers)),
agents_list: Some(all_agents),
listeners: config.listeners.clone(),
state_storage,
llm_provider_url,
span_attributes,
http_client: reqwest::Client::new(),
filter_pipeline,
})
}
debug!(connection_string = %connection_string, "postgres connection");
info!(
storage_type = "postgres",
"initializing conversation state storage"
);
Arc::new(
PostgreSQLConversationStorage::new(connection_string.clone())
.await
.expect("Failed to initialize Postgres state storage"),
)
}
};
Some(storage)
} else {
info!("no state_storage configured, conversation state management disabled");
None
};
/// Initialize the conversation state storage backend (if configured).
async fn init_state_storage(
config: &Configuration,
) -> Result<Option<Arc<dyn StateStorage>>, Box<dyn std::error::Error + Send + Sync>> {
let Some(storage_config) = &config.state_storage else {
info!("no state_storage configured, conversation state management disabled");
return Ok(None);
};
loop {
let (stream, _) = listener.accept().await?;
let peer_addr = stream.peer_addr()?;
let io = TokioIo::new(stream);
let storage: Arc<dyn StateStorage> = match storage_config.storage_type {
common::configuration::StateStorageType::Memory => {
info!(
storage_type = "memory",
"initialized conversation state storage"
);
Arc::new(MemoryConversationalStorage::new())
}
common::configuration::StateStorageType::Postgres => {
let connection_string = storage_config
.connection_string
.as_ref()
.ok_or("connection_string is required for postgres state_storage")?;
let router_service: Arc<RouterService> = Arc::clone(&router_service);
let orchestrator_service: Arc<OrchestratorService> = Arc::clone(&orchestrator_service);
let model_aliases: Arc<
Option<std::collections::HashMap<String, common::configuration::ModelAlias>>,
> = Arc::clone(&model_aliases);
let llm_provider_url = llm_provider_url.clone();
debug!(connection_string = %connection_string, "postgres connection");
info!(
storage_type = "postgres",
"initializing conversation state storage"
);
let llm_providers = llm_providers.clone();
let agents_list = combined_agents_filters_list.clone();
let listeners = listeners.clone();
let span_attributes = span_attributes.clone();
let state_storage = state_storage.clone();
let service = service_fn(move |req| {
let router_service = Arc::clone(&router_service);
let orchestrator_service = Arc::clone(&orchestrator_service);
let parent_cx = extract_context_from_request(&req);
let llm_provider_url = llm_provider_url.clone();
let llm_providers = llm_providers.clone();
let model_aliases = Arc::clone(&model_aliases);
let agents_list = agents_list.clone();
let listeners = listeners.clone();
let span_attributes = span_attributes.clone();
let state_storage = state_storage.clone();
Arc::new(
PostgreSQLConversationStorage::new(connection_string.clone())
.await
.map_err(|e| format!("failed to initialize Postgres state storage: {e}"))?,
)
}
};
async move {
let path = req.uri().path().to_string();
// Check if path starts with /agents
if path.starts_with("/agents") {
// Check if it matches one of the agent API paths
let stripped_path = path.strip_prefix("/agents").unwrap();
if matches!(
stripped_path,
CHAT_COMPLETIONS_PATH | MESSAGES_PATH | OPENAI_RESPONSES_API_PATH
) {
let fully_qualified_url = format!("{}{}", llm_provider_url, stripped_path);
return agent_chat(
req,
orchestrator_service,
fully_qualified_url,
agents_list,
listeners,
span_attributes,
llm_providers,
)
.with_context(parent_cx)
.await;
}
}
if let Some(stripped_path) = path.strip_prefix("/routing") {
let stripped_path = stripped_path.to_string();
if matches!(
stripped_path.as_str(),
CHAT_COMPLETIONS_PATH | MESSAGES_PATH | OPENAI_RESPONSES_API_PATH
) {
return routing_decision(
req,
router_service,
stripped_path,
span_attributes,
)
.with_context(parent_cx)
.await;
}
}
match (req.method(), path.as_str()) {
(
&Method::POST,
CHAT_COMPLETIONS_PATH | MESSAGES_PATH | OPENAI_RESPONSES_API_PATH,
) => {
let fully_qualified_url = format!("{}{}", llm_provider_url, path);
llm_chat(
req,
router_service,
fully_qualified_url,
model_aliases,
llm_providers,
span_attributes,
state_storage,
)
.with_context(parent_cx)
.await
}
(&Method::POST, "/function_calling") => {
let fully_qualified_url =
format!("{}{}", llm_provider_url, "/v1/chat/completions");
function_calling_chat_handler(req, fully_qualified_url)
.with_context(parent_cx)
.await
}
(&Method::GET, "/v1/models" | "/agents/v1/models") => {
Ok(list_models(llm_providers).await)
}
// hack for now to get openw-web-ui to work
(&Method::OPTIONS, "/v1/models" | "/agents/v1/models") => {
let mut response = Response::new(empty());
*response.status_mut() = StatusCode::NO_CONTENT;
response
.headers_mut()
.insert("Allow", "GET, OPTIONS".parse().unwrap());
response
.headers_mut()
.insert("Access-Control-Allow-Origin", "*".parse().unwrap());
response.headers_mut().insert(
"Access-Control-Allow-Headers",
"Authorization, Content-Type".parse().unwrap(),
);
response.headers_mut().insert(
"Access-Control-Allow-Methods",
"GET, POST, OPTIONS".parse().unwrap(),
);
response
.headers_mut()
.insert("Content-Type", "application/json".parse().unwrap());
Ok(Some(storage))
}
Ok(response)
}
_ => {
debug!(method = %req.method(), path = %path, "no route found");
let mut not_found = Response::new(empty());
*not_found.status_mut() = StatusCode::NOT_FOUND;
Ok(not_found)
}
}
}
});
// ---------------------------------------------------------------------------
// Request routing
// ---------------------------------------------------------------------------
tokio::task::spawn(async move {
debug!(peer = ?peer_addr, "accepted connection");
if let Err(err) = http1::Builder::new()
// .serve_connection(io, service_fn(chat_completion))
.serve_connection(io, service)
/// Route an incoming HTTP request to the appropriate handler.
async fn route(
req: Request<Incoming>,
state: Arc<AppState>,
) -> Result<Response<BoxBody<Bytes, hyper::Error>>, hyper::Error> {
let parent_cx = global::get_text_map_propagator(|p| p.extract(&HeaderExtractor(req.headers())));
let path = req.uri().path().to_string();
// --- Agent routes (/agents/...) ---
if let Some(stripped) = path.strip_prefix("/agents") {
if matches!(
stripped,
CHAT_COMPLETIONS_PATH | MESSAGES_PATH | OPENAI_RESPONSES_API_PATH
) {
return agent_chat(req, Arc::clone(&state))
.with_context(parent_cx)
.await;
}
}
// --- Routing decision routes (/routing/...) ---
if let Some(stripped) = path.strip_prefix("/routing") {
let stripped = stripped.to_string();
if matches!(
stripped.as_str(),
CHAT_COMPLETIONS_PATH | MESSAGES_PATH | OPENAI_RESPONSES_API_PATH
) {
return routing_decision(
req,
Arc::clone(&state.router_service),
stripped,
&state.span_attributes,
)
.with_context(parent_cx)
.await;
}
}
// --- Standard routes ---
match (req.method(), path.as_str()) {
(&Method::POST, CHAT_COMPLETIONS_PATH | MESSAGES_PATH | OPENAI_RESPONSES_API_PATH) => {
llm_chat(req, Arc::clone(&state))
.with_context(parent_cx)
.await
{
warn!(error = ?err, "error serving connection");
}
});
}
(&Method::POST, "/function_calling") => {
let url = format!("{}/v1/chat/completions", state.llm_provider_url);
function_calling_chat_handler(req, url)
.with_context(parent_cx)
.await
}
(&Method::GET, "/v1/models" | "/agents/v1/models") => {
Ok(list_models(Arc::clone(&state.llm_providers)).await)
}
(&Method::OPTIONS, "/v1/models" | "/agents/v1/models") => cors_preflight(),
_ => {
debug!(method = %req.method(), path = %path, "no route found");
let mut not_found = Response::new(empty());
*not_found.status_mut() = StatusCode::NOT_FOUND;
Ok(not_found)
}
}
}
// ---------------------------------------------------------------------------
// Server loop
// ---------------------------------------------------------------------------
/// Accept connections and spawn a task per connection.
///
/// Listens for `SIGINT` / `ctrl-c` and shuts down gracefully, allowing
/// in-flight connections to finish.
async fn run_server(state: Arc<AppState>) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
let bind_address = env::var("BIND_ADDRESS").unwrap_or_else(|_| BIND_ADDRESS.to_string());
let listener = TcpListener::bind(&bind_address).await?;
info!(address = %bind_address, "server listening");
let shutdown = tokio::signal::ctrl_c();
tokio::pin!(shutdown);
loop {
tokio::select! {
result = listener.accept() => {
let (stream, _) = result?;
let peer_addr = stream.peer_addr()?;
let io = TokioIo::new(stream);
let state = Arc::clone(&state);
tokio::task::spawn(async move {
debug!(peer = ?peer_addr, "accepted connection");
let service = service_fn(move |req| {
let state = Arc::clone(&state);
async move { route(req, state).await }
});
if let Err(err) = http1::Builder::new().serve_connection(io, service).await {
warn!(error = ?err, "error serving connection");
}
});
}
_ = &mut shutdown => {
info!("received shutdown signal, stopping server");
break;
}
}
}
Ok(())
}
// ---------------------------------------------------------------------------
// Entry point
// ---------------------------------------------------------------------------
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
let config = load_config()?;
let _tracer_provider = init_tracer(config.tracing.as_ref());
info!("loaded plano_config.yaml");
let state = Arc::new(init_app_state(&config).await?);
run_server(state).await
}

48
crates/brightstaff/src/router/http.rs Normal file
View file

@ -0,0 +1,48 @@
use hermesllm::apis::openai::ChatCompletionsResponse;
use hyper::header;
use thiserror::Error;
use tracing::warn;
#[derive(Debug, Error)]
pub enum HttpError {
#[error("Failed to send request: {0}")]
Request(#[from] reqwest::Error),
#[error("Failed to parse JSON response: {0}")]
Json(serde_json::Error, String),
}
/// Sends a POST request to the given URL and extracts the text content
/// from the first choice of the `ChatCompletionsResponse`.
///
/// Returns `Some((content, elapsed))` on success, or `None` if the response
/// had no choices or the first choice had no content.
pub async fn post_and_extract_content(
client: &reqwest::Client,
url: &str,
headers: header::HeaderMap,
body: String,
) -> Result<Option<(String, std::time::Duration)>, HttpError> {
let start_time = std::time::Instant::now();
let res = client.post(url).headers(headers).body(body).send().await?;
let body = res.text().await?;
let elapsed = start_time.elapsed();
let response: ChatCompletionsResponse = serde_json::from_str(&body).map_err(|err| {
warn!(error = %err, body = %body, "failed to parse json response");
HttpError::Json(err, format!("Failed to parse JSON: {}", body))
})?;
if response.choices.is_empty() {
warn!(body = %body, "no choices in response");
return Ok(None);
}
Ok(response.choices[0]
.message
.content
.as_ref()
.map(|c| (c.clone(), elapsed)))
}

128
crates/brightstaff/src/router/llm_router.rs → crates/brightstaff/src/router/llm.rs
View file

@ -4,16 +4,17 @@ use common::{
configuration::{LlmProvider, ModelUsagePreference, RoutingPreference},
consts::{ARCH_PROVIDER_HINT_HEADER, REQUEST_ID_HEADER, TRACE_PARENT_HEADER},
};
use hermesllm::apis::openai::{ChatCompletionsResponse, Message};
use hermesllm::apis::openai::Message;
use hyper::header;
use thiserror::Error;
use tokio::sync::RwLock;
use tracing::{debug, info, warn};
use crate::router::router_model_v1::{self};
use tracing::{debug, info};
use super::http::{self, post_and_extract_content};
use super::router_model::RouterModel;
use crate::router::router_model_v1;
const DEFAULT_SESSION_TTL_SECONDS: u64 = 600;
const DEFAULT_SESSION_MAX_ENTRIES: usize = 10_000;
@ -28,7 +29,6 @@ pub struct RouterService {
router_url: String,
client: reqwest::Client,
router_model: Arc<dyn RouterModel>,
#[allow(dead_code)]
routing_provider_name: String,
llm_usage_defined: bool,
session_cache: RwLock<HashMap<String, CachedRoute>>,
@ -38,11 +38,8 @@ pub struct RouterService {
#[derive(Debug, Error)]
pub enum RoutingError {
#[error("Failed to send request: {0}")]
RequestError(#[from] reqwest::Error),
#[error("Failed to parse JSON: {0}, JSON: {1}")]
JsonError(serde_json::Error, String),
#[error(transparent)]
Http(#[from] http::HttpError),
#[error("Router model error: {0}")]
RouterModelError(#[from] super::router_model::RoutingModelError),
@ -119,7 +116,6 @@ impl RouterService {
) {
let mut cache = self.session_cache.write().await;
if cache.len() >= self.session_max_entries && !cache.contains_key(&session_id) {
// Evict the oldest entry
if let Some(oldest_key) = cache
.iter()
.min_by_key(|(_, v)| v.cached_at)
@ -164,7 +160,9 @@ impl RouterService {
return Ok(None);
}
if (usage_preferences.is_none() || usage_preferences.as_ref().unwrap().len() < 2)
if usage_preferences
.as_ref()
.is_none_or(|prefs| prefs.len() < 2)
&& !self.llm_usage_defined
{
return Ok(None);
@ -180,88 +178,50 @@ impl RouterService {
"sending request to arch-router"
);
debug!(
body = %serde_json::to_string(&router_request).unwrap(),
"arch router request"
);
let body = serde_json::to_string(&router_request)
.map_err(super::router_model::RoutingModelError::from)?;
debug!(body = %body, "arch router request");
let mut llm_route_request_headers = header::HeaderMap::new();
llm_route_request_headers.insert(
let mut headers = header::HeaderMap::new();
headers.insert(
header::CONTENT_TYPE,
header::HeaderValue::from_static("application/json"),
);
llm_route_request_headers.insert(
header::HeaderName::from_static(ARCH_PROVIDER_HINT_HEADER),
header::HeaderValue::from_str(&self.routing_provider_name).unwrap(),
);
llm_route_request_headers.insert(
header::HeaderName::from_static(TRACE_PARENT_HEADER),
header::HeaderValue::from_str(traceparent).unwrap(),
);
llm_route_request_headers.insert(
header::HeaderName::from_static(REQUEST_ID_HEADER),
header::HeaderValue::from_str(request_id).unwrap(),
);
llm_route_request_headers.insert(
if let Ok(val) = header::HeaderValue::from_str(&self.routing_provider_name) {
headers.insert(
header::HeaderName::from_static(ARCH_PROVIDER_HINT_HEADER),
val,
);
}
if let Ok(val) = header::HeaderValue::from_str(traceparent) {
headers.insert(header::HeaderName::from_static(TRACE_PARENT_HEADER), val);
}
if let Ok(val) = header::HeaderValue::from_str(request_id) {
headers.insert(header::HeaderName::from_static(REQUEST_ID_HEADER), val);
}
headers.insert(
header::HeaderName::from_static("model"),
header::HeaderValue::from_static("arch-router"),
);
let start_time = std::time::Instant::now();
let res = self
.client
.post(&self.router_url)
.headers(llm_route_request_headers)
.body(serde_json::to_string(&router_request).unwrap())
.send()
.await?;
let body = res.text().await?;
let router_response_time = start_time.elapsed();
let chat_completion_response: ChatCompletionsResponse = match serde_json::from_str(&body) {
Ok(response) => response,
Err(err) => {
warn!(
error = %err,
body = %serde_json::to_string(&body).unwrap(),
"failed to parse json response"
);
return Err(RoutingError::JsonError(
err,
format!("Failed to parse JSON: {}", body),
));
}
let Some((content, elapsed)) =
post_and_extract_content(&self.client, &self.router_url, headers, body).await?
else {
return Ok(None);
};
if chat_completion_response.choices.is_empty() {
warn!(body = %body, "no choices in router response");
return Ok(None);
}
let parsed = self
.router_model
.parse_response(&content, &usage_preferences)?;
if let Some(content) = &chat_completion_response.choices[0].message.content {
let parsed_response = self
.router_model
.parse_response(content, &usage_preferences)?;
info!(
content = %content.replace("\n", "\\n"),
selected_model = ?parsed_response,
response_time_ms = router_response_time.as_millis(),
"arch-router determined route"
);
info!(
content = %content.replace("\n", "\\n"),
selected_model = ?parsed,
response_time_ms = elapsed.as_millis(),
"arch-router determined route"
);
if let Some(ref parsed_response) = parsed_response {
return Ok(Some(parsed_response.clone()));
}
Ok(None)
} else {
Ok(None)
}
Ok(parsed)
}
}
@ -306,7 +266,6 @@ mod tests {
let svc = make_router_service(0, 100);
svc.cache_route("s1".to_string(), "gpt-4o".to_string(), None)
.await;
// TTL is 0 seconds, so the entry should be expired immediately
assert!(svc.get_cached_route("s1").await.is_none());
}
@ -329,12 +288,10 @@ mod tests {
let svc = make_router_service(600, 2);
svc.cache_route("s1".to_string(), "model-a".to_string(), None)
.await;
// Small delay so s2 has a later cached_at
tokio::time::sleep(Duration::from_millis(10)).await;
svc.cache_route("s2".to_string(), "model-b".to_string(), None)
.await;
// Cache is full (2 entries). Adding s3 should evict s1 (oldest).
svc.cache_route("s3".to_string(), "model-c".to_string(), None)
.await;
@ -353,7 +310,6 @@ mod tests {
svc.cache_route("s2".to_string(), "model-b".to_string(), None)
.await;
// Updating s1 should not trigger eviction since key already exists
svc.cache_route(
"s1".to_string(),
"model-a-updated".to_string(),

5
crates/brightstaff/src/router/mod.rs
View file

@ -1,6 +1,7 @@
pub mod llm_router;
pub(crate) mod http;
pub mod llm;
pub mod orchestrator;
pub mod orchestrator_model;
pub mod orchestrator_model_v1;
pub mod plano_orchestrator;
pub mod router_model;
pub mod router_model_v1;

138
crates/brightstaff/src/router/orchestrator.rs Normal file
View file

@ -0,0 +1,138 @@
use std::{collections::HashMap, sync::Arc};
use common::{
configuration::{AgentUsagePreference, OrchestrationPreference},
consts::{ARCH_PROVIDER_HINT_HEADER, REQUEST_ID_HEADER},
};
use hermesllm::apis::openai::Message;
use hyper::header;
use opentelemetry::global;
use opentelemetry_http::HeaderInjector;
use thiserror::Error;
use tracing::{debug, info};
use super::http::{self, post_and_extract_content};
use super::orchestrator_model::OrchestratorModel;
use crate::router::orchestrator_model_v1;
pub struct OrchestratorService {
orchestrator_url: String,
client: reqwest::Client,
orchestrator_model: Arc<dyn OrchestratorModel>,
orchestrator_provider_name: String,
}
#[derive(Debug, Error)]
pub enum OrchestrationError {
#[error(transparent)]
Http(#[from] http::HttpError),
#[error("Orchestrator model error: {0}")]
OrchestratorModelError(#[from] super::orchestrator_model::OrchestratorModelError),
}
pub type Result<T> = std::result::Result<T, OrchestrationError>;
impl OrchestratorService {
pub fn new(
orchestrator_url: String,
orchestration_model_name: String,
orchestrator_provider_name: String,
) -> Self {
let agent_orchestrations: HashMap<String, Vec<OrchestrationPreference>> = HashMap::new();
let orchestrator_model = Arc::new(orchestrator_model_v1::OrchestratorModelV1::new(
agent_orchestrations,
orchestration_model_name.clone(),
orchestrator_model_v1::MAX_TOKEN_LEN,
));
OrchestratorService {
orchestrator_url,
client: reqwest::Client::new(),
orchestrator_model,
orchestrator_provider_name,
}
}
pub async fn determine_orchestration(
&self,
messages: &[Message],
usage_preferences: Option<Vec<AgentUsagePreference>>,
request_id: Option<String>,
) -> Result<Option<Vec<(String, String)>>> {
if messages.is_empty() {
return Ok(None);
}
if usage_preferences
.as_ref()
.is_none_or(|prefs| prefs.is_empty())
{
return Ok(None);
}
let orchestrator_request = self
.orchestrator_model
.generate_request(messages, &usage_preferences);
debug!(
model = %self.orchestrator_model.get_model_name(),
endpoint = %self.orchestrator_url,
"sending request to arch-orchestrator"
);
let body = serde_json::to_string(&orchestrator_request)
.map_err(super::orchestrator_model::OrchestratorModelError::from)?;
debug!(body = %body, "arch orchestrator request");
let mut headers = header::HeaderMap::new();
headers.insert(
header::CONTENT_TYPE,
header::HeaderValue::from_static("application/json"),
);
headers.insert(
header::HeaderName::from_static(ARCH_PROVIDER_HINT_HEADER),
header::HeaderValue::from_str(&self.orchestrator_provider_name)
.unwrap_or_else(|_| header::HeaderValue::from_static("plano-orchestrator")),
);
// Inject OpenTelemetry trace context from current span
global::get_text_map_propagator(|propagator| {
let cx =
tracing_opentelemetry::OpenTelemetrySpanExt::context(&tracing::Span::current());
propagator.inject_context(&cx, &mut HeaderInjector(&mut headers));
});
if let Some(ref request_id) = request_id {
if let Ok(val) = header::HeaderValue::from_str(request_id) {
headers.insert(header::HeaderName::from_static(REQUEST_ID_HEADER), val);
}
}
headers.insert(
header::HeaderName::from_static("model"),
header::HeaderValue::from_static("plano-orchestrator"),
);
let Some((content, elapsed)) =
post_and_extract_content(&self.client, &self.orchestrator_url, headers, body).await?
else {
return Ok(None);
};
let parsed = self
.orchestrator_model
.parse_response(&content, &usage_preferences)?;
info!(
content = %content.replace("\n", "\\n"),
selected_routes = ?parsed,
response_time_ms = elapsed.as_millis(),
"arch-orchestrator determined routes"
);
Ok(parsed)
}
}

168
crates/brightstaff/src/router/plano_orchestrator.rs
View file

@ -1,168 +0,0 @@
use std::{collections::HashMap, sync::Arc};
use common::{
configuration::{AgentUsagePreference, OrchestrationPreference},
consts::{ARCH_PROVIDER_HINT_HEADER, PLANO_ORCHESTRATOR_MODEL_NAME, REQUEST_ID_HEADER},
};
use hermesllm::apis::openai::{ChatCompletionsResponse, Message};
use hyper::header;
use opentelemetry::global;
use opentelemetry_http::HeaderInjector;
use thiserror::Error;
use tracing::{debug, info, warn};
use crate::router::orchestrator_model_v1::{self};
use super::orchestrator_model::OrchestratorModel;
pub struct OrchestratorService {
orchestrator_url: String,
client: reqwest::Client,
orchestrator_model: Arc<dyn OrchestratorModel>,
}
#[derive(Debug, Error)]
pub enum OrchestrationError {
#[error("Failed to send request: {0}")]
RequestError(#[from] reqwest::Error),
#[error("Failed to parse JSON: {0}, JSON: {1}")]
JsonError(serde_json::Error, String),
#[error("Orchestrator model error: {0}")]
OrchestratorModelError(#[from] super::orchestrator_model::OrchestratorModelError),
}
pub type Result<T> = std::result::Result<T, OrchestrationError>;
impl OrchestratorService {
pub fn new(orchestrator_url: String, orchestration_model_name: String) -> Self {
// Empty agent orchestrations - will be provided via usage_preferences in requests
let agent_orchestrations: HashMap<String, Vec<OrchestrationPreference>> = HashMap::new();
let orchestrator_model = Arc::new(orchestrator_model_v1::OrchestratorModelV1::new(
agent_orchestrations,
orchestration_model_name.clone(),
orchestrator_model_v1::MAX_TOKEN_LEN,
));
OrchestratorService {
orchestrator_url,
client: reqwest::Client::new(),
orchestrator_model,
}
}
pub async fn determine_orchestration(
&self,
messages: &[Message],
usage_preferences: Option<Vec<AgentUsagePreference>>,
request_id: Option<String>,
) -> Result<Option<Vec<(String, String)>>> {
if messages.is_empty() {
return Ok(None);
}
// Require usage_preferences to be provided
if usage_preferences.is_none() || usage_preferences.as_ref().unwrap().is_empty() {
return Ok(None);
}
let orchestrator_request = self
.orchestrator_model
.generate_request(messages, &usage_preferences);
debug!(
model = %self.orchestrator_model.get_model_name(),
endpoint = %self.orchestrator_url,
"sending request to arch-orchestrator"
);
debug!(
body = %serde_json::to_string(&orchestrator_request).unwrap(),
"arch orchestrator request"
);
let mut orchestration_request_headers = header::HeaderMap::new();
orchestration_request_headers.insert(
header::CONTENT_TYPE,
header::HeaderValue::from_static("application/json"),
);
orchestration_request_headers.insert(
header::HeaderName::from_static(ARCH_PROVIDER_HINT_HEADER),
header::HeaderValue::from_str(PLANO_ORCHESTRATOR_MODEL_NAME).unwrap(),
);
// Inject OpenTelemetry trace context from current span
global::get_text_map_propagator(|propagator| {
let cx =
tracing_opentelemetry::OpenTelemetrySpanExt::context(&tracing::Span::current());
propagator.inject_context(&cx, &mut HeaderInjector(&mut orchestration_request_headers));
});
if let Some(request_id) = request_id {
orchestration_request_headers.insert(
header::HeaderName::from_static(REQUEST_ID_HEADER),
header::HeaderValue::from_str(&request_id).unwrap(),
);
}
orchestration_request_headers.insert(
header::HeaderName::from_static("model"),
header::HeaderValue::from_static(PLANO_ORCHESTRATOR_MODEL_NAME),
);
let start_time = std::time::Instant::now();
let res = self
.client
.post(&self.orchestrator_url)
.headers(orchestration_request_headers)
.body(serde_json::to_string(&orchestrator_request).unwrap())
.send()
.await?;
let body = res.text().await?;
let orchestrator_response_time = start_time.elapsed();
let chat_completion_response: ChatCompletionsResponse = match serde_json::from_str(&body) {
Ok(response) => response,
Err(err) => {
warn!(
error = %err,
body = %serde_json::to_string(&body).unwrap(),
"failed to parse json response"
);
return Err(OrchestrationError::JsonError(
err,
format!("Failed to parse JSON: {}", body),
));
}
};
if chat_completion_response.choices.is_empty() {
warn!(body = %body, "no choices in orchestrator response");
return Ok(None);
}
if let Some(content) = &chat_completion_response.choices[0].message.content {
let parsed_response = self
.orchestrator_model
.parse_response(content, &usage_preferences)?;
info!(
content = %content.replace("\n", "\\n"),
selected_routes = ?parsed_response,
response_time_ms = orchestrator_response_time.as_millis(),
"arch-orchestrator determined routes"
);
if let Some(ref parsed_response) = parsed_response {
return Ok(Some(parsed_response.clone()));
}
Ok(None)
} else {
Ok(None)
}
}
}

29
crates/brightstaff/src/state/mod.rs
View file

@ -88,35 +88,18 @@ pub trait StateStorage: Send + Sync {
combined_input.extend(current_input);
debug!(
"PLANO | BRIGHTSTAFF | STATE_STORAGE | RESP_ID:{} | Merged state: prev_items={}, current_items={}, total_items={}, combined_json={}",
prev_state.response_id,
prev_count,
current_count,
combined_input.len(),
serde_json::to_string(&combined_input).unwrap_or_else(|_| "serialization_error".to_string())
response_id = %prev_state.response_id,
prev_items = prev_count,
current_items = current_count,
total_items = combined_input.len(),
combined_json = %serde_json::to_string(&combined_input).unwrap_or_else(|_| "serialization_error".to_string()),
"merged conversation state"
);
combined_input
}
}
/// Storage backend type enum
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum StorageBackend {
Memory,
Supabase,
}
impl StorageBackend {
pub fn parse_backend(s: &str) -> Option<Self> {
match s.to_lowercase().as_str() {
"memory" => Some(StorageBackend::Memory),
"supabase" => Some(StorageBackend::Supabase),
_ => None,
}
}
}
// === Utility functions for state management ===
/// Extract input items from InputParam, converting text to structured format

2
crates/brightstaff/src/state/response_state_processor.rs
View file

@ -7,8 +7,8 @@ use std::io::Read;
use std::sync::Arc;
use tracing::{debug, info, warn};
use crate::handlers::utils::StreamProcessor;
use crate::state::{OpenAIConversationState, StateStorage};
use crate::streaming::StreamProcessor;
/// Processor that wraps another processor and handles v1/responses state management
/// Captures response_id and output from streaming responses, stores state after completion

132
crates/brightstaff/src/handlers/utils.rs → crates/brightstaff/src/streaming.rs
View file

@ -1,16 +1,21 @@
use bytes::Bytes;
use common::configuration::ResolvedFilterChain;
use http_body_util::combinators::BoxBody;
use http_body_util::StreamBody;
use hyper::body::Frame;
use hyper::header::HeaderMap;
use opentelemetry::trace::TraceContextExt;
use opentelemetry::KeyValue;
use std::time::Instant;
use tokio::sync::mpsc;
use tokio_stream::wrappers::ReceiverStream;
use tokio_stream::StreamExt;
use tracing::{info, warn, Instrument};
use tracing::{debug, info, warn, Instrument};
use tracing_opentelemetry::OpenTelemetrySpanExt;
use crate::handlers::agents::pipeline::{PipelineError, PipelineProcessor};
const STREAM_BUFFER_SIZE: usize = 16;
use crate::signals::{InteractionQuality, SignalAnalyzer, TextBasedSignalAnalyzer, FLAG_MARKER};
use crate::tracing::{llm, set_service_name, signals as signal_constants};
use hermesllm::apis::openai::Message;
@ -31,6 +36,21 @@ pub trait StreamProcessor: Send + 'static {
fn on_error(&mut self, _error: &str) {}
}
impl StreamProcessor for Box<dyn StreamProcessor> {
fn process_chunk(&mut self, chunk: Bytes) -> Result<Option<Bytes>, String> {
(**self).process_chunk(chunk)
}
fn on_first_bytes(&mut self) {
(**self).on_first_bytes()
}
fn on_complete(&mut self) {
(**self).on_complete()
}
fn on_error(&mut self, error: &str) {
(**self).on_error(error)
}
}
/// A processor that tracks streaming metrics
pub struct ObservableStreamProcessor {
service_name: String,
@ -206,16 +226,12 @@ pub struct StreamingResponse {
pub processor_handle: tokio::task::JoinHandle<()>,
}
pub fn create_streaming_response<S, P>(
mut byte_stream: S,
mut processor: P,
buffer_size: usize,
) -> StreamingResponse
pub fn create_streaming_response<S, P>(mut byte_stream: S, mut processor: P) -> StreamingResponse
where
S: StreamExt<Item = Result<Bytes, reqwest::Error>> + Send + Unpin + 'static,
P: StreamProcessor,
{
let (tx, rx) = mpsc::channel::<Bytes>(buffer_size);
let (tx, rx) = mpsc::channel::<Bytes>(STREAM_BUFFER_SIZE);
// Capture the current span so the spawned task inherits the request context
let current_span = tracing::Span::current();
@ -277,6 +293,108 @@ where
}
}
/// Creates a streaming response that processes each raw chunk through output filters.
/// Filters receive the raw LLM response bytes and request path (any API shape; not limited to
/// chat completions). On filter error mid-stream the original chunk is passed through (headers already sent).
pub fn create_streaming_response_with_output_filter<S, P>(
mut byte_stream: S,
mut inner_processor: P,
output_chain: ResolvedFilterChain,
request_headers: HeaderMap,
request_path: String,
) -> StreamingResponse
where
S: StreamExt<Item = Result<Bytes, reqwest::Error>> + Send + Unpin + 'static,
P: StreamProcessor,
{
let (tx, rx) = mpsc::channel::<Bytes>(STREAM_BUFFER_SIZE);
let current_span = tracing::Span::current();
let processor_handle = tokio::spawn(
async move {
let mut is_first_chunk = true;
let mut pipeline_processor = PipelineProcessor::default();
let chain = output_chain.to_agent_filter_chain("output_filter");
while let Some(item) = byte_stream.next().await {
let chunk = match item {
Ok(chunk) => chunk,
Err(err) => {
let err_msg = format!("Error receiving chunk: {:?}", err);
warn!(error = %err_msg, "stream error");
inner_processor.on_error(&err_msg);
break;
}
};
if is_first_chunk {
inner_processor.on_first_bytes();
is_first_chunk = false;
}
// Pass raw chunk bytes through the output filter chain
let processed_chunk = match pipeline_processor
.process_raw_filter_chain(
&chunk,
&chain,
&output_chain.agents,
&request_headers,
&request_path,
)
.await
{
Ok(filtered) => filtered,
Err(PipelineError::ClientError {
agent,
status,
body,
}) => {
warn!(
agent = %agent,
status = %status,
body = %body,
"output filter client error, passing through original chunk"
);
chunk
}
Err(e) => {
warn!(error = %e, "output filter error, passing through original chunk");
chunk
}
};
// Pass through inner processor for metrics/observability
match inner_processor.process_chunk(processed_chunk) {
Ok(Some(final_chunk)) => {
if tx.send(final_chunk).await.is_err() {
warn!("receiver dropped");
break;
}
}
Ok(None) => continue,
Err(err) => {
warn!("processor error: {}", err);
inner_processor.on_error(&err);
break;
}
}
}
inner_processor.on_complete();
debug!("output filter streaming completed");
}
.instrument(current_span),
);
let stream = ReceiverStream::new(rx).map(|chunk| Ok::<_, hyper::Error>(Frame::data(chunk)));
let stream_body = BoxBody::new(StreamBody::new(stream));
StreamingResponse {
body: stream_body,
processor_handle,
}
}
/// Truncates a message to the specified maximum length, adding "..." if truncated.
pub fn truncate_message(message: &str, max_length: usize) -> String {
if message.chars().count() > max_length {

1
crates/brightstaff/src/tracing/custom_attributes.rs
View file

@ -52,6 +52,7 @@ pub fn collect_custom_trace_attributes(
attributes
}
#[allow(dead_code)]
pub fn append_span_attributes(
mut span_builder: SpanBuilder,
attributes: &HashMap<String, String>,

11
crates/brightstaff/src/utils/tracing.rs → crates/brightstaff/src/tracing/init.rs
View file

@ -11,7 +11,7 @@ use tracing_subscriber::registry::LookupSpan;
use tracing_subscriber::util::SubscriberInitExt;
use tracing_subscriber::EnvFilter;
use crate::tracing::ServiceNameOverrideExporter;
use super::ServiceNameOverrideExporter;
use common::configuration::Tracing;
struct BracketedTime;
@ -96,17 +96,16 @@ pub fn init_tracer(tracing_config: Option<&Tracing>) -> &'static SdkTracerProvid
tracing_enabled, otel_endpoint, random_sampling
);
// Create OTLP exporter to send spans to collector
if tracing_enabled {
// Set service name via environment if not already set
// Create OTLP exporter to send spans to collector.
// Use `if let` to destructure the endpoint, avoiding an unwrap.
if let Some(endpoint) = otel_endpoint.as_deref().filter(|_| tracing_enabled) {
if std::env::var("OTEL_SERVICE_NAME").is_err() {
std::env::set_var("OTEL_SERVICE_NAME", "plano");
}
// Create ServiceNameOverrideExporter to support per-span service names
// This allows spans to have different service names (e.g., plano(orchestrator),
// plano(filter), plano(llm)) by setting the "service.name.override" attribute
let exporter = ServiceNameOverrideExporter::new(otel_endpoint.as_ref().unwrap());
let exporter = ServiceNameOverrideExporter::new(endpoint);
let provider = SdkTracerProvider::builder()
.with_batch_exporter(exporter)

4
crates/brightstaff/src/tracing/mod.rs
View file

@ -1,11 +1,13 @@
mod constants;
mod custom_attributes;
mod init;
mod service_name_exporter;
pub use constants::{
error, http, llm, operation_component, routing, signals, OperationNameBuilder,
};
pub use custom_attributes::{append_span_attributes, collect_custom_trace_attributes};
pub use custom_attributes::collect_custom_trace_attributes;
pub use init::init_tracer;
pub use service_name_exporter::{ServiceNameOverrideExporter, SERVICE_NAME_OVERRIDE_KEY};
use opentelemetry::trace::get_active_span;

1
crates/brightstaff/src/utils/mod.rs
View file

@ -1 +0,0 @@
pub mod tracing;

68
crates/common/src/configuration.rs
View file

@ -35,14 +35,66 @@ pub struct AgentFilterChain {
pub id: String,
pub default: Option<bool>,
pub description: Option<String>,
pub filter_chain: Option<Vec<String>>,
pub input_filters: Option<Vec<String>>,
}
/// A filter chain with its agent references resolved to concrete Agent objects.
/// Bundles the ordered filter IDs with the agent lookup map so they stay in sync.
#[derive(Debug, Clone, Default)]
pub struct ResolvedFilterChain {
pub filter_ids: Vec<String>,
pub agents: HashMap<String, Agent>,
}
impl ResolvedFilterChain {
pub fn is_empty(&self) -> bool {
self.filter_ids.is_empty()
}
pub fn to_agent_filter_chain(&self, id: &str) -> AgentFilterChain {
AgentFilterChain {
id: id.to_string(),
default: None,
description: None,
input_filters: Some(self.filter_ids.clone()),
}
}
}
/// Holds resolved input and output filter chains for a model listener.
#[derive(Debug, Clone, Default)]
pub struct FilterPipeline {
pub input: Option<ResolvedFilterChain>,
pub output: Option<ResolvedFilterChain>,
}
impl FilterPipeline {
pub fn has_input_filters(&self) -> bool {
self.input.as_ref().is_some_and(|c| !c.is_empty())
}
pub fn has_output_filters(&self) -> bool {
self.output.as_ref().is_some_and(|c| !c.is_empty())
}
}
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
#[serde(rename_all = "lowercase")]
pub enum ListenerType {
Model,
Agent,
Prompt,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Listener {
#[serde(rename = "type")]
pub listener_type: ListenerType,
pub name: String,
pub router: Option<String>,
pub agents: Option<Vec<AgentFilterChain>>,
pub input_filters: Option<Vec<String>>,
pub output_filters: Option<Vec<String>>,
pub port: u16,
}
@ -67,6 +119,7 @@ pub struct Configuration {
pub model_providers: Vec<LlmProvider>,
pub model_aliases: Option<HashMap<String, ModelAlias>>,
pub overrides: Option<Overrides>,
pub routing: Option<Routing>,
pub system_prompt: Option<String>,
pub prompt_guards: Option<PromptGuards>,
pub prompt_targets: Option<Vec<PromptTarget>>,
@ -74,7 +127,6 @@ pub struct Configuration {
pub ratelimits: Option<Vec<Ratelimit>>,
pub tracing: Option<Tracing>,
pub mode: Option<GatewayMode>,
pub routing: Option<Routing>,
pub agents: Option<Vec<Agent>>,
pub filters: Option<Vec<Agent>>,
pub listeners: Vec<Listener>,
@ -86,6 +138,8 @@ pub struct Overrides {
pub prompt_target_intent_matching_threshold: Option<f64>,
pub optimize_context_window: Option<bool>,
pub use_agent_orchestrator: Option<bool>,
pub llm_routing_model: Option<String>,
pub agent_orchestration_model: Option<String>,
}
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
@ -209,8 +263,6 @@ pub struct EmbeddingProviver {
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Hash)]
pub enum LlmProviderType {
#[serde(rename = "arch")]
Arch,
#[serde(rename = "anthropic")]
Anthropic,
#[serde(rename = "deepseek")]
@ -239,12 +291,13 @@ pub enum LlmProviderType {
Qwen,
#[serde(rename = "amazon_bedrock")]
AmazonBedrock,
#[serde(rename = "plano")]
Plano,
}
impl Display for LlmProviderType {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
match self {
LlmProviderType::Arch => write!(f, "arch"),
LlmProviderType::Anthropic => write!(f, "anthropic"),
LlmProviderType::Deepseek => write!(f, "deepseek"),
LlmProviderType::Groq => write!(f, "groq"),
@ -259,6 +312,7 @@ impl Display for LlmProviderType {
LlmProviderType::Zhipu => write!(f, "zhipu"),
LlmProviderType::Qwen => write!(f, "qwen"),
LlmProviderType::AmazonBedrock => write!(f, "amazon_bedrock"),
LlmProviderType::Plano => write!(f, "plano"),
}
}
}
@ -593,14 +647,14 @@ mod test {
},
LlmProvider {
name: "arch-router".to_string(),
provider_interface: LlmProviderType::Arch,
provider_interface: LlmProviderType::Plano,
model: Some("Arch-Router".to_string()),
internal: Some(true),
..Default::default()
},
LlmProvider {
name: "plano-orchestrator".to_string(),
provider_interface: LlmProviderType::Arch,
provider_interface: LlmProviderType::Plano,
model: Some("Plano-Orchestrator".to_string()),
internal: Some(true),
..Default::default()

3
crates/common/src/consts.rs
View file

@ -34,5 +34,4 @@ pub const OTEL_COLLECTOR_HTTP: &str = "opentelemetry_collector_http";
pub const LLM_ROUTE_HEADER: &str = "x-arch-llm-route";
pub const ENVOY_RETRY_HEADER: &str = "x-envoy-max-retries";
pub const BRIGHT_STAFF_SERVICE_NAME: &str = "brightstaff";
pub const PLANO_ORCHESTRATOR_MODEL_NAME: &str = "Plano-Orchestrator";
pub const ARCH_FC_CLUSTER: &str = "arch";
pub const PLANO_FC_CLUSTER: &str = "plano";

366
crates/hermesllm/src/bin/provider_models.yaml
View file

@ -1,183 +1,16 @@
version: '1.0'
source: canonical-apis
providers:
mistralai:
- mistralai/mistral-medium-2505
- mistralai/mistral-medium-2508
- mistralai/mistral-medium-latest
- mistralai/mistral-medium
- mistralai/mistral-vibe-cli-with-tools
- mistralai/open-mistral-nemo
- mistralai/open-mistral-nemo-2407
- mistralai/mistral-tiny-2407
- mistralai/mistral-tiny-latest
- mistralai/mistral-large-2411
- mistralai/pixtral-large-2411
- mistralai/pixtral-large-latest
- mistralai/mistral-large-pixtral-2411
- mistralai/codestral-2508
- mistralai/codestral-latest
- mistralai/devstral-small-2507
- mistralai/devstral-medium-2507
- mistralai/devstral-2512
- mistralai/mistral-vibe-cli-latest
- mistralai/devstral-medium-latest
- mistralai/devstral-latest
- mistralai/labs-devstral-small-2512
- mistralai/devstral-small-latest
- mistralai/mistral-small-2506
- mistralai/mistral-small-latest
- mistralai/labs-mistral-small-creative
- mistralai/magistral-medium-2509
- mistralai/magistral-medium-latest
- mistralai/magistral-small-2509
- mistralai/magistral-small-latest
- mistralai/mistral-large-2512
- mistralai/mistral-large-latest
- mistralai/ministral-3b-2512
- mistralai/ministral-3b-latest
- mistralai/ministral-8b-2512
- mistralai/ministral-8b-latest
- mistralai/ministral-14b-2512
- mistralai/ministral-14b-latest
- mistralai/mistral-small-2501
- mistralai/mistral-embed-2312
- mistralai/mistral-embed
- mistralai/codestral-embed
- mistralai/codestral-embed-2505
openai:
- openai/gpt-4-0613
- openai/gpt-4
- openai/gpt-3.5-turbo
- openai/gpt-5.2-codex
- openai/gpt-3.5-turbo-instruct
- openai/gpt-3.5-turbo-instruct-0914
- openai/gpt-4-1106-preview
- openai/gpt-3.5-turbo-1106
- openai/gpt-4-0125-preview
- openai/gpt-4-turbo-preview
- openai/gpt-3.5-turbo-0125
- openai/gpt-4-turbo
- openai/gpt-4-turbo-2024-04-09
- openai/gpt-4o
- openai/gpt-4o-2024-05-13
- openai/gpt-4o-mini-2024-07-18
- openai/gpt-4o-mini
- openai/gpt-4o-2024-08-06
- openai/chatgpt-4o-latest
- openai/o1-2024-12-17
- openai/o1
- openai/computer-use-preview
- openai/o3-mini
- openai/o3-mini-2025-01-31
- openai/gpt-4o-2024-11-20
- openai/computer-use-preview-2025-03-11
- openai/gpt-4o-search-preview-2025-03-11
- openai/gpt-4o-search-preview
- openai/gpt-4o-mini-search-preview-2025-03-11
- openai/gpt-4o-mini-search-preview
- openai/o1-pro-2025-03-19
- openai/o1-pro
- openai/o3-2025-04-16
- openai/o4-mini-2025-04-16
- openai/o3
- openai/o4-mini
- openai/gpt-4.1-2025-04-14
- openai/gpt-4.1
- openai/gpt-4.1-mini-2025-04-14
- openai/gpt-4.1-mini
- openai/gpt-4.1-nano-2025-04-14
- openai/gpt-4.1-nano
- openai/o3-pro
- openai/o3-pro-2025-06-10
- openai/o4-mini-deep-research
- openai/o3-deep-research
- openai/o3-deep-research-2025-06-26
- openai/o4-mini-deep-research-2025-06-26
- openai/gpt-5-chat-latest
- openai/gpt-5-2025-08-07
- openai/gpt-5
- openai/gpt-5-mini-2025-08-07
- openai/gpt-5-mini
- openai/gpt-5-nano-2025-08-07
- openai/gpt-5-nano
- openai/gpt-5-codex
- openai/gpt-5-pro-2025-10-06
- openai/gpt-5-pro
- openai/gpt-5-search-api
- openai/gpt-5-search-api-2025-10-14
- openai/gpt-5.1-chat-latest
- openai/gpt-5.1-2025-11-13
- openai/gpt-5.1
- openai/gpt-5.1-codex
- openai/gpt-5.1-codex-mini
- openai/gpt-5.1-codex-max
- openai/gpt-5.2-2025-12-11
- openai/gpt-5.2
- openai/gpt-5.2-pro-2025-12-11
- openai/gpt-5.2-pro
- openai/gpt-5.2-chat-latest
- openai/gpt-3.5-turbo-16k
- openai/ft:gpt-3.5-turbo-0613:katanemo::8CMZbm0P
deepseek:
- deepseek/deepseek-chat
- deepseek/deepseek-reasoner
x-ai:
- x-ai/grok-2-vision-1212
- x-ai/grok-3
- x-ai/grok-3-mini
- x-ai/grok-4-0709
- x-ai/grok-4-1-fast-non-reasoning
- x-ai/grok-4-1-fast-reasoning
- x-ai/grok-4-fast-non-reasoning
- x-ai/grok-4-fast-reasoning
- x-ai/grok-code-fast-1
- x-ai/grok-imagine-image
- x-ai/grok-imagine-video
moonshotai:
- moonshotai/kimi-k2-thinking
- moonshotai/kimi-k2.5
- moonshotai/moonshot-v1-128k-vision-preview
- moonshotai/moonshot-v1-8k
- moonshotai/kimi-k2-turbo-preview
- moonshotai/moonshot-v1-128k
- moonshotai/moonshot-v1-32k-vision-preview
- moonshotai/kimi-k2-thinking-turbo
- moonshotai/kimi-latest
- moonshotai/moonshot-v1-32k
- moonshotai/moonshot-v1-auto
- moonshotai/kimi-k2-0711-preview
- moonshotai/kimi-k2-0905-preview
- moonshotai/moonshot-v1-8k-vision-preview
anthropic:
- anthropic/claude-opus-4-6
- anthropic/claude-opus-4-5-20251101
- anthropic/claude-opus-4-5
- anthropic/claude-haiku-4-5-20251001
- anthropic/claude-haiku-4-5
- anthropic/claude-sonnet-4-5-20250929
- anthropic/claude-sonnet-4-5
- anthropic/claude-opus-4-1-20250805
- anthropic/claude-opus-4-1
- anthropic/claude-opus-4-20250514
- anthropic/claude-opus-4
- anthropic/claude-sonnet-4-20250514
- anthropic/claude-sonnet-4
- anthropic/claude-3-7-sonnet-20250219
- anthropic/claude-3-7-sonnet
- anthropic/claude-3-5-haiku-20241022
- anthropic/claude-3-5-haiku
- anthropic/claude-3-haiku-20240307
- anthropic/claude-3-haiku
google:
- google/gemini-2.5-flash
- google/gemini-2.5-pro
- google/gemini-2.0-flash
- google/gemini-2.0-flash-001
- google/gemini-2.0-flash-exp-image-generation
- google/gemini-2.0-flash-lite-001
- google/gemini-2.0-flash-lite
- google/gemini-exp-1206
- google/gemini-2.5-flash-preview-tts
- google/gemini-2.5-pro-preview-tts
- google/gemma-3-1b-it
@ -191,12 +24,15 @@ providers:
- google/gemini-pro-latest
- google/gemini-2.5-flash-lite
- google/gemini-2.5-flash-image
- google/gemini-2.5-flash-preview-09-2025
- google/gemini-2.5-flash-lite-preview-09-2025
- google/gemini-3-pro-preview
- google/gemini-3-flash-preview
- google/gemini-3.1-pro-preview
- google/gemini-3.1-pro-preview-customtools
- google/gemini-3.1-flash-lite-preview
- google/gemini-3-pro-image-preview
- google/nano-banana-pro-preview
- google/gemini-3.1-flash-image-preview
- google/gemini-robotics-er-1.5-preview
- google/gemini-2.5-computer-use-preview-10-2025
- google/deep-research-pro-preview-12-2025
@ -212,7 +48,37 @@ providers:
- amazon/amazon.nova-premier-v1:0
- amazon/amazon.nova-lite-v1:0
- amazon/amazon.nova-micro-v1:0
x-ai:
- x-ai/grok-3
- x-ai/grok-3-mini
- x-ai/grok-4-0709
- x-ai/grok-4-1-fast-non-reasoning
- x-ai/grok-4-1-fast-reasoning
- x-ai/grok-4-fast-non-reasoning
- x-ai/grok-4-fast-reasoning
- x-ai/grok-4.20-beta-0309-non-reasoning
- x-ai/grok-4.20-beta-0309-reasoning
- x-ai/grok-4.20-multi-agent-beta-0309
- x-ai/grok-code-fast-1
- x-ai/grok-imagine-image
- x-ai/grok-imagine-video
z-ai:
- z-ai/glm-4.5
- z-ai/glm-4.5-air
- z-ai/glm-4.6
- z-ai/glm-4.7
- z-ai/glm-5
qwen:
- qwen/qwen3-asr-flash-2026-02-10
- qwen/qwen3.5-flash-2026-02-23
- qwen/qwen3.5-flash
- qwen/qwen3.5-122b-a10b
- qwen/qwen3.5-35b-a3b
- qwen/qwen3.5-27b
- qwen/qwen3-coder-next
- qwen/qwen3.5-397b-a17b
- qwen/qwen3.5-plus-2026-02-15
- qwen/qwen3.5-plus
- qwen/qwen3-vl-flash-2026-01-22
- qwen/qwen3-max-2026-01-23
- qwen/qwen-plus-character
@ -294,13 +160,161 @@ providers:
- qwen/qwen-max
- qwen/qwen-plus
- qwen/qwen-turbo
z-ai:
- z-ai/glm-4.5
- z-ai/glm-4.5-air
- z-ai/glm-4.6
- z-ai/glm-4.7
- z-ai/glm-5
mistralai:
- mistralai/mistral-medium-2505
- mistralai/mistral-medium-2508
- mistralai/mistral-medium-latest
- mistralai/mistral-medium
- mistralai/mistral-vibe-cli-with-tools
- mistralai/open-mistral-nemo
- mistralai/open-mistral-nemo-2407
- mistralai/mistral-tiny-2407
- mistralai/mistral-tiny-latest
- mistralai/codestral-2508
- mistralai/codestral-latest
- mistralai/devstral-2512
- mistralai/mistral-vibe-cli-latest
- mistralai/devstral-medium-latest
- mistralai/devstral-latest
- mistralai/mistral-small-2506
- mistralai/mistral-small-latest
- mistralai/labs-mistral-small-creative
- mistralai/magistral-medium-2509
- mistralai/magistral-medium-latest
- mistralai/magistral-small-2509
- mistralai/magistral-small-latest
- mistralai/mistral-large-2512
- mistralai/mistral-large-latest
- mistralai/ministral-3b-2512
- mistralai/ministral-3b-latest
- mistralai/ministral-8b-2512
- mistralai/ministral-8b-latest
- mistralai/ministral-14b-2512
- mistralai/ministral-14b-latest
- mistralai/mistral-large-2411
- mistralai/pixtral-large-2411
- mistralai/pixtral-large-latest
- mistralai/mistral-large-pixtral-2411
- mistralai/devstral-small-2507
- mistralai/devstral-medium-2507
- mistralai/labs-devstral-small-2512
- mistralai/devstral-small-latest
- mistralai/mistral-squarepoint-2602
- mistralai/mistral-embed-2312
- mistralai/mistral-embed
- mistralai/codestral-embed
- mistralai/codestral-embed-2505
moonshotai:
- moonshotai/kimi-k2.5
- moonshotai/kimi-k2-0905-preview
- moonshotai/moonshot-v1-32k
- moonshotai/moonshot-v1-128k
- moonshotai/kimi-k2-thinking-turbo
- moonshotai/moonshot-v1-8k-vision-preview
- moonshotai/kimi-k2-0711-preview
- moonshotai/moonshot-v1-auto
- moonshotai/kimi-k2-thinking
- moonshotai/moonshot-v1-128k-vision-preview
- moonshotai/kimi-k2-turbo-preview
- moonshotai/moonshot-v1-32k-vision-preview
- moonshotai/moonshot-v1-8k
anthropic:
- anthropic/claude-sonnet-4-6
- anthropic/claude-opus-4-6
- anthropic/claude-opus-4-5-20251101
- anthropic/claude-opus-4-5
- anthropic/claude-haiku-4-5-20251001
- anthropic/claude-haiku-4-5
- anthropic/claude-sonnet-4-5-20250929
- anthropic/claude-sonnet-4-5
- anthropic/claude-opus-4-1-20250805
- anthropic/claude-opus-4-1
- anthropic/claude-opus-4-20250514
- anthropic/claude-opus-4
- anthropic/claude-sonnet-4-20250514
- anthropic/claude-sonnet-4
- anthropic/claude-3-haiku-20240307
- anthropic/claude-3-haiku
openai:
- openai/gpt-4-0613
- openai/gpt-4
- openai/gpt-3.5-turbo
- openai/gpt-5.4
- openai/gpt-5.3-chat-latest
- openai/gpt-5.4-2026-03-05
- openai/gpt-5.4-pro
- openai/gpt-5.4-pro-2026-03-05
- openai/gpt-3.5-turbo-instruct
- openai/gpt-3.5-turbo-instruct-0914
- openai/gpt-4-1106-preview
- openai/gpt-3.5-turbo-1106
- openai/gpt-4-0125-preview
- openai/gpt-4-turbo-preview
- openai/gpt-3.5-turbo-0125
- openai/gpt-4-turbo
- openai/gpt-4-turbo-2024-04-09
- openai/gpt-4o
- openai/gpt-4o-2024-05-13
- openai/gpt-4o-mini-2024-07-18
- openai/gpt-4o-mini
- openai/gpt-4o-2024-08-06
- openai/o1-2024-12-17
- openai/o1
- openai/computer-use-preview
- openai/o3-mini
- openai/o3-mini-2025-01-31
- openai/gpt-4o-2024-11-20
- openai/computer-use-preview-2025-03-11
- openai/gpt-4o-mini-search-preview-2025-03-11
- openai/gpt-4o-mini-search-preview
- openai/o1-pro-2025-03-19
- openai/o1-pro
- openai/o3-2025-04-16
- openai/o4-mini-2025-04-16
- openai/o3
- openai/o4-mini
- openai/gpt-4.1-2025-04-14
- openai/gpt-4.1
- openai/gpt-4.1-mini-2025-04-14
- openai/gpt-4.1-mini
- openai/gpt-4.1-nano-2025-04-14
- openai/gpt-4.1-nano
- openai/o3-pro
- openai/o3-pro-2025-06-10
- openai/o4-mini-deep-research
- openai/o3-deep-research
- openai/o3-deep-research-2025-06-26
- openai/o4-mini-deep-research-2025-06-26
- openai/gpt-5-chat-latest
- openai/gpt-5-2025-08-07
- openai/gpt-5
- openai/gpt-5-mini-2025-08-07
- openai/gpt-5-mini
- openai/gpt-5-nano-2025-08-07
- openai/gpt-5-nano
- openai/gpt-5-codex
- openai/gpt-5-pro-2025-10-06
- openai/gpt-5-pro
- openai/gpt-5-search-api
- openai/gpt-5-search-api-2025-10-14
- openai/gpt-5.1-chat-latest
- openai/gpt-5.1-2025-11-13
- openai/gpt-5.1
- openai/gpt-5.1-codex
- openai/gpt-5.1-codex-mini
- openai/gpt-5.1-codex-max
- openai/gpt-5.2-2025-12-11
- openai/gpt-5.2
- openai/gpt-5.2-pro-2025-12-11
- openai/gpt-5.2-pro
- openai/gpt-5.2-chat-latest
- openai/gpt-5.2-codex
- openai/gpt-5.3-codex
- openai/gpt-4o-search-preview
- openai/gpt-4o-search-preview-2025-03-11
- openai/gpt-3.5-turbo-16k
- openai/ft:gpt-3.5-turbo-0613:katanemo::8CMZbm0P
metadata:
total_providers: 10
total_models: 289
last_updated: 2026-02-13T22:44:30.413065+00:00
total_models: 303
last_updated: 2026-03-15T16:47:22.207197+00:00

2
crates/hermesllm/src/lib.rs
View file

@ -35,7 +35,7 @@ mod tests {
ProviderId::Mistral
);
assert_eq!(ProviderId::try_from("groq").unwrap(), ProviderId::Groq);
assert_eq!(ProviderId::try_from("arch").unwrap(), ProviderId::Arch);
assert_eq!(ProviderId::try_from("plano").unwrap(), ProviderId::Plano);
// Test aliases
assert_eq!(ProviderId::try_from("google").unwrap(), ProviderId::Gemini);

10
crates/hermesllm/src/providers/id.rs
View file

@ -34,7 +34,7 @@ pub enum ProviderId {
Gemini,
Anthropic,
GitHub,
Arch,
Plano,
AzureOpenAI,
XAI,
TogetherAI,
@ -58,7 +58,7 @@ impl TryFrom<&str> for ProviderId {
"google" => Ok(ProviderId::Gemini), // alias
"anthropic" => Ok(ProviderId::Anthropic),
"github" => Ok(ProviderId::GitHub),
"arch" => Ok(ProviderId::Arch),
"plano" => Ok(ProviderId::Plano),
"azure_openai" => Ok(ProviderId::AzureOpenAI),
"xai" => Ok(ProviderId::XAI),
"together_ai" => Ok(ProviderId::TogetherAI),
@ -135,7 +135,7 @@ impl ProviderId {
| ProviderId::Groq
| ProviderId::Mistral
| ProviderId::Deepseek
| ProviderId::Arch
| ProviderId::Plano
| ProviderId::Gemini
| ProviderId::GitHub
| ProviderId::AzureOpenAI
@ -153,7 +153,7 @@ impl ProviderId {
| ProviderId::Groq
| ProviderId::Mistral
| ProviderId::Deepseek
| ProviderId::Arch
| ProviderId::Plano
| ProviderId::Gemini
| ProviderId::GitHub
| ProviderId::AzureOpenAI
@ -219,7 +219,7 @@ impl Display for ProviderId {
ProviderId::Gemini => write!(f, "Gemini"),
ProviderId::Anthropic => write!(f, "Anthropic"),
ProviderId::GitHub => write!(f, "GitHub"),
ProviderId::Arch => write!(f, "Arch"),
ProviderId::Plano => write!(f, "Plano"),
ProviderId::AzureOpenAI => write!(f, "azure_openai"),
ProviderId::XAI => write!(f, "xai"),
ProviderId::TogetherAI => write!(f, "together_ai"),

2
crates/llm_gateway/src/stream_context.rs
View file

@ -873,7 +873,7 @@ impl HttpContext for StreamContext {
// ensure that the provider has an endpoint if the access key is missing else return a bad request
if self.llm_provider.as_ref().unwrap().endpoint.is_none()
&& self.llm_provider.as_ref().unwrap().provider_interface
!= LlmProviderType::Arch
!= LlmProviderType::Plano
{
self.send_server_error(error, Some(StatusCode::BAD_REQUEST));
}

2
demos/advanced/model_choice_test_harness/pyproject.toml
View file

@ -5,7 +5,7 @@ description = "Benchmarking model alias routing with Plano."
authors = [{name = "Your Name", email = "your@email.com"}]
license = {text = "Apache 2.0"}
readme = "README.md"
requires-python = ">=3.10"
requires-python = ">=3.10,<3.14"
dependencies = [
"pydantic>=2.0",
"openai>=1.0",

156
demos/advanced/model_choice_test_harness/uv.lock generated
View file

@ -1,6 +1,6 @@
version = 1
revision = 3
requires-python = ">=3.10"
requires-python = ">=3.10, <3.14"
[[package]]
name = "annotated-types"
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2
demos/advanced/multi_turn_rag/pyproject.toml
View file

@ -1,7 +1,7 @@
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dependencies = [
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2
demos/agent_orchestration/multi_agent_crewai_langchain/pyproject.toml
View file

@ -3,7 +3,7 @@ name = "multi-framework-agents"
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750
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36
demos/agent_orchestration/travel_agents/README.md
View file

@ -123,6 +123,42 @@ Each agent:
Both agents run as native local processes and communicate with Plano running natively on the host.
## Running with local Plano-Orchestrator (via vLLM)
By default, Plano uses a hosted Plano-Orchestrator endpoint. To self-host the orchestrator model locally using vLLM on a server with an NVIDIA GPU:
1. Install vLLM and download the model:
```bash
pip install vllm
```
2. Start the vLLM server with the 4B model:
```bash
vllm serve katanemo/Plano-Orchestrator-4B \
--host 0.0.0.0 \
--port 8000 \
--tensor-parallel-size 1 \
--gpu-memory-utilization 0.3 \
--tokenizer katanemo/Plano-Orchestrator-4B \
--chat-template chat_template.jinja \
--served-model-name katanemo/Plano-Orchestrator-4B \
--enable-prefix-caching
```
3. Start the demo with the local orchestrator config:
```bash
./run_demo.sh --local-orchestrator
```
4. Test with curl:
```bash
curl -X POST http://localhost:8001/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model": "gpt-5.2", "messages": [{"role": "user", "content": "What is the weather in Istanbul?"}]}'
```
You should see Plano use your local orchestrator to route the request to the weather agent.
## Observability
This demo includes full OpenTelemetry (OTel) compatible distributed tracing to monitor and debug agent interactions:

66
demos/agent_orchestration/travel_agents/config_local_orchestrator.yaml Normal file
View file

@ -0,0 +1,66 @@
version: v0.3.0
overrides:
agent_orchestration_model: plano/katanemo/Plano-Orchestrator-4B
agents:
- id: weather_agent
url: http://localhost:10510
- id: flight_agent
url: http://localhost:10520
model_providers:
- model: plano/katanemo/Plano-Orchestrator-4B
base_url: http://localhost:8000
- model: openai/gpt-5.2
access_key: $OPENAI_API_KEY
default: true
- model: openai/gpt-4o-mini
access_key: $OPENAI_API_KEY # smaller, faster, cheaper model for extracting entities like location
listeners:
- type: agent
name: travel_booking_service
port: 8001
router: plano_orchestrator_v1
agents:
- id: weather_agent
description: |
WeatherAgent is a specialized AI assistant for real-time weather information and forecasts. It provides accurate weather data for any city worldwide using the Open-Meteo API, helping travelers plan their trips with up-to-date weather conditions.
Capabilities:
* Get real-time weather conditions and multi-day forecasts for any city worldwide using Open-Meteo API (free, no API key needed)
* Provides current temperature
* Provides multi-day forecasts
* Provides weather conditions
* Provides sunrise/sunset times
* Provides detailed weather information
* Understands conversation context to resolve location references from previous messages
* Handles weather-related questions including "What's the weather in [city]?", "What's the forecast for [city]?", "How's the weather in [city]?"
* When queries include both weather and other travel questions (e.g., flights, currency), this agent answers ONLY the weather part
- id: flight_agent
description: |
FlightAgent is an AI-powered tool specialized in providing live flight information between airports. It leverages the FlightAware AeroAPI to deliver real-time flight status, gate information, and delay updates.
Capabilities:
* Get live flight information between airports using FlightAware AeroAPI
* Shows real-time flight status
* Shows scheduled/estimated/actual departure and arrival times
* Shows gate and terminal information
* Shows delays
* Shows aircraft type
* Shows flight status
* Automatically resolves city names to airport codes (IATA/ICAO)
* Understands conversation context to infer origin/destination from follow-up questions
* Handles flight-related questions including "What flights go from [city] to [city]?", "Do flights go to [city]?", "Are there direct flights from [city]?"
* When queries include both flight and other travel questions (e.g., weather, currency), this agent answers ONLY the flight part
tracing:
random_sampling: 100
span_attributes:
header_prefixes:
- x-acme-

2
demos/agent_orchestration/travel_agents/pyproject.toml
View file

@ -3,7 +3,7 @@ name = "travel-agents"
version = "0.1.0"
description = "Travel Booking Agents - Weather, Flight, and Currency"
readme = "README.md"
requires-python = ">=3.10"
requires-python = ">=3.10,<3.14"
dependencies = [
"click>=8.2.1",
"pydantic>=2.11.7",

9
demos/agent_orchestration/travel_agents/run_demo.sh
View file

@ -31,8 +31,13 @@ start_demo() {
fi
# Step 4: Start Plano
echo "Starting Plano with config.yaml..."
planoai up config.yaml
PLANO_CONFIG="config.yaml"
if [ "$1" == "--local-orchestrator" ]; then
PLANO_CONFIG="config_local_orchestrator.yaml"
echo "Using local orchestrator config..."
fi
echo "Starting Plano with $PLANO_CONFIG..."
planoai up "$PLANO_CONFIG"
# Step 5: Start agents natively
echo "Starting agents..."

55
demos/agent_orchestration/travel_agents/uv.lock generated
View file

@ -1,6 +1,6 @@
version = 1
revision = 3
requires-python = ">=3.10"
requires-python = ">=3.10, <3.14"
[[package]]
name = "annotated-doc"
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26
demos/filter_chains/http_filter/README.md
View file

@ -1,31 +1,28 @@
# RAG Agent Demo
A multi-agent RAG system demonstrating plano's agent filter chain with MCP protocol.
A multi-agent RAG system demonstrating plano's agent filter chain with HTTP protocol.
## Architecture
This demo consists of four components:
1. **Input Guards** (MCP filter) - Validates queries are within TechCorp's domain
2. **Query Rewriter** (MCP filter) - Rewrites user queries for better retrieval
3. **Context Builder** (MCP filter) - Retrieves relevant context from knowledge base
1. **Input Guards** (HTTP filter) - Validates queries are within TechCorp's domain
2. **Query Rewriter** (HTTP filter) - Rewrites user queries for better retrieval
3. **Context Builder** (HTTP filter) - Retrieves relevant context from knowledge base
4. **RAG Agent** (REST) - Generates final responses based on augmented context
## Components
### Input Guards Filter (MCP)
### Input Guards Filter (HTTP)
- **Port**: 10500
- **Tool**: `input_guards`
- Validates queries are within TechCorp's domain
- Rejects queries about other companies or unrelated topics
### Query Rewrit3r Filter (MCP)
### Query Rewriter Filter (HTTP)
- **Port**: 10501
- **Tool**: `query_rewriter`
- Improves queries using LLM before retrieval
### Context Builder Filter (MCP)
### Context Builder Filter (HTTP)
- **Port**: 10502
- **Tool**: `context_builder`
- Augments queries with relevant passages from knowledge base
### RAG Agent (REST/OpenAI)
@ -93,7 +90,7 @@ filters:
## How It Works
1. User sends request to plano listener on port 8001
2. Request passes through MCP filter chain:
2. Request passes through HTTP filter chain:
- **Input Guards** validates the query is within TechCorp's domain
- **Query Rewriter** rewrites the query for better retrieval
- **Context Builder** augments query with relevant knowledge base passages
@ -102,10 +99,7 @@ filters:
## Additional Configuration
See `config.yaml` for the complete filter chain setup. The MCP filters use default settings:
- `type: mcp` (default)
- `transport: streamable-http` (default)
- Tool name defaults to filter ID
See `config.yaml` for the complete filter chain setup. The HTTP filters use `type: http` to indicate they communicate over plain HTTP POST rather than MCP.
See `sample_queries.md` for example queries to test the RAG system.
@ -123,7 +117,7 @@ curl -X POST http://localhost:8001/v1/chat/completions \
]
}'
```
- `LLM_GATEWAY_ENDPOINT` - lpano endpoint (default: `http://localhost:12000/v1`)
- `LLM_GATEWAY_ENDPOINT` - Plano endpoint (default: `http://localhost:12000/v1`)
- `OPENAI_API_KEY` - OpenAI API key for model providers
## Additional Resources

8
demos/filter_chains/http_filter/config.yaml
View file

@ -8,15 +8,9 @@ filters:
- id: input_guards
url: http://localhost:10500
type: http
# type: mcp (default)
# transport: streamable-http (default)
# tool: input_guards (default - same as filter id)
- id: query_rewriter
url: http://localhost:10501
type: http
# type: mcp (default)
# transport: streamable-http (default)
# tool: query_rewriter (default - same as filter id)
- id: context_builder
url: http://localhost:10502
type: http
@ -42,7 +36,7 @@ listeners:
agents:
- id: rag_agent
description: virtual assistant for retrieval augmented generation tasks
filter_chain:
input_filters:
- input_guards
- query_rewriter
- context_builder

2
demos/filter_chains/http_filter/pyproject.toml
View file

@ -3,7 +3,7 @@ name = "rag_agent"
version = "0.1.0"
description = "RAG Agent"
readme = "README.md"
requires-python = ">=3.10"
requires-python = ">=3.10,<3.14"
dependencies = [
"click>=8.2.1",
"mcp>=1.13.1",

16
demos/filter_chains/http_filter/src/rag_agent/context_builder.py
View file

@ -195,11 +195,11 @@ async def augment_query_with_context(
load_knowledge_base()
@app.post("/")
async def context_builder(
messages: List[ChatMessage], request: Request
) -> List[ChatMessage]:
@app.post("/{path:path}")
async def context_builder(path: str, request: Request) -> dict:
"""MCP tool that augments user queries with relevant context from the knowledge base."""
body = await request.json()
messages = [ChatMessage(**m) for m in body.get("messages", [])]
logger.info(f"Received chat completion request with {len(messages)} messages")
# Get traceparent header from MCP request
@ -219,8 +219,12 @@ async def context_builder(
messages, traceparent_header, request_id
)
# Return as dict to minimize text serialization
return [{"role": msg.role, "content": msg.content} for msg in updated_messages]
return {
**body,
"messages": [
{"role": msg.role, "content": msg.content} for msg in updated_messages
],
}
# Register MCP tool only if mcp is available

10
demos/filter_chains/http_filter/src/rag_agent/input_guards.py
View file

@ -126,14 +126,14 @@ Respond in JSON format:
# @mcp.tool
@app.post("/")
async def input_guards(
messages: List[ChatMessage], request: Request
) -> List[ChatMessage]:
@app.post("/{path:path}")
async def input_guards(path: str, request: Request) -> dict:
"""Input guard that validates queries are within TechCorp's domain.
If the query is out of scope, replaces the user message with a rejection notice.
"""
body = await request.json()
messages = [ChatMessage(**m) for m in body.get("messages", [])]
logger.info(f"Received request with {len(messages)} messages")
# Get traceparent header from HTTP request using FastMCP's dependency function
@ -164,7 +164,7 @@ async def input_guards(
)
logger.info("Query validation passed - forwarding to next filter")
return messages
return body
@app.get("/health")

23
demos/filter_chains/http_filter/src/rag_agent/query_rewriter.py
View file

@ -81,11 +81,11 @@ Return only the rewritten query, nothing else."""
return ""
@app.post("/")
async def query_rewriter_http(
messages: List[ChatMessage], request: Request
) -> List[ChatMessage]:
@app.post("/{path:path}")
async def query_rewriter_http(path: str, request: Request) -> dict:
"""HTTP filter endpoint used by Plano (type: http)."""
body = await request.json()
messages = [ChatMessage(**m) for m in body.get("messages", [])]
logger.info(f"Received request with {len(messages)} messages")
traceparent_header = request.headers.get("traceparent")
@ -99,25 +99,20 @@ async def query_rewriter_http(
rewritten_query = await rewrite_query_with_plano(
messages, traceparent_header, request_id
)
# Create updated messages with the rewritten query
updated_messages = messages.copy()
# Find and update the last user message with the rewritten query
updated_messages = [m.model_dump() for m in messages]
for i in range(len(updated_messages) - 1, -1, -1):
if updated_messages[i].role == "user":
original_query = updated_messages[i].content
updated_messages[i] = ChatMessage(role="user", content=rewritten_query)
if updated_messages[i]["role"] == "user":
original_query = updated_messages[i]["content"]
updated_messages[i]["content"] = rewritten_query
logger.info(
f"Updated user query from '{original_query}' to '{rewritten_query}'"
)
break
updated_messages_data = [
{"role": msg.role, "content": msg.content} for msg in updated_messages
]
updated_messages = [ChatMessage(**msg) for msg in updated_messages_data]
logger.info("Returning rewritten chat completion response")
return updated_messages
return {**body, "messages": updated_messages}
@app.get("/health")

152
demos/filter_chains/http_filter/uv.lock generated
View file

@ -1,6 +1,6 @@
version = 1
revision = 3
requires-python = ">=3.10"
requires-python = ">=3.10, <3.14"
[[package]]
name = "annotated-types"
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4
demos/filter_chains/mcp_filter/README.md
View file

@ -18,7 +18,7 @@ This demo consists of four components:
- Validates queries are within TechCorp's domain
- Rejects queries about other companies or unrelated topics
### Query Rewrit3r Filter (MCP)
### Query Rewriter Filter (MCP)
- **Port**: 10501
- **Tool**: `query_rewriter`
- Improves queries using LLM before retrieval
@ -123,7 +123,7 @@ curl -X POST http://localhost:8001/v1/chat/completions \
]
}'
```
- `LLM_GATEWAY_ENDPOINT` - lpano endpoint (default: `http://localhost:12000/v1`)
- `LLM_GATEWAY_ENDPOINT` - Plano endpoint (default: `http://localhost:12000/v1`)
- `OPENAI_API_KEY` - OpenAI API key for model providers
## Additional Resources

2
demos/filter_chains/mcp_filter/config.yaml
View file

@ -39,7 +39,7 @@ listeners:
agents:
- id: rag_agent
description: virtual assistant for retrieval augmented generation tasks
filter_chain:
input_filters:
- input_guards
- query_rewriter
- context_builder

2
demos/filter_chains/mcp_filter/pyproject.toml
View file

@ -3,7 +3,7 @@ name = "rag_agent"
version = "0.1.0"
description = "RAG Agent"
readme = "README.md"
requires-python = ">=3.10"
requires-python = ">=3.10,<3.14"
dependencies = [
"click>=8.2.1",
"mcp>=1.13.1",

19
demos/filter_chains/mcp_filter/src/rag_agent/context_builder.py
View file

@ -195,9 +195,14 @@ async def augment_query_with_context(
load_knowledge_base()
async def context_builder(messages: List[ChatMessage]) -> List[ChatMessage]:
"""MCP tool that augments user queries with relevant context from the knowledge base."""
logger.info(f"Received chat completion request with {len(messages)} messages")
async def context_builder(body: dict, path: str) -> dict:
"""MCP tool that augments user queries with relevant context from the knowledge base.
Receives the full request body dict and the API path hint (e.g. /v1/chat/completions).
Returns the body with the last user message augmented with retrieved context.
"""
messages = [ChatMessage(**m) for m in body.get("messages", [])]
logger.info(f"Received request with {len(messages)} messages at path {path}")
# Get traceparent header from MCP request
headers = get_http_headers()
@ -215,8 +220,12 @@ async def context_builder(messages: List[ChatMessage]) -> List[ChatMessage]:
messages, traceparent_header, request_id
)
# Return as dict to minimize text serialization
return [{"role": msg.role, "content": msg.content} for msg in updated_messages]
return {
**body,
"messages": [
{"role": msg.role, "content": msg.content} for msg in updated_messages
],
}
# Register MCP tool only if mcp is available

19
demos/filter_chains/mcp_filter/src/rag_agent/input_guards.py
View file

@ -1,15 +1,11 @@
import asyncio
import json
import time
from typing import List, Optional, Dict, Any
import uuid
from fastapi import FastAPI, Depends, Request
from fastmcp.exceptions import ToolError
from openai import AsyncOpenAI
import os
import logging
from .api import ChatCompletionRequest, ChatCompletionResponse, ChatMessage
from .api import ChatMessage
from . import mcp
from fastmcp.server.dependencies import get_http_headers
@ -30,8 +26,6 @@ plano_client = AsyncOpenAI(
api_key="EMPTY", # Plano doesn't require a real API key
)
app = FastAPI()
async def validate_query_scope(
messages: List[ChatMessage],
@ -127,12 +121,14 @@ Respond in JSON format:
@mcp.tool
async def input_guards(messages: List[ChatMessage]) -> List[ChatMessage]:
async def input_guards(body: dict, path: str) -> dict:
"""Input guard that validates queries are within TechCorp's domain.
If the query is out of scope, replaces the user message with a rejection notice.
Receives the full request body dict and the API path hint (e.g. /v1/chat/completions).
If the query is out of scope, raises a ToolError to block the request.
"""
logger.info(f"Received request with {len(messages)} messages")
messages = [ChatMessage(**m) for m in body.get("messages", [])]
logger.info(f"Received request with {len(messages)} messages at path {path}")
# Get traceparent header from HTTP request using FastMCP's dependency function
headers = get_http_headers()
@ -153,9 +149,8 @@ async def input_guards(messages: List[ChatMessage]) -> List[ChatMessage]:
reason = validation_result.get("reason", "Query is outside TechCorp's domain")
logger.warning(f"Query rejected: {reason}")
# Throw ToolError
error_message = f"I apologize, but I can only assist with questions related to TechCorp and its services. Your query appears to be outside this scope. {reason}\n\nPlease ask me about TechCorp's products, services, pricing, SLAs, or technical support."
raise ToolError(error_message)
logger.info("Query validation passed - forwarding to next filter")
return messages
return body

70
demos/filter_chains/mcp_filter/src/rag_agent/query_rewriter.py
View file

@ -1,14 +1,9 @@
import asyncio
import json
import time
from typing import List, Optional, Dict, Any
import uuid
from fastapi import FastAPI, Depends, Request
from typing import List, Optional
from openai import AsyncOpenAI
import os
import logging
from .api import ChatCompletionRequest, ChatCompletionResponse, ChatMessage
from .api import ChatMessage
from . import mcp
from fastmcp.server.dependencies import get_http_headers
@ -29,8 +24,6 @@ plano_client = AsyncOpenAI(
api_key="EMPTY", # Plano doesn't require a real API key
)
app = FastAPI()
async def rewrite_query_with_plano(
messages: List[ChatMessage],
@ -87,12 +80,14 @@ async def rewrite_query_with_plano(
return ""
async def query_rewriter(messages: List[ChatMessage]) -> List[ChatMessage]:
"""Chat completions endpoint that rewrites the last user query using Plano.
async def query_rewriter(body: dict, path: str) -> dict:
"""Rewrites the last user query in the request body using Plano.
Returns a dict with a 'messages' key containing the updated message list.
Receives the full request body dict and the API path hint (e.g. /v1/chat/completions).
Returns the body with the last user message rewritten for better retrieval.
"""
logger.info(f"Received chat completion request with {len(messages)} messages")
messages = [ChatMessage(**m) for m in body.get("messages", [])]
logger.info(f"Received request with {len(messages)} messages at path {path}")
# Get traceparent header from HTTP request using FastMCP's dependency function
headers = get_http_headers()
@ -109,57 +104,20 @@ async def query_rewriter(messages: List[ChatMessage]) -> List[ChatMessage]:
messages, traceparent_header, request_id
)
# Create updated messages with the rewritten query
updated_messages = messages.copy()
# Find and update the last user message with the rewritten query
updated_messages = [m.model_dump() for m in messages]
for i in range(len(updated_messages) - 1, -1, -1):
if updated_messages[i].role == "user":
original_query = updated_messages[i].content
updated_messages[i] = ChatMessage(role="user", content=rewritten_query)
if updated_messages[i]["role"] == "user":
logger.info(
f"Updated user query from '{original_query}' to '{rewritten_query}'"
f"Updated user query from '{updated_messages[i]['content']}' to '{rewritten_query}'"
)
updated_messages[i]["content"] = rewritten_query
break
# Return as dict to minimize text serialization
return [{"role": msg.role, "content": msg.content} for msg in updated_messages]
logger.info("Returning rewritten chat completion response")
return {**body, "messages": updated_messages}
# Register MCP tool only if mcp is available
if mcp is not None:
mcp.tool()(query_rewriter)
@app.post("/")
async def chat_completions_endpoint(
request_messages: List[ChatMessage], request: Request
) -> List[ChatMessage]:
"""FastAPI endpoint for chat completions with query rewriting."""
logger.info(
f"Received /v1/chat/completions request with {len(request_messages)} messages"
)
# Extract traceparent header
traceparent_header = request.headers.get("traceparent")
if traceparent_header:
logger.info(f"Received traceparent header: {traceparent_header}")
else:
logger.info("No traceparent header found")
# Call the query rewriter tool
updated_messages_data = await query_rewriter(request_messages)
# Convert back to ChatMessage objects
updated_messages = [ChatMessage(**msg) for msg in updated_messages_data]
logger.info("Returning rewritten chat completion response")
return updated_messages
def start_server(host: str = "0.0.0.0", port: int = 10501):
"""Start the FastAPI server for query rewriter."""
import uvicorn
logger.info(f"Starting Query Rewriter REST server on {host}:{port}")
uvicorn.run(app, host=host, port=port)

1599
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View file

File diff suppressed because it is too large Load diff

11
demos/filter_chains/model_listener_filter/Dockerfile Normal file
View file

@ -0,0 +1,11 @@
FROM python:3.14-slim
WORKDIR /app
RUN pip install --no-cache-dir fastapi uvicorn pydantic
COPY content_guard.py .
EXPOSE 10500
CMD ["uvicorn", "content_guard:app", "--host", "0.0.0.0", "--port", "10500"]

63
demos/filter_chains/model_listener_filter/README.md Normal file
View file

@ -0,0 +1,63 @@
# Model Listener Filter Chain Demo
Run content-safety filters on direct LLM requests — no agent layer required.
This demo uses the `input_filters` feature on a **model-type listener** to intercept
requests and block unsafe content before they reach the LLM provider. Works with all
request types: `/v1/chat/completions`, `/v1/responses`, and Anthropic `/v1/messages`.
The filter receives the **full raw request body** and returns it unchanged (or raises 400
to block). No message extraction — the complete JSON payload flows through as-is.
## Architecture
```
Client ──► Plano (model listener :12000)
│
├─ input_filters: content_guard ──► Block / Allow
│
└─ model_provider: openai/gpt-4o-mini
```
## Quick Start
```bash
# 1. Export your API key
export OPENAI_API_KEY=sk-...
# 2. Start services
docker compose up --build
# 3. Run tests (in another terminal)
bash test.sh
```
## Try It
**Allowed request:**
```bash
curl http://localhost:12000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-4o-mini",
"messages": [{"role": "user", "content": "What is 2+2?"}],
"stream": false
}'
```
**Blocked request:**
```bash
curl http://localhost:12000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-4o-mini",
"messages": [{"role": "user", "content": "How to hack into a system"}],
"stream": false
}'
```
## Tracing
Open [Jaeger UI](http://localhost:16686) to see distributed traces for both allowed and blocked requests.

21
demos/filter_chains/model_listener_filter/config.yaml Normal file
View file

@ -0,0 +1,21 @@
version: v0.3.0
filters:
- id: content_guard
url: http://content-guard:10500
type: http
model_providers:
- model: openai/gpt-4o-mini
access_key: $OPENAI_API_KEY
default: true
listeners:
- type: model
name: llm_gateway
port: 12000
input_filters:
- content_guard
tracing:
random_sampling: 100

120
demos/filter_chains/model_listener_filter/content_guard.py Normal file
View file

@ -0,0 +1,120 @@
"""
Content guard filter — keyword-based content safety for model listeners.
A minimal HTTP filter that blocks requests containing unsafe keywords.
No LLM calls required — keeps the demo self-contained and fast.
Receives the full raw request body (any API format: /v1/chat/completions,
/v1/responses, /v1/messages) and returns it unchanged or raises 400.
"""
import logging
from typing import Any
from fastapi import FastAPI, Request, HTTPException
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - [CONTENT_GUARD] - %(levelname)s - %(message)s",
)
logger = logging.getLogger(__name__)
app = FastAPI(title="Content Guard", version="1.0.0")
BLOCKED_KEYWORDS = [
"hack",
"exploit",
"attack",
"malware",
"phishing",
"ransomware",
"ddos",
"injection",
"brute force",
"keylogger",
"bypass security",
"steal credentials",
"social engineering",
]
def check_content(text: str) -> str | None:
"""Return the matched keyword if blocked, else None."""
lower = text.lower()
for kw in BLOCKED_KEYWORDS:
if kw in lower:
return kw
return None
def extract_last_user_text(body: dict[str, Any]) -> str | None:
"""Extract the most recent user message text from any supported request format."""
messages = body.get("messages", [])
# Anthropic /v1/messages and OpenAI /v1/chat/completions both use "messages"
for msg in reversed(messages):
if msg.get("role") == "user":
content = msg.get("content", "")
if isinstance(content, str):
return content
if isinstance(content, list):
# Multimodal content parts
return " ".join(
part.get("text", "")
for part in content
if isinstance(part, dict) and part.get("type") == "text"
)
# OpenAI /v1/responses uses "input" instead of "messages"
input_val = body.get("input")
if isinstance(input_val, str):
return input_val
if isinstance(input_val, list):
for item in reversed(input_val):
if isinstance(item, dict) and item.get("role") == "user":
content = item.get("content", "")
if isinstance(content, str):
return content
return None
@app.post("/{path:path}")
async def content_guard(path: str, request: Request) -> dict[str, Any]:
"""Block requests containing unsafe keywords. Returns the full request body unchanged.
The endpoint path encodes the API format:
/v1/chat/completions — check body["messages"]
/v1/responses — check body["input"]
/v1/messages — check body["messages"] (Anthropic format)
"""
endpoint = f"/{path}"
body = await request.json()
# /v1/responses uses "input" instead of "messages"
if endpoint == "/v1/responses":
input_val = body.get("input", "")
last_user_msg = input_val if isinstance(input_val, str) else None
else:
last_user_msg = extract_last_user_text(body)
if last_user_msg is None:
return body
matched = check_content(last_user_msg)
if matched:
logger.warning(f"Blocked request — matched keyword: '{matched}'")
raise HTTPException(
status_code=400,
detail={
"error": "content_blocked",
"message": f"Request blocked by content safety filter (matched: '{matched}').",
},
)
logger.info("Content check passed — forwarding request")
return body
@app.get("/health")
async def health():
return {"status": "healthy"}

25
demos/filter_chains/model_listener_filter/docker-compose.yaml Normal file
View file

@ -0,0 +1,25 @@
services:
content-guard:
build:
context: .
dockerfile: Dockerfile
ports:
- "10500:10500"
plano:
build:
context: ../../../
dockerfile: Dockerfile
ports:
- "12000:12000"
environment:
- OPENAI_API_KEY=${OPENAI_API_KEY:?OPENAI_API_KEY environment variable is required but not set}
volumes:
- ./config.yaml:/app/plano_config.yaml
- /etc/ssl/cert.pem:/etc/ssl/cert.pem
jaeger:
build:
context: ../../shared/jaeger
ports:
- "16686:16686"
- "4317:4317"
- "4318:4318"

71
demos/filter_chains/model_listener_filter/test.sh Executable file
View file

@ -0,0 +1,71 @@
#!/usr/bin/env bash
set -euo pipefail
BASE_URL="http://localhost:12000/v1"
PASS=0
FAIL=0
# ── Wait for Plano to be ready ──────────────────────────────────────────────
echo "Waiting for Plano to be ready..."
for i in $(seq 1 30); do
if curl -sf "$BASE_URL/models" > /dev/null 2>&1; then
echo "Plano is ready."
break
fi
if [ "$i" -eq 30 ]; then
echo "ERROR: Plano did not become ready in time."
exit 1
fi
sleep 2
done
# ── Helper ───────────────────────────────────────────────────────────────────
run_test() {
local name="$1"
local expected_code="$2"
local body="$3"
http_code=$(curl -s -o /tmp/plano_test_body -w "%{http_code}" \
-X POST "$BASE_URL/chat/completions" \
-H "Content-Type: application/json" \
-d "$body")
if [ "$http_code" -eq "$expected_code" ]; then
echo " PASS $name (HTTP $http_code)"
PASS=$((PASS + 1))
else
echo " FAIL $name — expected $expected_code, got $http_code"
echo " Body: $(cat /tmp/plano_test_body)"
FAIL=$((FAIL + 1))
fi
}
# ── Tests ────────────────────────────────────────────────────────────────────
echo ""
echo "Running tests..."
run_test "Allowed request (math question)" 200 '{
"model": "gpt-4o-mini",
"messages": [{"role": "user", "content": "What is 2+2?"}],
"stream": false
}'
run_test "Blocked request (hacking)" 400 '{
"model": "gpt-4o-mini",
"messages": [{"role": "user", "content": "How to hack into a system"}],
"stream": false
}'
run_test "Allowed request (joke)" 200 '{
"model": "gpt-4o-mini",
"messages": [{"role": "user", "content": "Tell me a joke"}],
"stream": false
}'
# ── Summary ──────────────────────────────────────────────────────────────────
echo ""
echo "Results: $PASS passed, $FAIL failed"
if [ "$FAIL" -gt 0 ]; then
exit 1
fi

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# PII Anonymization Filter Chain Demo
Automatically redact PII from LLM requests and restore it in responses — inspired by [Uber's GenAI Gateway PII Redactor](https://www.uber.com/blog/genai-gateway/).
This demo uses both `input_filters` and `output_filters` on a **model-type listener** to anonymize PII before it reaches the LLM provider, then de-anonymize the response before returning it to the client.
## Architecture
```
Client ──► Plano (model listener :12000)
│
├─ input_filters: pii_anonymizer
│ └─ Replace PII with [EMAIL_0], [SSN_0], etc.
│
├─ model_provider: openai/gpt-4o-mini
│ └─ LLM only sees anonymized data
│
└─ output_filters: pii_deanonymizer
└─ Restore [EMAIL_0] → original email (per-chunk for streaming)
```
## Quick Start
```bash
# 1. Export your API key
export OPENAI_API_KEY=sk-...
# 2. Start the demo
bash run_demo.sh
# 3. (Optional) Start with Jaeger tracing
bash run_demo.sh --with-ui
# 4. Run tests (in another terminal)
bash test.sh
# 5. Stop the demo
bash run_demo.sh down
```
## Try It
**Request with PII:**
```bash
curl http://localhost:12000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-4o-mini",
"messages": [{"role": "user", "content": "Contact john@example.com or call 555-123-4567"}],
"stream": false
}'
```
**Streaming with PII:**
```bash
curl --no-buffer http://localhost:12000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-4o-mini",
"messages": [{"role": "user", "content": "My SSN is 123-45-6789"}],
"stream": true
}'
```
## Verify Anonymization
Check the PII filter service logs in the terminal running `start_agents.sh`. You should see log lines like:
```
[PII_ANONYMIZER] - INFO - request_id=abc123 anonymized PII: EMAIL=1, PHONE=1
```
## Supported PII Types
| Type | Pattern | Example | Placeholder |
|------|---------|---------|-------------|
| SSN | `XXX-XX-XXXX` | `123-45-6789` | `[SSN_0]` |
| Credit Card | `XXXX XXXX XXXX XXXX` | `4111 1111 1111 1111` | `[CREDIT_CARD_0]` |
| Email | standard email format | `user@example.com` | `[EMAIL_0]` |
| Phone | US phone formats | `555-123-4567` | `[PHONE_0]` |
## Filter Contract
**Input filter (`/anonymize`)** receives the **full raw request body** and returns the modified body:
```json
{"model": "gpt-4o-mini", "messages": [{"role": "user", "content": "Contact john@example.com"}], "stream": true}
```
→ returns the same structure with PII replaced in the `messages` array.
**Output filter (`/deanonymize`)** receives the **raw LLM response bytes** and returns modified bytes:
- *Streaming*: raw SSE chunk, e.g. `data: {"choices":[{"delta":{"content":"Contact [EMAIL_0]"}}]}`
- *Non-streaming*: full JSON response body
## How Streaming De-anonymization Works
For streaming responses, each raw SSE chunk is sent through the output filter as it arrives from the LLM:
1. Plano receives a raw SSE chunk like `data: {"choices":[{"delta":{"content":"The email [EMAIL_0] belongs to..."}}]}`
2. The raw chunk bytes are sent to the `/deanonymize` endpoint
3. The filter parses the SSE, looks up the PII mapping (stored during anonymization), and replaces placeholders in the delta content
4. The restored chunk is returned and streamed to the client
Partial placeholders split across chunks (e.g., `[EMA` in one chunk, `IL_0]` in the next) are handled via internal buffering in the filter service.
## Limitations
- **No name detection** — regex cannot reliably detect names. For production, consider [Microsoft Presidio](https://github.com/microsoft/presidio) or spaCy NER.
- **US-centric patterns** — phone and SSN patterns are US-focused. International formats may not be detected.
- **Per-chunk latency** — streaming de-anonymization adds a small network round-trip per chunk (~1ms on local network).
## Tracing
Open [Jaeger UI](http://localhost:16686) to see distributed traces for requests flowing through the PII filter chain.

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version: v0.3.0
filters:
- id: pii_anonymizer
url: http://localhost:10501/anonymize
type: http
- id: pii_deanonymizer
url: http://localhost:10501/deanonymize
type: http
model_providers:
- model: openai/gpt-4o-mini
access_key: $OPENAI_API_KEY
default: true
- model: anthropic/claude-sonnet-4-20250514
access_key: $ANTHROPIC_API_KEY
listeners:
- type: model
name: llm_gateway
port: 12000
input_filters:
- pii_anonymizer
output_filters:
- pii_deanonymizer
tracing:
random_sampling: 100

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services:
jaeger:
build:
context: ../../shared/jaeger
ports:
- "16686:16686"
- "4317:4317"
- "4318:4318"
anythingllm:
image: mintplexlabs/anythingllm
restart: always
ports:
- "3001:3001"
cap_add:
- SYS_ADMIN
environment:
- STORAGE_DIR=/app/server/storage
- LLM_PROVIDER=generic-openai
- GENERIC_OPEN_AI_BASE_PATH=http://host.docker.internal:12000/v1
- GENERIC_OPEN_AI_MODEL_PREF=gpt-4o-mini
- GENERIC_OPEN_AI_MODEL_TOKEN_LIMIT=128000
- GENERIC_OPEN_AI_API_KEY=sk-placeholder
extra_hosts:
- "host.docker.internal:host-gateway"

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"""PII detection and anonymization utilities."""
import re
from typing import Any, Dict, List, Tuple
# Order matters: SSN before phone to avoid overlap
PII_PATTERNS = [
("SSN", re.compile(r"\b\d{3}-\d{2}-\d{4}\b")),
("CREDIT_CARD", re.compile(r"\b(?:\d{4}[-\s]?){3}\d{4}\b")),
("EMAIL", re.compile(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}")),
("PHONE", re.compile(r"(\+?1[-.\s]?)?\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4}")),
]
def anonymize_text(text: str) -> Tuple[str, Dict[str, str]]:
"""Replace PII with [TYPE_N] placeholders. Returns (anonymized_text, mapping)."""
mapping: Dict[str, str] = {}
counters: Dict[str, int] = {}
matched_spans: List[Tuple[int, int]] = []
for pii_type, pattern in PII_PATTERNS:
for match in pattern.finditer(text):
start, end = match.start(), match.end()
if any(s <= start < e or s < end <= e for s, e in matched_spans):
continue
matched_spans.append((start, end))
idx = counters.get(pii_type, 0)
counters[pii_type] = idx + 1
mapping[f"[{pii_type}_{idx}]"] = match.group()
# Replace right-to-left to preserve span indices
matched_spans.sort(reverse=True)
result = text
for start, end in matched_spans:
placeholder = next(k for k, v in mapping.items() if v == text[start:end])
result = result[:start] + placeholder + result[end:]
return result, mapping
def deanonymize_text(
text: str, mapping: Dict[str, str], buffer: str = ""
) -> Tuple[str, str]:
"""Replace placeholders back with original PII values.
Handles partial placeholders at chunk boundaries via a buffer.
Returns (processed_text, remaining_buffer).
"""
combined = buffer + text
# Build prefix set for all known placeholders (e.g. "[EMAIL_0" is a prefix of "[EMAIL_0]")
prefixes: set[str] = set()
for placeholder in mapping:
for i in range(1, len(placeholder)):
prefixes.add(placeholder[:i])
# If the tail looks like the start of a placeholder, hold it in the buffer
remaining_buffer = ""
last_bracket = combined.rfind("[")
if last_bracket != -1 and "]" not in combined[last_bracket:]:
tail = combined[last_bracket:]
if tail in prefixes:
remaining_buffer = tail
combined = combined[:last_bracket]
for placeholder, original in mapping.items():
combined = combined.replace(placeholder, original)
return combined, remaining_buffer
def anonymize_message_content(content: Any, all_mappings: Dict[str, str]) -> Any:
"""Anonymize string content or list of content parts."""
if isinstance(content, str):
anonymized, mapping = anonymize_text(content)
all_mappings.update(mapping)
return anonymized
if isinstance(content, list):
result = []
for part in content:
if isinstance(part, dict) and part.get("type") == "text":
anonymized, mapping = anonymize_text(part.get("text", ""))
all_mappings.update(mapping)
result.append({**part, "text": anonymized})
else:
result.append(part)
return result
return content

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"""
PII Anonymization filter — redact and restore PII in LLM requests/responses.
Inspired by Uber's GenAI Gateway PII Redactor. Two endpoints:
POST /anonymize — replace PII with placeholders (input filter)
POST /deanonymize — restore original PII from placeholders (output filter)
Input filter (/anonymize):
Receives the full raw request body (any API format). Anonymizes user message
content and returns the modified body.
Output filter (/deanonymize):
Receives raw LLM response bytes — SSE (streaming) or full JSON (non-streaming).
De-anonymizes content and returns modified bytes.
The path suffix encodes the upstream API format so each endpoint knows how to
parse the body (e.g. /anonymize/v1/chat/completions, /deanonymize/v1/messages).
"""
import logging
from typing import Any, Dict
from fastapi import FastAPI, Request
from fastapi.responses import Response
from pii import anonymize_text, anonymize_message_content
from store import get_mapping, store_mapping, deanonymize_sse, deanonymize_json
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - [PII_ANONYMIZER] - %(levelname)s - %(message)s",
)
logger = logging.getLogger(__name__)
app = FastAPI(title="PII Anonymizer", version="1.0.0")
@app.post("/anonymize/{path:path}")
async def anonymize(path: str, request: Request) -> dict[str, Any]:
"""Anonymize PII in user messages. Receives and returns the full raw request body.
The endpoint path encodes the API format:
/anonymize/v1/chat/completions — anonymize body["messages"]
/anonymize/v1/responses — anonymize body["input"] (string or items list)
/anonymize/v1/messages — anonymize body["messages"] (Anthropic format)
"""
request_id = request.headers.get("x-request-id", "unknown")
endpoint = f"/{path}"
body = await request.json()
all_mappings: Dict[str, str] = {}
if endpoint == "/v1/responses":
input_val = body.get("input", "")
if isinstance(input_val, str):
anonymized, mapping = anonymize_text(input_val)
all_mappings.update(mapping)
body = {**body, "input": anonymized}
elif isinstance(input_val, list):
items = [
(
{
**item,
"content": anonymize_message_content(
item.get("content", ""), all_mappings
),
}
if isinstance(item, dict) and item.get("role") == "user"
else item
)
for item in input_val
]
body = {**body, "input": items}
else:
# /v1/chat/completions and /v1/messages both use "messages"
messages = [
(
{
**msg,
"content": anonymize_message_content(
msg.get("content", ""), all_mappings
),
}
if msg.get("role") == "user"
else msg
)
for msg in body.get("messages", [])
]
if messages:
body = {**body, "messages": messages}
if all_mappings:
store_mapping(request_id, all_mappings)
logger.info("request_id=%s /anonymize mapping: %s", request_id, all_mappings)
else:
logger.info("request_id=%s no PII detected", request_id)
return body
@app.post("/deanonymize/{path:path}")
async def deanonymize(path: str, request: Request) -> Response:
"""De-anonymize PII placeholders in LLM response. Handles SSE (streaming) and JSON.
The path encodes the upstream API format:
/deanonymize/v1/chat/completions — OpenAI chat completions
/deanonymize/v1/messages — Anthropic messages
/deanonymize/v1/responses — OpenAI responses API
"""
endpoint = f"/{path}"
is_anthropic = endpoint == "/v1/messages"
request_id = request.headers.get("x-request-id", "unknown")
mapping = get_mapping(request_id)
raw_body = await request.body()
if not mapping:
logger.info("request_id=%s no mapping found, passing through", request_id)
return Response(content=raw_body, media_type="application/json")
body_str = raw_body.decode("utf-8", errors="replace")
if "data: " in body_str or "event: " in body_str:
return deanonymize_sse(request_id, body_str, mapping, is_anthropic)
return deanonymize_json(request_id, raw_body, body_str, mapping, is_anthropic)
@app.get("/health")
async def health():
return {"status": "healthy"}

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[project]
name = "pii_anonymizer"
version = "0.1.0"
description = "PII Anonymization filter for Plano filter chains"
readme = "README.md"
requires-python = ">=3.10,<3.14"
dependencies = [
"fastapi>=0.104.1",
"uvicorn>=0.24.0",
"pydantic>=2.0.0",
]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

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#!/bin/bash
set -e
# Function to start the demo
start_demo() {
# Step 1: Check if .env file exists
if [ -f ".env" ]; then
echo ".env file already exists. Skipping creation."
else
# Step 2: Create `.env` file and set OpenAI key
if [ -z "$OPENAI_API_KEY" ]; then
echo "Error: OPENAI_API_KEY environment variable is not set for the demo."
exit 1
fi
echo "Creating .env file..."
echo "OPENAI_API_KEY=$OPENAI_API_KEY" > .env
echo ".env file created with OPENAI_API_KEY."
fi
# Step 3: Optionally start UI services (Jaeger)
# Jaeger must start before Plano so it can bind the OTEL port (4317)
if [ "$1" == "--with-ui" ]; then
echo "Starting UI services (AnythingLLM, Jaeger)..."
docker compose up -d
fi
# Step 4: Start Plano
echo "Starting Plano with config.yaml..."
planoai up config.yaml
# Step 5: Start filter service natively
echo "Starting PII filter service..."
bash start_agents.sh &
}
# Function to stop the demo
stop_demo() {
# Stop filter service
echo "Stopping PII filter service..."
pkill -f start_agents.sh 2>/dev/null || true
# Stop Docker Compose services if running
docker compose down 2>/dev/null || true
# Stop Plano
echo "Stopping Plano..."
planoai down
}
# Main script logic
if [ "$1" == "down" ]; then
stop_demo
else
start_demo "$1"
fi

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#!/bin/bash
set -e
PIDS=()
log() { echo "$(date '+%F %T') - $*"; }
cleanup() {
log "Stopping agents..."
for PID in "${PIDS[@]}"; do
kill $PID 2>/dev/null && log "Stopped process $PID"
done
exit 0
}
trap cleanup EXIT INT TERM
log "Starting PII anonymizer service on port 10501..."
uv run uvicorn pii_anonymizer:app --host 0.0.0.0 --port 10501 &
PIDS+=($!)
for PID in "${PIDS[@]}"; do
wait "$PID"
done

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"""In-memory mapping store and LLM response processors for PII de-anonymization."""
import json
import logging
import threading
import time
from typing import Dict, Optional, Tuple
from fastapi.responses import Response
from pii import deanonymize_text
logger = logging.getLogger(__name__)
MAPPING_TTL_SECONDS = 300 # 5 minutes
_lock = threading.Lock()
_mappings: Dict[str, Tuple[Dict[str, str], float]] = {}
_buffers: Dict[str, str] = {} # partial placeholder buffers for streaming
def _cleanup_expired():
now = time.time()
expired = [k for k, (_, ts) in _mappings.items() if now - ts > MAPPING_TTL_SECONDS]
for k in expired:
del _mappings[k]
_buffers.pop(k, None)
def store_mapping(request_id: str, mapping: Dict[str, str]):
with _lock:
_cleanup_expired()
_mappings[request_id] = (mapping, time.time())
def get_mapping(request_id: str) -> Optional[Dict[str, str]]:
with _lock:
entry = _mappings.get(request_id)
return entry[0] if entry else None
def restore_streaming(request_id: str, content: str, mapping: Dict[str, str]) -> str:
"""Restore PII in one streaming chunk, maintaining the per-request partial buffer."""
with _lock:
buffer = _buffers.get(request_id, "")
restored, remaining = deanonymize_text(content, mapping, buffer)
with _lock:
if remaining:
_buffers[request_id] = remaining
else:
_buffers.pop(request_id, None)
if restored != content:
logger.info(
"request_id=%s restored '%s' -> '%s'", request_id, content, restored
)
return restored
def deanonymize_sse(
request_id: str, body_str: str, mapping: Dict[str, str], is_anthropic: bool
) -> Response:
result_lines = []
for line in body_str.split("\n"):
stripped = line.strip()
if not (stripped.startswith("data: ") and stripped[6:] != "[DONE]"):
result_lines.append(line)
continue
try:
chunk = json.loads(stripped[6:])
if is_anthropic:
# {"type": "content_block_delta", "delta": {"type": "text_delta", "text": "..."}}
if chunk.get("type") == "content_block_delta":
delta = chunk.get("delta", {})
if delta.get("type") == "text_delta" and delta.get("text"):
delta["text"] = restore_streaming(
request_id, delta["text"], mapping
)
else:
# {"choices": [{"delta": {"content": "..."}}]}
for choice in chunk.get("choices", []):
delta = choice.get("delta", {})
if delta.get("content"):
delta["content"] = restore_streaming(
request_id, delta["content"], mapping
)
result_lines.append("data: " + json.dumps(chunk))
except json.JSONDecodeError:
result_lines.append(line)
return Response(content="\n".join(result_lines), media_type="text/plain")
def deanonymize_json(
request_id: str,
raw_body: bytes,
body_str: str,
mapping: Dict[str, str],
is_anthropic: bool,
) -> Response:
try:
body = json.loads(body_str)
if is_anthropic:
# {"content": [{"type": "text", "text": "..."}]}
for part in body.get("content", []):
if (
isinstance(part, dict)
and part.get("type") == "text"
and part.get("text")
):
restored, _ = deanonymize_text(part["text"], mapping)
if restored != part["text"]:
logger.info(
"request_id=%s restored '%s' -> '%s'",
request_id,
part["text"],
restored,
)
part["text"] = restored
else:
# {"choices": [{"message": {"content": "..."}}]}
for choice in body.get("choices", []):
message = choice.get("message", {})
content = message.get("content")
if content and isinstance(content, str):
restored, _ = deanonymize_text(content, mapping)
if restored != content:
logger.info(
"request_id=%s restored '%s' -> '%s'",
request_id,
content,
restored,
)
message["content"] = restored
return Response(content=json.dumps(body), media_type="application/json")
except json.JSONDecodeError:
return Response(content=raw_body, media_type="application/json")

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#!/usr/bin/env bash
set -euo pipefail
BASE_URL="http://localhost:12000"
PASS=0
FAIL=0
# ── Wait for Plano to be ready ──────────────────────────────────────────────
echo "Waiting for Plano to be ready..."
for i in $(seq 1 30); do
if curl -sf "$BASE_URL/v1/models" > /dev/null 2>&1; then
echo "Plano is ready."
break
fi
if [ "$i" -eq 30 ]; then
echo "ERROR: Plano did not become ready in time."
exit 1
fi
sleep 2
done
# ── Helper ───────────────────────────────────────────────────────────────────
run_test() {
local name="$1"
local path="$2"
local expected_code="$3"
local body="$4"
http_code=$(curl -s -o /tmp/plano_test_body -w "%{http_code}" \
-X POST "$BASE_URL$path" \
-H "Content-Type: application/json" \
-d "$body")
if [ "$http_code" -eq "$expected_code" ]; then
echo " PASS $name (HTTP $http_code)"
PASS=$((PASS + 1))
else
echo " FAIL $name — expected $expected_code, got $http_code"
echo " Body: $(cat /tmp/plano_test_body)"
FAIL=$((FAIL + 1))
fi
}
# ── /v1/chat/completions ─────────────────────────────────────────────────────
echo ""
echo "=== /v1/chat/completions ==="
run_test "Non-streaming with PII (email + phone)" /v1/chat/completions 200 '{
"model": "gpt-4o-mini",
"messages": [{"role": "user", "content": "Contact me at john@example.com or call 555-123-4567"}],
"stream": false
}'
run_test "Streaming with PII (SSN)" /v1/chat/completions 200 '{
"model": "gpt-4o-mini",
"messages": [{"role": "user", "content": "My SSN is 123-45-6789, please help me file taxes"}],
"stream": true
}'
run_test "No PII (clean message)" /v1/chat/completions 200 '{
"model": "gpt-4o-mini",
"messages": [{"role": "user", "content": "What is 2+2?"}],
"stream": false
}'
run_test "Multiple PII types" /v1/chat/completions 200 '{
"model": "gpt-4o-mini",
"messages": [{"role": "user", "content": "Email: test@test.com, SSN: 987-65-4321, Card: 4111 1111 1111 1111"}],
"stream": false
}'
# ── /v1/responses ────────────────────────────────────────────────────────────
echo ""
echo "=== /v1/responses ==="
run_test "Non-streaming with PII (email)" /v1/responses 200 '{
"model": "gpt-4o-mini",
"input": "My email is jane@example.com — can you summarize it?"
}'
run_test "Non-streaming with PII (credit card)" /v1/responses 200 '{
"model": "gpt-4o-mini",
"input": "I need help disputing a charge on card 4111 1111 1111 1111"
}'
run_test "No PII" /v1/responses 200 '{
"model": "gpt-4o-mini",
"input": "What is the capital of France?"
}'
# ── /v1/messages (Anthropic) ─────────────────────────────────────────────────
echo ""
echo "=== /v1/messages ==="
run_test "Non-streaming with PII (phone)" /v1/messages 200 '{
"model": "claude-sonnet-4-20250514",
"max_tokens": 256,
"messages": [{"role": "user", "content": "Call me at 555-867-5309 to discuss my account"}]
}'
run_test "Non-streaming with PII (SSN)" /v1/messages 200 '{
"model": "claude-sonnet-4-20250514",
"max_tokens": 256,
"messages": [{"role": "user", "content": "My SSN is 123-45-6789"}]
}'
run_test "No PII" /v1/messages 200 '{
"model": "claude-sonnet-4-20250514",
"max_tokens": 256,
"messages": [{"role": "user", "content": "Hello, how are you?"}]
}'
# ── Summary ──────────────────────────────────────────────────────────────────
echo ""
echo "Results: $PASS passed, $FAIL failed"
echo ""
echo "To verify PII was anonymized before reaching the LLM, check the terminal running start_agents.sh"
if [ "$FAIL" -gt 0 ]; then
exit 1
fi

272
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113
demos/llm_routing/model_routing_service/README.md
View file

@ -1,6 +1,54 @@
# Model Routing Service Demo
This demo shows how to use the `/routing/v1/*` endpoints to get routing decisions without proxying requests to an LLM. The endpoint accepts standard LLM request formats and returns which model Plano's router would select.
Plano is an AI-native proxy and data plane for agentic apps — with built-in orchestration, safety, observability, and intelligent LLM routing.
```
┌───────────┐ ┌─────────────────────────────────┐ ┌──────────────┐
│ Client │ ───► │ Plano │ ───► │ OpenAI │
│ (any │ │ │ │ Anthropic │
│ language)│ │ Arch-Router (1.5B model) │ │ Any Provider│
└───────────┘ │ analyzes intent → picks model │ └──────────────┘
└─────────────────────────────────┘
```
- **One endpoint, many models** — apps call Plano using standard OpenAI/Anthropic APIs; Plano handles provider selection, keys, and failover
- **Intelligent routing** — a lightweight 1.5B router model classifies user intent and picks the best model per request
- **Platform governance** — centralize API keys, rate limits, guardrails, and observability without touching app code
- **Runs anywhere** — single binary; self-host the router for full data privacy
## How Routing Works
The entire routing configuration is plain YAML — no code:
```yaml
model_providers:
- model: openai/gpt-4o-mini
default: true # fallback for unmatched requests
- model: openai/gpt-4o
routing_preferences:
- name: complex_reasoning
description: complex reasoning tasks, multi-step analysis
- model: anthropic/claude-sonnet-4-20250514
routing_preferences:
- name: code_generation
description: generating new code, writing functions
```
When a request arrives, Plano sends the conversation and routing preferences to Arch-Router, which classifies the intent and returns the matching route:
```
1. Request arrives → "Write binary search in Python"
2. Preferences serialized → [{"name":"code_generation", ...}, {"name":"complex_reasoning", ...}]
3. Arch-Router classifies → {"route": "code_generation"}
4. Route → Model lookup → code_generation → anthropic/claude-sonnet-4-20250514
5. Request forwarded → Claude generates the response
```
No match? Arch-Router returns `other` → Plano falls back to the default model.
The `/routing/v1/*` endpoints return the routing decision **without** forwarding to the LLM — useful for testing and validating routing behavior before going to production.
## Setup
@ -112,6 +160,69 @@ routing:
Without the `X-Session-Id` header, routing runs fresh every time (no breaking change).
## Kubernetes Deployment (Self-hosted Arch-Router on GPU)
To run Arch-Router in-cluster using vLLM instead of the default hosted endpoint:
**0. Check your GPU node labels and taints**
```bash
kubectl get nodes --show-labels | grep -i gpu
kubectl get node <gpu-node-name> -o jsonpath='{.spec.taints}'
```
GPU nodes commonly have a `nvidia.com/gpu:NoSchedule` taint — `vllm-deployment.yaml` includes a matching toleration. If you have multiple GPU node pools and need to pin to a specific one, uncomment and set the `nodeSelector` in `vllm-deployment.yaml` using the label for your cloud provider.
**1. Deploy Arch-Router and Plano:**
```bash
# arch-router deployment
kubectl apply -f vllm-deployment.yaml
# plano deployment
kubectl create secret generic plano-secrets \
--from-literal=OPENAI_API_KEY=$OPENAI_API_KEY \
--from-literal=ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY
kubectl create configmap plano-config \
--from-file=plano_config.yaml=config_k8s.yaml \
--dry-run=client -o yaml | kubectl apply -f -
kubectl apply -f plano-deployment.yaml
```
**3. Wait for both pods to be ready:**
```bash
# Arch-Router downloads the model (~1 min) then vLLM loads it (~2 min)
kubectl get pods -l app=arch-router -w
kubectl rollout status deployment/plano
```
**4. Test:**
```bash
kubectl port-forward svc/plano 12000:12000
./demo.sh
```
To confirm requests are hitting your in-cluster Arch-Router (not just health checks):
```bash
kubectl logs -l app=arch-router -f --tail=0
# Look for POST /v1/chat/completions entries
```
**Updating the config:**
```bash
kubectl create configmap plano-config \
--from-file=plano_config.yaml=config_k8s.yaml \
--dry-run=client -o yaml | kubectl apply -f -
kubectl rollout restart deployment/plano
```
## Demo Output
```

33
demos/llm_routing/model_routing_service/config_k8s.yaml Normal file
View file

@ -0,0 +1,33 @@
version: v0.3.0
overrides:
llm_routing_model: plano/Arch-Router
listeners:
- type: model
name: model_listener
port: 12000
model_providers:
- model: plano/Arch-Router
base_url: http://arch-router:10000
- model: openai/gpt-4o-mini
access_key: $OPENAI_API_KEY
default: true
- model: openai/gpt-4o
access_key: $OPENAI_API_KEY
routing_preferences:
- name: complex_reasoning
description: complex reasoning tasks, multi-step analysis, or detailed explanations
- model: anthropic/claude-sonnet-4-20250514
access_key: $ANTHROPIC_API_KEY
routing_preferences:
- name: code_generation
description: generating new code, writing functions, or creating boilerplate
tracing:
random_sampling: 100

68
demos/llm_routing/model_routing_service/plano-deployment.yaml Normal file
View file

@ -0,0 +1,68 @@
apiVersion: apps/v1
kind: Deployment
metadata:
name: plano
labels:
app: plano
spec:
replicas: 1
selector:
matchLabels:
app: plano
template:
metadata:
labels:
app: plano
spec:
containers:
- name: plano
image: katanemo/plano:0.4.12
ports:
- containerPort: 12000 # LLM gateway (chat completions, model routing)
name: llm-gateway
envFrom:
- secretRef:
name: plano-secrets
env:
- name: LOG_LEVEL
value: "info"
volumeMounts:
- name: plano-config
mountPath: /app/plano_config.yaml
subPath: plano_config.yaml
readOnly: true
readinessProbe:
httpGet:
path: /healthz
port: 12000
initialDelaySeconds: 5
periodSeconds: 10
livenessProbe:
httpGet:
path: /healthz
port: 12000
initialDelaySeconds: 10
periodSeconds: 30
resources:
requests:
memory: "256Mi"
cpu: "250m"
limits:
memory: "512Mi"
cpu: "1000m"
volumes:
- name: plano-config
configMap:
name: plano-config
---
apiVersion: v1
kind: Service
metadata:
name: plano
spec:
selector:
app: plano
ports:
- name: llm-gateway
port: 12000
targetPort: 12000

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