Add direct API backend for agent execution
When api_base_url is configured, agents call the LLM directly via OpenAI-compatible API (vllm, llama.cpp, etc.) instead of shelling out to claude CLI. Implements the full tool loop: send prompt, if tool_calls execute them and send results back, repeat until text. This enables running agents against local/remote models like Qwen-27B on a RunPod B200, with no dependency on claude CLI. Config fields: api_base_url, api_key, api_model. Falls back to claude CLI when api_base_url is not set. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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115
poc-memory/src/agents/api.rs
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115
poc-memory/src/agents/api.rs
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// agents/api.rs — Direct API backend for agent execution
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//
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// Uses poc-agent's OpenAI-compatible API client to call models directly
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// (vllm, llama.cpp, OpenRouter, etc.) instead of shelling out to claude CLI.
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// Implements the tool loop: send prompt → if tool_calls, execute them →
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// send results back → repeat until text response.
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//
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// Activated when config has api_base_url set.
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use poc_agent::api::ApiClient;
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use poc_agent::types::*;
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use poc_agent::tools::{self, ProcessTracker};
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use poc_agent::ui_channel::StreamTarget;
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/// Run an agent prompt through the direct API with tool support.
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/// Returns the final text response after all tool calls are resolved.
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pub async fn call_api_with_tools(
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agent: &str,
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prompt: &str,
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log: &dyn Fn(&str),
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) -> Result<String, String> {
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let config = crate::config::get();
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let base_url = config.api_base_url.as_deref()
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.ok_or("api_base_url not configured")?;
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let api_key = config.api_key.as_deref().unwrap_or("");
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let model = config.api_model.as_deref().unwrap_or("qwen-2.5-27b");
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let client = ApiClient::new(base_url, api_key, model);
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// Set up a minimal UI channel (we just collect messages, no TUI)
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let (ui_tx, _ui_rx) = poc_agent::ui_channel::channel();
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// Build tool definitions — just bash for poc-memory commands
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let all_defs = tools::definitions();
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let tool_defs: Vec<ToolDef> = all_defs.into_iter()
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.filter(|d| d.function.name == "bash")
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.collect();
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let tracker = ProcessTracker::new();
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// Start with the prompt as a user message
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let mut messages = vec![Message::user(prompt)];
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let max_turns = 50;
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for turn in 0..max_turns {
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log(&format!("API turn {} ({} messages)", turn, messages.len()));
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let (msg, usage) = client.chat_completion_stream(
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&messages,
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Some(&tool_defs),
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&ui_tx,
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StreamTarget::Autonomous,
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"none",
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).await.map_err(|e| format!("API error: {}", e))?;
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if let Some(u) = &usage {
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log(&format!("tokens: {} prompt + {} completion",
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u.prompt_tokens, u.completion_tokens));
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}
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let has_content = msg.content.is_some();
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let has_tools = msg.tool_calls.as_ref().is_some_and(|tc| !tc.is_empty());
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if has_tools {
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// Push the assistant message with tool calls
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messages.push(msg.clone());
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// Execute each tool call
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for call in msg.tool_calls.as_ref().unwrap() {
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log(&format!("tool: {}({})",
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call.function.name,
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crate::util::first_n_chars(&call.function.arguments, 80)));
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let args: serde_json::Value = serde_json::from_str(&call.function.arguments)
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.unwrap_or_default();
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let output = tools::dispatch(&call.function.name, &args, &tracker).await;
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log(&format!("tool result: {} chars", output.text.len()));
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messages.push(Message::tool_result(&call.id, &output.text));
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}
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continue;
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}
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// Text-only response — we're done
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let text = msg.content_text().to_string();
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if text.is_empty() && !has_content {
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log("empty response, retrying");
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messages.push(Message::user(
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"[system] Your previous response was empty. Please respond with text or use a tool."
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));
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continue;
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}
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return Ok(text);
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}
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Err(format!("agent exceeded {} tool turns", max_turns))
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}
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/// Synchronous wrapper — creates a tokio runtime and blocks.
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/// Used by the existing sync call path in knowledge.rs.
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pub fn call_api_with_tools_sync(
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agent: &str,
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prompt: &str,
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log: &dyn Fn(&str),
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) -> Result<String, String> {
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let rt = tokio::runtime::Builder::new_current_thread()
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.enable_all()
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.build()
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.map_err(|e| format!("tokio runtime: {}", e))?;
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rt.block_on(call_api_with_tools(agent, prompt, log))
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}
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