Merge branch 'main' into adil/agent_format

This commit is contained in:
Adil Hafeez 2025-09-30 11:39:34 -07:00
commit 2cebc0c85f
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GPG key ID: 9B18EF7691369645
33 changed files with 1369 additions and 421 deletions

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@ -30,7 +30,7 @@ jobs:
- name: build arch docker image
run: |
cd ../../ && docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.13 -t katanemo/archgw:latest
cd ../../ && docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.14 -t katanemo/archgw:latest
- name: start archgw
env:

View file

@ -24,7 +24,7 @@ jobs:
- name: build arch docker image
run: |
docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.13
docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.14
- name: install poetry
run: |

View file

@ -24,7 +24,7 @@ jobs:
- name: build arch docker image
run: |
docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.13
docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.14
- name: install poetry
run: |

View file

@ -24,7 +24,7 @@ jobs:
- name: build arch docker image
run: |
docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.13
docker build -f arch/Dockerfile . -t katanemo/archgw -t katanemo/archgw:0.3.14
- name: validate arch config
run: |

View file

@ -83,7 +83,7 @@ Arch's CLI allows you to manage and interact with the Arch gateway efficiently.
```console
$ python3.12 -m venv venv
$ source venv/bin/activate # On Windows, use: venv\Scripts\activate
$ pip install archgw==0.3.13
$ pip install archgw==0.3.14
```
### Use Arch as a LLM Router
@ -272,7 +272,7 @@ endpoints:
```sh
$ archgw up arch_config.yaml
2024-12-05 16:56:27,979 - cli.main - INFO - Starting archgw cli version: 0.3.13
2024-12-05 16:56:27,979 - cli.main - INFO - Starting archgw cli version: 0.3.14
2024-12-05 16:56:28,485 - cli.utils - INFO - Schema validation successful!
2024-12-05 16:56:28,485 - cli.main - INFO - Starting arch model server and arch gateway
2024-12-05 16:56:51,647 - cli.core - INFO - Container is healthy!

View file

@ -140,7 +140,7 @@ static_resources:
route:
auto_host_rewrite: true
cluster: {{ llm_cluster_name }}
timeout: 60s
timeout: 300s
{% endfor %}
{% if agent_orchestrator %}
@ -153,7 +153,7 @@ static_resources:
route:
auto_host_rewrite: true
cluster: {{ agent_orchestrator }}
timeout: 60s
timeout: 300s
{% endif %}
http_filters:
- name: envoy.filters.http.compressor
@ -268,7 +268,7 @@ static_resources:
route:
auto_host_rewrite: true
cluster: {{ internal_cluster }}
timeout: 60s
timeout: 300s
{% endfor %}
{% for cluster_name, cluster in arch_clusters.items() %}
@ -281,7 +281,7 @@ static_resources:
route:
auto_host_rewrite: true
cluster: {{ cluster_name }}
timeout: 60s
timeout: 300s
{% endfor %}
http_filters:
- name: envoy.filters.http.router
@ -538,7 +538,7 @@ static_resources:
route:
auto_host_rewrite: true
cluster: {{ llm_cluster_name }}
timeout: 60s
timeout: 300s
{% endfor %}
- match:
prefix: "/"

View file

@ -19,7 +19,7 @@ source venv/bin/activate
### Step 3: Run the build script
```bash
pip install archgw==0.3.13
pip install archgw==0.3.14
```
## Uninstall Instructions: archgw CLI

View file

@ -10,4 +10,4 @@ SERVICE_NAME_MODEL_SERVER = "model_server"
SERVICE_ALL = "all"
MODEL_SERVER_LOG_FILE = "~/archgw_logs/modelserver.log"
ARCHGW_DOCKER_NAME = "archgw"
ARCHGW_DOCKER_IMAGE = os.getenv("ARCHGW_DOCKER_IMAGE", "katanemo/archgw:0.3.13")
ARCHGW_DOCKER_IMAGE = os.getenv("ARCHGW_DOCKER_IMAGE", "katanemo/archgw:0.3.14")

View file

@ -186,3 +186,93 @@ def stop_arch_modelserver():
except subprocess.CalledProcessError as e:
log.info(f"Failed to start model_server. Please check archgw_modelserver logs")
sys.exit(1)
def start_cli_agent(arch_config_file=None, settings_json="{}"):
"""Start a CLI client connected to Arch."""
with open(arch_config_file, "r") as file:
arch_config = file.read()
arch_config_yaml = yaml.safe_load(arch_config)
# Get egress listener configuration
egress_config = arch_config_yaml.get("listeners", {}).get("egress_traffic", {})
host = egress_config.get("host", "127.0.0.1")
port = egress_config.get("port", 12000)
# Parse additional settings from command line
try:
additional_settings = json.loads(settings_json) if settings_json else {}
except json.JSONDecodeError:
log.error("Settings must be valid JSON")
sys.exit(1)
# Set up environment variables
env = os.environ.copy()
env.update(
{
"ANTHROPIC_AUTH_TOKEN": "test", # Use test token for arch
"ANTHROPIC_API_KEY": "",
"ANTHROPIC_BASE_URL": f"http://{host}:{port}",
"NO_PROXY": host,
"DISABLE_TELEMETRY": "true",
"DISABLE_COST_WARNINGS": "true",
"API_TIMEOUT_MS": "600000",
}
)
# Set ANTHROPIC_SMALL_FAST_MODEL from additional_settings or model alias
if "ANTHROPIC_SMALL_FAST_MODEL" in additional_settings:
env["ANTHROPIC_SMALL_FAST_MODEL"] = additional_settings[
"ANTHROPIC_SMALL_FAST_MODEL"
]
else:
# Check if arch.claude.code.small.fast alias exists in model_aliases
model_aliases = arch_config_yaml.get("model_aliases", {})
if "arch.claude.code.small.fast" in model_aliases:
env["ANTHROPIC_SMALL_FAST_MODEL"] = "arch.claude.code.small.fast"
else:
log.info(
"Tip: Set an alias 'arch.claude.code.small.fast' in your model_aliases config to set a small fast model Claude Code"
)
log.info("Or provide ANTHROPIC_SMALL_FAST_MODEL in --settings JSON")
# Non-interactive mode configuration from additional_settings only
if additional_settings.get("NON_INTERACTIVE_MODE", False):
env.update(
{
"CI": "true",
"FORCE_COLOR": "0",
"NODE_NO_READLINE": "1",
"TERM": "dumb",
}
)
# Build claude command arguments
claude_args = []
# Add settings if provided, excluding those already handled as environment variables
if additional_settings:
# Filter out settings that are already processed as environment variables
claude_settings = {
k: v
for k, v in additional_settings.items()
if k not in ["ANTHROPIC_SMALL_FAST_MODEL", "NON_INTERACTIVE_MODE"]
}
if claude_settings:
claude_args.append(f"--settings={json.dumps(claude_settings)}")
# Use claude from PATH
claude_path = "claude"
log.info(f"Connecting Claude Code Agent to Arch at {host}:{port}")
try:
subprocess.run([claude_path] + claude_args, env=env, check=True)
except subprocess.CalledProcessError as e:
log.error(f"Error starting claude: {e}")
sys.exit(1)
except FileNotFoundError:
log.error(
f"{claude_path} not found. Make sure Claude Code is installed: npm install -g @anthropic-ai/claude-code"
)
sys.exit(1)

View file

@ -4,13 +4,20 @@ import sys
import subprocess
import multiprocessing
import importlib.metadata
import json
from cli import targets
from cli.docker_cli import docker_validate_archgw_schema, stream_gateway_logs
from cli.docker_cli import (
docker_validate_archgw_schema,
stream_gateway_logs,
docker_container_status,
)
from cli.utils import (
getLogger,
get_llm_provider_access_keys,
has_ingress_listener,
load_env_file_to_dict,
stream_access_logs,
find_config_file,
)
from cli.core import (
start_arch_modelserver,
@ -18,9 +25,11 @@ from cli.core import (
start_arch,
stop_docker_container,
download_models_from_hf,
start_cli_agent,
)
from cli.consts import (
ARCHGW_DOCKER_IMAGE,
ARCHGW_DOCKER_NAME,
KATANEMO_DOCKERHUB_REPO,
SERVICE_NAME_ARCHGW,
SERVICE_NAME_MODEL_SERVER,
@ -170,12 +179,8 @@ def up(file, path, service, foreground):
start_arch_modelserver(foreground)
return
if file:
# If a file is provided, process that file
arch_config_file = os.path.abspath(file)
else:
# If no file is provided, use the path and look for arch_config.yaml
arch_config_file = os.path.abspath(os.path.join(path, "arch_config.yaml"))
# Use the utility function to find config file
arch_config_file = find_config_file(path, file)
# Check if the file exists
if not os.path.exists(arch_config_file):
@ -183,7 +188,6 @@ def up(file, path, service, foreground):
return
log.info(f"Validating {arch_config_file}")
(
validation_return_code,
validation_stdout,
@ -240,8 +244,15 @@ def up(file, path, service, foreground):
if service == SERVICE_NAME_ARCHGW:
start_arch(arch_config_file, env, foreground=foreground)
else:
download_models_from_hf()
start_arch_modelserver(foreground)
# Check if ingress_traffic listener is configured before starting model_server
if has_ingress_listener(arch_config_file):
download_models_from_hf()
start_arch_modelserver(foreground)
else:
log.info(
"Skipping model_server startup: no ingress_traffic listener configured in arch_config.yaml"
)
start_arch(arch_config_file, env, foreground=foreground)
@ -321,10 +332,51 @@ def logs(debug, follow):
archgw_process.terminate()
@click.command()
@click.argument("type", type=click.Choice(["claude"]), required=True)
@click.argument("file", required=False) # Optional file argument
@click.option(
"--path", default=".", help="Path to the directory containing arch_config.yaml"
)
@click.option(
"--settings",
default="{}",
help="Additional settings as JSON string for the CLI agent.",
)
def cli_agent(type, file, path, settings):
"""Start a CLI agent connected to Arch.
CLI_AGENT: The type of CLI agent to start (currently only 'claude' is supported)
"""
# Check if archgw docker container is running
archgw_status = docker_container_status(ARCHGW_DOCKER_NAME)
if archgw_status != "running":
log.error(f"archgw docker container is not running (status: {archgw_status})")
log.error("Please start archgw using the 'archgw up' command.")
sys.exit(1)
# Determine arch_config.yaml path
arch_config_file = find_config_file(path, file)
if not os.path.exists(arch_config_file):
log.error(f"Config file not found: {arch_config_file}")
sys.exit(1)
try:
start_cli_agent(arch_config_file, settings)
except SystemExit:
# Re-raise SystemExit to preserve exit codes
raise
except Exception as e:
click.echo(f"Error: {e}")
sys.exit(1)
main.add_command(up)
main.add_command(down)
main.add_command(build)
main.add_command(logs)
main.add_command(cli_agent)
main.add_command(generate_prompt_targets)
if __name__ == "__main__":

View file

@ -21,6 +21,22 @@ def getLogger(name="cli"):
log = getLogger(__name__)
def has_ingress_listener(arch_config_file):
"""Check if the arch config file has ingress_traffic listener configured."""
try:
with open(arch_config_file) as f:
arch_config_dict = yaml.safe_load(f)
ingress_traffic = arch_config_dict.get("listeners", {}).get(
"ingress_traffic", {}
)
return bool(ingress_traffic)
except Exception as e:
log.error(f"Error reading config file {arch_config_file}: {e}")
return False
def convert_legacy_llm_providers(
listeners: dict | list, llm_providers: list | None
) -> tuple[list, dict | None, dict | None]:
@ -147,6 +163,19 @@ def load_env_file_to_dict(file_path):
return env_dict
def find_config_file(path=".", file=None):
"""Find the appropriate config file path."""
if file:
# If a file is provided, process that file
return os.path.abspath(file)
else:
# If no file is provided, use the path and look for arch_config.yaml first, then config.yaml for convenience
arch_config_file = os.path.abspath(os.path.join(path, "config.yaml"))
if not os.path.exists(arch_config_file):
arch_config_file = os.path.abspath(os.path.join(path, "arch_config.yaml"))
return arch_config_file
def stream_access_logs(follow):
"""
Get the archgw access logs

284
arch/tools/poetry.lock generated
View file

@ -2,7 +2,7 @@
[[package]]
name = "archgw_modelserver"
version = "0.3.13"
version = "0.3.14"
description = "A model server for serving models"
optional = false
python-versions = "*"
@ -139,72 +139,100 @@ referencing = ">=0.31.0"
[[package]]
name = "markupsafe"
version = "3.0.2"
version = "3.0.3"
description = "Safely add untrusted strings to HTML/XML markup."
optional = false
python-versions = ">=3.9"
files = [
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{file = "MarkupSafe-3.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9e2d922824181480953426608b81967de705c3cef4d1af983af849d7bd619158"},
{file = "MarkupSafe-3.0.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:38a9ef736c01fccdd6600705b09dc574584b89bea478200c5fbf112a6b0d5579"},
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]
[[package]]
@ -587,4 +635,4 @@ files = [
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "9b8ccf6f43b7bdd14d8e0bb20decbb4dca099737c54cc93999b78d58956dd9ab"
content-hash = "18a3144509286d68efc9711706943ace163d0d35cd3e700511b77d8c2ac1b303"

View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "archgw"
version = "0.3.13"
version = "0.3.14"
description = "Python-based CLI tool to manage Arch Gateway."
authors = ["Katanemo Labs, Inc."]
packages = [
@ -10,7 +10,7 @@ readme = "README.md"
[tool.poetry.dependencies]
python = "^3.10"
archgw_modelserver = "^0.3.13"
archgw_modelserver = "^0.3.14"
click = "^8.1.7"
jinja2 = "^3.1.4"
jsonschema = "^4.23.0"

View file

@ -135,8 +135,9 @@ pub async fn chat(
});
const MAX_MESSAGE_LENGTH: usize = 50;
let latest_message_for_log = if latest_message_for_log.len() > MAX_MESSAGE_LENGTH {
format!("{}...", &latest_message_for_log[..MAX_MESSAGE_LENGTH])
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
};

File diff suppressed because one or more lines are too long

View file

@ -88,6 +88,7 @@ pub struct ChatCompletionsRequest {
pub prediction: Option<StaticContent>,
// pub reasoning_effect: Option<bool>, // GOOD FIRST ISSUE: Future support for reasoning effects
pub response_format: Option<Value>,
pub reasoning_effort: Option<String>, // e.g., "none", "low", "medium", "high"
// pub safety_identifier: Option<String>, // GOOD FIRST ISSUE: Future support for safety identifiers
pub seed: Option<i32>,
pub service_tier: Option<String>,
@ -116,6 +117,13 @@ impl ChatCompletionsRequest {
self.max_tokens = None;
}
}
pub fn fix_temperature_if_gpt5(&mut self) {
let model = self.model.as_str();
if model.starts_with("gpt-5") {
self.temperature = Some(1.0);
}
}
}
// ============================================================================
@ -598,6 +606,7 @@ impl TryFrom<&[u8]> for ChatCompletionsRequest {
let mut req: ChatCompletionsRequest = serde_json::from_slice(bytes).map_err(OpenAIStreamError::from)?;
// Use the centralized suppression logic
req.suppress_max_tokens_if_o3();
req.fix_temperature_if_gpt5();
Ok(req)
}
}

View file

@ -111,6 +111,7 @@ impl TryFrom<AnthropicMessagesRequest> for ChatCompletionsRequest {
..Default::default()
};
_chat_completions_req.suppress_max_tokens_if_o3();
_chat_completions_req.fix_temperature_if_gpt5();
Ok(_chat_completions_req)
}
}
@ -352,6 +353,7 @@ impl TryFrom<ChatCompletionsStreamResponse> for MessagesStreamEvent {
let choice = &resp.choices[0];
// Handle final chunk with usage
let has_usage = resp.usage.is_some();
if let Some(usage) = resp.usage {
if let Some(finish_reason) = &choice.finish_reason {
let anthropic_stop_reason: MessagesStopReason = finish_reason.clone().into();
@ -403,11 +405,27 @@ impl TryFrom<ChatCompletionsStreamResponse> for MessagesStreamEvent {
return convert_tool_call_deltas(tool_calls.clone());
}
// Handle finish reason
// Handle finish reason - generate MessageDelta only (MessageStop comes later)
if let Some(finish_reason) = &choice.finish_reason {
if *finish_reason == FinishReason::Stop {
return Ok(MessagesStreamEvent::MessageStop);
// If we have usage data, it was already handled above
// If not, we need to generate MessageDelta with default usage
if !has_usage {
let anthropic_stop_reason: MessagesStopReason = finish_reason.clone().into();
return Ok(MessagesStreamEvent::MessageDelta {
delta: MessagesMessageDelta {
stop_reason: anthropic_stop_reason,
stop_sequence: None,
},
usage: MessagesUsage {
input_tokens: 0,
output_tokens: 0,
cache_creation_input_tokens: None,
cache_read_input_tokens: None,
},
});
}
// If usage was already handled above, we don't need to do anything more here
// MessageStop will be handled when [DONE] is encountered
}
// Default to ping for unhandled cases
@ -468,18 +486,6 @@ impl TryFrom<MessagesMessage> for Vec<Message> {
}
MessagesMessageContent::Blocks(blocks) => {
let (content_parts, tool_calls, tool_results) = blocks.split_for_openai()?;
// Create main message
let content = build_openai_content(content_parts, &tool_calls);
let main_message = Message {
role: message.role.into(),
content,
name: None,
tool_calls: if tool_calls.is_empty() { None } else { Some(tool_calls) },
tool_call_id: None,
};
result.push(main_message);
// Add tool result messages
for (tool_use_id, result_text, _is_error) in tool_results {
result.push(Message {
@ -490,6 +496,20 @@ impl TryFrom<MessagesMessage> for Vec<Message> {
tool_call_id: Some(tool_use_id),
});
}
// Only create main message if there's actual content or tool calls
// Skip creating empty content messages (e.g., when message only contains tool_result blocks)
if !content_parts.is_empty() || !tool_calls.is_empty() {
let content = build_openai_content(content_parts, &tool_calls);
let main_message = Message {
role: message.role.into(),
content,
name: None,
tool_calls: if tool_calls.is_empty() { None } else { Some(tool_calls) },
tool_call_id: None,
};
result.push(main_message);
}
}
}
@ -515,9 +535,11 @@ impl TryFrom<Message> for MessagesMessage {
MessagesContentBlock::ToolResult {
tool_use_id: tool_call_id,
is_error: None,
content: vec![MessagesContentBlock::Text {
content: ToolResultContent::Blocks(vec![MessagesContentBlock::Text {
text: message.content.extract_text(),
}],
cache_control: None,
}]),
cache_control: None,
},
]),
});
@ -551,7 +573,7 @@ impl ContentUtils<ToolCall> for Vec<MessagesContentBlock> {
for block in self {
match block {
MessagesContentBlock::ToolUse { id, name, input } |
MessagesContentBlock::ToolUse { id, name, input, .. } |
MessagesContentBlock::ServerToolUse { id, name, input } |
MessagesContentBlock::McpToolUse { id, name, input } => {
let arguments = serde_json::to_string(&input)?;
@ -575,7 +597,7 @@ impl ContentUtils<ToolCall> for Vec<MessagesContentBlock> {
for block in self {
match block {
MessagesContentBlock::Text { text } => {
MessagesContentBlock::Text { text, .. } => {
content_parts.push(ContentPart::Text { text: text.clone() });
}
MessagesContentBlock::Image { source } => {
@ -587,7 +609,7 @@ impl ContentUtils<ToolCall> for Vec<MessagesContentBlock> {
},
});
}
MessagesContentBlock::ToolUse { id, name, input } |
MessagesContentBlock::ToolUse { id, name, input, .. } |
MessagesContentBlock::ServerToolUse { id, name, input } |
MessagesContentBlock::McpToolUse { id, name, input } => {
let arguments = serde_json::to_string(&input)?;
@ -597,7 +619,10 @@ impl ContentUtils<ToolCall> for Vec<MessagesContentBlock> {
function: FunctionCall { name: name.clone(), arguments },
});
}
MessagesContentBlock::ToolResult { tool_use_id, content, is_error } |
MessagesContentBlock::ToolResult { tool_use_id, content, is_error, .. } => {
let result_text = content.extract_text();
tool_results.push((tool_use_id.clone(), result_text, is_error.unwrap_or(false)));
}
MessagesContentBlock::WebSearchToolResult { tool_use_id, content, is_error } |
MessagesContentBlock::CodeExecutionToolResult { tool_use_id, content, is_error } |
MessagesContentBlock::McpToolResult { tool_use_id, content, is_error } => {
@ -819,7 +844,7 @@ fn build_openai_content(content_parts: Vec<ContentPart>, tool_calls: &[ToolCall]
fn build_anthropic_content(content_blocks: Vec<MessagesContentBlock>) -> MessagesMessageContent {
if content_blocks.len() == 1 {
match &content_blocks[0] {
MessagesContentBlock::Text { text } => MessagesMessageContent::Single(text.clone()),
MessagesContentBlock::Text { text, .. } => MessagesMessageContent::Single(text.clone()),
_ => MessagesMessageContent::Blocks(content_blocks),
}
} else if content_blocks.is_empty() {
@ -835,12 +860,11 @@ fn convert_anthropic_content_to_openai(content: &[MessagesContentBlock]) -> Resu
for block in content {
match block {
MessagesContentBlock::Text { text } => {
MessagesContentBlock::Text { text, .. } => {
text_parts.push(text.clone());
}
MessagesContentBlock::Thinking { text } => {
// Include thinking as regular text for OpenAI
text_parts.push(format!("[Thinking: {}]", text));
MessagesContentBlock::Thinking { thinking, .. } => {
text_parts.push(format!("thinking: {}", thinking));
}
_ => {
// Skip other content types for basic text conversion
@ -860,14 +884,14 @@ fn convert_openai_message_to_anthropic_content(message: &Message) -> Result<Vec<
match &message.content {
MessageContent::Text(text) => {
if !text.is_empty() {
blocks.push(MessagesContentBlock::Text { text: text.clone() });
blocks.push(MessagesContentBlock::Text { text: text.clone(), cache_control: None });
}
}
MessageContent::Parts(parts) => {
for part in parts {
match part {
ContentPart::Text { text } => {
blocks.push(MessagesContentBlock::Text { text: text.clone() });
blocks.push(MessagesContentBlock::Text { text: text.clone(), cache_control: None });
}
ContentPart::ImageUrl { image_url } => {
let source = convert_image_url_to_source(image_url);
@ -886,6 +910,7 @@ fn convert_openai_message_to_anthropic_content(message: &Message) -> Result<Vec<
id: tool_call.id.clone(),
name: tool_call.function.name.clone(),
input,
cache_control: None,
});
}
}
@ -984,6 +1009,21 @@ fn convert_content_delta(delta: MessagesContentDelta) -> Result<ChatCompletionsS
None,
))
}
MessagesContentDelta::ThinkingDelta { thinking } => {
Ok(create_openai_chunk(
"stream",
"unknown",
MessageDelta {
role: None,
content: Some(format!("thinking: {}", thinking)),
refusal: None,
function_call: None,
tool_calls: None,
},
None,
None,
))
}
MessagesContentDelta::InputJsonDelta { partial_json } => {
Ok(create_openai_chunk(
"stream",
@ -1023,6 +1063,7 @@ fn convert_tool_call_deltas(tool_calls: Vec<ToolCallDelta>) -> Result<MessagesSt
id: id.clone(),
name: name.clone(),
input: Value::Object(serde_json::Map::new()),
cache_control: None,
},
});
}
@ -1254,6 +1295,7 @@ mod tests {
id: "call_123".to_string(),
name: "get_weather".to_string(),
input: json!({}),
cache_control: None,
},
};
@ -1566,6 +1608,7 @@ mod tests {
id: "call_weather".to_string(),
name: "get_weather".to_string(),
input: json!({}),
cache_control: None,
},
};

View file

@ -269,6 +269,13 @@ impl TryFrom<(&[u8], &SupportedAPIs, &SupportedAPIs)> for ProviderStreamResponse
Ok(ProviderStreamResponseType::ChatCompletionsStreamResponse(chat_resp))
}
(SupportedAPIs::OpenAIChatCompletions(_), SupportedAPIs::AnthropicMessagesAPI(_)) => {
// Special case: Handle [DONE] marker for OpenAI -> Anthropic conversion
if bytes == b"[DONE]" {
return Ok(ProviderStreamResponseType::MessagesStreamEvent(
crate::apis::anthropic::MessagesStreamEvent::MessageStop
));
}
let openai_resp: crate::apis::openai::ChatCompletionsStreamResponse = serde_json::from_slice(bytes)?;
// Transform to Anthropic Messages stream format using the transformer
@ -287,8 +294,8 @@ impl TryFrom<(SseEvent, &SupportedAPIs, &SupportedAPIs)> for SseEvent {
// Create a new transformed event based on the original
let mut transformed_event = sse_event;
// If not [DONE] and has data, parse the data as a provider stream response (business logic layer)
if !transformed_event.is_done() && transformed_event.data.is_some() {
// If has data, parse the data as a provider stream response (business logic layer)
if transformed_event.data.is_some() {
let data_str = transformed_event.data.as_ref().unwrap();
let data_bytes = data_str.as_bytes();
let transformed_response = ProviderStreamResponseType::try_from((data_bytes, client_api, upstream_api))?;
@ -380,6 +387,7 @@ where
I::Item: AsRef<str>,
{
pub lines: I,
pub done_seen: bool,
}
impl<I> SseStreamIter<I>
@ -388,7 +396,7 @@ where
I::Item: AsRef<str>,
{
pub fn new(lines: I) -> Self {
Self { lines }
Self { lines, done_seen: false }
}
}
@ -411,14 +419,20 @@ where
type Item = SseEvent;
fn next(&mut self) -> Option<Self::Item> {
// If we already returned [DONE], terminate the stream
if self.done_seen {
return None;
}
for line in &mut self.lines {
let line_str = line.as_ref();
// Try to parse as either data: or event: line
if let Ok(event) = line_str.parse::<SseEvent>() {
// For data: lines, check if this is the [DONE] marker - if so, end the stream
// For data: lines, check if this is the [DONE] marker
if event.data.is_some() && event.is_done() {
return None;
self.done_seen = true;
return Some(event); // Return [DONE] event for transformation
}
// For data: lines, skip events that should be filtered at the transport layer
if event.data.is_some() && event.should_skip() {
@ -706,7 +720,11 @@ mod tests {
assert!(event2.data.as_ref().unwrap().contains("msg2"));
assert!(!event2.should_skip());
// Iterator should end at [DONE] (no more events)
// Third event should be [DONE]
let done_event = iter.next().unwrap();
assert!(done_event.is_done());
// Iterator should end after [DONE]
assert!(iter.next().is_none());
}
@ -745,7 +763,11 @@ mod tests {
assert!(!event4.is_event_only());
assert!(event4.data.as_ref().unwrap().contains("Hello"));
// Iterator should end at [DONE]
// Fifth event should be [DONE]
let done_event = iter.next().unwrap();
assert!(done_event.is_done());
// Iterator should end after [DONE]
assert!(iter.next().is_none());
}
@ -776,4 +798,25 @@ mod tests {
let provider_type = ProviderStreamResponseType::ChatCompletionsStreamResponse(openai_event);
assert_eq!(provider_type.event_type(), None);
}
#[test]
fn test_done_marker_handled_in_stream_response_transformation() {
use crate::apis::anthropic::AnthropicApi;
// Test that [DONE] marker is properly converted to MessageStop in the transformation layer
let done_bytes = b"[DONE]";
let client_api = SupportedAPIs::AnthropicMessagesAPI(AnthropicApi::Messages);
let upstream_api = SupportedAPIs::OpenAIChatCompletions(crate::apis::openai::OpenAIApi::ChatCompletions);
let result = ProviderStreamResponseType::try_from((done_bytes.as_slice(), &client_api, &upstream_api));
assert!(result.is_ok());
if let Ok(ProviderStreamResponseType::MessagesStreamEvent(event)) = result {
// Verify it's a MessageStop event
assert_eq!(event.event_type(), Some("message_stop"));
assert!(matches!(event, crate::apis::anthropic::MessagesStreamEvent::MessageStop));
} else {
panic!("Expected MessagesStreamEvent::MessageStop");
}
}
}

View file

@ -395,23 +395,15 @@ impl StreamContext {
}
}
fn debug_log_body(&self, body: &[u8]) {
debug!(
"[ARCHGW_REQ_ID:{}] UPSTREAM_RAW_RESPONSE: body_size={} content={}",
self.request_identifier(),
body.len(),
String::from_utf8_lossy(body)
);
}
fn handle_streaming_response(
&mut self,
body: &[u8],
provider_id: ProviderId,
) -> Result<Vec<u8>, Action> {
debug!(
"[ARCHGW_REQ_ID:{}] STREAMING_PROCESS: provider_id={:?} chunk_size={}",
"[ARCHGW_REQ_ID:{}] STREAMING_PROCESS: client={:?} provider_id={:?} chunk_size={}",
self.request_identifier(),
self.client_api,
provider_id,
body.len()
);
@ -958,7 +950,12 @@ impl HttpContext for StreamContext {
Err(action) => return action,
};
self.debug_log_body(&body);
debug!(
"[ARCHGW_REQ_ID:{}] UPSTREAM_RAW_RESPONSE: body_size={} content={}",
self.request_identifier(),
body.len(),
String::from_utf8_lossy(&body)
);
let provider_id = self.get_provider_id();
if self.streaming_response {

View file

@ -0,0 +1,146 @@
# Claude Code Router - Multi-Model Access with Intelligent Routing
Arch Gateway extends Claude Code to access multiple LLM providers through a single interface. Offering two key benefits:
1. **Access to Models**: Connect to Grok, Mistral, Gemini, DeepSeek, GPT models, Claude, and local models via Ollama
2. **Intelligent Routing via Preferences for Coding Tasks**: Configure which models handle specific development tasks:
- Code generation and implementation
- Code reviews and analysis
- Architecture and system design
- Debugging and optimization
- Documentation and explanations
Uses a [1.5B preference-aligned router LLM](https://arxiv.org/abs/2506.16655) to automatically select the best model based on your request type.
## Benefits
- **Single Interface**: Access multiple LLM providers through the same Claude Code CLI
- **Task-Aware Routing**: Requests are analyzed and routed to models based on task type (code generation, debugging, architecture, documentation)
- **Provider Flexibility**: Add or remove LLM providers without changing your workflow
- **Routing Transparency**: See which model handles each request and why
## How It Works
Arch Gateway sits between Claude Code and multiple LLM providers, analyzing each request to route it to the most suitable model:
```
Your Request → Arch Gateway → Suitable Model → Response
[Task Analysis & Model Selection]
```
**Supported Providers**: OpenAI-compatible, Anthropic, DeepSeek, Grok, Gemini, Llama, Mistral, local models via Ollama. See [full list of supported providers](https://docs.archgw.com/concepts/llm_providers/supported_providers.html).
## Quick Start (5 minutes)
### Prerequisites
```bash
# Install Claude Code if you haven't already
npm install -g @anthropic-ai/claude-code
# Ensure Docker is running
docker --version
```
### Step 1: Get Configuration
```bash
# Clone and navigate to demo
git clone https://github.com/katanemo/arch.git
cd arch/demos/use_cases/claude_code
```
### Step 2: Set API Keys
```bash
# Copy the sample environment file
cp .env .env.local
# Edit with your actual API keys
export OPENAI_API_KEY="your-openai-key-here"
export ANTHROPIC_API_KEY="your-anthropic-key-here"
# Add other providers as needed
```
### Step 3: Start Arch Gateway
```bash
# Install and start the gateway
pip install archgw
archgw up
```
### Step 4: Launch Enhanced Claude Code
```bash
# This will launch Claude Code with multi-model routing
archgw cli-agent claude
```
![claude code](claude_code.png)
### Monitor Model Selection in Real-Time
While using Claude Code, open a **second terminal** and run this helper script to watch routing decisions. This script shows you:
- **Which model** was selected for each request
- **Real-time routing decisions** as you work
```bash
# In a new terminal window (from the same directory)
sh pretty_model_resolution.sh
```
![model_selection](model_selection.png)
## Understanding the Configuration
The `config.yaml` file defines your multi-model setup:
```yaml
llm_providers:
- model: openai/gpt-4.1-2025-04-14
access_key: $OPENAI_API_KEY
routing_preferences:
- name: code generation
description: generating new code snippets and functions
- model: anthropic/claude-3-5-sonnet-20241022
access_key: $ANTHROPIC_API_KEY
routing_preferences:
- name: code understanding
description: explaining and analyzing existing code
```
## Advanced Usage
### Override Model Selection
```bash
# Force a specific model for this session
archgw cli-agent claude --settings='{"ANTHROPIC_SMALL_FAST_MODEL": "deepseek-coder-v2"}'
### Environment Variables
The system automatically configures these variables for Claude Code:
```bash
ANTHROPIC_BASE_URL=http://127.0.0.1:12000 # Routes through Arch Gateway
ANTHROPIC_SMALL_FAST_MODEL=arch.claude.code.small.fast # Uses intelligent alias
```
### Custom Routing Configuration
Edit `config.yaml` to define custom task→model mappings:
```yaml
llm_providers:
# OpenAI Models
- model: openai/gpt-5-2025-08-07
access_key: $OPENAI_API_KEY
routing_preferences:
- name: code generation
description: generating new code snippets, functions, or boilerplate based on user prompts or requirements
- model: openai/gpt-4.1-2025-04-14
access_key: $OPENAI_API_KEY
routing_preferences:
- name: code understanding
description: understand and explain existing code snippets, functions, or libraries
```
## Technical Details
**How routing works:** Arch intercepts Claude Code requests, analyzes the content using preference-aligned routing, and forwards to the configured model.
**Research foundation:** Built on our research in [Preference-Aligned LLM Routing](https://arxiv.org/abs/2506.16655)
**Documentation:** [docs.archgw.com](https://docs.archgw.com) for advanced configuration and API details.

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@ -0,0 +1,41 @@
version: v0.1
listeners:
egress_traffic:
address: 0.0.0.0
port: 12000
message_format: openai
timeout: 30s
llm_providers:
# OpenAI Models
- model: openai/gpt-5-2025-08-07
access_key: $OPENAI_API_KEY
routing_preferences:
- name: code generation
description: generating new code snippets, functions, or boilerplate based on user prompts or requirements
- model: openai/gpt-4.1-2025-04-14
access_key: $OPENAI_API_KEY
routing_preferences:
- name: code understanding
description: understand and explain existing code snippets, functions, or libraries
# Anthropic Models
- model: anthropic/claude-sonnet-4-5
default: true
access_key: $ANTHROPIC_API_KEY
- model: anthropic/claude-3-haiku-20240307
access_key: $ANTHROPIC_API_KEY
# Ollama Models
- model: ollama/llama3.1
base_url: http://host.docker.internal:11434
# Model aliases - friendly names that map to actual provider names
model_aliases:
# Alias for a small faster Claude model
arch.claude.code.small.fast:
target: claude-3-haiku-20240307

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@ -0,0 +1,33 @@
#!/usr/bin/env bash
# Pretty-print ArchGW MODEL_RESOLUTION lines from docker logs
# - hides Arch-Router
# - prints timestamp
# - colors MODEL_RESOLUTION red
# - colors req_model cyan
# - colors resolved_model magenta
# - removes provider and streaming
docker logs -f archgw 2>&1 \
| awk '
/MODEL_RESOLUTION:/ && $0 !~ /Arch-Router/ {
# extract timestamp between first [ and ]
ts=""
if (match($0, /\[[0-9-]+ [0-9:.]+\]/)) {
ts=substr($0, RSTART+1, RLENGTH-2)
}
# split out after MODEL_RESOLUTION:
n = split($0, parts, /MODEL_RESOLUTION: */)
line = parts[2]
# remove provider and streaming fields
sub(/ *provider='\''[^'\'']+'\''/, "", line)
sub(/ *streaming=(true|false)/, "", line)
# highlight fields
gsub(/req_model='\''[^'\'']+'\''/, "\033[36m&\033[0m", line)
gsub(/resolved_model='\''[^'\'']+'\''/, "\033[35m&\033[0m", line)
# print timestamp + MODEL_RESOLUTION
printf "\033[90m[%s]\033[0m \033[31mMODEL_RESOLUTION\033[0m: %s\n", ts, line
}'

View file

@ -24,7 +24,7 @@ llm_providers:
access_key: $OPENAI_API_KEY
# Anthropic Models
- model: anthropic/claude-3-5-sonnet-20241022
- model: anthropic/claude-sonnet-4-20250514
access_key: $ANTHROPIC_API_KEY
- model: anthropic/claude-3-haiku-20240307
@ -56,7 +56,7 @@ model_aliases:
# Alias for creative tasks -> Claude model
arch.creative.v1:
target: claude-3-5-sonnet-20241022
target: claude-sonnet-4-20250514
# Alias for quick responses -> fast model
arch.fast.v1:
@ -67,7 +67,7 @@ model_aliases:
target: gpt-5-mini-2025-08-07
chat-model:
target: llama3.1
target: gpt-5-mini-2025-08-07
creative-model:
target: claude-3-5-sonnet-20241022
target: claude-sonnet-4-20250514

View file

@ -12,7 +12,7 @@ python = ">=3.10,<3.13.3"
pydantic = "^2.0"
openai = "^1.0"
pyyaml = "^6.0"
archgw ="^0.3.13"
archgw ="^0.3.14"
[tool.poetry.group.dev.dependencies]
pytest = "^8.3"

View file

@ -14,9 +14,9 @@ Make sure your machine is up to date with [latest version of archgw]([url](https
2. start archgw in the foreground
```bash
(venv) $ archgw up --service archgw --foreground
2025-05-30 18:00:09,953 - cli.main - INFO - Starting archgw cli version: 0.3.13
2025-05-30 18:00:09,953 - cli.main - INFO - Starting archgw cli version: 0.3.14
2025-05-30 18:00:09,953 - cli.main - INFO - Validating /Users/adilhafeez/src/intelligent-prompt-gateway/demos/use_cases/preference_based_routing/arch_config.yaml
2025-05-30 18:00:10,422 - cli.core - INFO - Starting arch gateway, image name: archgw, tag: katanemo/archgw:0.3.13
2025-05-30 18:00:10,422 - cli.core - INFO - Starting arch gateway, image name: archgw, tag: katanemo/archgw:0.3.14
2025-05-30 18:00:10,662 - cli.core - INFO - archgw status: running, health status: starting
2025-05-30 18:00:11,712 - cli.core - INFO - archgw status: running, health status: starting
2025-05-30 18:00:12,761 - cli.core - INFO - archgw is running and is healthy!

View file

@ -15,7 +15,7 @@ from sphinxawesome_theme.postprocess import Icons
project = "Arch Docs"
copyright = "2025, Katanemo Labs, Inc"
author = "Katanemo Labs, Inc"
release = " v0.3.13"
release = " v0.3.14"
# -- General configuration ---------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

View file

@ -25,7 +25,7 @@ Arch's CLI allows you to manage and interact with the Arch gateway efficiently.
$ python -m venv venv
$ source venv/bin/activate # On Windows, use: venv\Scripts\activate
$ pip install archgw==0.3.13
$ pip install archgw==0.3.14
Build AI Agent with Arch Gateway

422
model_server/poetry.lock generated
View file

@ -45,13 +45,13 @@ files = [
[[package]]
name = "anyio"
version = "4.10.0"
version = "4.11.0"
description = "High-level concurrency and networking framework on top of asyncio or Trio"
optional = false
python-versions = ">=3.9"
files = [
{file = "anyio-4.10.0-py3-none-any.whl", hash = "sha256:60e474ac86736bbfd6f210f7a61218939c318f43f9972497381f1c5e930ed3d1"},
{file = "anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6"},
{file = "anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc"},
{file = "anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4"},
]
[package.dependencies]
@ -61,17 +61,17 @@ sniffio = ">=1.1"
typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
[package.extras]
trio = ["trio (>=0.26.1)"]
trio = ["trio (>=0.31.0)"]
[[package]]
name = "asgiref"
version = "3.9.1"
version = "3.9.2"
description = "ASGI specs, helper code, and adapters"
optional = false
python-versions = ">=3.9"
files = [
{file = "asgiref-3.9.1-py3-none-any.whl", hash = "sha256:f3bba7092a48005b5f5bacd747d36ee4a5a61f4a269a6df590b43144355ebd2c"},
{file = "asgiref-3.9.1.tar.gz", hash = "sha256:a5ab6582236218e5ef1648f242fd9f10626cfd4de8dc377db215d5d5098e3142"},
{file = "asgiref-3.9.2-py3-none-any.whl", hash = "sha256:0b61526596219d70396548fc003635056856dba5d0d086f86476f10b33c75960"},
{file = "asgiref-3.9.2.tar.gz", hash = "sha256:a0249afacb66688ef258ffe503528360443e2b9a8d8c4581b6ebefa58c841ef1"},
]
[package.dependencies]
@ -371,69 +371,79 @@ grpc = ["grpcio (>=1.44.0,<2.0.0)"]
[[package]]
name = "grpcio"
version = "1.75.0"
version = "1.75.1"
description = "HTTP/2-based RPC framework"
optional = false
python-versions = ">=3.9"
files = [
{file = "grpcio-1.75.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:1ec9cbaec18d9597c718b1ed452e61748ac0b36ba350d558f9ded1a94cc15ec7"},
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]
[[package]]

View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "archgw_modelserver"
version = "0.3.13"
version = "0.3.14"
description = "A model server for serving models"
authors = ["Katanemo Labs, Inc <info@katanemo.com>"]
license = "Apache 2.0"

View file

@ -199,8 +199,7 @@ def test_400_error_handling_with_alias():
try:
completion = client.chat.completions.create(
model="arch.summarize.v1", # This should resolve to gpt-5-mini-2025-08-07
max_completion_tokens=50,
temperature=0.7, # This is a typo - should be "temperature", which should trigger a 400 error
max_tokens=50,
messages=[
{
"role": "user",
@ -350,3 +349,57 @@ def test_direct_model_4o_mini_anthropic():
response_content = "".join(b.text for b in message.content if b.type == "text")
logger.info(f"Response from direct 4o-mini via Anthropic: {response_content}")
assert response_content == "Hello from direct 4o-mini via Anthropic!"
def test_anthropic_thinking_mode_streaming():
# Anthropic base_url should be the root, not /v1/chat/completions
base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
client = anthropic.Anthropic(
api_key=os.environ.get("ANTHROPIC_API_KEY", "test-key"),
base_url=base_url,
)
thinking_block_started = False
thinking_delta_seen = False
text_delta_seen = False
with client.messages.stream(
model="claude-sonnet-4-20250514",
max_tokens=2048,
thinking={"type": "enabled", "budget_tokens": 1024}, # <- idiomatic
messages=[{"role": "user", "content": "Explain briefly what 2+2 equals"}],
) as stream:
for event in stream:
# 1) detect when a thinking block starts
if event.type == "content_block_start" and getattr(
event, "content_block", None
):
if getattr(event.content_block, "type", None) == "thinking":
thinking_block_started = True
# 2) collect text vs thinking deltas
if event.type == "content_block_delta" and getattr(event, "delta", None):
if event.delta.type == "text_delta":
text_delta_seen = True
elif event.delta.type == "thinking_delta":
# some SDKs expose .thinking, others .text for this delta; not needed here
thinking_delta_seen = True
final = stream.get_final_message()
# Basic integrity
assert final is not None
assert final.content and len(final.content) > 0
# Normal text should have streamed
assert text_delta_seen, "Expected normal text deltas in stream"
# With thinking enabled, we expect a thinking block and at least one thinking delta
assert thinking_block_started, "No thinking block started"
assert thinking_delta_seen, "No thinking deltas observed"
# Optional: double-check on the assembled message
final_block_types = [blk.type for blk in final.content]
assert "text" in final_block_types
assert "thinking" in final_block_types

View file

@ -417,12 +417,12 @@ def test_anthropic_client_with_openai_model_streaming():
client = anthropic.Anthropic(api_key="test-key", base_url=base_url)
with client.messages.stream(
model="gpt-4o-mini", # OpenAI model via Anthropic client
max_tokens=50,
model="gpt-5-mini-2025-08-07", # OpenAI model via Anthropic client
max_tokens=500,
messages=[
{
"role": "user",
"content": "Hello, please respond with exactly: Hello from GPT-4o-mini via Anthropic!",
"content": "Hello, please respond with exactly: Hello from ChatGPT!",
}
],
) as stream:
@ -435,8 +435,8 @@ def test_anthropic_client_with_openai_model_streaming():
# A safe way to reassemble text from the content blocks:
final_text = "".join(b.text for b in final.content if b.type == "text")
assert full_text == "Hello from GPT-4o-mini via Anthropic!"
assert final_text == "Hello from GPT-4o-mini via Anthropic!"
assert full_text == "Hello from ChatGPT!"
assert final_text == "Hello from ChatGPT!"
def test_openai_gpt4o_mini_v1_messages_api():