From 84f7b67ee090e379798214afdcf207f9f8e3b1bf Mon Sep 17 00:00:00 2001 From: inesaranab Date: Sat, 25 Jul 2026 20:03:54 +0200 Subject: [PATCH 1/2] feat: added memory compaction to agent --- harness/memory.py | 71 +++++++++++++++++++++++++ harness/runtime.py | 70 ++++++++++++++++++------- pyproject.toml | 1 + uv.lock | 127 +++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 249 insertions(+), 20 deletions(-) create mode 100644 harness/memory.py diff --git a/harness/memory.py b/harness/memory.py new file mode 100644 index 0000000..d8c8882 --- /dev/null +++ b/harness/memory.py @@ -0,0 +1,71 @@ +import json +from functools import cache + +import tiktoken +from openai import AsyncOpenAI + +from config import settings +from harness.system_prompt import SYSTEM_PROMPT + +# Small on purpose so a short task triggers compaction. +MAX_CONTEXT_TOKENS = 500 +KEEP_CONTEXT_TOKENS = 200 + +client = AsyncOpenAI(api_key=settings.openai_api_key) + + +@cache +def _encoder() -> tiktoken.Encoding: + # Built once, reused forever (lazy). + return tiktoken.get_encoding("o200k_base") + + +def _as_text(message: object) -> str: + # Messages are mixed dicts (role/content, function_call_output, ...) + return message if isinstance(message, str) else json.dumps(message) + + +# ── estimate: how many tokens a list of messages costs ────────────── +def estimate_tokens(messages: list) -> int: + text = "\n".join(_as_text(m) for m in messages) + return len(_encoder().encode(text)) + + +# ── CONTEXT: what the model actually sees THIS turn, assembled fresh ─ +def build_context(task: str, summary: str, turns: list) -> list: + context: list = [ + {"role": "system", "content": SYSTEM_PROMPT}, + {"role": "user", "content": task}, # the goal is pinned, never summarized away + ] + if summary: + context.append({"role": "system", "content": f"Summary of earlier work so far:\n{summary}"}) + for turn in turns: + context.extend(turn) # the most recent turns, verbatim + return context + + +# ── STATE: compress old turns into the running summary (an LLM call) ─ +async def summarize(old_turns: list, prior_summary: str) -> str: + transcript = "\n".join(_as_text(m) for turn in old_turns for m in turn)[:6000] + resp = await client.responses.create( + model="gpt-5.6-luna", + input=[ + { + "role": "system", + "content": ( + "You compress an agent's work log into a short running summary. " + "Preserve concrete facts: item ids, categories, draft ids, amounts, " + "and what was already sent. Be terse." + ), + }, + { + "role": "user", + "content": ( + f"Prior summary:\n{prior_summary or '(none)'}\n\n" + f"Fold in this newer work:\n{transcript}\n\n" + "Return the updated summary." + ), + }, + ], + ) + return resp.output_text diff --git a/harness/runtime.py b/harness/runtime.py index 03fe34e..a8800ce 100644 --- a/harness/runtime.py +++ b/harness/runtime.py @@ -12,15 +12,21 @@ from config import settings from harness.bus import emit -from harness.system_prompt import SYSTEM_PROMPT +from harness.memory import ( + KEEP_CONTEXT_TOKENS, + MAX_CONTEXT_TOKENS, + build_context, + estimate_tokens, + summarize, +) from harness.tools import TOOL_SCHEMAS, run_tool DBOS(config=DBOSConfig(name="ines-harness", system_database_url=settings.database_url)) client = AsyncOpenAI(api_key=settings.openai_api_key) -# Loop guard -MAX_STEPS = 10 + +MAX_STEPS = 30 @DBOS.step() @@ -29,13 +35,18 @@ async def emit_step(event: dict) -> None: @DBOS.step() -async def model_turn(workflow_id: str, messages: list) -> dict: +async def summarize_step(old_turns: list, prior_summary: str) -> str: + return await summarize(old_turns, prior_summary) + + +@DBOS.step() +async def model_turn(workflow_id: str, context: list) -> dict: tool_calls = [] - # Stream — we match on the SDK's event CLASSES, not strings. + # One model turn over the HYDRATED context (not the whole history). async with client.responses.stream( model="gpt-5.6-luna", - input=messages, + input=context, tools=TOOL_SCHEMAS, ) as stream: async for event in stream: @@ -69,12 +80,9 @@ async def model_turn(workflow_id: str, messages: list) -> dict: "error": event.message, } ) - # Stop the turn instead of falling into get_final_response() - # on an already-errored stream. raise RuntimeError(event.message) final = await stream.get_final_response() - # Return only serializable data — DBOS checkpoints this to Postgres. return { "output": [ item.model_dump(exclude={"status", "parsed_arguments"}) for item in final.output @@ -105,18 +113,39 @@ async def agent_workflow(user_input: str) -> str: workflow_id = DBOS.workflow_id await emit_step({"type": "workflow.started", "workflowId": workflow_id, "input": user_input}) - # STATE - messages: list = [ - {"role": "system", "content": SYSTEM_PROMPT}, - {"role": "user", "content": user_input}, - ] + # Conversation as a list of TURNS to compact at clean boundaries. + turns: list = [] + summary = "" - # THE LOOP. step = 0 while step < MAX_STEPS: - turn = await model_turn(workflow_id, messages) # ty: ignore[invalid-argument-type] - # Append the model's output to history so the next turn sees it. - messages += turn["output"] + # 1. Compact: while the recent window is over budget, peel the oldest + # turns into the running summary (keeping at least the last turn). + if estimate_tokens([m for turn in turns for m in turn]) > MAX_CONTEXT_TOKENS: + old: list = [] + while ( + len(turns) > 1 + and estimate_tokens([m for turn in turns for m in turn]) > KEEP_CONTEXT_TOKENS + ): + old.append(turns.pop(0)) + if old: + summary = await summarize_step(old, summary) + context_tokens = estimate_tokens(build_context(user_input, summary, turns)) + await emit_step( + { + "type": "memory.compacted", + "workflowId": workflow_id, + "summarizedTurns": len(old), + "contextTokens": context_tokens, + "summary": summary, + } + ) + + # 2 + 3. Hydrate the context and run one turn over it. + context = build_context(user_input, summary, turns) + turn = await model_turn(workflow_id, context) # ty: ignore[invalid-argument-type] + + turn_messages: list = list(turn["output"]) # No tool calls means the model answered. We're done. if not turn["tool_calls"]: @@ -132,10 +161,10 @@ async def agent_workflow(user_input: str) -> str: ) return turn["output_text"] - # Run each requested tool with NO mediation, feed the result back. + # Run each requested tool, feed the result into THIS turn's messages. for call in turn["tool_calls"]: result = await tool_step(workflow_id, call) # ty: ignore[invalid-argument-type] - messages.append( + turn_messages.append( { "type": "function_call_output", "call_id": call["call_id"], @@ -143,6 +172,7 @@ async def agent_workflow(user_input: str) -> str: } ) + turns.append(turn_messages) step += 1 await emit_step( diff --git a/pyproject.toml b/pyproject.toml index 23aaa2e..71ee542 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,6 +12,7 @@ dependencies = [ "psycopg2-binary>=2.9.12", "pydantic-settings>=2.14.2", "sqlmodel>=0.0.39", + "tiktoken>=0.13.0", "uvicorn[standard]>=0.51.0", ] diff --git a/uv.lock b/uv.lock index fd1da59..2595c7a 100644 --- a/uv.lock +++ b/uv.lock 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+] + [[package]] name = "uvicorn" version = "0.51.0" From 2e484e82a1c42a74c504c2b35c65a91d3851f3b1 Mon Sep 17 00:00:00 2001 From: inesaranab Date: Sat, 25 Jul 2026 20:19:09 +0200 Subject: [PATCH 2/2] fix: address relevant PR comments --- harness/memory.py | 3 ++- harness/runtime.py | 9 +++++---- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/harness/memory.py b/harness/memory.py index d8c8882..f440e19 100644 --- a/harness/memory.py +++ b/harness/memory.py @@ -28,7 +28,8 @@ def _as_text(message: object) -> str: # ── estimate: how many tokens a list of messages costs ────────────── def estimate_tokens(messages: list) -> int: text = "\n".join(_as_text(m) for m in messages) - return len(_encoder().encode(text)) + # encode_ordinary never raises on text that looks like a special token + return len(_encoder().encode_ordinary(text)) # ── CONTEXT: what the model actually sees THIS turn, assembled fresh ─ diff --git a/harness/runtime.py b/harness/runtime.py index a8800ce..710cc85 100644 --- a/harness/runtime.py +++ b/harness/runtime.py @@ -119,13 +119,14 @@ async def agent_workflow(user_input: str) -> str: step = 0 while step < MAX_STEPS: - # 1. Compact: while the recent window is over budget, peel the oldest - # turns into the running summary (keeping at least the last turn). - if estimate_tokens([m for turn in turns for m in turn]) > MAX_CONTEXT_TOKENS: + # 1. Compact: while the FULL assembled context (system prompt + task + + # summary + turns) is over budget, peel the oldest turns into the + # running summary (keeping at least the last turn). + if estimate_tokens(build_context(user_input, summary, turns)) > MAX_CONTEXT_TOKENS: old: list = [] while ( len(turns) > 1 - and estimate_tokens([m for turn in turns for m in turn]) > KEEP_CONTEXT_TOKENS + and estimate_tokens(build_context(user_input, summary, turns)) > KEEP_CONTEXT_TOKENS ): old.append(turns.pop(0)) if old: