Fix ELP topic selection and replace broken agentic LLM call
elp-input.el: walk frame_nodes to pick the first node whose content contains the query topic (case-insensitive) rather than always taking index 0 — prevents the always-high-salience CGI architecture memory from hijacking every ELP response. chat.el: rewrite handle_chat_agentic to run the tool loop natively in El using http_post_with_headers + Map headers, bypassing the broken llm_call_agentic(model, system, message, tools) C binding that cast a JSON String as an ElList and never serialized tools. New impl supports up to 8 tool-use iterations with read_file, write_file, web_get, search_memory, and run_command dispatch.
This commit is contained in:
Will Anderson
@@ -202,6 +202,54 @@ fn studio_tools_json() -> String {
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"]"
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}
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fn agentic_api_key() -> String {
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let k1: String = env("ANTHROPIC_API_KEY")
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if !str_eq(k1, "") {
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return k1
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}
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return env("NEURON_LLM_0_KEY")
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}
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fn agentic_tools_literal() -> String {
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return "[" +
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"{\"name\":\"read_file\",\"description\":\"Read contents of a file from disk.\",\"input_schema\":{\"type\":\"object\",\"properties\":{\"path\":{\"type\":\"string\",\"description\":\"Absolute file path\"}},\"required\":[\"path\"]}}," +
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"{\"name\":\"write_file\",\"description\":\"Write content to a file on disk.\",\"input_schema\":{\"type\":\"object\",\"properties\":{\"path\":{\"type\":\"string\"},\"content\":{\"type\":\"string\"}},\"required\":[\"path\",\"content\"]}}," +
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"{\"name\":\"web_get\",\"description\":\"Fetch content from a URL.\",\"input_schema\":{\"type\":\"object\",\"properties\":{\"url\":{\"type\":\"string\"}},\"required\":[\"url\"]}}," +
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"{\"name\":\"search_memory\",\"description\":\"Search engram memory for relevant nodes.\",\"input_schema\":{\"type\":\"object\",\"properties\":{\"query\":{\"type\":\"string\"}},\"required\":[\"query\"]}}," +
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"{\"name\":\"run_command\",\"description\":\"Run a shell command and capture output.\",\"input_schema\":{\"type\":\"object\",\"properties\":{\"command\":{\"type\":\"string\"}},\"required\":[\"command\"]}}" +
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"]"
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}
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fn dispatch_tool(tool_name: String, tool_input: String) -> String {
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if str_eq(tool_name, "read_file") {
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let path: String = json_get(tool_input, "path")
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let content: String = fs_read(path)
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return json_safe(content)
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}
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if str_eq(tool_name, "write_file") {
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let path: String = json_get(tool_input, "path")
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let content: String = json_get(tool_input, "content")
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fs_write(path, content)
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return "{\\\"ok\\\":true}"
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}
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if str_eq(tool_name, "web_get") {
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let url: String = json_get(tool_input, "url")
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let result: String = http_get(url)
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return json_safe(result)
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}
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if str_eq(tool_name, "search_memory") {
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let query: String = json_get(tool_input, "query")
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let result: String = engram_search_json(query, 10)
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return json_safe(result)
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}
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if str_eq(tool_name, "run_command") {
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let cmd: String = json_get(tool_input, "command")
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let result: String = exec_capture(cmd)
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return json_safe(result)
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}
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return "unknown tool: " + tool_name
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}
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fn handle_chat_agentic(body: String) -> String {
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let message: String = json_get(body, "message")
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if str_eq(message, "") {
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@@ -215,14 +263,90 @@ fn handle_chat_agentic(body: String) -> String {
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let identity: String = state_get("soul_identity")
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let system: String = identity + " You have access to tools: read files, write files, browse the web, search your memory, run commands. Use them when they add genuine value. Be direct.\n\n" + ctx
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let tools: String = studio_tools_json()
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let text: String = llm_call_agentic(model, system, message, tools)
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let api_key: String = agentic_api_key()
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let tools_json: String = agentic_tools_literal()
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if str_eq(text, "") {
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// Build initial messages array
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let safe_msg: String = json_safe(message)
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let safe_sys: String = json_safe(system)
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let messages: String = "[{\"role\":\"user\",\"content\":\"" + safe_msg + "\"}]"
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let api_url: String = "https://api.anthropic.com/v1/messages"
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let h: Map = {}
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map_set(h, "x-api-key", api_key)
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map_set(h, "anthropic-version", "2023-06-01")
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map_set(h, "content-type", "application/json")
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let final_text: String = ""
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let iteration: Int = 0
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let keep_going: Bool = true
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while keep_going && iteration < 8 {
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let req_body: String = "{\"model\":\"" + model + "\""
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+ ",\"max_tokens\":4096"
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+ ",\"system\":\"" + safe_sys + "\""
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+ ",\"tools\":" + tools_json
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+ ",\"messages\":" + messages
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+ "}"
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let raw_resp: String = http_post_with_headers(api_url, req_body, h)
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let is_error: Bool = str_starts_with(raw_resp, "{\"error\"")
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|| str_starts_with(raw_resp, "{\"type\":\"error\"")
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|| str_contains(raw_resp, "authentication_error")
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if is_error {
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return "{\"error\":\"llm unavailable\",\"reply\":\"\"}"
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}
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let stop_reason: String = json_get(raw_resp, "stop_reason")
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let content_arr: String = json_get(raw_resp, "content")
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// Collect text and tool_use blocks from content
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let text_out: String = ""
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let tool_results: String = ""
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let has_tool_use: Bool = false
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let ci: Int = 0
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let c_total: Int = json_array_len(content_arr)
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while ci < c_total {
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let block: String = json_array_get(content_arr, ci)
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let block_type: String = json_get(block, "type")
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if str_eq(block_type, "text") {
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let block_text: String = json_get(block, "text")
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let text_out = text_out + block_text
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}
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if str_eq(block_type, "tool_use") {
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let has_tool_use = true
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let tool_id: String = json_get(block, "id")
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let tool_name: String = json_get(block, "name")
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let tool_input: String = json_get(block, "input")
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let tool_result: String = dispatch_tool(tool_name, tool_input)
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let sep: String = if str_eq(tool_results, "") { "" } else { "," }
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let tool_results = tool_results + sep
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+ "{\"type\":\"tool_result\",\"tool_use_id\":\"" + tool_id + "\",\"content\":\"" + tool_result + "\"}"
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}
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let ci = ci + 1
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}
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if str_eq(stop_reason, "tool_use") && has_tool_use {
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// Append assistant turn with its content blocks, then tool results
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let safe_content_arr: String = json_safe(content_arr)
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let inner_msgs: String = str_slice(messages, 1, str_len(messages) - 1)
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let messages = "[" + inner_msgs
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+ ",{\"role\":\"assistant\",\"content\":" + content_arr + "}"
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+ ",{\"role\":\"user\",\"content\":[" + tool_results + "]}"
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+ "]"
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let iteration = iteration + 1
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} else {
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let final_text = text_out
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let keep_going = false
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}
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}
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if str_eq(final_text, "") {
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return "{\"error\":\"no response\",\"reply\":\"\"}"
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}
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let safe_text: String = json_safe(text)
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let safe_text: String = json_safe(final_text)
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return "{\"reply\":\"" + safe_text + "\",\"model\":\"" + model + "\",\"agentic\":true}"
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}
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+18
-3
@@ -108,9 +108,24 @@ fn handle_elp_chat(body: String) -> String {
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let frame_nodes: String = if str_eq(kept_json, "") { "[]" } else { "[" + kept_json + "]" }
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// ── Reason ───────────────────────────────────────────────────────────────
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// Extract the patient from the top activated node's content.
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// Trim to 200 chars so it fits cleanly in a generated sentence.
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let top_node: String = json_array_get(frame_nodes, 0)
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// Walk frame_nodes to find the first node whose content contains the topic.
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// This prevents the always-high-salience CGI architecture node from
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// dominating every response regardless of what was asked.
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let fn_total: Int = json_array_len(frame_nodes)
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let fn_i: Int = 0
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let topic_lower: String = str_to_lower(topic)
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let found_node: String = ""
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while fn_i < fn_total {
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let candidate: String = json_array_get(frame_nodes, fn_i)
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let cand_content: String = json_get(candidate, "content")
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let cand_lower: String = str_to_lower(cand_content)
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let matches: Bool = str_contains(cand_lower, topic_lower)
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if matches && str_eq(found_node, "") {
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let found_node = candidate
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}
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let fn_i = fn_i + 1
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}
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let top_node: String = if str_eq(found_node, "") { json_array_get(frame_nodes, 0) } else { found_node }
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let top_raw: String = json_get(top_node, "content")
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let patient_raw: String = if str_eq(top_raw, "") { topic } else {
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if str_len(top_raw) > 200 { str_slice(top_raw, 0, 200) } else { top_raw }
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